From 23d2df704ec4e230cedc7338af157cd3708fec73 Mon Sep 17 00:00:00 2001 From: kenxxxxx Date: Tue, 16 Jul 2024 20:15:42 +0800 Subject: [PATCH 01/16] Initial commit --- egs/vc/noro_inference.sh | 0 egs/vc/noro_train_clean.sh | 0 egs/vc/noro_train_noisy.sh | 91 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 91 insertions(+) create mode 100644 egs/vc/noro_inference.sh create mode 100644 egs/vc/noro_train_clean.sh create mode 100644 egs/vc/noro_train_noisy.sh diff --git a/egs/vc/noro_inference.sh b/egs/vc/noro_inference.sh new file mode 100644 index 00000000..e69de29b diff --git a/egs/vc/noro_train_clean.sh b/egs/vc/noro_train_clean.sh new file mode 100644 index 00000000..e69de29b diff --git a/egs/vc/noro_train_noisy.sh b/egs/vc/noro_train_noisy.sh new file mode 100644 index 00000000..6d3de92c --- /dev/null +++ b/egs/vc/noro_train_noisy.sh @@ -0,0 +1,91 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +export PYTHONPATH="./" + + + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $(dirname $exp_dir))) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +# 从这里开始 +export http_proxy=http://127.0.0.1:7890 +export https_proxy=http://127.0.0.1:7890 +export HF_ENDPOINT=https://hf-mirror.com + + +#镜像 export HF_ENDPOINT=https://hf-mirror.com + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config_4gpu_clean.json +fi + + + +echo "Exprimental Configuration File: $exp_config" + +# hubertnew="/mnt/petrelfs/hehaorui/data/ckpt/vc/newmhubert/model.safetensors" + +hubertold="/mnt/data2/hehaorui/ckpt/zs-vc-ckpt/vc_mls_clean/model.safetensors" +whisperold="/mnt/data3/hehaorui/pretrained_models/VC/old_whisper/pytorch_model.bin" +hubert="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602/model.safetensors" +hubert_se="/mnt/petrelfs/hehaorui/data/ckpt/vc/mhubert-noise-se/checkpoint/epoch-0000_step-0080000_loss-1.515860/pytorch_model.bin" +whisper="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_whisper/checkpoint/epoch-0000_step-0400001_loss-1.194134/model.safetensors" +whisper_se="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_whisper_aug/checkpoint/epoch-0000_step-0468003_loss-2.859798/model.safetensors" +whisper_se_spk="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_whisper_aug_spk/checkpoint/epoch-0000_step-0583003_loss-3.672843/model.safetensors" +hubert_se="/mnt/data2/hehaorui/ckpt/zs-vc-ckpt/epoch-0001_step-0796000_loss-0.567479/model.safetensors" +hubert_se_both="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert_aug_spk_both/checkpoint/epoch-0001_step-0844000_loss-1.542532/model.safetensors" + +hubert_ref_noise="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert_ref_noise/checkpoint/epoch-0003_step-1171002_loss-1.399222/model.safetensors" + +old_ref_noise="/mnt/data2/hehaorui/ckpt/zs-vc-ckpt/vc_mls_libri_robust/model.safetensors" +#模型的 +# hubert_clean="xxx" +# hubert_ref_noise="xx" +hubert_both_noise="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert_both_noise/checkpoint/epoch-0002_step-0741002_loss-1.452612/model.safetensors" + +checkpoint_path=$hubert_ref_noise + +# gpu的编号：一般用6/7,换卡 +cuda_id=1 + +#prompt就是reference， target就是ground truth +zero_shot_json_file_path="/mnt/data2/hehaorui/datasets/VCTK/zero_shot_json.json" #测试用例的json文件 +output_dir="/mnt/data2/hehaorui/exp_out_noro" # +vocoder_path="/mnt/data2/wangyuancheng/model_ckpts/ns2/bigvgan/g_00490000" +wavlm_path="/mnt/data3/hehaorui/pretrained_models/wavlm/wavlm-base-plus-sv" +#加一个ASR模型的path +#用来算WER + + +echo "CUDA ID: $cuda_id" +echo "Zero Shot Json File Path: $zero_shot_json_file_path" +echo "Checkpoint Path: $checkpoint_path" +echo "Output Directory: $output_dir" +echo "Vocoder Path: $vocoder_path" +echo "WavLM Path: $wavlm_path" + + +python "${work_dir}"/models/tts/vc/vc_inference.py \ + --config $exp_config \ + --checkpoint_path $checkpoint_path \ + --zero_shot_json_file_path $zero_shot_json_file_path \ + --output_dir $output_dir \ + --cuda_id ${cuda_id} \ + --vocoder_path $vocoder_path \ + --wavlm_path $wavlm_path \ + --ref_noisy \ \ No newline at end of file From 7333555eea775d0f6d194c79c97c834e14a99336 Mon Sep 17 00:00:00 2001 From: kenxxxxx Date: Wed, 17 Jul 2024 23:01:01 +0800 Subject: [PATCH 02/16] successful run code --- bins/vc/train.py | 86 ++ config/vc.json | 77 ++ egs/vc/exp_config_4gpu_clean.json | 57 ++ egs/vc/exp_config_4gpu_noisy.json | 57 ++ egs/vc/exp_config_base.json | 101 +++ egs/vc/exp_config_testing.json | 48 + egs/vc/noro_inference.sh | 52 ++ egs/vc/noro_train_clean.sh | 49 + egs/vc/noro_train_noisy.sh | 80 +- models/base/vc_dataset.py | 344 ++++++++ models/vc/base_trainer.py | 762 ++++++++++++++++ models/vc/hubert_kmeans.py | 142 +++ models/vc/noro_inference.py | 142 +++ models/vc/ns2_uniamphion.py | 1375 +++++++++++++++++++++++++++++ models/vc/vc_dataset.py | 442 ++++++++++ models/vc/vc_loss.py | 89 ++ models/vc/vc_trainer.py | 530 +++++++++++ models/vc/vc_utils.py | 94 ++ 18 files changed, 4466 insertions(+), 61 deletions(-) create mode 100644 bins/vc/train.py create mode 100644 config/vc.json create mode 100644 egs/vc/exp_config_4gpu_clean.json create mode 100644 egs/vc/exp_config_4gpu_noisy.json create mode 100644 egs/vc/exp_config_base.json create mode 100644 egs/vc/exp_config_testing.json create mode 100644 models/base/vc_dataset.py create mode 100644 models/vc/base_trainer.py create mode 100644 models/vc/hubert_kmeans.py create mode 100644 models/vc/noro_inference.py create mode 100644 models/vc/ns2_uniamphion.py create mode 100644 models/vc/vc_dataset.py create mode 100644 models/vc/vc_loss.py create mode 100644 models/vc/vc_trainer.py create mode 100644 models/vc/vc_utils.py diff --git a/bins/vc/train.py b/bins/vc/train.py new file mode 100644 index 00000000..219b16b3 --- /dev/null +++ b/bins/vc/train.py @@ -0,0 +1,86 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import argparse + +import torch + +from models.tts.valle.valle_trainer import VALLETrainer +from models.vc.vc_trainer import VCTrainer +from utils.util import load_config + + + +def build_trainer(args, cfg): + supported_trainer = { + "VC": VCTrainer, + } + trainer_class = supported_trainer[cfg.model_type] + trainer = trainer_class(args, cfg) + return trainer + + +def cuda_relevant(deterministic=False): + torch.cuda.empty_cache() + # TF32 on Ampere and above + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.enabled = True + torch.backends.cudnn.allow_tf32 = True + # Deterministic + torch.backends.cudnn.deterministic = deterministic + torch.backends.cudnn.benchmark = not deterministic + torch.use_deterministic_algorithms(deterministic) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + default="config.json", + help="json files for configurations.", + required=True, + ) + parser.add_argument( + "--exp_name", + type=str, + default="exp_name", + help="A specific name to note the experiment", + required=True, + ) + parser.add_argument( + "--resume", action="store_true", help="The model name to restore" + ) + parser.add_argument( + "--log_level", default="warning", help="logging level (debug, info, warning)" + ) + parser.add_argument( + "--resume_type", + type=str, + default="resume", + help="Resume training or finetuning.", + ) + parser.add_argument( + "--checkpoint_path", + type=str, + default=None, + help="Checkpoint for resume training or finetuning.", + ) + VALLETrainer.add_arguments(parser) + args = parser.parse_args() + cfg = load_config(args.config) + print("experiment name: ", args.exp_name) + # # CUDA settings + cuda_relevant() + # Build trainer + print(f"Building {cfg.model_type} trainer") + trainer = build_trainer(args, cfg) + torch.set_num_threads(1) + torch.set_num_interop_threads(1) + print(f"Start training {cfg.model_type} model") + trainer.train_loop() + + +if __name__ == "__main__": + main() diff --git a/config/vc.json b/config/vc.json new file mode 100644 index 00000000..06afff8d --- /dev/null +++ b/config/vc.json @@ -0,0 +1,77 @@ +{ + "base_config": "config/tts.json", + "model_type": "VC", + "dataset": ["mls"], + "model": { + "reference_encoder": { + "encoder_layer": 6, + "encoder_hidden": 512, + "encoder_head": 8, + "conv_filter_size": 2048, + "conv_kernel_size": 9, + "encoder_dropout": 0.2, + "use_skip_connection": false, + "use_new_ffn": true, + "ref_in_dim": 80, + "ref_out_dim": 512, + "use_query_emb": true, + "num_query_emb": 32 + }, + "diffusion": { + "diffusion_type": "diffusion", // Option: "diffusion" or "flow matching" + "beta_min": 0.05, + "beta_max": 20, + "sigma": 1.0, + "noise_factor": 1.0, + "ode_solve_method": "euler", + "diff_model_type": "WaveNet", // Transformer or WaveNet + "diff_wavenet":{ + "input_size": 80, + "hidden_size": 512, + "out_size": 80, + "num_layers": 47, + "cross_attn_per_layer": 3, + "dilation_cycle": 2, + "attn_head": 8, + "drop_out": 0.2 + } + }, + "prior_encoder": { + "encoder_layer": 6, + "encoder_hidden": 512, + "encoder_head": 8, + "conv_filter_size": 2048, + "conv_kernel_size": 9, + "encoder_dropout": 0.2, + "use_skip_connection": false, + "use_new_ffn": true, + "vocab_size": 256, + "cond_dim": 512, + "duration_predictor": { + "input_size": 512, + "filter_size": 512, + "kernel_size": 3, + "conv_layers": 30, + "cross_attn_per_layer": 3, + "attn_head": 8, + "drop_out": 0.2 + }, + "pitch_predictor": { + "input_size": 512, + "filter_size": 512, + "kernel_size": 5, + "conv_layers": 30, + "cross_attn_per_layer": 3, + "attn_head": 8, + "drop_out": 0.5 + }, + "pitch_min": 50, + "pitch_max": 1100, + "pitch_bins_num": 512 + }, + "vc_feature": { + "content_feature_dim": 768, + "hidden_dim": 512 + } + } +} \ No newline at end of file diff --git a/egs/vc/exp_config_4gpu_clean.json b/egs/vc/exp_config_4gpu_clean.json new file mode 100644 index 00000000..83cff952 --- /dev/null +++ b/egs/vc/exp_config_4gpu_clean.json @@ -0,0 +1,57 @@ +{ + "base_config": "egs/vc/exp_config_base.json", + "dataset": [ + "mls" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + "train_file": "train.json", + "valid_file": "test.json", + "read_metadata": true, + "metadata_dir": "metadata" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "/mnt/data2/hehaorui/ckpt/vc_new_exp", + "train": { + // New trainer and Accelerator + "gradient_accumulation_step": 1, + "tracker": ["tensorboard"], + "max_epoch": 10, + "save_checkpoint_stride": [1000], + "keep_last": [20], + "run_eval": [true], + "dataloader": { + "num_worker": 64, + "pin_memory": true + }, + "adam": { + "lr": 5e-5 + }, + "use_dynamic_batchsize": true, + "batch_size": 8, + "max_tokens": 3200000, + "max_sentences": 64, + "lr_warmup_steps": 5000, + "lr_scheduler": "cosine", + "num_train_steps": 800000 + }, + "trans_exp": { + "directory_list":[ + "/mnt/data4/hehaorui/medium_15s", + "/mnt/data4/hehaorui/large_15s", + "/mnt/data4/hehaorui/small_15s" + ], + "test_directory_list": [ + "/mnt/data2/wangyuancheng/mls_english/test/audio" + ], + "use_speaker": false, + "use_ref_noise": false, + "use_source_noise": false, + "snr_lower": 2, + "snr_upper": 42, + "total_snrlevels": 5, + "noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_train", + "test_noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_test" + } + } \ No newline at end of file diff --git a/egs/vc/exp_config_4gpu_noisy.json b/egs/vc/exp_config_4gpu_noisy.json new file mode 100644 index 00000000..683326b6 --- /dev/null +++ b/egs/vc/exp_config_4gpu_noisy.json @@ -0,0 +1,57 @@ +{ + "base_config": "egs/vc/exp_config_base.json", + "dataset": [ + "mls" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + "train_file": "train.json", + "valid_file": "test.json", + "read_metadata": true, + "metadata_dir": "metadata" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "/mnt/data2/hehaorui/ckpt/vc_new_exp", + "train": { + // New trainer and Accelerator + "gradient_accumulation_step": 1, + "tracker": ["tensorboard"], + "max_epoch": 10, + "save_checkpoint_stride": [1000], + "keep_last": [20], + "run_eval": [true], + "dataloader": { + "num_worker": 64, + "pin_memory": true + }, + "adam": { + "lr": 5e-5 + }, + "use_dynamic_batchsize": true, + "batch_size": 8, + "max_tokens": 3200000, + "max_sentences": 64, + "lr_warmup_steps": 5000, + "lr_scheduler": "cosine", + "num_train_steps": 800000 + }, + "trans_exp": { + "directory_list":[ + "/mnt/data4/hehaorui/medium_15s", + "/mnt/data4/hehaorui/large_15s", + "/mnt/data4/hehaorui/small_15s" + ], + "test_directory_list": [ + "/mnt/data2/wangyuancheng/mls_english/test/audio" + ], + "use_speaker": false, + "use_ref_noise": true, + "use_source_noise": false, + "snr_lower": 2, + "snr_upper": 42, + "total_snrlevels": 5, + "noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_train", + "test_noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_test" + } + } \ No newline at end of file diff --git a/egs/vc/exp_config_base.json b/egs/vc/exp_config_base.json new file mode 100644 index 00000000..89cc15b1 --- /dev/null +++ b/egs/vc/exp_config_base.json @@ -0,0 +1,101 @@ +{ + "base_config": "config/vc.json", + "model_type": "VC", + "dataset": [ + "mls" + ], + "preprocess": { + "use_mel": false, + "use_code": true, + "use_spkid": true, + "use_pitch": true, + "use_duration": true, + "use_phone": true, + "use_len": true, + "use_cross_reference": true, + "train_file": "train.json", + "valid_file": "test.json", + "melspec_dir": "mel", + "code_dir": "code", + "pitch_dir": "pitch", + "duration_dir": "duration", + "metadata_dir": "metadata", + "read_metadata": true, + "clip_mode": "start" + }, + "model": { + "reference_encoder": { + "encoder_layer": 6, + "encoder_hidden": 512, + "encoder_head": 8, + "conv_filter_size": 2048, + "conv_kernel_size": 9, + "encoder_dropout": 0.2, + "use_skip_connection": false, + "use_new_ffn": true, + "ref_in_dim": 80, + // "ref_in_dim": 96, + "ref_out_dim": 512, + "use_query_emb": true, + "num_query_emb": 32 + }, + "diffusion": { + "diffusion_type": "diffusion", // Option: "diffusion" or "flow matching" + "beta_min": 0.05, + "beta_max": 20, + "sigma": 1.0, + "noise_factor": 1.0, + "ode_solve_method": "euler", + "diff_model_type": "WaveNet", // Transformer or WaveNet + "diff_wavenet":{ + // "input_size": 96, + "input_size": 80, + "hidden_size": 512, + "out_size": 80, + // "out_size": 96, + "num_layers": 47, + "cross_attn_per_layer": 3, + "dilation_cycle": 2, + "attn_head": 8, + "drop_out": 0.2 + } + }, + "prior_encoder": { + "encoder_layer": 6, + "encoder_hidden": 512, + "encoder_head": 8, + "conv_filter_size": 2048, + "conv_kernel_size": 9, + "encoder_dropout": 0.2, + "use_skip_connection": false, + "use_new_ffn": true, + "vocab_size": 256, + "cond_dim": 512, + "duration_predictor": { + "input_size": 512, + "filter_size": 512, + "kernel_size": 3, + "conv_layers": 30, + "cross_attn_per_layer": 3, + "attn_head": 8, + "drop_out": 0.2 + }, + "pitch_predictor": { + "input_size": 512, + "filter_size": 512, + "kernel_size": 5, + "conv_layers": 30, + "cross_attn_per_layer": 3, + "attn_head": 8, + "drop_out": 0.5 + }, + "pitch_min": 50, + "pitch_max": 1100, + "pitch_bins_num": 512 + }, + "vc_feature": { + "content_feature_dim": 768, + "hidden_dim": 512 + } + } +} \ No newline at end of file diff --git a/egs/vc/exp_config_testing.json b/egs/vc/exp_config_testing.json new file mode 100644 index 00000000..70bb8067 --- /dev/null +++ b/egs/vc/exp_config_testing.json @@ -0,0 +1,48 @@ +{ + "base_config": "egs/vc/exp_config_base.json", + "dataset": [ + "mls" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + "train_file": "train.json", + "valid_file": "test.json", + "read_metadata": true, + "metadata_dir": "metadata" + }, + // Specify the output root path to save model ckpts and logs + "log_dir": "/mnt/data2/hehaorui/ckpt/vc", + "train": { + // New trainer and Accelerator + "gradient_accumulation_step": 1, + "tracker": ["tensorboard"], + "max_epoch": 5, + "save_checkpoint_stride": [1000], + "keep_last": [100], + "run_eval": [true], + "dataloader": { + "num_worker": 64, + "pin_memory": true + }, + "adam": { + "lr": 5e-5 + }, + "use_dynamic_batchsize": true, + "batch_size": 8, + "max_tokens": 800000, + "max_sentences": 64, + "lr_warmup_steps": 5000, + "lr_scheduler": "cosine", + "num_train_steps": 800000 + }, + "trans_exp": { + "dataset_list":["librilight_large","librilight_medium","librilight_small"], + "test_dataset_list": ["mls_test"], + "cache_dir": "/mnt/data2/hehaorui/exp_cache/vc/newcache", + "use_speaker": true, + "use_noise": true, + "noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_train", + "test_noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_test" + } + } diff --git a/egs/vc/noro_inference.sh b/egs/vc/noro_inference.sh index e69de29b..517a2b74 100644 --- a/egs/vc/noro_inference.sh +++ b/egs/vc/noro_inference.sh @@ -0,0 +1,52 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +export PYTHONPATH="./" + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $exp_dir)) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config_testing.json +fi + +echo "Exprimental Configuration File: $exp_config" + +hubert="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602/model.safetensors" + +checkpoint_path=$hubert + +cuda_id=0 + +output_dir="/home/hehaorui/code/Amphion-1/egs/vc" # +source_path="/home/hehaorui/code/Amphion-1/egs/vc/p233_001.wav" +reference_path="/home/hehaorui/code/Amphion-1/egs/vc/p275_425.wav" + +echo "CUDA ID: $cuda_id" +echo "Zero Shot Json File Path: $zero_shot_json_file_path" +echo "Checkpoint Path: $checkpoint_path" +echo "Output Directory: $output_dir" + + +python "${work_dir}"/models/vc/noro_inference.py \ + --config $exp_config \ + --checkpoint_path $checkpoint_path \ + --output_dir $output_dir \ + --cuda_id ${cuda_id} \ + --source_path $source_path \ + --ref_path $reference_path \ diff --git a/egs/vc/noro_train_clean.sh b/egs/vc/noro_train_clean.sh index e69de29b..6681335e 100644 --- a/egs/vc/noro_train_clean.sh +++ b/egs/vc/noro_train_clean.sh @@ -0,0 +1,49 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + + +######## Build Experiment Environment ########### +exp_dir=$(cd `dirname $0`; pwd) +work_dir=$(dirname $(dirname $exp_dir)) + +export WORK_DIR=$work_dir +export PYTHONPATH=$work_dir +export PYTHONIOENCODING=UTF-8 + +cd $work_dir/modules/monotonic_align +mkdir -p monotonic_align +python setup.py build_ext --inplace +cd $work_dir + +# 这一行开始 + +if [ -z "$exp_config" ]; then + exp_config="${exp_dir}"/exp_config_4gpu_noisy.json +fi +echo "Exprimental Configuration File: $exp_config" + +#这里需要跟exp_config保持对应 +exp_name="new" + +#训练的时候用0，1，2，3 +#测试的时候用6/7 +if [ -z "$gpu" ]; then + gpu="0,6" +fi + +######## Train Model ########### +echo "Exprimental Name: $exp_name" + + + +# 这是指定一个checkpoint 文件夹 +CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ +"${work_dir}"/bins/vc/train.py \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug \ + --resume \ + --resume_type resume \ + --checkpoint_path /mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602 \ No newline at end of file diff --git a/egs/vc/noro_train_noisy.sh b/egs/vc/noro_train_noisy.sh index 6d3de92c..0004e56d 100644 --- a/egs/vc/noro_train_noisy.sh +++ b/egs/vc/noro_train_noisy.sh @@ -3,89 +3,47 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. - -export PYTHONPATH="./" - - ######## Build Experiment Environment ########### exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $(dirname $exp_dir))) +work_dir=$(dirname $(dirname $exp_dir)) export WORK_DIR=$work_dir export PYTHONPATH=$work_dir export PYTHONIOENCODING=UTF-8 - cd $work_dir/modules/monotonic_align mkdir -p monotonic_align python setup.py build_ext --inplace cd $work_dir -# 从这里开始 -export http_proxy=http://127.0.0.1:7890 -export https_proxy=http://127.0.0.1:7890 -export HF_ENDPOINT=https://hf-mirror.com - - -#镜像 export HF_ENDPOINT=https://hf-mirror.com +# 这一行开始 if [ -z "$exp_config" ]; then exp_config="${exp_dir}"/exp_config_4gpu_clean.json fi - - - echo "Exprimental Configuration File: $exp_config" -# hubertnew="/mnt/petrelfs/hehaorui/data/ckpt/vc/newmhubert/model.safetensors" - -hubertold="/mnt/data2/hehaorui/ckpt/zs-vc-ckpt/vc_mls_clean/model.safetensors" -whisperold="/mnt/data3/hehaorui/pretrained_models/VC/old_whisper/pytorch_model.bin" -hubert="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602/model.safetensors" -hubert_se="/mnt/petrelfs/hehaorui/data/ckpt/vc/mhubert-noise-se/checkpoint/epoch-0000_step-0080000_loss-1.515860/pytorch_model.bin" -whisper="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_whisper/checkpoint/epoch-0000_step-0400001_loss-1.194134/model.safetensors" -whisper_se="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_whisper_aug/checkpoint/epoch-0000_step-0468003_loss-2.859798/model.safetensors" -whisper_se_spk="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_whisper_aug_spk/checkpoint/epoch-0000_step-0583003_loss-3.672843/model.safetensors" -hubert_se="/mnt/data2/hehaorui/ckpt/zs-vc-ckpt/epoch-0001_step-0796000_loss-0.567479/model.safetensors" -hubert_se_both="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert_aug_spk_both/checkpoint/epoch-0001_step-0844000_loss-1.542532/model.safetensors" - -hubert_ref_noise="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert_ref_noise/checkpoint/epoch-0003_step-1171002_loss-1.399222/model.safetensors" - -old_ref_noise="/mnt/data2/hehaorui/ckpt/zs-vc-ckpt/vc_mls_libri_robust/model.safetensors" -#模型的 -# hubert_clean="xxx" -# hubert_ref_noise="xx" -hubert_both_noise="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert_both_noise/checkpoint/epoch-0002_step-0741002_loss-1.452612/model.safetensors" +#这里需要跟exp_config保持对应 +exp_name="new" -checkpoint_path=$hubert_ref_noise - -# gpu的编号：一般用6/7,换卡 -cuda_id=1 - -#prompt就是reference， target就是ground truth -zero_shot_json_file_path="/mnt/data2/hehaorui/datasets/VCTK/zero_shot_json.json" #测试用例的json文件 -output_dir="/mnt/data2/hehaorui/exp_out_noro" # -vocoder_path="/mnt/data2/wangyuancheng/model_ckpts/ns2/bigvgan/g_00490000" -wavlm_path="/mnt/data3/hehaorui/pretrained_models/wavlm/wavlm-base-plus-sv" -#加一个ASR模型的path -#用来算WER +#训练的时候用0，1，2，3 +#测试的时候用6/7 +if [ -z "$gpu" ]; then + gpu="0,6" +fi +######## Train Model ########### +echo "Exprimental Name: $exp_name" -echo "CUDA ID: $cuda_id" -echo "Zero Shot Json File Path: $zero_shot_json_file_path" -echo "Checkpoint Path: $checkpoint_path" -echo "Output Directory: $output_dir" -echo "Vocoder Path: $vocoder_path" -echo "WavLM Path: $wavlm_path" -python "${work_dir}"/models/tts/vc/vc_inference.py \ +# 这是指定一个checkpoint 文件夹 +CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ +"${work_dir}"/bins/vc/train.py \ --config $exp_config \ - --checkpoint_path $checkpoint_path \ - --zero_shot_json_file_path $zero_shot_json_file_path \ - --output_dir $output_dir \ - --cuda_id ${cuda_id} \ - --vocoder_path $vocoder_path \ - --wavlm_path $wavlm_path \ - --ref_noisy \ \ No newline at end of file + --exp_name $exp_name \ + --log_level debug \ + --resume \ + --resume_type resume \ + --checkpoint_path /mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602 \ No newline at end of file diff --git a/models/base/vc_dataset.py b/models/base/vc_dataset.py new file mode 100644 index 00000000..8639af3c --- /dev/null +++ b/models/base/vc_dataset.py @@ -0,0 +1,344 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import numpy as np +import torch.utils.data +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from processors.acoustic_extractor import cal_normalized_mel +from text import text_to_sequence +from text.text_token_collation import phoneIDCollation + + +class BaseDataset(torch.utils.data.Dataset): + def __init__(self, cfg, dataset, is_valid=False): + """ + Args: + cfg: config + dataset: dataset name + is_valid: whether to use train or valid dataset + """ + + assert isinstance(dataset, str) + + # self.data_root = processed_data_dir + self.cfg = cfg + + processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) + meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file + self.metafile_path = os.path.join(processed_data_dir, meta_file) + self.metadata = self.get_metadata() + + """ + load spk2id and utt2spk from json file + spk2id: {spk1: 0, spk2: 1, ...} + utt2spk: {dataset_uid: spk1, ...} + """ + if cfg.preprocess.use_spkid: + spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id) + with open(spk2id_path, "r") as f: + self.spk2id = json.load(f) + + utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk) + self.utt2spk = dict() + with open(utt2spk_path, "r") as f: + for line in f.readlines(): + utt, spk = line.strip().split("\t") + self.utt2spk[utt] = spk + + if cfg.preprocess.use_uv: + self.utt2uv_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + self.utt2uv_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.uv_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_pitch: + self.utt2frame_pitch_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_pitch_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.pitch_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_frame_energy: + self.utt2frame_energy_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2frame_energy_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.energy_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_mel: + self.utt2mel_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2mel_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.mel_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_linear: + self.utt2linear_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2linear_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.linear_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_audio: + self.utt2audio_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2audio_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.audio_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_label: + self.utt2label_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2label_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.label_dir, + uid + ".npy", + ) + elif cfg.preprocess.use_one_hot: + self.utt2one_hot_path = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + self.utt2one_hot_path[utt] = os.path.join( + cfg.preprocess.processed_dir, + dataset, + cfg.preprocess.one_hot_dir, + uid + ".npy", + ) + + if cfg.preprocess.use_text or cfg.preprocess.use_phone: + self.utt2seq = {} + for utt_info in self.metadata: + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + if cfg.preprocess.use_text: + text = utt_info["Text"] + sequence = text_to_sequence(text, cfg.preprocess.text_cleaners) + elif cfg.preprocess.use_phone: + # load phoneme squence from phone file + phone_path = os.path.join( + processed_data_dir, cfg.preprocess.phone_dir, uid + ".phone" + ) + with open(phone_path, "r") as fin: + phones = fin.readlines() + assert len(phones) == 1 + phones = phones[0].strip() + phones_seq = phones.split(" ") + + phon_id_collator = phoneIDCollation(cfg, dataset=dataset) + sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq) + + self.utt2seq[utt] = sequence + + def get_metadata(self): + with open(self.metafile_path, "r", encoding="utf-8") as f: + metadata = json.load(f) + + return metadata + + def get_dataset_name(self): + return self.metadata[0]["Dataset"] + + def __getitem__(self, index): + utt_info = self.metadata[index] + + dataset = utt_info["Dataset"] + uid = utt_info["Uid"] + utt = "{}_{}".format(dataset, uid) + + single_feature = dict() + + if self.cfg.preprocess.use_spkid: + single_feature["spk_id"] = np.array( + [self.spk2id[self.utt2spk[utt]]], dtype=np.int32 + ) + + if self.cfg.preprocess.use_mel: + mel = np.load(self.utt2mel_path[utt]) + assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] + if self.cfg.preprocess.use_min_max_norm_mel: + # do mel norm + mel = cal_normalized_mel(mel, utt_info["Dataset"], self.cfg.preprocess) + + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = mel.shape[1] + single_feature["mel"] = mel.T # [T, n_mels] + + if self.cfg.preprocess.use_linear: + linear = np.load(self.utt2linear_path[utt]) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = linear.shape[1] + single_feature["linear"] = linear.T # [T, n_linear] + + if self.cfg.preprocess.use_frame_pitch: + frame_pitch_path = self.utt2frame_pitch_path[utt] + frame_pitch = np.load(frame_pitch_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_pitch) + aligned_frame_pitch = align_length( + frame_pitch, single_feature["target_len"] + ) + single_feature["frame_pitch"] = aligned_frame_pitch + + if self.cfg.preprocess.use_uv: + frame_uv_path = self.utt2uv_path[utt] + frame_uv = np.load(frame_uv_path) + aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) + aligned_frame_uv = [ + 0 if frame_uv else 1 for frame_uv in aligned_frame_uv + ] + aligned_frame_uv = np.array(aligned_frame_uv) + single_feature["frame_uv"] = aligned_frame_uv + + if self.cfg.preprocess.use_frame_energy: + frame_energy_path = self.utt2frame_energy_path[utt] + frame_energy = np.load(frame_energy_path) + if "target_len" not in single_feature.keys(): + single_feature["target_len"] = len(frame_energy) + aligned_frame_energy = align_length( + frame_energy, single_feature["target_len"] + ) + single_feature["frame_energy"] = aligned_frame_energy + + if self.cfg.preprocess.use_audio: + audio = np.load(self.utt2audio_path[utt]) + single_feature["audio"] = audio + single_feature["audio_len"] = audio.shape[0] + + if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text: + single_feature["phone_seq"] = np.array(self.utt2seq[utt]) + single_feature["phone_len"] = len(self.utt2seq[utt]) + + return single_feature + + def __len__(self): + return len(self.metadata) + + +class BaseCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "phone_len": + packed_batch_features["phone_len"] = torch.LongTensor( + [b["phone_len"] for b in batch] + ) + masks = [ + torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["phn_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "audio_len": + packed_batch_features["audio_len"] = torch.LongTensor( + [b["audio_len"] for b in batch] + ) + masks = [ + torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch + ] + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + return packed_batch_features + + +class BaseTestDataset(torch.utils.data.Dataset): + def __init__(self, cfg, args): + raise NotImplementedError + + def get_metadata(self): + raise NotImplementedError + + def __getitem__(self, index): + raise NotImplementedError + + def __len__(self): + return len(self.metadata) + + +class BaseTestCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + raise NotImplementedError + + def __call__(self, batch): + raise NotImplementedError \ No newline at end of file diff --git a/models/vc/base_trainer.py b/models/vc/base_trainer.py new file mode 100644 index 00000000..5a34a04d --- /dev/null +++ b/models/vc/base_trainer.py @@ -0,0 +1,762 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import shutil +import torch +import time +from pathlib import Path +import torch +from tqdm import tqdm +import re +import json5 +import accelerate +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration +from torch.utils.data import ConcatDataset, DataLoader +from accelerate import DistributedDataParallelKwargs +from schedulers.scheduler import Eden +from models.base.base_sampler import build_samplers +from models.base.new_trainer import BaseTrainer + + +class TTSTrainer(BaseTrainer): + r"""The base trainer for all TTS models. It inherits from BaseTrainer and implements + ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this + class, and implement ``_build_model``, ``_forward_step``. + """ + + def __init__(self, args=None, cfg=None): + self.args = args + self.cfg = cfg + + cfg.exp_name = args.exp_name + + # init with accelerate + self._init_accelerator() + self.accelerator.wait_for_everyone() + + with self.accelerator.main_process_first(): + self.logger = get_logger(args.exp_name, log_level="INFO") + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New training process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + self.logger.info(f"Experiment name: {args.exp_name}") + self.logger.info(f"Experiment directory: {self.exp_dir}") + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # init counts + self.batch_count: int = 0 + self.step: int = 0 + self.epoch: int = 0 + self.max_epoch = ( + self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") + ) + self.logger.info( + "Max epoch: {}".format( + self.max_epoch if self.max_epoch < float("inf") else "Unlimited" + ) + ) + + # Check values + if self.accelerator.is_main_process: + self.__check_basic_configs() + # Set runtime configs + self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride + self.checkpoints_path = [ + [] for _ in range(len(self.save_checkpoint_stride)) + ] + self.keep_last = [ + i if i > 0 else float("inf") for i in self.cfg.train.keep_last + ] + self.run_eval = self.cfg.train.run_eval + + # set random seed + with self.accelerator.main_process_first(): + # start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + # self.logger.debug( + # f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + # ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # setup data_loader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.train_dataloader, self.valid_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # # save phone table to exp dir. Should be done before building model due to loading phone table in model + # if cfg.preprocess.use_phone and cfg.preprocess.phone_extractor != "lexicon": + # self._save_phone_symbols_file_to_exp_path() + + # setup model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + self.logger.debug(self.model) + self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") + self.logger.info( + f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" + ) + + # optimizer & scheduler + with self.accelerator.main_process_first(): + self.logger.info("Building optimizer and scheduler...") + start = time.monotonic_ns() + self.optimizer = self._build_optimizer() + self.scheduler = self._build_scheduler() + end = time.monotonic_ns() + self.logger.info( + f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" + ) + + # create criterion + with self.accelerator.main_process_first(): + self.logger.info("Building criterion...") + start = time.monotonic_ns() + self.criterion = self._build_criterion() + end = time.monotonic_ns() + self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") + + # Resume or Finetune + with self.accelerator.main_process_first(): + self._check_resume() + + # accelerate prepare + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + self._accelerator_prepare() + end = time.monotonic_ns() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") + + # save config file path + self.config_save_path = os.path.join(self.exp_dir, "args.json") + self.device = self.accelerator.device + + if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training: + self.speakers = self._build_speaker_lut() + self.utt2spk_dict = self._build_utt2spk_dict() + + # Only for TTS tasks + self.task_type = "TTS" + self.logger.info("Task type: {}".format(self.task_type)) + + def _check_resume(self): + # if args.resume: + if self.args.resume or ( + self.cfg.model_type == "VALLE" and self.args.train_stage == 2 + ): + if self.cfg.model_type == "VALLE" and self.args.train_stage == 2: + self.args.resume_type = "finetune" + + self.logger.info("Resuming from checkpoint...") + self.ckpt_path = self._load_model( + self.checkpoint_dir, self.args.checkpoint_path, self.args.resume_type + ) + self.checkpoints_path = json.load( + open(os.path.join(self.ckpt_path, "ckpts.json"), "r") + ) + + def _init_accelerator(self): + self.exp_dir = os.path.join( + os.path.abspath(self.cfg.log_dir), self.args.exp_name + ) + project_config = ProjectConfiguration( + project_dir=self.exp_dir, + logging_dir=os.path.join(self.exp_dir, "log"), + ) + kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + self.accelerator = accelerate.Accelerator( + gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, + log_with=self.cfg.train.tracker, + project_config=project_config, + kwargs_handlers=[kwargs], + ) + if self.accelerator.is_main_process: + os.makedirs(project_config.project_dir, exist_ok=True) + os.makedirs(project_config.logging_dir, exist_ok=True) + with self.accelerator.main_process_first(): + self.accelerator.init_trackers(self.args.exp_name) + + def _accelerator_prepare(self): + ( + self.train_dataloader, + self.valid_dataloader, + ) = self.accelerator.prepare( + self.train_dataloader, + self.valid_dataloader, + ) + + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key] = self.accelerator.prepare(self.model[key]) + else: + self.model = self.accelerator.prepare(self.model) + + if isinstance(self.optimizer, dict): + for key in self.optimizer.keys(): + self.optimizer[key] = self.accelerator.prepare(self.optimizer[key]) + else: + self.optimizer = self.accelerator.prepare(self.optimizer) + + if isinstance(self.scheduler, dict): + for key in self.scheduler.keys(): + self.scheduler[key] = self.accelerator.prepare(self.scheduler[key]) + else: + self.scheduler = self.accelerator.prepare(self.scheduler) + + ### Following are methods only for TTS tasks ### + def _build_dataset(self): + pass + + def _build_criterion(self): + pass + + def _build_model(self): + pass + + def _build_dataloader(self): + """Build dataloader which merges a series of datasets.""" + # Build dataset instance for each dataset and combine them by ConcatDataset + Dataset, Collator = self._build_dataset() + + # Build train set + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=False) + datasets_list.append(subdataset) + train_dataset = ConcatDataset(datasets_list) + train_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + + # Build test set + datasets_list = [] + for dataset in self.cfg.dataset: + subdataset = Dataset(self.cfg, dataset, is_valid=True) + datasets_list.append(subdataset) + valid_dataset = ConcatDataset(datasets_list) + valid_collate = Collator(self.cfg) + _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid") + valid_loader = DataLoader( + valid_dataset, + collate_fn=valid_collate, + batch_sampler=batch_sampler, + num_workers=self.cfg.train.dataloader.num_worker, + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + return train_loader, valid_loader + + def _build_optimizer(self): + pass + + def _build_scheduler(self): + pass + + def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): + """Load model from checkpoint. If a folder is given, it will + load the latest checkpoint in checkpoint_dir. If a path is given + it will load the checkpoint specified by checkpoint_path. + **Only use this method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None or checkpoint_path == "": + ls = [str(i) for i in Path(checkpoint_dir).glob("*")] + # example path epoch-0000_step-0017000_loss-1.972191, 找step最大的 + checkpoint_path = max(ls, key=lambda x: int(x.split("_")[-2].split("-")[-1])) + + if self.accelerator.is_main_process: + self.logger.info("Load model from {}".format(checkpoint_path)) + print("Load model from {}".format(checkpoint_path)) + # if resume_type == "resume": + # self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + # self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 + # self.accelerator.load_state(checkpoint_path) + if resume_type == "resume": + self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + if isinstance(self.model, dict): + for idx, sub_model in enumerate(self.model.keys()): + try: + if idx == 0: + ckpt_name = "pytorch_model.bin" + else: + ckpt_name = "pytorch_model_{}.bin".format(idx) + + self.model[sub_model].load_state_dict( + torch.load(os.path.join(checkpoint_path, ckpt_name)) + ) + except: + if idx == 0: + ckpt_name = "model.safetensors" + else: + ckpt_name = "model_{}.safetensors".format(idx) + + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model[sub_model]), + os.path.join(checkpoint_path, ckpt_name), + ) + + self.model[sub_model].cuda(self.accelerator.device) + else: + try: + self.model.load_state_dict( + torch.load(os.path.join(checkpoint_path, "pytorch_model.bin")) + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'pytorch_model.bin' for resume") + except: + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "model.safetensors"), + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'model.safetensors' for resume") + self.model.cuda(self.accelerator.device) + if self.accelerator.is_main_process: + self.logger.info("Load model weights SUCCESS!") + elif resume_type == "finetune": + if isinstance(self.model, dict): + for idx, sub_model in enumerate(self.model.keys()): + try: + if idx == 0: + ckpt_name = "pytorch_model.bin" + else: + ckpt_name = "pytorch_model_{}.bin".format(idx) + + self.model[sub_model].load_state_dict( + torch.load(os.path.join(checkpoint_path, ckpt_name)) + ) + except: + if idx == 0: + ckpt_name = "model.safetensors" + else: + ckpt_name = "model_{}.safetensors".format(idx) + + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model[sub_model]), + os.path.join(checkpoint_path, ckpt_name), + ) + + self.model[sub_model].cuda(self.accelerator.device) + else: + try: + self.model.load_state_dict( + torch.load(os.path.join(checkpoint_path, "pytorch_model.bin")) + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'pytorch_model.bin' for finetune") + except: + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "model.safetensors"), + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'model.safetensors' for finetune") + self.model.cuda(self.accelerator.device) + if self.accelerator.is_main_process: + self.logger.info("Load model weights for finetune SUCCESS!") + else: + raise ValueError("Unsupported resume type: {}".format(resume_type)) + return checkpoint_path + + ### THIS IS MAIN ENTRY ### + def train_loop(self): + r"""Training loop. The public entry of training process.""" + # Wait everyone to prepare before we move on + self.accelerator.wait_for_everyone() + # dump config file + if self.accelerator.is_main_process: + self.__dump_cfg(self.config_save_path) + self.accelerator.wait_for_everyone() + while self.epoch < self.max_epoch: + self.logger.info("\n") + self.logger.info("-" * 32) + self.logger.info("Epoch {}: ".format(self.epoch)) + + # Do training & validating epoch + train_total_loss, train_losses = self._train_epoch() + if isinstance(train_losses, dict): + for key, loss in train_losses.items(): + self.logger.info(" |- Train/{} Loss: {:.6f}".format(key, loss)) + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.epoch, + ) + + valid_total_loss, valid_losses = self._valid_epoch() + if isinstance(valid_losses, dict): + for key, loss in valid_losses.items(): + self.logger.info(" |- Valid/{} Loss: {:.6f}".format(key, loss)) + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.epoch, + ) + + self.logger.info(" |- Train/Loss: {:.6f}".format(train_total_loss)) + self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_total_loss)) + self.accelerator.log( + { + "Epoch/Train Loss": train_total_loss, + "Epoch/Valid Loss": valid_total_loss, + }, + step=self.epoch, + ) + + self.accelerator.wait_for_everyone() + + # Check if hit save_checkpoint_stride and run_eval + run_eval = False + if self.accelerator.is_main_process: + save_checkpoint = False + hit_dix = [] + for i, num in enumerate(self.save_checkpoint_stride): + if self.epoch % num == 0: + save_checkpoint = True + hit_dix.append(i) + run_eval |= self.run_eval[i] + + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process and save_checkpoint: + path = os.path.join( + self.checkpoint_dir, + "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, train_total_loss + ), + ) + self.accelerator.save_state(path) + + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + + # Remove old checkpoints + to_remove = [] + for idx in hit_dix: + self.checkpoints_path[idx].append(path) + while len(self.checkpoints_path[idx]) > self.keep_last[idx]: + to_remove.append((idx, self.checkpoints_path[idx].pop(0))) + + # Search conflicts + total = set() + for i in self.checkpoints_path: + total |= set(i) + do_remove = set() + for idx, path in to_remove[::-1]: + if path in total: + self.checkpoints_path[idx].insert(0, path) + else: + do_remove.add(path) + + # Remove old checkpoints + for path in do_remove: + shutil.rmtree(path, ignore_errors=True) + self.logger.debug(f"Remove old checkpoint: {path}") + + self.accelerator.wait_for_everyone() + if run_eval: + # TODO: run evaluation + pass + + # Update info for each epoch + self.epoch += 1 + + # Finish training and save final checkpoint + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process: + path = os.path.join( + self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_total_loss + ), + ) + self.accelerator.save_state( + os.path.join( + self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, valid_total_loss + ), + ) + ) + + json.dump( + self.checkpoints_path, + open(os.path.join(path, "ckpts.json"), "w"), + ensure_ascii=False, + indent=4, + ) + + self.accelerator.end_training() + + ### Following are methods that can be used directly in child classes ### + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].train() + else: + self.model.train() + + epoch_sum_loss: float = 0.0 + epoch_losses: dict = {} + epoch_step: int = 0 + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + # Do training step and BP + with self.accelerator.accumulate(self.model): + total_loss, train_losses, _ = self._train_step(batch) + self.batch_count += 1 + + # Update info for each step + # TODO: step means BP counts or batch counts? + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + if isinstance(self.scheduler, dict): + for key in self.scheduler.keys(): + self.scheduler[key].step() + else: + if isinstance(self.scheduler, Eden): + self.scheduler.step_batch(self.step) + else: + self.scheduler.step() + + epoch_sum_loss += total_loss + + if isinstance(train_losses, dict): + for key, value in train_losses.items(): + epoch_losses[key] += value + + if isinstance(train_losses, dict): + for key, loss in train_losses.items(): + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.step, + ) + + self.step += 1 + epoch_step += 1 + + self.accelerator.wait_for_everyone() + + epoch_sum_loss = ( + epoch_sum_loss + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + + for key in epoch_losses.keys(): + epoch_losses[key] = ( + epoch_losses[key] + / len(self.train_dataloader) + * self.cfg.train.gradient_accumulation_step + ) + return epoch_sum_loss, epoch_losses + + @torch.inference_mode() + def _valid_epoch(self): + r"""Testing epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].eval() + else: + self.model.eval() + + epoch_sum_loss = 0.0 + epoch_losses = dict() + for batch in tqdm( + self.valid_dataloader, + desc=f"Validating Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + total_loss, valid_losses, valid_stats = self._valid_step(batch) + epoch_sum_loss += total_loss + if isinstance(valid_losses, dict): + for key, value in valid_losses.items(): + if key not in epoch_losses.keys(): + epoch_losses[key] = value + else: + epoch_losses[key] += value + + epoch_sum_loss = epoch_sum_loss / len(self.valid_dataloader) + for key in epoch_losses.keys(): + epoch_losses[key] = epoch_losses[key] / len(self.valid_dataloader) + + self.accelerator.wait_for_everyone() + + return epoch_sum_loss, epoch_losses + + def _train_step(self): + pass + + def _valid_step(self, batch): + pass + + def _inference(self): + pass + + def _is_valid_pattern(self, directory_name): + directory_name = str(directory_name) + pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}" + return re.match(pattern, directory_name) is not None + + def _check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + + def __dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) + + def __check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + # TODO: check other values + + @staticmethod + def __count_parameters(model): + model_param = 0.0 + if isinstance(model, dict): + for key, value in model.items(): + model_param += sum(p.numel() for p in model[key].parameters()) + else: + model_param = sum(p.numel() for p in model.parameters()) + return model_param + + def _build_speaker_lut(self): + # combine speakers + if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)): + speakers = {} + else: + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "r" + ) as speaker_file: + speakers = json.load(speaker_file) + for dataset in self.cfg.dataset: + speaker_lut_path = os.path.join( + self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id + ) + with open(speaker_lut_path, "r") as speaker_lut_path: + singer_lut = json.load(speaker_lut_path) + for singer in singer_lut.keys(): + if singer not in speakers: + speakers[singer] = len(speakers) + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "w" + ) as speaker_file: + json.dump(speakers, speaker_file, indent=4, ensure_ascii=False) + print( + "speakers have been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) + ) + ) + return speakers + + def _build_utt2spk_dict(self): + # combine speakers + utt2spk = {} + if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk)): + utt2spk = {} + else: + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "r" + ) as utt2spk_file: + for line in utt2spk_file.readlines(): + utt, spk = line.strip().split("\t") + utt2spk[utt] = spk + for dataset in self.cfg.dataset: + utt2spk_dict_path = os.path.join( + self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.utt2spk + ) + with open(utt2spk_dict_path, "r") as utt2spk_dict: + for line in utt2spk_dict.readlines(): + utt, spk = line.strip().split("\t") + if utt not in utt2spk.keys(): + utt2spk[utt] = spk + with open( + os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "w" + ) as utt2spk_file: + for utt, spk in utt2spk.items(): + utt2spk_file.write(utt + "\t" + spk + "\n") + print( + "utterance and speaker mapper have been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk) + ) + ) + return utt2spk + + def _save_phone_symbols_file_to_exp_path(self): + phone_symbols_file = os.path.join( + self.cfg.preprocess.processed_dir, + self.cfg.dataset[0], + self.cfg.preprocess.symbols_dict, + ) + phone_symbols_file_to_exp_path = os.path.join( + self.exp_dir, self.cfg.preprocess.symbols_dict + ) + shutil.copy(phone_symbols_file, phone_symbols_file_to_exp_path) + print( + "phone symbols been dumped to {}".format( + os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict) + ) + ) \ No newline at end of file diff --git a/models/vc/hubert_kmeans.py b/models/vc/hubert_kmeans.py new file mode 100644 index 00000000..80886771 --- /dev/null +++ b/models/vc/hubert_kmeans.py @@ -0,0 +1,142 @@ +from pathlib import Path +import torch +from torch import nn, einsum +from torchaudio.functional import resample +from einops import rearrange, repeat, pack, unpack +import torch.nn.functional as F + +# from audiolm_pytorch.utils import curtail_to_multiple + +# suppress a few warnings + + +def noop(*args, **kwargs): + pass + + +import warnings +import logging + +logging.root.setLevel(logging.ERROR) + +warnings.warn = noop + +# import fairseq and joblib for hubert + +import joblib +import fairseq + +# helper functions + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def round_down_nearest_multiple(num, divisor): + return num // divisor * divisor + + +def curtail_to_multiple(t, mult, from_left=False): + data_len = t.shape[-1] + rounded_seq_len = round_down_nearest_multiple(data_len, mult) + seq_slice = ( + slice(None, rounded_seq_len) if not from_left else slice(-rounded_seq_len, None) + ) + return t[..., seq_slice] + + +class HubertWithKmeans(nn.Module): + """ + checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert + or you can train your own + """ + + def __init__( + self, + checkpoint_path= '/mnt/data3/hehaorui/pretrained_models/mhubert/mhubert_base_vp_en_es_fr_it3.pt', + kmeans_path = '/mnt/data3/hehaorui/pretrained_models/mhubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', + target_sample_hz=16000, + seq_len_multiple_of=None, + output_layer=9, + ): + super().__init__() + + self.target_sample_hz = target_sample_hz + self.seq_len_multiple_of = seq_len_multiple_of + self.output_layer = output_layer + + model_path = Path(checkpoint_path) + kmeans_path = Path(kmeans_path) + + assert model_path.exists(), f"path {checkpoint_path} does not exist" + assert kmeans_path.exists(), f"path {kmeans_path} does not exist" + + checkpoint = torch.load(checkpoint_path) + load_model_input = {checkpoint_path: checkpoint} + model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task( + load_model_input + ) + + self.model = model[0] + self.model.eval() + + kmeans = joblib.load(kmeans_path) + + self.kmeans = kmeans + + self.register_buffer( + "cluster_centers", torch.from_numpy(kmeans.cluster_centers_) + ) + + @property + def groups(self): + return 1 + + @property + def codebook_size(self): + return self.kmeans.n_clusters + + @property + def downsample_factor(self): + # todo: double check + return 320 + + @torch.inference_mode() + def forward(self, wav_input, flatten=True, input_sample_hz=None): + batch, device = wav_input.shape[0], wav_input.device + wav_input = F.pad(wav_input, (40, 40), "reflect") + + if exists(input_sample_hz): + wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz) + + if exists(self.seq_len_multiple_of): + wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of) + + embed = self.model( + wav_input, + features_only=True, + mask=False, # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code + output_layer=self.output_layer, + )["x"] + + embed = embed.permute((0, 2, 1)) + embed = F.interpolate(embed, scale_factor=8, mode="nearest") + embed = F.interpolate(embed, scale_factor=0.2, mode="nearest") + embed = embed.permute((0, 2, 1)) + + batched_cluster_centers = repeat( + self.cluster_centers, "c d -> b c d", b=embed.shape[0] + ) + dists = -torch.cdist(embed, batched_cluster_centers, p=2) + clusters = dists.argmax(dim=-1) # (batch, seq_len) + quantize = F.embedding(clusters, self.cluster_centers) + + if flatten: + return clusters, quantize + + return rearrange(clusters, "b ... -> b (...)"), quantize \ No newline at end of file diff --git a/models/vc/noro_inference.py b/models/vc/noro_inference.py new file mode 100644 index 00000000..440e9485 --- /dev/null +++ b/models/vc/noro_inference.py @@ -0,0 +1,142 @@ +import argparse +import torch +import numpy as np +import torch +from safetensors.torch import load_model +import librosa +from utils.util import load_config +from models.vc.vc_trainer import VCTrainer +from utils.util import load_config +from models.vc.ns2_uniamphion import UniAmphionVC +from models.vc.hubert_kmeans import HubertWithKmeans +from models.vc.vc_utils import mel_spectrogram, extract_world_f0 +import os + + +def build_trainer(args, cfg): + supported_trainer = { + "VC": VCTrainer, + } + trainer_class = supported_trainer[cfg.model_type] + trainer = trainer_class(args, cfg) + return trainer + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--config", + default="config.json", + help="json files for configurations.", + required=True, + ) + parser.add_argument( + "--checkpoint_path", + type=str, + help="Checkpoint for resume training or finetuning.", + required=True, + ) + parser.add_argument( + "--output_dir", + help="output path", + required=True, + ) + parser.add_argument( + "--ref_path", + type=str, + help="Reference voice path", + ) + parser.add_argument( + "--source_path", + type=str, + help="source voice path", + ) + parser.add_argument( + "--cuda_id", + type=int, + default=7, + help="Cuda id for training." + ) + + + parser.add_argument("--local_rank", default=-1, type=int) + args = parser.parse_args() + cfg = load_config(args.config) + if not os.path.exists(args.output_dir): + os.makedirs(args.output_dir) + + cuda_id = args.cuda_id + args.local_rank = torch.device(f"cuda:{cuda_id}") + print("local rank", args.local_rank) + + + args.content_extractor = "mhubert" + + with torch.cuda.device(args.local_rank): + torch.cuda.empty_cache() + ckpt_path = args.checkpoint_path + + w2v = HubertWithKmeans() + + + w2v = w2v.to(device=args.local_rank) + w2v.eval() + + model = UniAmphionVC(cfg=cfg.model) + print("loading model") + + load_model(model, ckpt_path) + print("model loaded") + model.cuda(args.local_rank) + model.eval() + + wav_path = args.source_path + ref_wav_path = args.ref_path + + wav, _ = librosa.load(wav_path, sr=16000) + wav = np.pad(wav, (0, 1600 - len(wav) % 1600)) + audio = torch.from_numpy(wav).to(args.local_rank) + audio = audio[None, :] + + + ref_wav,_ = librosa.load(ref_wav_path, sr=16000) + ref_wav = np.pad(ref_wav, (0, 200 - len(ref_wav) % 200)) + ref_audio = torch.from_numpy(ref_wav).to(args.local_rank) + ref_audio = ref_audio[None, :] + + + with torch.no_grad(): + ref_mel = mel_spectrogram(ref_audio) + ref_mel = ref_mel.transpose(1, 2).to(device=args.local_rank) + ref_mask = torch.ones(ref_mel.shape[0], ref_mel.shape[1]).to(args.local_rank).bool() + + + + _, content_feature = w2v(audio) + content_feature = content_feature.to(device=args.local_rank) + + pitch_raw = extract_world_f0(audio) + pitch = (pitch_raw - pitch_raw.mean(dim=1, keepdim=True)) / ( + pitch_raw.std(dim=1, keepdim=True) + 1e-6 + ) + + # pitch = content_feature.mean(dim=2) + # pitch.zero_() + + x0 = model.inference( + content_feature=content_feature, + pitch=pitch, + x_ref=ref_mel, + x_ref_mask=ref_mask, + inference_steps=200, + sigma=1.2, + )# 150-300 0.95-1.5 + + recon_path = f"{args.output_dir}/recon_mel.npy" + np.save(recon_path, x0.transpose(1, 2).detach().cpu().numpy()) + print(f"mel saved to: {recon_path}") + + + +if __name__ == "__main__": + main() diff --git a/models/vc/ns2_uniamphion.py b/models/vc/ns2_uniamphion.py new file mode 100644 index 00000000..b6a699d8 --- /dev/null +++ b/models/vc/ns2_uniamphion.py @@ -0,0 +1,1375 @@ +import torch +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +import math +import json5 +from librosa.filters import mel as librosa_mel_fn +from einops.layers.torch import Rearrange + +sr = 16000 +MAX_WAV_VALUE = 32768.0 + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + return np.log(np.clip(x, a_min=clip_val, a_max=None) * C) + + +def dynamic_range_decompression(x, C=1): + return np.exp(x) / C + + +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression_torch(x, C=1): + return torch.exp(x) / C + + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def spectral_de_normalize_torch(magnitudes): + output = dynamic_range_decompression_torch(magnitudes) + return output + + +mel_basis = {} +hann_window = {} +init_mel_and_hann = False + + +def mel_spectrogram( + y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False +): + if torch.min(y) < -1.0: + print("min value is ", torch.min(y)) + if torch.max(y) > 1.0: + print("max value is ", torch.max(y)) + + global mel_basis, hann_window, init_mel_and_hann + if not init_mel_and_hann: + mel = librosa_mel_fn( + sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax + ) + mel_basis[str(fmax) + "_" + str(y.device)] = ( + torch.from_numpy(mel).float().to(y.device) + ) + hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device) + init_mel_and_hann = True + + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + + # complex tensor as default, then use view_as_real for future pytorch compatibility + spec = torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + + return spec + + +class Diffusion(nn.Module): + def __init__(self, cfg, diff_model): + super().__init__() + + self.cfg = cfg + self.diff_estimator = diff_model + self.beta_min = cfg.beta_min + self.beta_max = cfg.beta_max + self.sigma = cfg.sigma + self.noise_factor = cfg.noise_factor + + def forward(self, x, condition_embedding, x_mask, reference_embedding, offset=1e-5): + diffusion_step = torch.rand( + x.shape[0], dtype=x.dtype, device=x.device, requires_grad=False + ) + diffusion_step = torch.clamp(diffusion_step, offset, 1.0 - offset) + xt, z = self.forward_diffusion(x0=x, diffusion_step=diffusion_step) + + cum_beta = self.get_cum_beta(diffusion_step.unsqueeze(-1).unsqueeze(-1)) + x0_pred = self.diff_estimator( + xt, condition_embedding, x_mask, reference_embedding, diffusion_step + ) + mean_pred = x0_pred * torch.exp(-0.5 * cum_beta / (self.sigma**2)) + variance = (self.sigma**2) * (1.0 - torch.exp(-cum_beta / (self.sigma**2))) + noise_pred = (xt - mean_pred) / (torch.sqrt(variance) * self.noise_factor) + noise = z + diff_out = {"x0_pred": x0_pred, "noise_pred": noise_pred, "noise": noise} + return diff_out + + @torch.no_grad() + def get_cum_beta(self, time_step): + return self.beta_min * time_step + 0.5 * (self.beta_max - self.beta_min) * ( + time_step**2 + ) + + @torch.no_grad() + def get_beta_t(self, time_step): + return self.beta_min + (self.beta_max - self.beta_min) * time_step + + @torch.no_grad() + def forward_diffusion(self, x0, diffusion_step): + time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1) + cum_beta = self.get_cum_beta(time_step) + mean = x0 * torch.exp(-0.5 * cum_beta / (self.sigma**2)) + variance = (self.sigma**2) * (1 - torch.exp(-cum_beta / (self.sigma**2))) + z = torch.randn(x0.shape, dtype=x0.dtype, device=x0.device, requires_grad=False) + xt = mean + z * torch.sqrt(variance) * self.noise_factor + return xt, z + + @torch.no_grad() + def cal_dxt( + self, xt, condition_embedding, x_mask, reference_embedding, diffusion_step, h + ): + time_step = diffusion_step.unsqueeze(-1).unsqueeze(-1) + cum_beta = self.get_cum_beta(time_step=time_step) + beta_t = self.get_beta_t(time_step=time_step) + x0_pred = self.diff_estimator( + xt, condition_embedding, x_mask, reference_embedding, diffusion_step + ) + mean_pred = x0_pred * torch.exp(-0.5 * cum_beta / (self.sigma**2)) + noise_pred = xt - mean_pred + variance = (self.sigma**2) * (1.0 - torch.exp(-cum_beta / (self.sigma**2))) + logp = -noise_pred / (variance + 1e-8) + dxt = -0.5 * h * beta_t * (logp + xt / (self.sigma**2)) + return dxt + + @torch.no_grad() + def reverse_diffusion( + self, z, condition_embedding, x_mask, reference_embedding, n_timesteps + ): + h = 1.0 / max(n_timesteps, 1) + xt = z + for i in range(n_timesteps): + t = (1.0 - (i + 0.5) * h) * torch.ones( + z.shape[0], dtype=z.dtype, device=z.device + ) + dxt = self.cal_dxt( + xt, + condition_embedding, + x_mask, + reference_embedding, + diffusion_step=t, + h=h, + ) + xt_ = xt - dxt + if self.cfg.ode_solve_method == "midpoint": + x_mid = 0.5 * (xt_ + xt) + dxt = self.cal_dxt( + x_mid, + condition_embedding, + x_mask, + reference_embedding, + diffusion_step=t + 0.5 * h, + h=h, + ) + xt = xt - dxt + elif self.cfg.ode_solve_method == "euler": + xt = xt_ + return xt + + @torch.no_grad() + def reverse_diffusion_from_t( + self, z, condition_embedding, x_mask, reference_embedding, n_timesteps, t_start + ): + h = t_start / max(n_timesteps, 1) + xt = z + for i in range(n_timesteps): + t = (t_start - (i + 0.5) * h) * torch.ones( + z.shape[0], dtype=z.dtype, device=z.device + ) + dxt = self.cal_dxt( + xt, + x_mask, + condition_embedding, + x_mask, + reference_embedding, + diffusion_step=t, + h=h, + ) + xt_ = xt - dxt + if self.cfg.ode_solve_method == "midpoint": + x_mid = 0.5 * (xt_ + xt) + dxt = self.cal_dxt( + x_mid, + condition_embedding, + x_mask, + reference_embedding, + diffusion_step=t + 0.5 * h, + h=h, + ) + xt = xt - dxt + elif self.cfg.ode_solve_method == "euler": + xt = xt_ + return xt + + +class SinusoidalPosEmb(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + device = x.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device) * -emb) + emb = x[:, None] * emb[None, :] + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb + + +class Linear2(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + self.linear_1 = nn.Linear(dim, dim * 2) + self.linear_2 = nn.Linear(dim * 2, dim) + self.linear_1.weight.data.normal_(0.0, 0.02) + self.linear_2.weight.data.normal_(0.0, 0.02) + + def forward(self, x): + x = self.linear_1(x) + x = F.silu(x) + x = self.linear_2(x) + return x + + +class StyleAdaptiveLayerNorm(nn.Module): + def __init__(self, normalized_shape, eps=1e-5): + super().__init__() + self.in_dim = normalized_shape + self.norm = nn.LayerNorm(self.in_dim, eps=eps, elementwise_affine=False) + self.style = nn.Linear(self.in_dim, self.in_dim * 2) + self.style.bias.data[: self.in_dim] = 1 + self.style.bias.data[self.in_dim :] = 0 + + def forward(self, x, condition): + # x: (B, T, d); condition: (B, T, d) + + style = self.style(torch.mean(condition, dim=1, keepdim=True)) + + gamma, beta = style.chunk(2, -1) + + out = self.norm(x) + + out = gamma * out + beta + return out + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model, dropout, max_len=5000): + super().__init__() + + self.dropout = dropout + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(max_len, 1, d_model) + pe[:, 0, 0::2] = torch.sin(position * div_term) + pe[:, 0, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x): + x = x + self.pe[: x.size(0)] + return F.dropout(x, self.dropout, training=self.training) + + +class TransformerFFNLayer(nn.Module): + def __init__( + self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout + ): + super().__init__() + + self.encoder_hidden = encoder_hidden + self.conv_filter_size = conv_filter_size + self.conv_kernel_size = conv_kernel_size + self.encoder_dropout = encoder_dropout + + self.ffn_1 = nn.Conv1d( + self.encoder_hidden, + self.conv_filter_size, + self.conv_kernel_size, + padding=self.conv_kernel_size // 2, + ) + self.ffn_1.weight.data.normal_(0.0, 0.02) + self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden) + self.ffn_2.weight.data.normal_(0.0, 0.02) + + def forward(self, x): + # x: (B, T, d) + x = self.ffn_1(x.permute(0, 2, 1)).permute( + 0, 2, 1 + ) # (B, T, d) -> (B, d, T) -> (B, T, d) + x = F.silu(x) + x = F.dropout(x, self.encoder_dropout, training=self.training) + x = self.ffn_2(x) + return x + + +class TransformerFFNLayerOld(nn.Module): + def __init__( + self, encoder_hidden, conv_filter_size, conv_kernel_size, encoder_dropout + ): + super().__init__() + + self.encoder_hidden = encoder_hidden + self.conv_filter_size = conv_filter_size + self.conv_kernel_size = conv_kernel_size + self.encoder_dropout = encoder_dropout + + self.ffn_1 = nn.Linear(self.encoder_hidden, self.conv_filter_size) + self.ffn_1.weight.data.normal_(0.0, 0.02) + self.ffn_2 = nn.Linear(self.conv_filter_size, self.encoder_hidden) + self.ffn_2.weight.data.normal_(0.0, 0.02) + + def forward(self, x): + x = self.ffn_1(x) + x = F.silu(x) + x = F.dropout(x, self.encoder_dropout, training=self.training) + x = self.ffn_2(x) + return x + + +class TransformerEncoderLayer(nn.Module): + def __init__( + self, + encoder_hidden, + encoder_head, + conv_filter_size, + conv_kernel_size, + encoder_dropout, + use_cln, + use_skip_connection, + use_new_ffn, + add_diff_step, + ): + super().__init__() + self.encoder_hidden = encoder_hidden + self.encoder_head = encoder_head + self.conv_filter_size = conv_filter_size + self.conv_kernel_size = conv_kernel_size + self.encoder_dropout = encoder_dropout + self.use_cln = use_cln + self.use_skip_connection = use_skip_connection + self.use_new_ffn = use_new_ffn + self.add_diff_step = add_diff_step + + if not self.use_cln: + self.ln_1 = nn.LayerNorm(self.encoder_hidden) + self.ln_2 = nn.LayerNorm(self.encoder_hidden) + else: + self.ln_1 = StyleAdaptiveLayerNorm(self.encoder_hidden) + self.ln_2 = StyleAdaptiveLayerNorm(self.encoder_hidden) + + self.self_attn = nn.MultiheadAttention( + self.encoder_hidden, self.encoder_head, batch_first=True + ) + + if self.use_new_ffn: + self.ffn = TransformerFFNLayer( + self.encoder_hidden, + self.conv_filter_size, + self.conv_kernel_size, + self.encoder_dropout, + ) + else: + self.ffn = TransformerFFNLayerOld( + self.encoder_hidden, + self.conv_filter_size, + self.conv_kernel_size, + self.encoder_dropout, + ) + + if self.use_skip_connection: + self.skip_linear = nn.Linear(self.encoder_hidden * 2, self.encoder_hidden) + self.skip_linear.weight.data.normal_(0.0, 0.02) + self.skip_layernorm = nn.LayerNorm(self.encoder_hidden) + + if self.add_diff_step: + self.diff_step_emb = SinusoidalPosEmb(dim=self.encoder_hidden) + # self.diff_step_projection = nn.linear(self.encoder_hidden, self.encoder_hidden) + # self.encoder_hidden.weight.data.normal_(0.0, 0.02) + self.diff_step_projection = Linear2(self.encoder_hidden) + + def forward( + self, x, key_padding_mask, conditon=None, skip_res=None, diffusion_step=None + ): + # x: (B, T, d); key_padding_mask: (B, T), mask is 0; condition: (B, T, d); skip_res: (B, T, d); diffusion_step: (B,) + + if self.use_skip_connection and skip_res != None: + x = torch.cat([x, skip_res], dim=-1) # (B, T, 2*d) + x = self.skip_linear(x) + x = self.skip_layernorm(x) + + if self.add_diff_step and diffusion_step != None: + diff_step_embedding = self.diff_step_emb(diffusion_step) + diff_step_embedding = self.diff_step_projection(diff_step_embedding) + x = x + diff_step_embedding.unsqueeze(1) + + residual = x + + # pre norm + if self.use_cln: + x = self.ln_1(x, conditon) + else: + x = self.ln_1(x) + + # self attention + if key_padding_mask != None: + key_padding_mask_input = ~(key_padding_mask.bool()) + else: + key_padding_mask_input = None + x, _ = self.self_attn( + query=x, key=x, value=x, key_padding_mask=key_padding_mask_input + ) + x = F.dropout(x, self.encoder_dropout, training=self.training) + + x = residual + x + + # pre norm + residual = x + if self.use_cln: + x = self.ln_2(x, conditon) + else: + x = self.ln_2(x) + + # ffn + x = self.ffn(x) + + x = residual + x + return x + + +class TransformerEncoder(nn.Module): + def __init__( + self, + enc_emb_tokens=None, + encoder_layer=None, + encoder_hidden=None, + encoder_head=None, + conv_filter_size=None, + conv_kernel_size=None, + encoder_dropout=None, + use_cln=None, + use_skip_connection=None, + use_new_ffn=None, + add_diff_step=None, + cfg=None, + ): + super().__init__() + + self.encoder_layer = ( + encoder_layer if encoder_layer is not None else cfg.encoder_layer + ) + self.encoder_hidden = ( + encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden + ) + self.encoder_head = ( + encoder_head if encoder_head is not None else cfg.encoder_head + ) + self.conv_filter_size = ( + conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size + ) + self.conv_kernel_size = ( + conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size + ) + self.encoder_dropout = ( + encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout + ) + self.use_cln = use_cln if use_cln is not None else cfg.use_cln + self.use_skip_connection = ( + use_skip_connection + if use_skip_connection is not None + else cfg.use_skip_connection + ) + self.add_diff_step = ( + add_diff_step if add_diff_step is not None else cfg.add_diff_step + ) + self.use_new_ffn = use_new_ffn if use_new_ffn is not None else cfg.use_new_ffn + + if enc_emb_tokens != None: + self.use_enc_emb = True + self.enc_emb_tokens = enc_emb_tokens + else: + self.use_enc_emb = False + + self.position_emb = PositionalEncoding( + self.encoder_hidden, self.encoder_dropout + ) + + self.layers = nn.ModuleList([]) + if self.use_skip_connection: + self.layers.extend( + [ + TransformerEncoderLayer( + self.encoder_hidden, + self.encoder_head, + self.conv_filter_size, + self.conv_kernel_size, + self.encoder_dropout, + self.use_cln, + use_skip_connection=False, + use_new_ffn=self.use_new_ffn, + add_diff_step=self.add_diff_step, + ) + for i in range( + (self.encoder_layer + 1) // 2 + ) # for example: 12 -> 6; 13 -> 7 + ] + ) + self.layers.extend( + [ + TransformerEncoderLayer( + self.encoder_hidden, + self.encoder_head, + self.conv_filter_size, + self.conv_kernel_size, + self.encoder_dropout, + self.use_cln, + use_skip_connection=True, + use_new_ffn=self.use_new_ffn, + add_diff_step=self.add_diff_step, + ) + for i in range( + self.encoder_layer - (self.encoder_layer + 1) // 2 + ) # 12 -> 6; 13 -> 6 + ] + ) + else: + self.layers.extend( + [ + TransformerEncoderLayer( + self.encoder_hidden, + self.encoder_head, + self.conv_filter_size, + self.conv_kernel_size, + self.encoder_dropout, + self.use_cln, + use_new_ffn=self.use_new_ffn, + add_diff_step=self.add_diff_step, + use_skip_connection=False, + ) + for i in range(self.encoder_layer) + ] + ) + + if self.use_cln: + self.last_ln = StyleAdaptiveLayerNorm(self.encoder_hidden) + else: + self.last_ln = nn.LayerNorm(self.encoder_hidden) + + if self.add_diff_step: + self.diff_step_emb = SinusoidalPosEmb(dim=self.encoder_hidden) + # self.diff_step_projection = nn.linear(self.encoder_hidden, self.encoder_hidden) + # self.encoder_hidden.weight.data.normal_(0.0, 0.02) + self.diff_step_projection = Linear2(self.encoder_hidden) + + def forward(self, x, key_padding_mask, condition=None, diffusion_step=None): + if len(x.shape) == 2 and self.use_enc_emb: + x = self.enc_emb_tokens(x) + x = self.position_emb(x) + else: + x = self.position_emb(x) # (B, T, d) + + if self.add_diff_step and diffusion_step != None: + diff_step_embedding = self.diff_step_emb(diffusion_step) + diff_step_embedding = self.diff_step_projection(diff_step_embedding) + x = x + diff_step_embedding.unsqueeze(1) + + if self.use_skip_connection: + skip_res_list = [] + # down + for layer in self.layers[: self.encoder_layer // 2]: + x = layer(x, key_padding_mask, condition) + res = x + skip_res_list.append(res) + # middle + for layer in self.layers[ + self.encoder_layer // 2 : (self.encoder_layer + 1) // 2 + ]: + x = layer(x, key_padding_mask, condition) + # up + for layer in self.layers[(self.encoder_layer + 1) // 2 :]: + skip_res = skip_res_list.pop() + x = layer(x, key_padding_mask, condition, skip_res) + else: + for layer in self.layers: + x = layer(x, key_padding_mask, condition) + + if self.use_cln: + x = self.last_ln(x, condition) + else: + x = self.last_ln(x) + + return x + + +class DiffTransformer(nn.Module): + def __init__( + self, + encoder_layer=None, + encoder_hidden=None, + encoder_head=None, + conv_filter_size=None, + conv_kernel_size=None, + encoder_dropout=None, + use_cln=None, + use_skip_connection=None, + use_new_ffn=None, + add_diff_step=None, + cat_diff_step=None, + in_dim=None, + out_dim=None, + cond_dim=None, + cfg=None, + ): + super().__init__() + + self.encoder_layer = ( + encoder_layer if encoder_layer is not None else cfg.encoder_layer + ) + self.encoder_hidden = ( + encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden + ) + self.encoder_head = ( + encoder_head if encoder_head is not None else cfg.encoder_head + ) + self.conv_filter_size = ( + conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size + ) + self.conv_kernel_size = ( + conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size + ) + self.encoder_dropout = ( + encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout + ) + self.use_cln = use_cln if use_cln is not None else cfg.use_cln + self.use_skip_connection = ( + use_skip_connection + if use_skip_connection is not None + else cfg.use_skip_connection + ) + self.use_new_ffn = use_new_ffn if use_new_ffn is not None else cfg.use_new_ffn + self.add_diff_step = ( + add_diff_step if add_diff_step is not None else cfg.add_diff_step + ) + self.cat_diff_step = ( + cat_diff_step if cat_diff_step is not None else cfg.cat_diff_step + ) + self.in_dim = in_dim if in_dim is not None else cfg.in_dim + self.out_dim = out_dim if out_dim is not None else cfg.out_dim + self.cond_dim = cond_dim if cond_dim is not None else cfg.cond_dim + + if self.in_dim != self.encoder_hidden: + self.in_linear = nn.Linear(self.in_dim, self.encoder_hidden) + self.in_linear.weight.data.normal_(0.0, 0.02) + else: + self.in_dim = None + + if self.out_dim != self.encoder_hidden: + self.out_linear = nn.Linear(self.encoder_hidden, self.out_dim) + self.out_linear.weight.data.normal_(0.0, 0.02) + else: + self.out_dim = None + + assert not ((self.cat_diff_step == True) and (self.add_diff_step == True)) + + self.transformer_encoder = TransformerEncoder( + encoder_layer=self.encoder_layer, + encoder_hidden=self.encoder_hidden, + encoder_head=self.encoder_head, + conv_kernel_size=self.conv_kernel_size, + conv_filter_size=self.conv_filter_size, + encoder_dropout=self.encoder_dropout, + use_cln=self.use_cln, + use_skip_connection=self.use_skip_connection, + use_new_ffn=self.use_new_ffn, + add_diff_step=self.add_diff_step, + ) + + self.cond_project = nn.Linear(self.cond_dim, self.encoder_hidden) + self.cond_project.weight.data.normal_(0.0, 0.02) + self.cat_linear = nn.Linear(self.encoder_hidden * 2, self.encoder_hidden) + self.cat_linear.weight.data.normal_(0.0, 0.02) + + if self.cat_diff_step: + self.diff_step_emb = SinusoidalPosEmb(dim=self.encoder_hidden) + self.diff_step_projection = Linear2(self.encoder_hidden) + + def forward( + self, + x, + condition_embedding, + key_padding_mask=None, + reference_embedding=None, + diffusion_step=None, + ): + # x: shape is (B, T, d_x) + # key_padding_mask: shape is (B, T), mask is 0 + # condition_embedding: from condition adapter, shape is (B, T, d_c) + # reference_embedding: from reference encoder, shape is (B, N, d_r), or (B, 1, d_r), or (B, d_r) + + # TODO: How to add condition emedding? concatenate then linear (or FilM?) or cross attention? + # concatenate then linear + # TODO: How to add reference embedding to the model? use style adaptive norm or cross attention? + # use style adaptive norm + # TODO: How to add diffusion step embedding? add a timestep token? add timestep embedding in each layers? use style adaptive norm? + # choose: (add_diff_step) add timestep embedding in each layers followed by a linear layer / (cat_diff_step) cat a timestep token before the first tokens + + if self.in_linear != None: + x = self.in_linear(x) + condition_embedding = self.cond_project(condition_embedding) + + x = torch.cat([x, condition_embedding], dim=-1) + x = self.cat_linear(x) + + if self.cat_diff_step and diffusion_step != None: + diff_step_embedding = self.diff_step_emb(diffusion_step) + diff_step_embedding = self.diff_step_projection( + diff_step_embedding + ).unsqueeze( + 1 + ) # (B, 1, d) + x = torch.cat([diff_step_embedding, x], dim=1) + if key_padding_mask != None: + key_padding_mask = torch.cat( + [ + key_padding_mask, + torch.ones(key_padding_mask.shape[0], 1).to( + key_padding_mask.device + ), + ], + dim=1, + ) + + x = self.transformer_encoder( + x, + key_padding_mask=key_padding_mask, + condition=reference_embedding, + diffusion_step=diffusion_step, + ) + + if self.cat_diff_step and diffusion_step != None: + x = x[:, 1:, :] + + if self.out_linear != None: + x = self.out_linear(x) + + return x + + +class ReferenceEncoder(nn.Module): + def __init__( + self, + encoder_layer=None, + encoder_hidden=None, + encoder_head=None, + conv_filter_size=None, + conv_kernel_size=None, + encoder_dropout=None, + use_skip_connection=None, + use_new_ffn=None, + ref_in_dim=None, + ref_out_dim=None, + use_query_emb=None, + num_query_emb=None, + cfg=None, + ): + super().__init__() + + self.encoder_layer = ( + encoder_layer if encoder_layer is not None else cfg.encoder_layer + ) + self.encoder_hidden = ( + encoder_hidden if encoder_hidden is not None else cfg.encoder_hidden + ) + self.encoder_head = ( + encoder_head if encoder_head is not None else cfg.encoder_head + ) + self.conv_filter_size = ( + conv_filter_size if conv_filter_size is not None else cfg.conv_filter_size + ) + self.conv_kernel_size = ( + conv_kernel_size if conv_kernel_size is not None else cfg.conv_kernel_size + ) + self.encoder_dropout = ( + encoder_dropout if encoder_dropout is not None else cfg.encoder_dropout + ) + self.use_skip_connection = ( + use_skip_connection + if use_skip_connection is not None + else cfg.use_skip_connection + ) + self.use_new_ffn = use_new_ffn if use_new_ffn is not None else cfg.use_new_ffn + self.in_dim = ref_in_dim if ref_in_dim is not None else cfg.ref_in_dim + self.out_dim = ref_out_dim if ref_out_dim is not None else cfg.ref_out_dim + self.use_query_emb = ( + use_query_emb if use_query_emb is not None else cfg.use_query_emb + ) + self.num_query_emb = ( + num_query_emb if num_query_emb is not None else cfg.num_query_emb + ) + + if self.in_dim != self.encoder_hidden: + self.in_linear = nn.Linear(self.in_dim, self.encoder_hidden) + self.in_linear.weight.data.normal_(0.0, 0.02) + else: + self.in_dim = None + + if self.out_dim != self.encoder_hidden: + self.out_linear = nn.Linear(self.encoder_hidden, self.out_dim) + self.out_linear.weight.data.normal_(0.0, 0.02) + else: + self.out_linear = None + + self.transformer_encoder = TransformerEncoder( + encoder_layer=self.encoder_layer, + encoder_hidden=self.encoder_hidden, + encoder_head=self.encoder_head, + conv_kernel_size=self.conv_kernel_size, + conv_filter_size=self.conv_filter_size, + encoder_dropout=self.encoder_dropout, + use_new_ffn=self.use_new_ffn, + use_cln=False, + use_skip_connection=False, + add_diff_step=False, + ) + + if self.use_query_emb: + # 32 x 512 + self.query_embs = nn.Embedding(self.num_query_emb, self.encoder_hidden) + self.query_attn = nn.MultiheadAttention( + self.encoder_hidden, self.encoder_hidden // 64, batch_first=True + ) + + def forward(self, x_ref, key_padding_mask=None): + # x_ref: (B, T, d_ref) + # key_padding_mask: (B, T) + # return speaker embedding: x_spk + # if self.use_query_embs: shape is (B, N_query, d_out) + # else: shape is (B, T, d_out) + + if self.in_linear != None: + # print('x_ref:',x_ref.shape) + x = self.in_linear(x_ref) + + x = self.transformer_encoder( + x, key_padding_mask=key_padding_mask, condition=None, diffusion_step=None + ) # B, T, d_out + + if self.use_query_emb: + spk_query_emb = self.query_embs( + torch.arange(self.num_query_emb).to(x.device) + ).repeat(x.shape[0], 1, 1) + #k/v b x t x d + #q b x n x d + spk_embs, _ = self.query_attn( + query=spk_query_emb, + key=x, + value=x, + key_padding_mask=( + ~(key_padding_mask.bool()) if key_padding_mask is not None else None + ), + )# B, N_query, d_out + if self.out_linear != None: + spk_embs = self.out_linear(spk_embs) + + else: + spk_query_emb = None + # B x n x d + # b x t x d + return spk_embs, x + + +def pad(input_ele, mel_max_length=None): + if mel_max_length: + max_len = mel_max_length + else: + max_len = max([input_ele[i].size(0) for i in range(len(input_ele))]) + + out_list = list() + for i, batch in enumerate(input_ele): + if len(batch.shape) == 1: + one_batch_padded = F.pad( + batch, (0, max_len - batch.size(0)), "constant", 0.0 + ) + elif len(batch.shape) == 2: + one_batch_padded = F.pad( + batch, (0, 0, 0, max_len - batch.size(0)), "constant", 0.0 + ) + out_list.append(one_batch_padded) + out_padded = torch.stack(out_list) + return out_padded + + +class FiLM(nn.Module): + def __init__(self, in_dim, cond_dim): + super().__init__() + + self.gain = Linear(cond_dim, in_dim) + self.bias = Linear(cond_dim, in_dim) + + nn.init.xavier_uniform_(self.gain.weight) + nn.init.constant_(self.gain.bias, 1) + + nn.init.xavier_uniform_(self.bias.weight) + nn.init.constant_(self.bias.bias, 0) + + def forward(self, x, condition): + gain = self.gain(condition) + bias = self.bias(condition) + if gain.dim() == 2: + gain = gain.unsqueeze(-1) + if bias.dim() == 2: + bias = bias.unsqueeze(-1) + return x * gain + bias + + +class Mish(nn.Module): + def forward(self, x): + return x * torch.tanh(F.softplus(x)) + + +def Conv1d(*args, **kwargs): + layer = nn.Conv1d(*args, **kwargs) + layer.weight.data.normal_(0.0, 0.02) + return layer + + +def Linear(*args, **kwargs): + layer = nn.Linear(*args, **kwargs) + layer.weight.data.normal_(0.0, 0.02) + return layer + + +class ResidualBlock(nn.Module): + def __init__(self, hidden_dim, attn_head, dilation, drop_out, has_cattn=False): + super().__init__() + + self.hidden_dim = hidden_dim + self.dilation = dilation + self.has_cattn = has_cattn + self.attn_head = attn_head + self.drop_out = drop_out + + self.dilated_conv = Conv1d( + hidden_dim, 2 * hidden_dim, 3, padding=dilation, dilation=dilation + ) + self.diffusion_proj = Linear(hidden_dim, hidden_dim) + + self.cond_proj = Conv1d(hidden_dim, hidden_dim * 2, 1) + self.out_proj = Conv1d(hidden_dim, hidden_dim * 2, 1) + + if self.has_cattn: + self.attn = nn.MultiheadAttention( + hidden_dim, attn_head, 0.1, batch_first=True + ) + self.film = FiLM(hidden_dim * 2, hidden_dim) + + self.ln = nn.LayerNorm(hidden_dim) + + self.dropout = nn.Dropout(self.drop_out) + + def forward(self, x, x_mask, cond, diffusion_step, spk_query_emb): + diffusion_step = self.diffusion_proj(diffusion_step).unsqueeze(-1) # (B, d, 1) + cond = self.cond_proj(cond) # (B, 2*d, T) + + y = x + diffusion_step + if x_mask != None: + y = y * x_mask.to(y.dtype)[:, None, :] # (B, 2*d, T) + + if self.has_cattn: + y_ = y.transpose(1, 2) + y_ = self.ln(y_) + + y_, _ = self.attn(y_, spk_query_emb, spk_query_emb) # (B, T, d) + + y = self.dilated_conv(y) + cond # (B, 2*d, T) + + if self.has_cattn: + y = self.film(y.transpose(1, 2), y_) # (B, T, 2*d) + y = y.transpose(1, 2) # (B, 2*d, T) + + gate, filter_ = torch.chunk(y, 2, dim=1) + y = torch.sigmoid(gate) * torch.tanh(filter_) + + y = self.out_proj(y) + + residual, skip = torch.chunk(y, 2, dim=1) + + if x_mask != None: + residual = residual * x_mask.to(y.dtype)[:, None, :] + skip = skip * x_mask.to(y.dtype)[:, None, :] + + return (x + residual) / math.sqrt(2.0), skip + + +class DiffWaveNet(nn.Module): + def __init__( + self, + cfg=None, + ): + super().__init__() + + self.cfg = cfg + self.in_dim = cfg.input_size + self.hidden_dim = cfg.hidden_size + self.out_dim = cfg.out_size + self.num_layers = cfg.num_layers + self.cross_attn_per_layer = cfg.cross_attn_per_layer + self.dilation_cycle = cfg.dilation_cycle + self.attn_head = cfg.attn_head + self.drop_out = cfg.drop_out + + self.in_proj = Conv1d(self.in_dim, self.hidden_dim, 1) + self.diffusion_embedding = SinusoidalPosEmb(self.hidden_dim) + + self.mlp = nn.Sequential( + Linear(self.hidden_dim, self.hidden_dim * 4), + Mish(), + Linear(self.hidden_dim * 4, self.hidden_dim), + ) + + self.cond_ln = nn.LayerNorm(self.hidden_dim) + + self.layers = nn.ModuleList( + [ + ResidualBlock( + self.hidden_dim, + self.attn_head, + 2 ** (i % self.dilation_cycle), + self.drop_out, + has_cattn=(i % self.cross_attn_per_layer == 0), + ) + for i in range(self.num_layers) + ] + ) + + self.skip_proj = Conv1d(self.hidden_dim, self.hidden_dim, 1) + self.out_proj = Conv1d(self.hidden_dim, self.out_dim, 1) + + nn.init.zeros_(self.out_proj.weight) + + def forward( + self, + x, + condition_embedding, + key_padding_mask=None, + reference_embedding=None, + diffusion_step=None, + ): + x = x.transpose(1, 2) # (B, T, d) -> (B, d, T) + x_mask = key_padding_mask + cond = condition_embedding + spk_query_emb = reference_embedding + diffusion_step = diffusion_step + + cond = self.cond_ln(cond) + cond_input = cond.transpose(1, 2) + + x_input = self.in_proj(x) + + x_input = F.relu(x_input) + + diffusion_step = self.diffusion_embedding(diffusion_step).to(x.dtype) + diffusion_step = self.mlp(diffusion_step) + + skip = [] + for _, layer in enumerate(self.layers): + x_input, skip_connection = layer( + x_input, x_mask, cond_input, diffusion_step, spk_query_emb + ) + skip.append(skip_connection) + + x_input = torch.sum(torch.stack(skip), dim=0) / math.sqrt(self.num_layers) + + x_out = self.skip_proj(x_input) + + x_out = F.relu(x_out) + + x_out = self.out_proj(x_out) # (B, 80, T) + + x_out = x_out.transpose(1, 2) + + return x_out + + +def override_config(base_config, new_config): + """Update new configurations in the original dict with the new dict + + Args: + base_config (dict): original dict to be overridden + new_config (dict): dict with new configurations + + Returns: + dict: updated configuration dict + """ + for k, v in new_config.items(): + if type(v) == dict: + if k not in base_config.keys(): + base_config[k] = {} + base_config[k] = override_config(base_config[k], v) + else: + base_config[k] = v + return base_config + + +def get_lowercase_keys_config(cfg): + """Change all keys in cfg to lower case + + Args: + cfg (dict): dictionary that stores configurations + + Returns: + dict: dictionary that stores configurations + """ + updated_cfg = dict() + for k, v in cfg.items(): + if type(v) == dict: + v = get_lowercase_keys_config(v) + updated_cfg[k.lower()] = v + return updated_cfg + + + +def save_config(save_path, cfg): + """Save configurations into a json file + + Args: + save_path (str): path to save configurations + cfg (dict): dictionary that stores configurations + """ + with open(save_path, "w") as f: + json5.dump( + cfg, f, ensure_ascii=False, indent=4, quote_keys=True, sort_keys=True + ) + + +class JsonHParams: + def __init__(self, **kwargs): + for k, v in kwargs.items(): + if type(v) == dict: + v = JsonHParams(**v) + self[k] = v + + def keys(self): + return self.__dict__.keys() + + def items(self): + return self.__dict__.items() + + def values(self): + return self.__dict__.values() + + def __len__(self): + return len(self.__dict__) + + def __getitem__(self, key): + return getattr(self, key) + + def __setitem__(self, key, value): + return setattr(self, key, value) + + def __contains__(self, key): + return key in self.__dict__ + + def __repr__(self): + return self.__dict__.__repr__() + + +class UniAmphionVC(nn.Module): + def __init__(self, cfg, use_source_noise = False, use_ref_noise = False): + super().__init__() + self.cfg = cfg + self.use_source_noise = use_source_noise + self.use_ref_noise = use_ref_noise + self.reference_encoder = ReferenceEncoder(cfg=cfg.reference_encoder) + if cfg.diffusion.diff_model_type == "Transformer": + self.diffusion = Diffusion( + cfg=cfg.diffusion, + diff_model=DiffTransformer(cfg=cfg.diffusion.diff_transformer), + ) + elif cfg.diffusion.diff_model_type == "WaveNet": + self.diffusion = Diffusion( + cfg=cfg.diffusion, + diff_model=DiffWaveNet(cfg=cfg.diffusion.diff_wavenet), + ) + else: + raise NotImplementedError() + pitch_dim = 1 + self.content_f0_enc = nn.Sequential( + nn.LayerNorm( + cfg.vc_feature.content_feature_dim + pitch_dim + ), # 768 (for mhubert) + 1 (for f0) + Rearrange("b t d -> b d t"), + nn.Conv1d( + cfg.vc_feature.content_feature_dim + pitch_dim, + cfg.vc_feature.hidden_dim, + kernel_size=3, + padding=1, + ), + Rearrange("b d t -> b t d"), + ) + + self.reset_parameters() + + def forward( + self, + x=None, + content_feature=None, + pitch=None, + x_ref=None, + x_mask=None, + x_ref_mask=None, + noisy_x_ref=None, + noisy_content_feature=None, + noisy_pitch=None, + ): + noisy_reference_embedding = None + noisy_condition_embedding = None + + reference_embedding, encoded_x = self.reference_encoder( + x_ref=x_ref, key_padding_mask=x_ref_mask + ) + + # print(f'reference_embedding shape: {reference_embedding.shape}') + # content_feature: B x T x D + # pitch: B x T x 1 + # B x t x D+1 + # 2B x T + condition_embedding = torch.cat([content_feature, pitch[:, :, None]], dim=-1) + condition_embedding = self.content_f0_enc(condition_embedding) + + # print(f'condition_embedding shape: {condition_embedding.shape}') + + # 2B x T x D + + if self.use_ref_noise: + # noisy_reference + noisy_reference_embedding, _ = self.reference_encoder( + x_ref=noisy_x_ref, key_padding_mask=x_ref_mask + ) + combined_reference_embedding = (noisy_reference_embedding + reference_embedding) / 2 + else: + combined_reference_embedding = reference_embedding + + if self.use_source_noise: + # condition_embedding + noisy_condition_embedding = torch.cat([noisy_content_feature, noisy_pitch[:, :, None]], dim=-1) + noisy_condition_embedding = self.content_f0_enc(noisy_condition_embedding) + combined_condition_embedding = condition_embedding + else: + combined_condition_embedding = condition_embedding + + diff_out = self.diffusion( + x=x, + condition_embedding=combined_condition_embedding, + x_mask=x_mask, + reference_embedding=combined_reference_embedding, + ) + return diff_out, (reference_embedding, noisy_reference_embedding), (condition_embedding, noisy_condition_embedding) + + @torch.no_grad() + def inference( + self, + content_feature=None, + pitch=None, + x_ref=None, + x_ref_mask=None, + inference_steps=1000, + sigma=1.2, + ): + reference_embedding, _ = self.reference_encoder( + x_ref=x_ref, key_padding_mask=x_ref_mask + ) + + condition_embedding = torch.cat([content_feature, pitch[:, :, None]], dim=-1) + condition_embedding = self.content_f0_enc(condition_embedding) + + bsz, l, _ = condition_embedding.shape + if self.cfg.diffusion.diff_model_type == "Transformer": + z = ( + torch.randn(bsz, l, self.cfg.diffusion.diff_transformer.in_dim).to( + condition_embedding.device + ) + / sigma + ) + elif self.cfg.diffusion.diff_model_type == "WaveNet": + z = ( + torch.randn(bsz, l, self.cfg.diffusion.diff_wavenet.input_size).to( + condition_embedding.device + ) + / sigma + ) + + x0 = self.diffusion.reverse_diffusion( + z=z, + condition_embedding=condition_embedding, + x_mask=None, + reference_embedding=reference_embedding, + n_timesteps=inference_steps, + ) + + return x0 + + def reset_parameters(self): + def _reset_parameters(m): + if isinstance(m, nn.MultiheadAttention): + if m._qkv_same_embed_dim: + nn.init.normal_(m.in_proj_weight, std=0.02) + else: + nn.init.normal_(m.q_proj_weight, std=0.02) + nn.init.normal_(m.k_proj_weight, std=0.02) + nn.init.normal_(m.v_proj_weight, std=0.02) + + if m.in_proj_bias is not None: + nn.init.constant_(m.in_proj_bias, 0.0) + nn.init.constant_(m.out_proj.bias, 0.0) + if m.bias_k is not None: + nn.init.xavier_normal_(m.bias_k) + if m.bias_v is not None: + nn.init.xavier_normal_(m.bias_v) + + elif ( + isinstance(m, nn.Conv1d) + or isinstance(m, nn.ConvTranspose1d) + or isinstance(m, nn.Conv2d) + or isinstance(m, nn.ConvTranspose2d) + ): + m.weight.data.normal_(0.0, 0.02) + + elif isinstance(m, nn.Linear): + m.weight.data.normal_(mean=0.0, std=0.02) + if m.bias is not None: + m.bias.data.zero_() + + elif isinstance(m, nn.Embedding): + m.weight.data.normal_(mean=0.0, std=0.02) + if m.padding_idx is not None: + m.weight.data[m.padding_idx].zero_() + + self.apply(_reset_parameters) \ No newline at end of file diff --git a/models/vc/vc_dataset.py b/models/vc/vc_dataset.py new file mode 100644 index 00000000..f20ff145 --- /dev/null +++ b/models/vc/vc_dataset.py @@ -0,0 +1,442 @@ +import os +import numpy as np +import librosa +import torch +from torch.utils.data import Dataset +from torch.nn.utils.rnn import pad_sequence +from utils.data_utils import * +from models.base.vc_dataset import ( + BaseCollator, +) +from multiprocessing import Pool, Lock +import random +import torchaudio +import rir_generator as rir + + +NUM_WORKERS = 64 +lock = Lock() # 创建一个全局锁 +SAMPLE_RATE = 16000 + +def get_metadata(file_path): + metadata = torchaudio.info(file_path) + return file_path, metadata.num_frames + +def get_speaker(file_path): + speaker_id = file_path.split(os.sep)[-3] + if 'mls' in file_path: + speaker = 'mls_' + speaker_id + else: + speaker = 'libri_' + speaker_id + return file_path, speaker + +def safe_write_to_file(data, file_path, mode='w'): + try: + with lock, open(file_path, mode, encoding='utf-8') as f: + json.dump(data, f) + f.flush() + os.fsync(f.fileno()) + except IOError as e: + print(f"Error writing to {file_path}: {e}") + + +class VCDataset(Dataset): + def __init__(self, args, TRAIN_MODE=True): + print(f"Initializing VCDataset") + if TRAIN_MODE: + directory_list = args.directory_list + else: + directory_list = args.test_directory_list + random.shuffle(directory_list) + + # 配置噪声和说话人使用 + self.use_source_noise = args.use_source_noise + self.use_ref_noise = args.use_ref_noise + self.use_speaker = args.use_speaker + + print(f"use_source_noise: {self.use_source_noise}") + print(f"use_ref_noise: {self.use_ref_noise}") + print(f"use_speaker: {self.use_speaker}") + + # number of workers + print(f"Using {NUM_WORKERS} workers") + self.directory_list = directory_list + print(f"Loading {len(directory_list)} directories: {directory_list}") + + # Load metadata cache + # metadata_cache: {file_path: num_frames} + self.metadata_cache_path = '/mnt/data2/hehaorui/ckpt/rp_metadata_cache.json' + print(f"Loading metadata_cache from {self.metadata_cache_path}") + if os.path.exists(self.metadata_cache_path): + with open(self.metadata_cache_path, 'r', encoding='utf-8') as f: + self.metadata_cache = json.load(f) + print(f"Loaded {len(self.metadata_cache)} metadata_cache") + else: + print(f"metadata_cache not found, creating new") + self.metadata_cache = {} + + # Load speaker cache + # speaker_cache: {file_path: speaker} + self.speaker_cache_path = '/mnt/data2/hehaorui/ckpt/rp_file2speaker.json' + print(f"Loading speaker_cache from {self.speaker_cache_path}") + if os.path.exists(self.speaker_cache_path): + with open(self.speaker_cache_path, 'r', encoding='utf-8') as f: + self.speaker_cache = json.load(f) + print(f"Loaded {len(self.speaker_cache)} speaker_cache") + else: + print(f"speaker_cache not found, creating new") + self.speaker_cache = {} + + self.files = [] + # Load all flac files + for directory in directory_list: + print(f"Loading {directory}") + files = self.get_flac_files(directory) + random.shuffle(files) + print(f"Loaded {len(files)} files") + self.files.extend(files) + del files + print(f"Now {len(self.files)} files") + self.meta_data_cache = self.process_files() + self.speaker_cache = self.process_speakers() + temp_cache_path = self.metadata_cache_path.replace('.json', f'_{directory.split("/")[-1]}.json') + if not os.path.exists(temp_cache_path): + safe_write_to_file(self.meta_data_cache, temp_cache_path) + print(f"Saved metadata cache to {temp_cache_path}") + temp_cache_path = self.speaker_cache_path.replace('.json', f'_{directory.split("/")[-1]}.json') + if not os.path.exists(temp_cache_path): + safe_write_to_file(self.speaker_cache, temp_cache_path) + print(f"Saved speaker cache to {temp_cache_path}") + + print(f"Loaded {len(self.files)} files") + random.shuffle(self.files) # Shuffle the files. + + self.filtered_files, self.all_num_frames, index2numframes, index2speakerid = self.filter_files() + #只有3-30s的语音才会被保留 + print(f"Loaded {len(self.filtered_files)} files") + + self.index2numframes = index2numframes#index to 每条utt的长度 + self.index2speaker = index2speakerid #index to 每条utt的speaker + self.speaker2id = self.create_speaker2id() #每条utt的speaker to 每条utt的speaker_id + self.num_frame_sorted = np.array(sorted(self.all_num_frames)) + self.num_frame_indices = np.array( + sorted( + range(len(self.all_num_frames)), key=lambda k: self.all_num_frames[k] + ) + ) + del self.meta_data_cache, self.speaker_cache + + if self.use_ref_noise or self.use_source_noise: + if TRAIN_MODE: + self.noise_filenames = self.get_all_flac(args.noise_dir) + else: + self.noise_filenames = self.get_all_flac(args.test_noise_dir) + + def process_files(self): + print(f"Processing metadata...") + files_to_process = [file for file in self.files if file not in self.metadata_cache] + if files_to_process: + with Pool(processes=NUM_WORKERS) as pool: + results = list(tqdm(pool.imap_unordered(get_metadata, files_to_process), total=len(files_to_process))) + for file, num_frames in results: + self.metadata_cache[file] = num_frames + safe_write_to_file(self.metadata_cache, self.metadata_cache_path) + else: + print(f"Skipping processing metadata, loaded {len(self.metadata_cache)} files") + return self.metadata_cache + + def process_speakers(self): + print(f"Processing speakers...") + files_to_process = [file for file in self.files if file not in self.speaker_cache] + if files_to_process: + with Pool(processes=NUM_WORKERS) as pool: + results = list(tqdm(pool.imap_unordered(get_speaker, files_to_process), total=len(files_to_process))) + for file, speaker in results: + self.speaker_cache[file] = speaker + safe_write_to_file(self.speaker_cache, self.speaker_cache_path) + else: + print(f"Skipping processing speakers, loaded {len(self.speaker_cache)} files") + return self.speaker_cache + + def get_flac_files(self, directory): + flac_files = [] + for root, dirs, files in os.walk(directory): + for file in files: + # flac or wav + if file.endswith(".flac") or file.endswith(".wav"): + flac_files.append(os.path.join(root, file)) + return flac_files + + def get_all_flac(self, directory): + directories = [os.path.join(directory, d) for d in os.listdir(directory) if os.path.isdir(os.path.join(directory, d))] + if not directories: + return self.get_flac_files(directory) + with Pool(processes=NUM_WORKERS) as pool: + results = [] + for result in tqdm(pool.imap_unordered(self.get_flac_files, directories), total=len(directories), desc="Processing"): + results.extend(result) + print(f"Found {len(results)} waveform files") + return results + + def get_num_frames(self, index): + return self.index2numframes[index] + + def filter_files(self): + # Filter files + metadata_cache = self.meta_data_cache + speaker_cache = self.speaker_cache + filtered_files = [] + all_num_frames = [] + index2numframes = {} + index2speaker = {} + for file in self.files: + num_frames = metadata_cache[file] + if SAMPLE_RATE * 3 <= num_frames <= SAMPLE_RATE * 30: + filtered_files.append(file) + all_num_frames.append(num_frames) + index2speaker[len(filtered_files) - 1] = speaker_cache[file] + index2numframes[len(filtered_files) - 1] = num_frames + return filtered_files, all_num_frames, index2numframes, index2speaker + + def create_speaker2id(self): + speaker2id = {} + unique_id = 0 # 开始的唯一 ID + print(f"Creating speaker2id from {len(self.index2speaker)} utterences") + for _, speaker in tqdm(self.index2speaker.items()): + if speaker not in speaker2id: + speaker2id[speaker] = unique_id + unique_id += 1 # 为下一个唯一 speaker 增加 ID + print(f"Created speaker2id with {len(speaker2id)} speakers") + return speaker2id + + def snr_mixer(self, clean, noise, snr): + # Normalizing to -25 dB FS + rmsclean = (clean**2).mean()**0.5 + epsilon = 1e-10 + rmsclean = max(rmsclean, epsilon) + scalarclean = 10 ** (-25 / 20) / rmsclean + clean = clean * scalarclean + + rmsnoise = (noise**2).mean()**0.5 + scalarnoise = 10 ** (-25 / 20) /rmsnoise + noise = noise * scalarnoise + rmsnoise = (noise**2).mean()**0.5 + + # Set the noise level for a given SNR + noisescalar = np.sqrt(rmsclean / (10**(snr/20)) / rmsnoise) + noisenewlevel = noise * noisescalar + noisyspeech = clean + noisenewlevel + noisyspeech_tensor = torch.tensor(noisyspeech, dtype=torch.float32) + return noisyspeech_tensor + + def add_noise(self, clean): + # self.noise_filenames: list of noise files + random_idx = np.random.randint(0, np.size(self.noise_filenames)) + noise, _ = librosa.load(self.noise_filenames[random_idx], sr=SAMPLE_RATE) + clean = clean.cpu().numpy() + if len(noise)>=len(clean): + noise = noise[0:len(clean)] #截取噪声的长度 + else: + while len(noise)<=len(clean): #如果噪声的长度小于语音的长度 + random_idx = (random_idx + 1)%len(self.noise_filenames) #随机读一个噪声 + newnoise, fs = librosa.load(self.noise_filenames[random_idx], sr=SAMPLE_RATE) + noiseconcat = np.append(noise, np.zeros(int(fs * 0.2)))#在噪声后面加上0.2静音 + noise = np.append(noiseconcat, newnoise)#拼接噪声 + noise = noise[0:len(clean)] #截取噪声的长度 + #随机sample一个小于20大于0的随机数 + snr = random.uniform(0.0,20.0) + noisyspeech = self.snr_mixer(clean=clean, noise=noise, snr=snr) #根据随机的SNR级别，混合生成带噪音频 + del noise + return noisyspeech + + def add_reverb(self, speech): + room_dim = [np.random.uniform(1, 12) for _ in range(3)] # [length, width, height] + mic_pos = [np.random.uniform(0, dim) for dim in room_dim] # 随机选择麦克风位置 + distance = np.random.normal(2, 4) # 确定声源与麦克风的距离 + while distance <= 0 or distance > 5: + distance = np.random.normal(2, 4) + source_pos = [mic_pos[0] + distance, mic_pos[1], mic_pos[2]] # 随机选择声源位置，确保它在以麦克风为中心的球内 + rt60 = np.random.uniform(0.05, 1.0) # 随机选择RT60值 + try: + rir_filter = rir.generate( + c=340, # 声速 + fs=SAMPLE_RATE, + r=[mic_pos], # 麦克风位置 + s=source_pos, # 声源位置 + L=room_dim, # 房间尺寸 + reverberation_time=rt60,# RT60值 + nsample=4096, # IR长度 + ) + # 应用混响 + speech_reverb = np.convolve(speech.cpu().numpy(), rir_filter[:, 0], mode='same') + speech = torch.tensor(speech_reverb, dtype=torch.float32) + return speech + except: + return speech #如果遇到ValueError: s is outside the room，直接返回没加混响的声音 + + + def __len__(self): + return len(self.files) + + def __getitem__(self, idx): + file_path = self.filtered_files[idx] + speech, _ = librosa.load(file_path, sr=SAMPLE_RATE) + if len(speech) > 30 * SAMPLE_RATE: + speech = speech[:30 * SAMPLE_RATE] + speech = torch.tensor(speech, dtype=torch.float32) + # inputs = self._get_reference_vc(speech, hop_length=320) + inputs = self._get_reference_vc(speech, hop_length=200) + speaker = self.index2speaker[idx] + speaker_id = self.speaker2id[speaker] + inputs["speaker_id"] = speaker_id + return inputs + + def _get_reference_vc(self, speech, hop_length): + pad_size = 1600 - speech.shape[0] % 1600 + speech = torch.nn.functional.pad(speech, (0, pad_size)) # 保证语音长度是1600的倍数 + + #hop_size + frame_nums = speech.shape[0] // hop_length + clip_frame_nums = np.random.randint(int(frame_nums * 0.25), int(frame_nums * 0.45)) + clip_frame_nums += (frame_nums - clip_frame_nums) % 8 + start_frames, end_frames = 0, clip_frame_nums + + ref_speech = speech[start_frames * hop_length : end_frames * hop_length] + new_speech = torch.cat((speech[:start_frames * hop_length], speech[end_frames * hop_length:]), 0) + + ref_mask = torch.ones(len(ref_speech) // hop_length) + mask = torch.ones(len(new_speech) // hop_length) + + if not (self.use_source_noise or self.use_ref_noise): + # 不使用噪声 + return {"speech": new_speech, "ref_speech": ref_speech, "ref_mask": ref_mask, "mask": mask} + elif self.use_source_noise and self.use_ref_noise: + # 使用噪声 + noisy_ref_speech = self.add_noise(ref_speech) # 添加噪声 + nosiy_speech = self.add_noise(new_speech) # 添加噪声 + return {"speech": new_speech, "noisy_speech":nosiy_speech, "ref_speech": ref_speech, "noisy_ref_speech": noisy_ref_speech, "ref_mask": ref_mask, "mask": mask} + elif self.use_source_noise and not self.use_ref_noise: + # 只使用源噪声 + noisy_speech = self.add_noise(new_speech) + return {"speech": new_speech, "noisy_speech": noisy_speech, "ref_speech": ref_speech, "ref_mask": ref_mask, "mask": mask} + elif self.use_ref_noise and not self.use_source_noise: + # 只使用参考噪声 + noisy_ref_speech = self.add_noise(ref_speech) + return {"speech": new_speech, "ref_speech": ref_speech, "noisy_ref_speech": noisy_ref_speech, "ref_mask": ref_mask, "mask": mask} + +class VCCollator(BaseCollator): + def __init__(self, cfg): + BaseCollator.__init__(self, cfg) + #self.use_noise = cfg.trans_exp.use_noise + + self.use_source_noise = self.cfg.trans_exp.use_source_noise + self.use_ref_noise = self.cfg.trans_exp.use_ref_noise + + print(f"use_source_noise: {self.use_source_noise}") + print(f"use_ref_noise: {self.use_ref_noise}") + + + def __call__(self, batch): + packed_batch_features = dict() + + # Function to handle tensor copying + def process_tensor(data, dtype=torch.float32): + if isinstance(data, torch.Tensor): + return data.clone().detach() + else: + return torch.tensor(data, dtype=dtype) + + # Process 'speech' data + speeches = [process_tensor(b['speech']) for b in batch] + packed_batch_features['speech'] = pad_sequence(speeches, batch_first=True, padding_value=0) + + # Process 'ref_speech' data + ref_speeches = [process_tensor(b['ref_speech']) for b in batch] + packed_batch_features['ref_speech'] = pad_sequence(ref_speeches, batch_first=True, padding_value=0) + + # Process 'mask' data + masks = [process_tensor(b['mask']) for b in batch] + packed_batch_features['mask'] = pad_sequence(masks, batch_first=True, padding_value=0) + + # Process 'ref_mask' data + ref_masks = [process_tensor(b['ref_mask']) for b in batch] + packed_batch_features['ref_mask'] = pad_sequence(ref_masks, batch_first=True, padding_value=0) + + # Process 'speaker_id' data + speaker_ids = [process_tensor(b['speaker_id'], dtype=torch.int64) for b in batch] + packed_batch_features['speaker_id'] = torch.stack(speaker_ids, dim=0) + if self.use_source_noise: + # Process 'noisy_speech' data + noisy_speeches = [process_tensor(b['noisy_speech']) for b in batch] + packed_batch_features['noisy_speech'] = pad_sequence(noisy_speeches, batch_first=True, padding_value=0) + if self.use_ref_noise: + # Process 'noisy_ref_speech' data + noisy_ref_speeches = [process_tensor(b['noisy_ref_speech']) for b in batch] + packed_batch_features['noisy_ref_speech'] = pad_sequence(noisy_ref_speeches, batch_first=True, padding_value=0) + return packed_batch_features + + +def _is_batch_full(batch, num_tokens, max_tokens, max_sentences): + if len(batch) == 0: + return 0 + if len(batch) == max_sentences: + return 1 + if num_tokens > max_tokens: + return 1 + return 0 + + +def batch_by_size( + indices, + num_tokens_fn, + max_tokens=None, + max_sentences=None, + required_batch_size_multiple=1, +): + """ + Yield mini-batches of indices bucketed by size. Batches may contain + sequences of different lengths. + + Args: + indices (List[int]): ordered list of dataset indices + num_tokens_fn (callable): function that returns the number of tokens at + a given index + max_tokens (int, optional): max number of tokens in each batch + (default: None). + max_sentences (int, optional): max number of sentences in each + batch (default: None). + required_batch_size_multiple (int, optional): require batch size to + be a multiple of N (default: 1). + """ + bsz_mult = required_batch_size_multiple + + sample_len = 0 + sample_lens = [] + batch = [] + batches = [] + for i in range(len(indices)): + idx = indices[i] + num_tokens = num_tokens_fn(idx) + sample_lens.append(num_tokens) + sample_len = max(sample_len, num_tokens) + + assert ( + sample_len <= max_tokens + ), "sentence at index {} of size {} exceeds max_tokens " "limit of {}!".format( + idx, sample_len, max_tokens + ) + num_tokens = (len(batch) + 1) * sample_len + + if _is_batch_full(batch, num_tokens, max_tokens, max_sentences): + mod_len = max( + bsz_mult * (len(batch) // bsz_mult), + len(batch) % bsz_mult, + ) + batches.append(batch[:mod_len]) + batch = batch[mod_len:] + sample_lens = sample_lens[mod_len:] + sample_len = max(sample_lens) if len(sample_lens) > 0 else 0 + batch.append(idx) + if len(batch) > 0: + batches.append(batch) + return batches diff --git a/models/vc/vc_loss.py b/models/vc/vc_loss.py new file mode 100644 index 00000000..d2f9356f --- /dev/null +++ b/models/vc/vc_loss.py @@ -0,0 +1,89 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +cosine_loss = nn.CosineEmbeddingLoss() + +class AMSoftmaxLoss(nn.Module): + def __init__(self, nOut, nClasses, margin=0.3, scale=30): + super(AMSoftmaxLoss, self).__init__() + self.m = margin + self.s = scale + self.in_feats = nOut # input feature size + self.nClasses = nClasses # number of classes (speaker numbers) + self.W = nn.Parameter(torch.randn(nOut, nClasses), requires_grad=True) + nn.init.xavier_normal_(self.W, gain=1) + self.ce = nn.CrossEntropyLoss(reduction='mean') + print('Initialised AMSoftmax m=%.3f s=%.3f'%(self.m,self.s)) + + def forward(self, x, label=None): + # x : B, H + # label: B + assert x.size()[0] == label.size()[0] + assert x.size()[1] == self.in_feats + label = label.reshape(-1) + x_norm = torch.norm(x, p=2, dim=1, keepdim=True).clamp(min=1e-12) + x_norm = torch.div(x, x_norm) + w_norm = torch.norm(self.W, p=2, dim=0, keepdim=True).clamp(min=1e-12) + w_norm = torch.div(self.W, w_norm) + costh = torch.mm(x_norm, w_norm) + label_view = label.view(-1, 1) + if label_view.is_cuda: + label_view = label_view.cpu() + delt_costh = torch.zeros(costh.size()).scatter_(1, label_view, self.m) + if x.is_cuda: + delt_costh = delt_costh.to(x.device) + costh_m = costh - delt_costh + costh_m_s = self.s * costh_m + loss = self.ce(costh_m_s, label) + return loss + + +def noise_clean_similarity_loss(ref_emb, noisy_ref_emb): + target = torch.tensor([1], dtype=torch.float).to(ref_emb.device) + ref_emb = ref_emb.reshape(ref_emb.size(0), -1) # (B, 32, 512) --> (B, 32 * 512) + noisy_ref_emb = noisy_ref_emb.reshape(noisy_ref_emb.size(0), -1) # (B, 32, 512) --> (B, 32 * 512) + loss = cosine_loss(ref_emb, noisy_ref_emb, target) + 1e-6 + return loss + +def cross_entropy_loss(preds, targets, reduction='none'): + log_softmax = nn.LogSoftmax(dim=-1) + loss = (-targets * log_softmax(preds)).sum(1) + if reduction == "none": + return loss + elif reduction == "mean": + return loss.mean() + +class ConstractiveSpeakerLoss(nn.Module): + def __init__(self, temperature=1.): + super(ConstractiveSpeakerLoss, self).__init__() + self.temperature = temperature + + def forward(self, x, speaker_ids): + # x : B, H + # speaker_ids: B 3 4 3 + speaker_ids = speaker_ids.reshape(-1) + speaker_ids_expand = torch.zeros(len(speaker_ids),len(speaker_ids)).to(speaker_ids.device) + speaker_ids_expand = (speaker_ids.view(-1,1) == speaker_ids).float() #形成一个mask + x_t = x.transpose(0,1) # B, C --> C,B + logits = (x @ x_t) / self.temperature # B, H * H, B --> B, B + targets = F.softmax(speaker_ids_expand / self.temperature, dim=-1) + loss = cross_entropy_loss(logits, targets, reduction='none') + return loss.mean() + +def diff_loss(pred, target, mask, loss_type="l1"): + # pred: (B, T, d) + # target: (B, T, d) + # mask: (B, T) + if loss_type == "l1": + loss = F.l1_loss(pred, target, reduction="none").float() * ( + mask.to(pred.dtype).unsqueeze(-1) + ) + elif loss_type == "l2": + loss = F.mse_loss(pred, target, reduction="none").float() * ( + mask.to(pred.dtype).unsqueeze(-1) + ) + else: + raise NotImplementedError() + loss = (torch.mean(loss, dim=-1)).sum() / (mask.to(pred.dtype).sum()) + return loss \ No newline at end of file diff --git a/models/vc/vc_trainer.py b/models/vc/vc_trainer.py new file mode 100644 index 00000000..116b726b --- /dev/null +++ b/models/vc/vc_trainer.py @@ -0,0 +1,530 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import shutil +import time +import json5 +import torch +import numpy as np +from tqdm import tqdm +from utils.util import ValueWindow +from torch.utils.data import DataLoader +from models.vc.base_trainer import TTSTrainer +from torch.nn import functional as F +from models.base.base_sampler import VariableSampler + +from diffusers import get_scheduler + +import accelerate +from accelerate.logging import get_logger +from accelerate.utils import ProjectConfiguration + +from models.vc.ns2_uniamphion import UniAmphionVC +from models.vc.vc_dataset import VCCollator, VCDataset, batch_by_size +# from models.tts.vc.vc_new_dataset import VCCollator, VCDataset, batch_by_size # used on ailab sever +from models.vc.hubert_kmeans import HubertWithKmeans +from models.vc.vc_loss import diff_loss, ConstractiveSpeakerLoss +from models.vc.vc_utils import mel_spectrogram, extract_world_f0 + + +# import descript_audio_codec.dac +# from audiotools import AudioSignal + + +class VCTrainer(TTSTrainer): + def __init__(self, args, cfg): + self.args = args + self.cfg = cfg + cfg.exp_name = args.exp_name + self.content_extractor = "mhubert" + + # 初始化加速器，并确保所有进程都已就绪 + self._init_accelerator() + self.accelerator.wait_for_everyone() + + # 在主进程中初始化日志记录器，避免在每个进程中重复记录 + if self.accelerator.is_main_process: + self.logger = get_logger(args.exp_name, log_level="INFO") + + # 配置噪声和说话人使用 + self.use_source_noise = self.cfg.trans_exp.use_source_noise + self.use_ref_noise = self.cfg.trans_exp.use_ref_noise + self.use_speaker = self.cfg.trans_exp.use_speaker + + # 在主进程中记录配置信息 + if self.accelerator.is_main_process: + self.logger.info(f"use_source_noise: {self.use_source_noise}") + self.logger.info(f"use_ref_noise: {self.use_ref_noise}") + self.logger.info(f"use_speaker: {self.use_speaker}") + + # 初始化一个时间窗口，用于监控或记录某些度量 + self.time_window = ValueWindow(50) + + # 记录训练开始信息 + if self.accelerator.is_main_process: + self.logger.info("=" * 56) + self.logger.info("||\t\tNew training process started.\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + self.logger.info(f"Experiment name: {args.exp_name}") + self.logger.info(f"Experiment directory: {self.exp_dir}") + + + # 初始化检查点目录，确保仅在主进程中执行 + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # 初始化训练计数器 + self.batch_count: int = 0 + self.step: int = 0 + self.epoch: int = 0 + self.max_epoch = (self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf")) + if self.accelerator.is_main_process: + self.logger.info(f"Max epoch: {self.max_epoch if self.max_epoch < float('inf') else 'Unlimited'}") + + # 检查基本配置 + if self.accelerator.is_main_process: + self._check_basic_configs() + self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride + self.keep_last = [i if i > 0 else float("inf") for i in self.cfg.train.keep_last] + self.run_eval = self.cfg.train.run_eval + + # 在所有进程中设置随机种子 + with self.accelerator.main_process_first(): + self._set_random_seed(self.cfg.train.random_seed) + + # setup data_loader + with self.accelerator.main_process_first(): + if self.accelerator.is_main_process: + self.logger.info("Building dataset...") + self.train_dataloader = self._build_dataloader() + self.speaker_num = len(self.train_dataloader.dataset.speaker2id) + if self.accelerator.is_main_process: + self.logger.info("Speaker num: {}".format(self.speaker_num)) + + # build model + with self.accelerator.main_process_first(): + if self.accelerator.is_main_process: + self.logger.info("Building model...") + self.model, self.w2v = self._build_model() + + with self.accelerator.main_process_first(): + if self.accelerator.is_main_process: + self.logger.info("Resume training: {}".format(args.resume)) + if args.resume: + if self.accelerator.is_main_process: + self.logger.info("Resuming from checkpoint...") + ckpt_path = self._load_model( + self.checkpoint_dir, + args.checkpoint_path, + resume_type=args.resume_type, + ) + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + if self.accelerator.is_main_process: + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # optimizer & scheduler + with self.accelerator.main_process_first(): + if self.accelerator.is_main_process: + self.logger.info("Building optimizer and scheduler...") + self.optimizer = self._build_optimizer() + self.scheduler = self._build_scheduler() + + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key] = self.accelerator.prepare(self.model[key]) + else: + self.model = self.accelerator.prepare(self.model) + + # prepare w2v to accelerator + if isinstance(self.w2v, dict): + for key in self.w2v.keys(): + self.w2v[key] = self.accelerator.prepare(self.w2v[key]) + else: + self.w2v = self.accelerator.prepare(self.w2v) + + if isinstance(self.optimizer, dict): + for key in self.optimizer.keys(): + self.optimizer[key] = self.accelerator.prepare(self.optimizer[key]) + else: + self.optimizer = self.accelerator.prepare(self.optimizer) + + if isinstance(self.scheduler, dict): + for key in self.scheduler.keys(): + self.scheduler[key] = self.accelerator.prepare(self.scheduler[key]) + else: + self.scheduler = self.accelerator.prepare(self.scheduler) + + with self.accelerator.main_process_first(): + if self.accelerator.is_main_process: + self.logger.info("Building criterion...") + self.criterion = self._build_criterion() + + self.config_save_path = os.path.join(self.exp_dir, "args.json") + + self.task_type = "VC" + + self.contrastive_speaker_loss = ConstractiveSpeakerLoss() + + if self.accelerator.is_main_process: + self.logger.info("Task type: {}".format(self.task_type)) + + + def _init_accelerator(self): + self.exp_dir = os.path.join( + os.path.abspath(self.cfg.log_dir), self.args.exp_name + ) + project_config = ProjectConfiguration( + project_dir=self.exp_dir, + logging_dir=os.path.join(self.exp_dir, "log"), + ) + self.accelerator = accelerate.Accelerator( + gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, + log_with=self.cfg.train.tracker, + project_config=project_config, + ) + if self.accelerator.is_main_process: + os.makedirs(project_config.project_dir, exist_ok=True) + os.makedirs(project_config.logging_dir, exist_ok=True) + self.accelerator.wait_for_everyone() + with self.accelerator.main_process_first(): + self.accelerator.init_trackers(self.args.exp_name) + + + def _build_model(self): + w2v = HubertWithKmeans() + self.cfg.model.vc_feature.content_feature_dim = 768 + model = UniAmphionVC(cfg=self.cfg.model, use_ref_noise=self.use_ref_noise, use_source_noise=self.use_source_noise) + return model, w2v + + def _build_dataset(self): + return VCDataset, VCCollator + + def _build_dataloader(self): + np.random.seed(int(time.time())) + if self.accelerator.is_main_process: + self.logger.info("Use Dynamic Batchsize......") + train_dataset = VCDataset(self.cfg.trans_exp) + train_collate = VCCollator(self.cfg) + batch_sampler = batch_by_size( + train_dataset.num_frame_indices, + train_dataset.get_num_frames, + max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes, + max_sentences=self.cfg.train.max_sentences + * self.accelerator.num_processes, + required_batch_size_multiple=self.accelerator.num_processes, + ) + np.random.shuffle(batch_sampler) + batches = [ + x[ + self.accelerator.local_process_index :: self.accelerator.num_processes + ] + for x in batch_sampler + if len(x) % self.accelerator.num_processes == 0 + ] + train_loader = DataLoader( + train_dataset, + collate_fn=train_collate, + num_workers=self.cfg.train.dataloader.num_worker, + batch_sampler=VariableSampler( + batches, drop_last=False, use_random_sampler=True + ), + pin_memory=self.cfg.train.dataloader.pin_memory, + ) + self.accelerator.wait_for_everyone() + return train_loader + + def _build_optimizer(self): + optimizer = torch.optim.AdamW( + filter(lambda p: p.requires_grad, self.model.parameters()), + **self.cfg.train.adam, + ) + return optimizer + + def _build_scheduler(self): + lr_scheduler = get_scheduler( + self.cfg.train.lr_scheduler, + optimizer=self.optimizer, + num_warmup_steps=self.cfg.train.lr_warmup_steps, + num_training_steps=self.cfg.train.num_train_steps + ) + return lr_scheduler + + def _build_criterion(self): + criterion = torch.nn.L1Loss(reduction="mean") + return criterion + + def _count_parameters(self, model): + model_param = 0.0 + if isinstance(model, dict): + for key, _ in model.items(): + model_param += sum(p.numel() for p in model[key].parameters()) + else: + model_param = sum(p.numel() for p in model.parameters()) + return model_param + + def _dump_cfg(self, path): + os.makedirs(os.path.dirname(path), exist_ok=True) + json5.dump( + self.cfg, + open(path, "w"), + indent=4, + sort_keys=True, + ensure_ascii=False, + quote_keys=True, + ) + + def get_state_dict(self): + state_dict = { + "model": self.model.state_dict(), + "optimizer": self.optimizer.state_dict(), + "scheduler": self.scheduler.state_dict(), + "step": self.step, + "epoch": self.epoch, + "batch_size": self.cfg.train.batch_size, + } + return state_dict + + def load_model(self, checkpoint): + self.step = checkpoint["step"] + self.epoch = checkpoint["epoch"] + self.model.load_state_dict(checkpoint["model"]) + self.optimizer.load_state_dict(checkpoint["optimizer"]) + self.scheduler.load_state_dict(checkpoint["scheduler"]) + + def _train_step(self, batch): + total_loss = 0.0 + train_losses = {} + device = self.accelerator.device + + # 将所有Tensor类型的数据迁移到指定设备 + batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()} + + speech = batch["speech"] + ref_speech = batch["ref_speech"] + + with torch.set_grad_enabled(False): + # 提取需要的特征和光谱图 + mel = mel_spectrogram(speech).transpose(1, 2) + ref_mel = mel_spectrogram(ref_speech).transpose(1, 2) + # print('-----------------------------------') + # print(ref_mel.shape) + mask = batch["mask"] + ref_mask = batch["ref_mask"] + + # 提取 pitch 和 content_feature + if not self.use_source_noise: + pitch = extract_world_f0(speech) + pitch = (pitch - pitch.mean(dim=1, keepdim=True)) / (pitch.std(dim=1, keepdim=True) + 1e-6) # Normalize pitch (B,T) + _, content_feature = self.w2v(speech) # semantic (B, T, 768) + + if self.use_ref_noise: + noisy_ref_mel = mel_spectrogram(batch["noisy_ref_speech"]).transpose(1, 2) + + if self.use_source_noise: + combined_speech = torch.cat((speech, batch["noisy_speech"]), dim=0) + _, combined_features = self.w2v(combined_speech) + content_feature, noisy_content_feature = torch.split(combined_features, speech.shape[0], dim=0) + combined_pitch = extract_world_f0(combined_speech) + clean_pitch, noisy_pitch = torch.split(combined_pitch, speech.shape[0], dim=0) + pitch = (clean_pitch - clean_pitch.mean(dim=1, keepdim=True)) / (clean_pitch.std(dim=1, keepdim=True) + 1e-6) + noisy_pitch = (noisy_pitch - noisy_pitch.mean(dim=1, keepdim=True)) / (noisy_pitch.std(dim=1, keepdim=True) + 1e-6) + + # FORWARD 模型 + if self.use_ref_noise and self.use_source_noise: + diff_out, (ref_emb, noisy_ref_emb), (cond_emb, noisy_cond_emb) = self.model( + x=mel, content_feature=content_feature, pitch=pitch, x_ref=ref_mel, + x_mask=mask, x_ref_mask=ref_mask, noisy_x_ref=noisy_ref_mel, + noisy_content_feature=noisy_content_feature, noisy_pitch=noisy_pitch + ) + elif self.use_ref_noise: + diff_out, (ref_emb, noisy_ref_emb), (cond_emb, _) = self.model( + x=mel, content_feature=content_feature, pitch=pitch, x_ref=ref_mel, + x_mask=mask, x_ref_mask=ref_mask, noisy_x_ref=noisy_ref_mel + ) + else: + diff_out, (ref_emb, _), (cond_emb, _) = self.model( + x=mel, content_feature=content_feature, pitch=pitch, x_ref=ref_mel, + x_mask=mask, x_ref_mask=ref_mask + ) + + if self.use_ref_noise: + # B x N_query x D + ref_emb = torch.mean(ref_emb, dim=1) # B x D + noisy_ref_emb = torch.mean(noisy_ref_emb, dim=1) # B x D + all_ref_emb = torch.cat([ref_emb, noisy_ref_emb], dim=0) # 2B x D + all_speaker_ids = torch.cat([batch["speaker_id"], batch["speaker_id"]], dim=0) # 2B + cs_loss = self.contrastive_speaker_loss(all_ref_emb, all_speaker_ids) * 0.25 + total_loss += cs_loss + train_losses["ref_loss"] = cs_loss + + if self.use_source_noise: + # B x T x D + diff_loss_cond = F.l1_loss(noisy_cond_emb, cond_emb, reduction="mean") * 2.0 + total_loss += diff_loss_cond + train_losses["source_loss"] = diff_loss_cond + + diff_loss_x0 = diff_loss(diff_out["x0_pred"], mel, mask=mask) + total_loss += diff_loss_x0 + train_losses["diff_loss_x0"] = diff_loss_x0 + + diff_loss_noise = diff_loss(diff_out["noise_pred"], diff_out["noise"], mask=mask) + total_loss += diff_loss_noise + train_losses["diff_loss_noise"] = diff_loss_noise + train_losses["total_loss"] = total_loss + + self.optimizer.zero_grad() + self.accelerator.backward(total_loss) + if self.accelerator.sync_gradients: + self.accelerator.clip_grad_norm_(filter(lambda p: p.requires_grad, self.model.parameters()), 0.5) + self.optimizer.step() + self.scheduler.step() + + for item in train_losses: + train_losses[item] = train_losses[item].item() + + train_losses['learning_rate'] = f"{self.optimizer.param_groups[0]['lr']:.1e}" + train_losses["batch_size"] = batch["speaker_id"].shape[0] + + return (train_losses["total_loss"], train_losses, None) + + + + def _train_epoch(self): + r"""Training epoch. Should return average loss of a batch (sample) over + one epoch. See ``train_loop`` for usage. + """ + if isinstance(self.model, dict): + for key in self.model.keys(): + self.model[key].train() + else: + self.model.train() + if isinstance(self.w2v, dict): + for key in self.w2v.keys(): + self.w2v[key].eval() + else: + self.w2v.eval() + + epoch_sum_loss: float = 0.0 # total loss + # Put the data to cuda device + device = self.accelerator.device + with device: + torch.cuda.empty_cache() + self.model = self.model.to(device) + self.w2v = self.w2v.to(device) + + for batch in tqdm( + self.train_dataloader, + desc=f"Training Epoch {self.epoch}", + unit="batch", + colour="GREEN", + leave=False, + dynamic_ncols=True, + smoothing=0.04, + disable=not self.accelerator.is_main_process, + ): + + speech = batch["speech"].cpu().numpy() + speech = speech[0] + self.batch_count += 1 + self.step += 1 + #epoch_step += 1 + if len(speech) >= 16000 * 25: + continue + with self.accelerator.accumulate(self.model): + total_loss, train_losses, _ = self._train_step(batch) + + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: + epoch_sum_loss += total_loss + self.current_loss = total_loss + if isinstance(train_losses, dict): + for key, loss in train_losses.items(): + self.accelerator.log( + {"Epoch/Train {} Loss".format(key): loss}, + step=self.step, + ) + if (self.accelerator.is_main_process and self.batch_count % 10 == 0): + self.echo_log(train_losses, mode="Training") + + self.save_checkpoint() + self.accelerator.wait_for_everyone() + + return epoch_sum_loss, None + + def train_loop(self): + r"""Training loop. The public entry of training process.""" + # Wait everyone to prepare before we move on + self.accelerator.wait_for_everyone() + # dump config file + if self.accelerator.is_main_process: + self._dump_cfg(self.config_save_path) + + # Wait to ensure good to go + self.accelerator.wait_for_everyone() + # stop when meet max epoch or self.cfg.train.num_train_steps + print(self.step) + while self.epoch < self.max_epoch and self.step < self.cfg.train.num_train_steps: + if self.accelerator.is_main_process: + self.logger.info("\n") + self.logger.info("-" * 32) + self.logger.info("Epoch {}: ".format(self.epoch)) + self.logger.info("Start training......") + + train_total_loss, _ = self._train_epoch() + + self.epoch += 1 + self.accelerator.wait_for_everyone() + if isinstance(self.scheduler, dict): + for key in self.scheduler.keys(): + self.scheduler[key].step() + else: + self.scheduler.step() + + # Finish training and save final checkpoint + self.accelerator.wait_for_everyone() + if self.accelerator.is_main_process: + self.accelerator.save_state( + os.path.join( + self.checkpoint_dir, + "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, train_total_loss + ), + ) + ) + self.accelerator.end_training() + if self.accelerator.is_main_process: + self.logger.info("Training finished......") + + def save_checkpoint(self): + self.accelerator.wait_for_everyone() + # main process only + if self.accelerator.is_main_process: + if self.batch_count % self.save_checkpoint_stride[0] == 0: + keep_last = self.keep_last[0] + # 读取self.checkpoint_dir所有的folder + all_ckpts = os.listdir(self.checkpoint_dir) + # 排除非文件夹 + all_ckpts = [ckpt for ckpt in all_ckpts if os.path.isdir(os.path.join(self.checkpoint_dir, ckpt))] + if len(all_ckpts) > keep_last: + # 只保留keep_last个的folder in self.checkpoint_dir, sort by step "epoch-{:04d}_step-{:07d}_loss-{:.6f}" + all_ckpts = sorted(all_ckpts, key=lambda x: int(x.split("_")[1].split('-')[1])) + + # 只保留keep_last个的folder in self.checkpoint_dir, sort by step "epoch-{:04d}_step-{:07d}_loss-{:.6f}" + all_ckpts = sorted(all_ckpts, key=lambda x: int(x.split("_")[1].split('-')[1])) + for ckpt in all_ckpts[:-keep_last]: + shutil.rmtree(os.path.join(self.checkpoint_dir, ckpt)) + checkpoint_filename = "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( + self.epoch, self.step, self.current_loss + ) + path = os.path.join(self.checkpoint_dir, checkpoint_filename) + self.logger.info("Saving state to {}...".format(path)) + self.accelerator.save_state(path) + self.logger.info("Finished saving state.") + self.accelerator.wait_for_everyone() diff --git a/models/vc/vc_utils.py b/models/vc/vc_utils.py new file mode 100644 index 00000000..6fb0e948 --- /dev/null +++ b/models/vc/vc_utils.py @@ -0,0 +1,94 @@ +import torch +import pyworld as pw +import numpy as np +from librosa.filters import mel as librosa_mel_fn +from torchaudio.functional import detect_pitch_frequency +from torchaudio.functional import pitch_shift + +mel_basis = {} +hann_window = {} +init_mel_and_hann = False + +# online feature extraction +def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): + return torch.log(torch.clamp(x, min=clip_val) * C) + +def spectral_normalize_torch(magnitudes): + output = dynamic_range_compression_torch(magnitudes) + return output + + +def mel_spectrogram( + y, n_fft=1024, num_mels=80, sampling_rate=16000, hop_size=200, win_size=800, fmin=0, fmax=8000, center=False +): + global mel_basis, hann_window, init_mel_and_hann + device = y.device + if not init_mel_and_hann: + mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) + mel_basis[str(fmax) + "_" + str(y.device)] = (torch.from_numpy(mel).float().to(device)) + hann_window[str(device)] = torch.hann_window(win_size).to(device) + init_mel_and_hann = True + y = torch.nn.functional.pad( + y.unsqueeze(1), + (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), + mode="reflect", + ) + y = y.squeeze(1) + spec = torch.stft( + y, + n_fft, + hop_length=hop_size, + win_length=win_size, + window=hann_window[str(y.device)], + center=center, + pad_mode="reflect", + normalized=False, + onesided=True, + return_complex=True, + ) + spec = torch.view_as_real(spec) + spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) + spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) + spec = spectral_normalize_torch(spec) + return spec + +def interpolate(f0): + uv = f0 == 0 + if len(f0[~uv]) > 0: + # interpolate the unvoiced f0 + f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) + uv = uv.astype("float") + uv = np.min(np.array([uv[:-2], uv[1:-1], uv[2:]]), axis=0) + uv = np.pad(uv, (1, 1)) + return f0, uv + +def extract_world_f0(speech): + audio = speech.cpu().numpy() + f0s = [] + for i in range(audio.shape[0]): + wav = audio[i] + frame_num = len(wav) // 200 + f0, t = pw.dio(wav.astype(np.float64), 16000, frame_period=12.5) + f0 = pw.stonemask(wav.astype(np.float64), f0, t, 16000) + f0, _ = interpolate(f0) + f0 = torch.from_numpy(f0).to(speech.device) + f0s.append(f0[:frame_num]) + f0s = torch.stack(f0s, dim=0).float() + return f0s + + +def get_pitch_shifted_speech(speech, sr = 16000): + # pitch shift + shifted_speech = torch.zeros_like(speech) + need_shift = False + for i in range(speech.shape[0]): + sub_speech = speech[i] + f0_mean = detect_pitch_frequency(sub_speech, sr).mean() + if f0_mean > 350: + need_shift = True + n_step = -12 * torch.log2(f0_mean / 300) + shifted_speech[i] = pitch_shift(sub_speech, sr, n_steps=n_step) + else: + shifted_speech[i] = sub_speech + shifted_speech = shifted_speech.to(speech.device) + return shifted_speech, need_shift \ No newline at end of file From 138b6a16aba286963425ffde2446eb86b99ba569 Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Thu, 18 Jul 2024 18:19:11 +0800 Subject: [PATCH 03/16] Create README.md --- egs/vc/README.md | 108 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 egs/vc/README.md diff --git a/egs/vc/README.md b/egs/vc/README.md new file mode 100644 index 00000000..ae3b00c0 --- /dev/null +++ b/egs/vc/README.md @@ -0,0 +1,108 @@ +# Noro: A Noise-Robust One-shot Voice Conversion System + +## Project Overview +Noro is a noise-robust one-shot voice conversion (VC) system designed to convert the timbre of speech from a source speaker to a target speaker using only a single reference speech sample, while preserving the semantic content of the original speech. Noro introduces innovative components tailored for VC using noisy reference speeches, including a dual-branch reference encoding module and a noise-agnostic contrastive speaker loss. + +## Features +- **Noise-Robust Voice Conversion**: Utilizes a dual-branch reference encoding module and noise-agnostic contrastive speaker loss to maintain high-quality voice conversion in noisy environments. +- **One-shot Voice Conversion**: Achieves timbre conversion using only one reference speech sample. +- **Speaker Representation Learning**: Explores the potential of the reference encoder as a self-supervised speaker encoder. + +## Installation Requirement + +Set up your environment as in Amphion README (you'll need a conda environment, and we recommend using Linux). + +### Prepare Hubert Model + +Humbert checkpoint and kmeans can be downloaded [here](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert). +Set the downloded model path at `models/vc/hubert_kmeans.py`. + + +## Usage + +### Download pretrained weights +You need to download our pretrained weights from [Google Drive](https://drive.google.com/drive/folders/1NPzSIuSKO8o87g5ImNzpw_BgbhsZaxNg?usp=drive_link). + +### Inference +1. Configure inference parameters: + Modify the pretrained checkpoint path, source voice path and reference voice path at egs/vc/noro_inference.sh` file. + Currently it's at line 35. +``` + checkpoint_path="path/to/checkpoint/model.safetensors" + output_dir="path/to/output/directory" + source_path="path/to/source/audio.wav" + reference_path="path/to/reference/audio.wav" +``` +2. Start inference: + ```bash + bash path/to/Amphion/egs/vc/noro_inference.sh + ``` + +3. You got the reconstructed mel spectrum saved to the output direction. + Then use the [BigVGAN](https://github.com/NVIDIA/BigVGAN) to construct the wav file. + +## Training from Scratch + +### Data Preparation + +We use the LibriLight dataset for training and evaluation. You can download it using the following commands: +```bash + wget https://dl.fbaipublicfiles.com/librilight/data/large.tar + wget https://dl.fbaipublicfiles.com/librilight/data/medium.tar + wget https://dl.fbaipublicfiles.com/librilight/data/small.tar +``` + +### Training the Model with Clean Reference Voice + +Configure training parameters: +Our configuration file for training clean Noro model is at "egs/vc/exp_config_4gpu_clean.json", and Nosiy Noro model at "egs/vc/exp_config_4gpu_noisy.json". + +To train your model, you need to modify the `dataset` variable in the json configurations. +Currently it's at line 40, you should modify the "data_dir" to your dataset's root directory. +``` + "directory_list": [ + "path/to/your/training_data_directory1", + "path/to/your/training_data_directory2", + "path/to/your/training_data_directory3" + ], +``` +You should also select a reasonable batch size at the "batch_size" entry (currently it's set at 8). + +You can change other experiment settings in the `/egs/tts/VALLE_V2/exp_ar_libritts.json` such as the learning rate, optimizer and the dataset. + + **Set smaller batch_size if you are out of memory😢😢** + +I used max_tokens = 3200000 to successfully run on a single card, if you'r out of memory, try smaller. + +```json + "batch_size": 8, + "max_tokens": 3200000, + "max_sentences": 64, +``` +### Resume from existing checkpoint +Our framework supports resuming from existing checkpoint. +If this is a new experiment, use the following command: +``` +CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ +"${work_dir}/bins/vc/train.py" \ + --config $exp_config \ + --exp_name $exp_name \ + --log_level debug +``` +To resume training or fine-tune from a checkpoint, use the following command: +Ensure the options `--resume`, `--resume_type resume`, and `--checkpoint_path` are set. + +### Run the command to Train model +Start clean training: + ```bash + bash path/to/Amphion/egs/vc/noro_train_clean.sh + ``` + + +Start noisy training: + ```bash + bash path/to/Amphion/egs/vc/noro_train_noisy.sh + ``` + + + From 4a484fb2d5096cd3b62ac9f054e1ed71ecf65eb2 Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Thu, 18 Jul 2024 18:31:10 +0800 Subject: [PATCH 04/16] Update README.md Add news --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index fccbae6e..3e1f9d0e 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ In addition to the specific generation tasks, Amphion includes several **vocoders** and **evaluation metrics**. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. Moreover, Amphion is dedicated to advancing audio generation in real-world applications, such as building **large-scale datasets** for speech synthesis. ## 🚀 News +- **2024/7/17**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system. The Noro model significantly improves performance in noisy environments by introducing a dual-branch reference encoding module and noise-agnostic contrastive speaker loss. It performs well in both clean and noisy conditions, making it suitable for real-world applications. Additionally, we explore the potential of the reference encoder as a self-supervised speaker encoder, showing competitive performance in speaker representation tasks. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) - **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md) - **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/tts/VALLE_V2/README.md) - **2024/03/12**: Amphion now support **NaturalSpeech3 FACodec** and release pretrained checkpoints. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2403.03100) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/naturalspeech3_facodec) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/naturalspeech3_facodec) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](models/codec/ns3_codec/README.md) From 070df5b1914e86c2ac6d3016eb667cb947941051 Mon Sep 17 00:00:00 2001 From: kenxxxxx Date: Thu, 18 Jul 2024 18:46:26 +0800 Subject: [PATCH 05/16] Code cleaned --- egs/vc/exp_config_4gpu_clean.json | 94 +++++++++--------- egs/vc/exp_config_4gpu_noisy.json | 84 ++++++++-------- egs/vc/exp_config_testing.json | 82 +++++++-------- egs/vc/noro_inference.sh | 36 +++---- egs/vc/noro_train_clean.sh | 38 ++++--- egs/vc/noro_train_noisy.sh | 38 ++++--- models/vc/hubert_kmeans.py | 4 +- models/vc/noro_inference.py | 60 ++++------- models/vc/vc_trainer.py | 160 ++++++++++++------------------ 9 files changed, 283 insertions(+), 313 deletions(-) diff --git a/egs/vc/exp_config_4gpu_clean.json b/egs/vc/exp_config_4gpu_clean.json index 83cff952..d5cef390 100644 --- a/egs/vc/exp_config_4gpu_clean.json +++ b/egs/vc/exp_config_4gpu_clean.json @@ -1,57 +1,57 @@ { - "base_config": "egs/vc/exp_config_base.json", - "dataset": [ - "mls" - ], - "preprocess": { - // Specify the output root path to save the processed data - "processed_dir": "data", - "train_file": "train.json", - "valid_file": "test.json", - "read_metadata": true, - "metadata_dir": "metadata" + "base_config": "egs/vc/exp_config_base.json", + "dataset": [ + "mls" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + "train_file": "train.json", + "valid_file": "test.json", + "read_metadata": true, + "metadata_dir": "metadata" + }, + // Specify the output root path to save model checkpoints and logs + "log_dir": "path/to/your/checkpoint/directory", + "train": { + // New trainer and Accelerator + "gradient_accumulation_step": 1, + "tracker": ["tensorboard"], + "max_epoch": 10, + "save_checkpoint_stride": [1000], + "keep_last": [20], + "run_eval": [true], + "dataloader": { + "num_worker": 64, + "pin_memory": true + }, + "adam": { + "lr": 5e-5 }, - // Specify the output root path to save model ckpts and logs - "log_dir": "/mnt/data2/hehaorui/ckpt/vc_new_exp", - "train": { - // New trainer and Accelerator - "gradient_accumulation_step": 1, - "tracker": ["tensorboard"], - "max_epoch": 10, - "save_checkpoint_stride": [1000], - "keep_last": [20], - "run_eval": [true], - "dataloader": { - "num_worker": 64, - "pin_memory": true - }, - "adam": { - "lr": 5e-5 - }, - "use_dynamic_batchsize": true, - "batch_size": 8, - "max_tokens": 3200000, - "max_sentences": 64, - "lr_warmup_steps": 5000, - "lr_scheduler": "cosine", - "num_train_steps": 800000 - }, - "trans_exp": { - "directory_list":[ - "/mnt/data4/hehaorui/medium_15s", - "/mnt/data4/hehaorui/large_15s", - "/mnt/data4/hehaorui/small_15s" - ], + "use_dynamic_batchsize": true, + "batch_size": 8, + "max_tokens": 3200000, + "max_sentences": 64, + "lr_warmup_steps": 5000, + "lr_scheduler": "cosine", + "num_train_steps": 800000 + }, + "trans_exp": { + "directory_list": [ + "path/to/your/training_data_directory1", + "path/to/your/training_data_directory2", + "path/to/your/training_data_directory3" + ], "test_directory_list": [ - "/mnt/data2/wangyuancheng/mls_english/test/audio" + "path/to/your/test/testing_data_directory" ], "use_speaker": false, "use_ref_noise": false, "use_source_noise": false, "snr_lower": 2, "snr_upper": 42, - "total_snrlevels": 5, - "noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_train", - "test_noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_test" + "total_snrlevels": 5, + "noise_dir": "path/to/your/noise/train/directory", + "test_noise_dir": "path/to/your/noise/test/directory" } - } \ No newline at end of file +} \ No newline at end of file diff --git a/egs/vc/exp_config_4gpu_noisy.json b/egs/vc/exp_config_4gpu_noisy.json index 683326b6..16132244 100644 --- a/egs/vc/exp_config_4gpu_noisy.json +++ b/egs/vc/exp_config_4gpu_noisy.json @@ -4,54 +4,54 @@ "mls" ], "preprocess": { - // Specify the output root path to save the processed data + // Specify the output root path to save the processed data "processed_dir": "data", "train_file": "train.json", "valid_file": "test.json", "read_metadata": true, "metadata_dir": "metadata" }, - // Specify the output root path to save model ckpts and logs - "log_dir": "/mnt/data2/hehaorui/ckpt/vc_new_exp", + // Specify the output root path to save model checkpoints and logs + "log_dir": "path/to/your/checkpoint/directory", "train": { - // New trainer and Accelerator - "gradient_accumulation_step": 1, - "tracker": ["tensorboard"], - "max_epoch": 10, - "save_checkpoint_stride": [1000], - "keep_last": [20], - "run_eval": [true], - "dataloader": { - "num_worker": 64, - "pin_memory": true - }, - "adam": { - "lr": 5e-5 - }, - "use_dynamic_batchsize": true, - "batch_size": 8, - "max_tokens": 3200000, - "max_sentences": 64, - "lr_warmup_steps": 5000, - "lr_scheduler": "cosine", - "num_train_steps": 800000 - }, + // New trainer and Accelerator + "gradient_accumulation_step": 1, + "tracker": ["tensorboard"], + "max_epoch": 10, + "save_checkpoint_stride": [1000], + "keep_last": [20], + "run_eval": [true], + "dataloader": { + "num_worker": 64, + "pin_memory": true + }, + "adam": { + "lr": 5e-5 + }, + "use_dynamic_batchsize": true, + "batch_size": 8, + "max_tokens": 3200000, + "max_sentences": 64, + "lr_warmup_steps": 5000, + "lr_scheduler": "cosine", + "num_train_steps": 800000 + }, "trans_exp": { - "directory_list":[ - "/mnt/data4/hehaorui/medium_15s", - "/mnt/data4/hehaorui/large_15s", - "/mnt/data4/hehaorui/small_15s" - ], - "test_directory_list": [ - "/mnt/data2/wangyuancheng/mls_english/test/audio" - ], - "use_speaker": false, - "use_ref_noise": true, - "use_source_noise": false, - "snr_lower": 2, - "snr_upper": 42, - "total_snrlevels": 5, - "noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_train", - "test_noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_test" - } + "directory_list": [ + "path/to/your/training_data_directory1", + "path/to/your/training_data_directory2", + "path/to/your/training_data_directory3" + ], + "test_directory_list": [ + "path/to/your/test/testing_data_directory" + ], + "use_speaker": false, + "use_ref_noise": true, + "use_source_noise": false, + "snr_lower": 2, + "snr_upper": 42, + "total_snrlevels": 5, + "noise_dir": "path/to/your/noise/train/directory", + "test_noise_dir": "path/to/your/noise/test/directory" + } } \ No newline at end of file diff --git a/egs/vc/exp_config_testing.json b/egs/vc/exp_config_testing.json index 70bb8067..5c91ea84 100644 --- a/egs/vc/exp_config_testing.json +++ b/egs/vc/exp_config_testing.json @@ -1,48 +1,48 @@ { - "base_config": "egs/vc/exp_config_base.json", - "dataset": [ - "mls" - ], - "preprocess": { - // Specify the output root path to save the processed data - "processed_dir": "data", - "train_file": "train.json", - "valid_file": "test.json", - "read_metadata": true, - "metadata_dir": "metadata" + "base_config": "egs/vc/exp_config_base.json", + "dataset": [ + "mls" + ], + "preprocess": { + // Specify the output root path to save the processed data + "processed_dir": "data", + "train_file": "train.json", + "valid_file": "test.json", + "read_metadata": true, + "metadata_dir": "metadata" + }, + // Specify the output root path to save model checkpoints and logs + "log_dir": "path/to/your/checkpoint/directory", + "train": { + // New trainer and Accelerator + "gradient_accumulation_step": 1, + "tracker": ["tensorboard"], + "max_epoch": 5, + "save_checkpoint_stride": [1000], + "keep_last": [100], + "run_eval": [true], + "dataloader": { + "num_worker": 64, + "pin_memory": true }, - // Specify the output root path to save model ckpts and logs - "log_dir": "/mnt/data2/hehaorui/ckpt/vc", - "train": { - // New trainer and Accelerator - "gradient_accumulation_step": 1, - "tracker": ["tensorboard"], - "max_epoch": 5, - "save_checkpoint_stride": [1000], - "keep_last": [100], - "run_eval": [true], - "dataloader": { - "num_worker": 64, - "pin_memory": true - }, - "adam": { - "lr": 5e-5 - }, - "use_dynamic_batchsize": true, - "batch_size": 8, - "max_tokens": 800000, - "max_sentences": 64, - "lr_warmup_steps": 5000, - "lr_scheduler": "cosine", - "num_train_steps": 800000 - }, + "adam": { + "lr": 5e-5 + }, + "use_dynamic_batchsize": true, + "batch_size": 8, + "max_tokens": 800000, + "max_sentences": 64, + "lr_warmup_steps": 5000, + "lr_scheduler": "cosine", + "num_train_steps": 800000 + }, "trans_exp": { - "dataset_list":["librilight_large","librilight_medium","librilight_small"], + "dataset_list": ["librilight_large", "librilight_medium", "librilight_small"], "test_dataset_list": ["mls_test"], - "cache_dir": "/mnt/data2/hehaorui/exp_cache/vc/newcache", + "cache_dir": "path/to/your/exp_cache/directory", "use_speaker": true, "use_noise": true, - "noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_train", - "test_noise_dir": "/home/hehaorui/code/Amphion/MS-SNSD/noise_test" - } + "noise_dir": "path/to/your/noise/train/directory", + "test_noise_dir": "path/to/your/noise/test/directory" } +} diff --git a/egs/vc/noro_inference.sh b/egs/vc/noro_inference.sh index 517a2b74..83e8f78c 100644 --- a/egs/vc/noro_inference.sh +++ b/egs/vc/noro_inference.sh @@ -3,50 +3,52 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. - +# Set the PYTHONPATH to the current directory export PYTHONPATH="./" - + ######## Build Experiment Environment ########### +# Get the current directory of the script exp_dir=$(cd `dirname $0`; pwd) +# Get the parent directory of the experiment directory work_dir=$(dirname $(dirname $exp_dir)) +# Export environment variables for the working directory and Python path export WORK_DIR=$work_dir export PYTHONPATH=$work_dir export PYTHONIOENCODING=UTF-8 - +# Build the monotonic alignment module cd $work_dir/modules/monotonic_align mkdir -p monotonic_align python setup.py build_ext --inplace cd $work_dir - if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config_testing.json + exp_config="${exp_dir}/exp_config_testing.json" fi -echo "Exprimental Configuration File: $exp_config" - -hubert="/mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602/model.safetensors" - -checkpoint_path=$hubert +echo "Experimental Configuration File: $exp_config" cuda_id=0 -output_dir="/home/hehaorui/code/Amphion-1/egs/vc" # -source_path="/home/hehaorui/code/Amphion-1/egs/vc/p233_001.wav" -reference_path="/home/hehaorui/code/Amphion-1/egs/vc/p275_425.wav" +# Set paths (modify these paths to your own) +checkpoint_path="path/to/checkpoint/model.safetensors" +output_dir="path/to/output/directory" +source_path="path/to/source/audio.wav" +reference_path="path/to/reference/audio.wav" echo "CUDA ID: $cuda_id" -echo "Zero Shot Json File Path: $zero_shot_json_file_path" echo "Checkpoint Path: $checkpoint_path" echo "Output Directory: $output_dir" +echo "Source Audio Path: $source_path" +echo "Reference Audio Path: $reference_path" - -python "${work_dir}"/models/vc/noro_inference.py \ +# Run the voice conversion inference script +python "${work_dir}/models/vc/noro_inference.py" \ --config $exp_config \ --checkpoint_path $checkpoint_path \ --output_dir $output_dir \ --cuda_id ${cuda_id} \ --source_path $source_path \ - --ref_path $reference_path \ + --ref_path $reference_path + diff --git a/egs/vc/noro_train_clean.sh b/egs/vc/noro_train_clean.sh index 6681335e..0a5f0bc4 100644 --- a/egs/vc/noro_train_clean.sh +++ b/egs/vc/noro_train_clean.sh @@ -3,47 +3,53 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. - ######## Build Experiment Environment ########### exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $exp_dir)) +work_dir=$(dirname $(dirname $exp_dir)) export WORK_DIR=$work_dir export PYTHONPATH=$work_dir export PYTHONIOENCODING=UTF-8 - + cd $work_dir/modules/monotonic_align mkdir -p monotonic_align python setup.py build_ext --inplace cd $work_dir -# 这一行开始 - if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config_4gpu_noisy.json + exp_config="${exp_dir}/exp_config_4gpu_clean.json" fi -echo "Exprimental Configuration File: $exp_config" +echo "Experimental Configuration File: $exp_config" -#这里需要跟exp_config保持对应 -exp_name="new" +# Set experiment name +exp_name="experiment_name" -#训练的时候用0，1，2，3 -#测试的时候用6/7 +# Set CUDA ID if [ -z "$gpu" ]; then - gpu="0,6" + gpu="0,1,2,3" fi ######## Train Model ########### -echo "Exprimental Name: $exp_name" +echo "Experimental Name: $exp_name" + +# Specify the checkpoint folder (modify this path to your own) +checkpoint_path="path/to/checkpoint/noro_checkpoint" +# If this is a new experiment, use the following command: +# CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ +# "${work_dir}/bins/vc/train.py" \ +# --config $exp_config \ +# --exp_name $exp_name \ +# --log_level debug -# 这是指定一个checkpoint 文件夹 +# To resume training or fine-tune from a checkpoint, use the following command: +# Ensure the options --resume, --resume_type resume, and --checkpoint_path are set CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ -"${work_dir}"/bins/vc/train.py \ +"${work_dir}/bins/vc/train.py" \ --config $exp_config \ --exp_name $exp_name \ --log_level debug \ --resume \ --resume_type resume \ - --checkpoint_path /mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602 \ No newline at end of file + --checkpoint_path $checkpoint_path diff --git a/egs/vc/noro_train_noisy.sh b/egs/vc/noro_train_noisy.sh index 0004e56d..192b27d6 100644 --- a/egs/vc/noro_train_noisy.sh +++ b/egs/vc/noro_train_noisy.sh @@ -3,47 +3,53 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. - ######## Build Experiment Environment ########### exp_dir=$(cd `dirname $0`; pwd) -work_dir=$(dirname $(dirname $exp_dir)) +work_dir=$(dirname $(dirname $exp_dir)) export WORK_DIR=$work_dir export PYTHONPATH=$work_dir export PYTHONIOENCODING=UTF-8 - + cd $work_dir/modules/monotonic_align mkdir -p monotonic_align python setup.py build_ext --inplace cd $work_dir -# 这一行开始 - if [ -z "$exp_config" ]; then - exp_config="${exp_dir}"/exp_config_4gpu_clean.json + exp_config="${exp_dir}/exp_config_4gpu_noisy.json" fi -echo "Exprimental Configuration File: $exp_config" +echo "Experimental Configuration File: $exp_config" -#这里需要跟exp_config保持对应 -exp_name="new" +# Set experiment name +exp_name="experiment_name" -#训练的时候用0，1，2，3 -#测试的时候用6/7 +# Set CUDA ID if [ -z "$gpu" ]; then - gpu="0,6" + gpu="0,1,2,3" fi ######## Train Model ########### -echo "Exprimental Name: $exp_name" +echo "Experimental Name: $exp_name" + +# Specify the checkpoint folder (modify this path to your own) +checkpoint_path="path/to/checkpoint/noro_checkpoint" +# If this is a new experiment, use the following command: +# CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ +# "${work_dir}/bins/vc/train.py" \ +# --config $exp_config \ +# --exp_name $exp_name \ +# --log_level debug -# 这是指定一个checkpoint 文件夹 +# To resume training or fine-tune from a checkpoint, use the following command: +# Ensure the options --resume, --resume_type resume, and --checkpoint_path are set CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ -"${work_dir}"/bins/vc/train.py \ +"${work_dir}/bins/vc/train.py" \ --config $exp_config \ --exp_name $exp_name \ --log_level debug \ --resume \ --resume_type resume \ - --checkpoint_path /mnt/data2/hehaorui/ckpt/vc_new_exp/new_mhubert/checkpoint/epoch-0002_step-0689002_loss-0.571602 \ No newline at end of file + --checkpoint_path $checkpoint_path \ No newline at end of file diff --git a/models/vc/hubert_kmeans.py b/models/vc/hubert_kmeans.py index 80886771..70df5d7a 100644 --- a/models/vc/hubert_kmeans.py +++ b/models/vc/hubert_kmeans.py @@ -58,8 +58,8 @@ class HubertWithKmeans(nn.Module): def __init__( self, - checkpoint_path= '/mnt/data3/hehaorui/pretrained_models/mhubert/mhubert_base_vp_en_es_fr_it3.pt', - kmeans_path = '/mnt/data3/hehaorui/pretrained_models/mhubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', + checkpoint_path= 'path/to/your/mhubert_model/mhubert_base_vp_en_es_fr_it3.pt', + kmeans_path = 'path/to/your/mhubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', target_sample_hz=16000, seq_len_multiple_of=None, output_layer=9, diff --git a/models/vc/noro_inference.py b/models/vc/noro_inference.py index 440e9485..8eb62dcb 100644 --- a/models/vc/noro_inference.py +++ b/models/vc/noro_inference.py @@ -1,17 +1,14 @@ import argparse import torch import numpy as np -import torch -from safetensors.torch import load_model import librosa +from safetensors.torch import load_model +import os from utils.util import load_config from models.vc.vc_trainer import VCTrainer -from utils.util import load_config from models.vc.ns2_uniamphion import UniAmphionVC from models.vc.hubert_kmeans import HubertWithKmeans from models.vc.vc_utils import mel_spectrogram, extract_world_f0 -import os - def build_trainer(args, cfg): supported_trainer = { @@ -21,24 +18,23 @@ def build_trainer(args, cfg): trainer = trainer_class(args, cfg) return trainer - def main(): parser = argparse.ArgumentParser() parser.add_argument( "--config", default="config.json", - help="json files for configurations.", + help="JSON file for configurations.", required=True, ) parser.add_argument( "--checkpoint_path", - type=str, - help="Checkpoint for resume training or finetuning.", + type=str, + help="Checkpoint for resume training or fine-tuning.", required=True, ) parser.add_argument( - "--output_dir", - help="output path", + "--output_dir", + help="Output path", required=True, ) parser.add_argument( @@ -49,16 +45,15 @@ def main(): parser.add_argument( "--source_path", type=str, - help="source voice path", + help="Source voice path", ) parser.add_argument( - "--cuda_id", - type=int, - default=7, - help="Cuda id for training." + "--cuda_id", + type=int, + default=0, + help="CUDA id for training." ) - parser.add_argument("--local_rank", default=-1, type=int) args = parser.parse_args() cfg = load_config(args.config) @@ -67,8 +62,7 @@ def main(): cuda_id = args.cuda_id args.local_rank = torch.device(f"cuda:{cuda_id}") - print("local rank", args.local_rank) - + print("Local rank:", args.local_rank) args.content_extractor = "mhubert" @@ -76,17 +70,15 @@ def main(): torch.cuda.empty_cache() ckpt_path = args.checkpoint_path - w2v = HubertWithKmeans() - - + w2v = HubertWithKmeans() w2v = w2v.to(device=args.local_rank) w2v.eval() model = UniAmphionVC(cfg=cfg.model) - print("loading model") + print("Loading model") load_model(model, ckpt_path) - print("model loaded") + print("Model loaded") model.cuda(args.local_rank) model.eval() @@ -98,45 +90,37 @@ def main(): audio = torch.from_numpy(wav).to(args.local_rank) audio = audio[None, :] - - ref_wav,_ = librosa.load(ref_wav_path, sr=16000) + ref_wav, _ = librosa.load(ref_wav_path, sr=16000) ref_wav = np.pad(ref_wav, (0, 200 - len(ref_wav) % 200)) ref_audio = torch.from_numpy(ref_wav).to(args.local_rank) ref_audio = ref_audio[None, :] - with torch.no_grad(): ref_mel = mel_spectrogram(ref_audio) ref_mel = ref_mel.transpose(1, 2).to(device=args.local_rank) ref_mask = torch.ones(ref_mel.shape[0], ref_mel.shape[1]).to(args.local_rank).bool() - - _, content_feature = w2v(audio) content_feature = content_feature.to(device=args.local_rank) pitch_raw = extract_world_f0(audio) pitch = (pitch_raw - pitch_raw.mean(dim=1, keepdim=True)) / ( pitch_raw.std(dim=1, keepdim=True) + 1e-6 - ) - - # pitch = content_feature.mean(dim=2) - # pitch.zero_() + ) x0 = model.inference( content_feature=content_feature, pitch=pitch, x_ref=ref_mel, x_ref_mask=ref_mask, - inference_steps=200, + inference_steps=200, sigma=1.2, - )# 150-300 0.95-1.5 + ) # 150-300 0.95-1.5 recon_path = f"{args.output_dir}/recon_mel.npy" np.save(recon_path, x0.transpose(1, 2).detach().cpu().numpy()) - print(f"mel saved to: {recon_path}") + print(f"Mel spectrogram saved to: {recon_path}") - - if __name__ == "__main__": main() + diff --git a/models/vc/vc_trainer.py b/models/vc/vc_trainer.py index 116b726b..80daca4a 100644 --- a/models/vc/vc_trainer.py +++ b/models/vc/vc_trainer.py @@ -2,6 +2,7 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. + import os import shutil import time @@ -22,17 +23,14 @@ from accelerate.utils import ProjectConfiguration from models.vc.ns2_uniamphion import UniAmphionVC -from models.vc.vc_dataset import VCCollator, VCDataset, batch_by_size -# from models.tts.vc.vc_new_dataset import VCCollator, VCDataset, batch_by_size # used on ailab sever +from models.vc.vc_dataset import VCCollator, VCDataset, batch_by_size from models.vc.hubert_kmeans import HubertWithKmeans from models.vc.vc_loss import diff_loss, ConstractiveSpeakerLoss from models.vc.vc_utils import mel_spectrogram, extract_world_f0 - # import descript_audio_codec.dac # from audiotools import AudioSignal - class VCTrainer(TTSTrainer): def __init__(self, args, cfg): self.args = args @@ -40,29 +38,29 @@ def __init__(self, args, cfg): cfg.exp_name = args.exp_name self.content_extractor = "mhubert" - # 初始化加速器，并确保所有进程都已就绪 + # Initialize accelerator and ensure all processes are ready self._init_accelerator() self.accelerator.wait_for_everyone() - # 在主进程中初始化日志记录器，避免在每个进程中重复记录 + # Initialize logger on the main process if self.accelerator.is_main_process: self.logger = get_logger(args.exp_name, log_level="INFO") - # 配置噪声和说话人使用 + # Configure noise and speaker usage self.use_source_noise = self.cfg.trans_exp.use_source_noise self.use_ref_noise = self.cfg.trans_exp.use_ref_noise self.use_speaker = self.cfg.trans_exp.use_speaker - # 在主进程中记录配置信息 + # Log configuration on the main process if self.accelerator.is_main_process: self.logger.info(f"use_source_noise: {self.use_source_noise}") self.logger.info(f"use_ref_noise: {self.use_ref_noise}") self.logger.info(f"use_speaker: {self.use_speaker}") - # 初始化一个时间窗口，用于监控或记录某些度量 + # Initialize a time window for monitoring metrics self.time_window = ValueWindow(50) - # 记录训练开始信息 + # Log the start of training if self.accelerator.is_main_process: self.logger.info("=" * 56) self.logger.info("||\t\tNew training process started.\t\t||") @@ -72,33 +70,32 @@ def __init__(self, args, cfg): self.logger.info(f"Experiment name: {args.exp_name}") self.logger.info(f"Experiment directory: {self.exp_dir}") - - # 初始化检查点目录，确保仅在主进程中执行 + # Initialize checkpoint directory self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") if self.accelerator.is_main_process: os.makedirs(self.checkpoint_dir, exist_ok=True) self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") - # 初始化训练计数器 + # Initialize training counters self.batch_count: int = 0 self.step: int = 0 self.epoch: int = 0 - self.max_epoch = (self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf")) + self.max_epoch = self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") if self.accelerator.is_main_process: self.logger.info(f"Max epoch: {self.max_epoch if self.max_epoch < float('inf') else 'Unlimited'}") - # 检查基本配置 + # Check basic configuration if self.accelerator.is_main_process: self._check_basic_configs() self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride self.keep_last = [i if i > 0 else float("inf") for i in self.cfg.train.keep_last] self.run_eval = self.cfg.train.run_eval - # 在所有进程中设置随机种子 + # Set random seed with self.accelerator.main_process_first(): self._set_random_seed(self.cfg.train.random_seed) - # setup data_loader + # Setup data loader with self.accelerator.main_process_first(): if self.accelerator.is_main_process: self.logger.info("Building dataset...") @@ -106,13 +103,14 @@ def __init__(self, args, cfg): self.speaker_num = len(self.train_dataloader.dataset.speaker2id) if self.accelerator.is_main_process: self.logger.info("Speaker num: {}".format(self.speaker_num)) - - # build model + + # Build model with self.accelerator.main_process_first(): if self.accelerator.is_main_process: self.logger.info("Building model...") self.model, self.w2v = self._build_model() + # Resume training if specified with self.accelerator.main_process_first(): if self.accelerator.is_main_process: self.logger.info("Resume training: {}".format(args.resume)) @@ -130,53 +128,32 @@ def __init__(self, args, cfg): if self.accelerator.is_main_process: self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") - # optimizer & scheduler + # Initialize optimizer & scheduler with self.accelerator.main_process_first(): if self.accelerator.is_main_process: self.logger.info("Building optimizer and scheduler...") self.optimizer = self._build_optimizer() self.scheduler = self._build_scheduler() - if isinstance(self.model, dict): - for key in self.model.keys(): - self.model[key] = self.accelerator.prepare(self.model[key]) - else: - self.model = self.accelerator.prepare(self.model) - - # prepare w2v to accelerator - if isinstance(self.w2v, dict): - for key in self.w2v.keys(): - self.w2v[key] = self.accelerator.prepare(self.w2v[key]) - else: - self.w2v = self.accelerator.prepare(self.w2v) - - if isinstance(self.optimizer, dict): - for key in self.optimizer.keys(): - self.optimizer[key] = self.accelerator.prepare(self.optimizer[key]) - else: - self.optimizer = self.accelerator.prepare(self.optimizer) - - if isinstance(self.scheduler, dict): - for key in self.scheduler.keys(): - self.scheduler[key] = self.accelerator.prepare(self.scheduler[key]) - else: - self.scheduler = self.accelerator.prepare(self.scheduler) + # Prepare model, w2v, optimizer, and scheduler for accelerator + self.model = self._prepare_for_accelerator(self.model) + self.w2v = self._prepare_for_accelerator(self.w2v) + self.optimizer = self._prepare_for_accelerator(self.optimizer) + self.scheduler = self._prepare_for_accelerator(self.scheduler) + # Build criterion with self.accelerator.main_process_first(): if self.accelerator.is_main_process: self.logger.info("Building criterion...") self.criterion = self._build_criterion() self.config_save_path = os.path.join(self.exp_dir, "args.json") - self.task_type = "VC" - self.contrastive_speaker_loss = ConstractiveSpeakerLoss() if self.accelerator.is_main_process: self.logger.info("Task type: {}".format(self.task_type)) - def _init_accelerator(self): self.exp_dir = os.path.join( os.path.abspath(self.cfg.log_dir), self.args.exp_name @@ -197,9 +174,8 @@ def _init_accelerator(self): with self.accelerator.main_process_first(): self.accelerator.init_trackers(self.args.exp_name) - def _build_model(self): - w2v = HubertWithKmeans() + w2v = HubertWithKmeans() self.cfg.model.vc_feature.content_feature_dim = 768 model = UniAmphionVC(cfg=self.cfg.model, use_ref_noise=self.use_ref_noise, use_source_noise=self.use_source_noise) return model, w2v @@ -210,22 +186,19 @@ def _build_dataset(self): def _build_dataloader(self): np.random.seed(int(time.time())) if self.accelerator.is_main_process: - self.logger.info("Use Dynamic Batchsize......") + self.logger.info("Use Dynamic Batchsize...") train_dataset = VCDataset(self.cfg.trans_exp) train_collate = VCCollator(self.cfg) batch_sampler = batch_by_size( train_dataset.num_frame_indices, train_dataset.get_num_frames, max_tokens=self.cfg.train.max_tokens * self.accelerator.num_processes, - max_sentences=self.cfg.train.max_sentences - * self.accelerator.num_processes, + max_sentences=self.cfg.train.max_sentences * self.accelerator.num_processes, required_batch_size_multiple=self.accelerator.num_processes, ) np.random.shuffle(batch_sampler) batches = [ - x[ - self.accelerator.local_process_index :: self.accelerator.num_processes - ] + x[self.accelerator.local_process_index :: self.accelerator.num_processes] for x in batch_sampler if len(x) % self.accelerator.num_processes == 0 ] @@ -269,7 +242,7 @@ def _count_parameters(self, model): else: model_param = sum(p.numel() for p in model.parameters()) return model_param - + def _dump_cfg(self, path): os.makedirs(os.path.dirname(path), exist_ok=True) json5.dump( @@ -291,7 +264,7 @@ def get_state_dict(self): "batch_size": self.cfg.train.batch_size, } return state_dict - + def load_model(self, checkpoint): self.step = checkpoint["step"] self.epoch = checkpoint["epoch"] @@ -299,27 +272,33 @@ def load_model(self, checkpoint): self.optimizer.load_state_dict(checkpoint["optimizer"]) self.scheduler.load_state_dict(checkpoint["scheduler"]) + def _prepare_for_accelerator(self, component): + if isinstance(component, dict): + for key in component.keys(): + component[key] = self.accelerator.prepare(component[key]) + else: + component = self.accelerator.prepare(component) + return component + def _train_step(self, batch): total_loss = 0.0 train_losses = {} - device = self.accelerator.device + device = self.accelerator.device - # 将所有Tensor类型的数据迁移到指定设备 + # Move all Tensor data to the specified device batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()} - speech = batch["speech"] - ref_speech = batch["ref_speech"] - + speech = batch["speech"] + ref_speech = batch["ref_speech"] + with torch.set_grad_enabled(False): - # 提取需要的特征和光谱图 + # Extract features and spectrograms mel = mel_spectrogram(speech).transpose(1, 2) ref_mel = mel_spectrogram(ref_speech).transpose(1, 2) - # print('-----------------------------------') - # print(ref_mel.shape) mask = batch["mask"] ref_mask = batch["ref_mask"] - - # 提取 pitch 和 content_feature + + # Extract pitch and content features if not self.use_source_noise: pitch = extract_world_f0(speech) pitch = (pitch - pitch.mean(dim=1, keepdim=True)) / (pitch.std(dim=1, keepdim=True) + 1e-6) # Normalize pitch (B,T) @@ -327,7 +306,7 @@ def _train_step(self, batch): if self.use_ref_noise: noisy_ref_mel = mel_spectrogram(batch["noisy_ref_speech"]).transpose(1, 2) - + if self.use_source_noise: combined_speech = torch.cat((speech, batch["noisy_speech"]), dim=0) _, combined_features = self.w2v(combined_speech) @@ -336,8 +315,8 @@ def _train_step(self, batch): clean_pitch, noisy_pitch = torch.split(combined_pitch, speech.shape[0], dim=0) pitch = (clean_pitch - clean_pitch.mean(dim=1, keepdim=True)) / (clean_pitch.std(dim=1, keepdim=True) + 1e-6) noisy_pitch = (noisy_pitch - noisy_pitch.mean(dim=1, keepdim=True)) / (noisy_pitch.std(dim=1, keepdim=True) + 1e-6) - - # FORWARD 模型 + + # Forward pass through the model if self.use_ref_noise and self.use_source_noise: diff_out, (ref_emb, noisy_ref_emb), (cond_emb, noisy_cond_emb) = self.model( x=mel, content_feature=content_feature, pitch=pitch, x_ref=ref_mel, @@ -356,7 +335,7 @@ def _train_step(self, batch): ) if self.use_ref_noise: - # B x N_query x D + # B x N_query x D ref_emb = torch.mean(ref_emb, dim=1) # B x D noisy_ref_emb = torch.mean(noisy_ref_emb, dim=1) # B x D all_ref_emb = torch.cat([ref_emb, noisy_ref_emb], dim=0) # 2B x D @@ -392,10 +371,8 @@ def _train_step(self, batch): train_losses['learning_rate'] = f"{self.optimizer.param_groups[0]['lr']:.1e}" train_losses["batch_size"] = batch["speaker_id"].shape[0] - - return (train_losses["total_loss"], train_losses, None) - + return (train_losses["total_loss"], train_losses, None) def _train_epoch(self): r"""Training epoch. Should return average loss of a batch (sample) over @@ -429,18 +406,16 @@ def _train_epoch(self): dynamic_ncols=True, smoothing=0.04, disable=not self.accelerator.is_main_process, - ): - + ): speech = batch["speech"].cpu().numpy() speech = speech[0] self.batch_count += 1 self.step += 1 - #epoch_step += 1 if len(speech) >= 16000 * 25: continue with self.accelerator.accumulate(self.model): total_loss, train_losses, _ = self._train_step(batch) - + if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: epoch_sum_loss += total_loss self.current_loss = total_loss @@ -450,33 +425,32 @@ def _train_epoch(self): {"Epoch/Train {} Loss".format(key): loss}, step=self.step, ) - if (self.accelerator.is_main_process and self.batch_count % 10 == 0): + if self.accelerator.is_main_process and self.batch_count % 10 == 0: self.echo_log(train_losses, mode="Training") - + self.save_checkpoint() self.accelerator.wait_for_everyone() return epoch_sum_loss, None - + def train_loop(self): r"""Training loop. The public entry of training process.""" # Wait everyone to prepare before we move on self.accelerator.wait_for_everyone() - # dump config file + # Dump config file if self.accelerator.is_main_process: self._dump_cfg(self.config_save_path) # Wait to ensure good to go self.accelerator.wait_for_everyone() - # stop when meet max epoch or self.cfg.train.num_train_steps - print(self.step) + # Stop when meeting max epoch or self.cfg.train.num_train_steps while self.epoch < self.max_epoch and self.step < self.cfg.train.num_train_steps: if self.accelerator.is_main_process: self.logger.info("\n") self.logger.info("-" * 32) self.logger.info("Epoch {}: ".format(self.epoch)) - self.logger.info("Start training......") - + self.logger.info("Start training...") + train_total_loss, _ = self._train_epoch() self.epoch += 1 @@ -500,23 +474,20 @@ def train_loop(self): ) self.accelerator.end_training() if self.accelerator.is_main_process: - self.logger.info("Training finished......") + self.logger.info("Training finished...") def save_checkpoint(self): self.accelerator.wait_for_everyone() - # main process only + # Main process only if self.accelerator.is_main_process: if self.batch_count % self.save_checkpoint_stride[0] == 0: keep_last = self.keep_last[0] - # 读取self.checkpoint_dir所有的folder + # Read all folders in self.checkpoint_dir all_ckpts = os.listdir(self.checkpoint_dir) - # 排除非文件夹 + # Exclude non-folders all_ckpts = [ckpt for ckpt in all_ckpts if os.path.isdir(os.path.join(self.checkpoint_dir, ckpt))] if len(all_ckpts) > keep_last: - # 只保留keep_last个的folder in self.checkpoint_dir, sort by step "epoch-{:04d}_step-{:07d}_loss-{:.6f}" - all_ckpts = sorted(all_ckpts, key=lambda x: int(x.split("_")[1].split('-')[1])) - - # 只保留keep_last个的folder in self.checkpoint_dir, sort by step "epoch-{:04d}_step-{:07d}_loss-{:.6f}" + # Keep only the last keep_last folders in self.checkpoint_dir, sorted by step "epoch-{:04d}_step-{:07d}_loss-{:.6f}" all_ckpts = sorted(all_ckpts, key=lambda x: int(x.split("_")[1].split('-')[1])) for ckpt in all_ckpts[:-keep_last]: shutil.rmtree(os.path.join(self.checkpoint_dir, ckpt)) @@ -528,3 +499,4 @@ def save_checkpoint(self): self.accelerator.save_state(path) self.logger.info("Finished saving state.") self.accelerator.wait_for_everyone() + From 962dd2764bdf838fd6137fe89180218358e40991 Mon Sep 17 00:00:00 2001 From: kenxxxxx Date: Wed, 28 Aug 2024 23:31:15 +0800 Subject: [PATCH 06/16] code clean --- bins/vc/{ => Noro}/train.py | 10 +- config/{vc.json => noro.json} | 5 +- config/tts.json | 2 +- egs/vc/{ => Noro}/README.md | 0 egs/vc/Noro/exp_config_base.json | 61 ++ .../exp_config_clean.json} | 14 - .../exp_config_noisy.json} | 19 +- egs/vc/{ => Noro}/noro_inference.sh | 2 +- egs/vc/{ => Noro}/noro_train_clean.sh | 4 +- egs/vc/{ => Noro}/noro_train_noisy.sh | 2 +- egs/vc/exp_config_base.json | 101 --- egs/vc/exp_config_testing.json | 48 -- models/base/vc_dataset.py | 344 -------- models/tts/naturalspeech2/ns2_trainer.py | 63 +- models/vc/{ => Noro}/hubert_kmeans.py | 27 +- models/vc/Noro/noro_base_trainer.py | 280 +++++++ .../{vc_dataset.py => Noro/noro_dataset.py} | 148 ++-- models/vc/{ => Noro}/noro_inference.py | 30 +- models/vc/Noro/noro_loss.py | 50 ++ .../{ns2_uniamphion.py => Noro/noro_model.py} | 52 +- .../{vc_trainer.py => Noro/noro_trainer.py} | 102 +-- models/vc/base_trainer.py | 762 ------------------ models/vc/vc_loss.py | 89 -- models/vc/vc_utils.py | 94 --- processors/audio_features_extractor.py | 18 + processors/content_extractor.py | 58 ++ 26 files changed, 643 insertions(+), 1742 deletions(-) rename bins/vc/{ => Noro}/train.py (92%) rename config/{vc.json => noro.json} (91%) rename egs/vc/{ => Noro}/README.md (100%) create mode 100644 egs/vc/Noro/exp_config_base.json rename egs/vc/{exp_config_4gpu_clean.json => Noro/exp_config_clean.json} (73%) rename egs/vc/{exp_config_4gpu_noisy.json => Noro/exp_config_noisy.json} (66%) rename egs/vc/{ => Noro}/noro_inference.sh (96%) rename egs/vc/{ => Noro}/noro_train_clean.sh (94%) rename egs/vc/{ => Noro}/noro_train_noisy.sh (96%) delete mode 100644 egs/vc/exp_config_base.json delete mode 100644 egs/vc/exp_config_testing.json delete mode 100644 models/base/vc_dataset.py rename models/vc/{ => Noro}/hubert_kmeans.py (86%) create mode 100644 models/vc/Noro/noro_base_trainer.py rename models/vc/{vc_dataset.py => Noro/noro_dataset.py} (79%) rename models/vc/{ => Noro}/noro_inference.py (77%) create mode 100644 models/vc/Noro/noro_loss.py rename models/vc/{ns2_uniamphion.py => Noro/noro_model.py} (95%) rename models/vc/{vc_trainer.py => Noro/noro_trainer.py} (82%) delete mode 100644 models/vc/base_trainer.py delete mode 100644 models/vc/vc_loss.py delete mode 100644 models/vc/vc_utils.py diff --git a/bins/vc/train.py b/bins/vc/Noro/train.py similarity index 92% rename from bins/vc/train.py rename to bins/vc/Noro/train.py index 219b16b3..8418c5cd 100644 --- a/bins/vc/train.py +++ b/bins/vc/Noro/train.py @@ -6,16 +6,12 @@ import argparse import torch - -from models.tts.valle.valle_trainer import VALLETrainer -from models.vc.vc_trainer import VCTrainer +from models.vc.Noro.noro_trainer import NoroTrainer from utils.util import load_config - - def build_trainer(args, cfg): supported_trainer = { - "VC": VCTrainer, + "VC": NoroTrainer, } trainer_class = supported_trainer[cfg.model_type] trainer = trainer_class(args, cfg) @@ -67,7 +63,7 @@ def main(): default=None, help="Checkpoint for resume training or finetuning.", ) - VALLETrainer.add_arguments(parser) + NoroTrainer.add_arguments(parser) args = parser.parse_args() cfg = load_config(args.config) print("experiment name: ", args.exp_name) diff --git a/config/vc.json b/config/noro.json similarity index 91% rename from config/vc.json rename to config/noro.json index 06afff8d..3c9d5c12 100644 --- a/config/vc.json +++ b/config/noro.json @@ -1,5 +1,5 @@ { - "base_config": "config/tts.json", + "base_config": "config/base.json", "model_type": "VC", "dataset": ["mls"], "model": { @@ -18,13 +18,12 @@ "num_query_emb": 32 }, "diffusion": { - "diffusion_type": "diffusion", // Option: "diffusion" or "flow matching" "beta_min": 0.05, "beta_max": 20, "sigma": 1.0, "noise_factor": 1.0, "ode_solve_method": "euler", - "diff_model_type": "WaveNet", // Transformer or WaveNet + "diff_model_type": "WaveNet", "diff_wavenet":{ "input_size": 80, "hidden_size": 512, diff --git a/config/tts.json b/config/tts.json index 882726db..0e804bda 100644 --- a/config/tts.json +++ b/config/tts.json @@ -19,7 +19,7 @@ "add_blank": true }, "model": { - "text_token_num": 512, + "text_token_num": 512 } } diff --git a/egs/vc/README.md b/egs/vc/Noro/README.md similarity index 100% rename from egs/vc/README.md rename to egs/vc/Noro/README.md diff --git a/egs/vc/Noro/exp_config_base.json b/egs/vc/Noro/exp_config_base.json new file mode 100644 index 00000000..27832d4d --- /dev/null +++ b/egs/vc/Noro/exp_config_base.json @@ -0,0 +1,61 @@ +{ + "base_config": "config/noro.json", + "model_type": "VC", + "dataset": [ + "mls" + ], + "sample_rate": 16000, + "n_fft": 1024, + "n_mel": 80, + "hop_size": 200, + "win_size": 800, + "fmin": 0, + "fmax": 8000, + "preprocess": { + "kmeans_model_path": "path/to/kmeans_model", + "hubert_model_path": "path/to/hubert_model", + "sample_rate": 16000, + "hop_size": 200, + "f0_min": 50, + "f0_max": 500, + "frame_period": 12.5 + }, + "model": { + "reference_encoder": { + "encoder_layer": 6, + "encoder_hidden": 512, + "encoder_head": 8, + "conv_filter_size": 2048, + "conv_kernel_size": 9, + "encoder_dropout": 0.2, + "use_skip_connection": false, + "use_new_ffn": true, + "ref_in_dim": 80, + "ref_out_dim": 512, + "use_query_emb": true, + "num_query_emb": 32 + }, + "diffusion": { + "beta_min": 0.05, + "beta_max": 20, + "sigma": 1.0, + "noise_factor": 1.0, + "ode_solve_method": "euler", + "diff_model_type": "WaveNet", + "diff_wavenet":{ + "input_size": 80, + "hidden_size": 512, + "out_size": 80, + "num_layers": 47, + "cross_attn_per_layer": 3, + "dilation_cycle": 2, + "attn_head": 8, + "drop_out": 0.2 + } + }, + "vc_feature": { + "content_feature_dim": 768, + "hidden_dim": 512 + } + } +} \ No newline at end of file diff --git a/egs/vc/exp_config_4gpu_clean.json b/egs/vc/Noro/exp_config_clean.json similarity index 73% rename from egs/vc/exp_config_4gpu_clean.json rename to egs/vc/Noro/exp_config_clean.json index d5cef390..5656db3f 100644 --- a/egs/vc/exp_config_4gpu_clean.json +++ b/egs/vc/Noro/exp_config_clean.json @@ -3,14 +3,6 @@ "dataset": [ "mls" ], - "preprocess": { - // Specify the output root path to save the processed data - "processed_dir": "data", - "train_file": "train.json", - "valid_file": "test.json", - "read_metadata": true, - "metadata_dir": "metadata" - }, // Specify the output root path to save model checkpoints and logs "log_dir": "path/to/your/checkpoint/directory", "train": { @@ -29,7 +21,6 @@ "lr": 5e-5 }, "use_dynamic_batchsize": true, - "batch_size": 8, "max_tokens": 3200000, "max_sentences": 64, "lr_warmup_steps": 5000, @@ -42,12 +33,7 @@ "path/to/your/training_data_directory2", "path/to/your/training_data_directory3" ], - "test_directory_list": [ - "path/to/your/test/testing_data_directory" - ], - "use_speaker": false, "use_ref_noise": false, - "use_source_noise": false, "snr_lower": 2, "snr_upper": 42, "total_snrlevels": 5, diff --git a/egs/vc/exp_config_4gpu_noisy.json b/egs/vc/Noro/exp_config_noisy.json similarity index 66% rename from egs/vc/exp_config_4gpu_noisy.json rename to egs/vc/Noro/exp_config_noisy.json index 16132244..7e5d7e75 100644 --- a/egs/vc/exp_config_4gpu_noisy.json +++ b/egs/vc/Noro/exp_config_noisy.json @@ -3,14 +3,6 @@ "dataset": [ "mls" ], - "preprocess": { - // Specify the output root path to save the processed data - "processed_dir": "data", - "train_file": "train.json", - "valid_file": "test.json", - "read_metadata": true, - "metadata_dir": "metadata" - }, // Specify the output root path to save model checkpoints and logs "log_dir": "path/to/your/checkpoint/directory", "train": { @@ -29,7 +21,6 @@ "lr": 5e-5 }, "use_dynamic_batchsize": true, - "batch_size": 8, "max_tokens": 3200000, "max_sentences": 64, "lr_warmup_steps": 5000, @@ -38,19 +29,11 @@ }, "trans_exp": { "directory_list": [ - "path/to/your/training_data_directory1", + "path/to/your/training_data_directory1", "path/to/your/training_data_directory2", "path/to/your/training_data_directory3" ], - "test_directory_list": [ - "path/to/your/test/testing_data_directory" - ], - "use_speaker": false, "use_ref_noise": true, - "use_source_noise": false, - "snr_lower": 2, - "snr_upper": 42, - "total_snrlevels": 5, "noise_dir": "path/to/your/noise/train/directory", "test_noise_dir": "path/to/your/noise/test/directory" } diff --git a/egs/vc/noro_inference.sh b/egs/vc/Noro/noro_inference.sh similarity index 96% rename from egs/vc/noro_inference.sh rename to egs/vc/Noro/noro_inference.sh index 83e8f78c..5f10a9cd 100644 --- a/egs/vc/noro_inference.sh +++ b/egs/vc/Noro/noro_inference.sh @@ -24,7 +24,7 @@ python setup.py build_ext --inplace cd $work_dir if [ -z "$exp_config" ]; then - exp_config="${exp_dir}/exp_config_testing.json" + exp_config="${exp_dir}/exp_config_base.json" fi echo "Experimental Configuration File: $exp_config" diff --git a/egs/vc/noro_train_clean.sh b/egs/vc/Noro/noro_train_clean.sh similarity index 94% rename from egs/vc/noro_train_clean.sh rename to egs/vc/Noro/noro_train_clean.sh index 0a5f0bc4..e04c16a8 100644 --- a/egs/vc/noro_train_clean.sh +++ b/egs/vc/Noro/noro_train_clean.sh @@ -17,7 +17,7 @@ python setup.py build_ext --inplace cd $work_dir if [ -z "$exp_config" ]; then - exp_config="${exp_dir}/exp_config_4gpu_clean.json" + exp_config="${exp_dir}/exp_config_clean.json" fi echo "Experimental Configuration File: $exp_config" @@ -46,7 +46,7 @@ checkpoint_path="path/to/checkpoint/noro_checkpoint" # To resume training or fine-tune from a checkpoint, use the following command: # Ensure the options --resume, --resume_type resume, and --checkpoint_path are set CUDA_VISIBLE_DEVICES=$gpu accelerate launch --main_process_port 26667 --mixed_precision fp16 \ -"${work_dir}/bins/vc/train.py" \ +"${work_dir}/bins/vc/Noro/train.py" \ --config $exp_config \ --exp_name $exp_name \ --log_level debug \ diff --git a/egs/vc/noro_train_noisy.sh b/egs/vc/Noro/noro_train_noisy.sh similarity index 96% rename from egs/vc/noro_train_noisy.sh rename to egs/vc/Noro/noro_train_noisy.sh index 192b27d6..0c910bf6 100644 --- a/egs/vc/noro_train_noisy.sh +++ b/egs/vc/Noro/noro_train_noisy.sh @@ -17,7 +17,7 @@ python setup.py build_ext --inplace cd $work_dir if [ -z "$exp_config" ]; then - exp_config="${exp_dir}/exp_config_4gpu_noisy.json" + exp_config="${exp_dir}/exp_config_noisy.json" fi echo "Experimental Configuration File: $exp_config" diff --git a/egs/vc/exp_config_base.json b/egs/vc/exp_config_base.json deleted file mode 100644 index 89cc15b1..00000000 --- a/egs/vc/exp_config_base.json +++ /dev/null @@ -1,101 +0,0 @@ -{ - "base_config": "config/vc.json", - "model_type": "VC", - "dataset": [ - "mls" - ], - "preprocess": { - "use_mel": false, - "use_code": true, - "use_spkid": true, - "use_pitch": true, - "use_duration": true, - "use_phone": true, - "use_len": true, - "use_cross_reference": true, - "train_file": "train.json", - "valid_file": "test.json", - "melspec_dir": "mel", - "code_dir": "code", - "pitch_dir": "pitch", - "duration_dir": "duration", - "metadata_dir": "metadata", - "read_metadata": true, - "clip_mode": "start" - }, - "model": { - "reference_encoder": { - "encoder_layer": 6, - "encoder_hidden": 512, - "encoder_head": 8, - "conv_filter_size": 2048, - "conv_kernel_size": 9, - "encoder_dropout": 0.2, - "use_skip_connection": false, - "use_new_ffn": true, - "ref_in_dim": 80, - // "ref_in_dim": 96, - "ref_out_dim": 512, - "use_query_emb": true, - "num_query_emb": 32 - }, - "diffusion": { - "diffusion_type": "diffusion", // Option: "diffusion" or "flow matching" - "beta_min": 0.05, - "beta_max": 20, - "sigma": 1.0, - "noise_factor": 1.0, - "ode_solve_method": "euler", - "diff_model_type": "WaveNet", // Transformer or WaveNet - "diff_wavenet":{ - // "input_size": 96, - "input_size": 80, - "hidden_size": 512, - "out_size": 80, - // "out_size": 96, - "num_layers": 47, - "cross_attn_per_layer": 3, - "dilation_cycle": 2, - "attn_head": 8, - "drop_out": 0.2 - } - }, - "prior_encoder": { - "encoder_layer": 6, - "encoder_hidden": 512, - "encoder_head": 8, - "conv_filter_size": 2048, - "conv_kernel_size": 9, - "encoder_dropout": 0.2, - "use_skip_connection": false, - "use_new_ffn": true, - "vocab_size": 256, - "cond_dim": 512, - "duration_predictor": { - "input_size": 512, - "filter_size": 512, - "kernel_size": 3, - "conv_layers": 30, - "cross_attn_per_layer": 3, - "attn_head": 8, - "drop_out": 0.2 - }, - "pitch_predictor": { - "input_size": 512, - "filter_size": 512, - "kernel_size": 5, - "conv_layers": 30, - "cross_attn_per_layer": 3, - "attn_head": 8, - "drop_out": 0.5 - }, - "pitch_min": 50, - "pitch_max": 1100, - "pitch_bins_num": 512 - }, - "vc_feature": { - "content_feature_dim": 768, - "hidden_dim": 512 - } - } -} \ No newline at end of file diff --git a/egs/vc/exp_config_testing.json b/egs/vc/exp_config_testing.json deleted file mode 100644 index 5c91ea84..00000000 --- a/egs/vc/exp_config_testing.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "base_config": "egs/vc/exp_config_base.json", - "dataset": [ - "mls" - ], - "preprocess": { - // Specify the output root path to save the processed data - "processed_dir": "data", - "train_file": "train.json", - "valid_file": "test.json", - "read_metadata": true, - "metadata_dir": "metadata" - }, - // Specify the output root path to save model checkpoints and logs - "log_dir": "path/to/your/checkpoint/directory", - "train": { - // New trainer and Accelerator - "gradient_accumulation_step": 1, - "tracker": ["tensorboard"], - "max_epoch": 5, - "save_checkpoint_stride": [1000], - "keep_last": [100], - "run_eval": [true], - "dataloader": { - "num_worker": 64, - "pin_memory": true - }, - "adam": { - "lr": 5e-5 - }, - "use_dynamic_batchsize": true, - "batch_size": 8, - "max_tokens": 800000, - "max_sentences": 64, - "lr_warmup_steps": 5000, - "lr_scheduler": "cosine", - "num_train_steps": 800000 - }, - "trans_exp": { - "dataset_list": ["librilight_large", "librilight_medium", "librilight_small"], - "test_dataset_list": ["mls_test"], - "cache_dir": "path/to/your/exp_cache/directory", - "use_speaker": true, - "use_noise": true, - "noise_dir": "path/to/your/noise/train/directory", - "test_noise_dir": "path/to/your/noise/test/directory" - } -} diff --git a/models/base/vc_dataset.py b/models/base/vc_dataset.py deleted file mode 100644 index 8639af3c..00000000 --- a/models/base/vc_dataset.py +++ /dev/null @@ -1,344 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -import numpy as np -import torch.utils.data -from torch.nn.utils.rnn import pad_sequence -from utils.data_utils import * -from processors.acoustic_extractor import cal_normalized_mel -from text import text_to_sequence -from text.text_token_collation import phoneIDCollation - - -class BaseDataset(torch.utils.data.Dataset): - def __init__(self, cfg, dataset, is_valid=False): - """ - Args: - cfg: config - dataset: dataset name - is_valid: whether to use train or valid dataset - """ - - assert isinstance(dataset, str) - - # self.data_root = processed_data_dir - self.cfg = cfg - - processed_data_dir = os.path.join(cfg.preprocess.processed_dir, dataset) - meta_file = cfg.preprocess.valid_file if is_valid else cfg.preprocess.train_file - self.metafile_path = os.path.join(processed_data_dir, meta_file) - self.metadata = self.get_metadata() - - """ - load spk2id and utt2spk from json file - spk2id: {spk1: 0, spk2: 1, ...} - utt2spk: {dataset_uid: spk1, ...} - """ - if cfg.preprocess.use_spkid: - spk2id_path = os.path.join(processed_data_dir, cfg.preprocess.spk2id) - with open(spk2id_path, "r") as f: - self.spk2id = json.load(f) - - utt2spk_path = os.path.join(processed_data_dir, cfg.preprocess.utt2spk) - self.utt2spk = dict() - with open(utt2spk_path, "r") as f: - for line in f.readlines(): - utt, spk = line.strip().split("\t") - self.utt2spk[utt] = spk - - if cfg.preprocess.use_uv: - self.utt2uv_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - self.utt2uv_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.uv_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_frame_pitch: - self.utt2frame_pitch_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2frame_pitch_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.pitch_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_frame_energy: - self.utt2frame_energy_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2frame_energy_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.energy_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_mel: - self.utt2mel_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2mel_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.mel_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_linear: - self.utt2linear_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2linear_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.linear_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_audio: - self.utt2audio_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2audio_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.audio_dir, - uid + ".npy", - ) - elif cfg.preprocess.use_label: - self.utt2label_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2label_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.label_dir, - uid + ".npy", - ) - elif cfg.preprocess.use_one_hot: - self.utt2one_hot_path = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - self.utt2one_hot_path[utt] = os.path.join( - cfg.preprocess.processed_dir, - dataset, - cfg.preprocess.one_hot_dir, - uid + ".npy", - ) - - if cfg.preprocess.use_text or cfg.preprocess.use_phone: - self.utt2seq = {} - for utt_info in self.metadata: - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - if cfg.preprocess.use_text: - text = utt_info["Text"] - sequence = text_to_sequence(text, cfg.preprocess.text_cleaners) - elif cfg.preprocess.use_phone: - # load phoneme squence from phone file - phone_path = os.path.join( - processed_data_dir, cfg.preprocess.phone_dir, uid + ".phone" - ) - with open(phone_path, "r") as fin: - phones = fin.readlines() - assert len(phones) == 1 - phones = phones[0].strip() - phones_seq = phones.split(" ") - - phon_id_collator = phoneIDCollation(cfg, dataset=dataset) - sequence = phon_id_collator.get_phone_id_sequence(cfg, phones_seq) - - self.utt2seq[utt] = sequence - - def get_metadata(self): - with open(self.metafile_path, "r", encoding="utf-8") as f: - metadata = json.load(f) - - return metadata - - def get_dataset_name(self): - return self.metadata[0]["Dataset"] - - def __getitem__(self, index): - utt_info = self.metadata[index] - - dataset = utt_info["Dataset"] - uid = utt_info["Uid"] - utt = "{}_{}".format(dataset, uid) - - single_feature = dict() - - if self.cfg.preprocess.use_spkid: - single_feature["spk_id"] = np.array( - [self.spk2id[self.utt2spk[utt]]], dtype=np.int32 - ) - - if self.cfg.preprocess.use_mel: - mel = np.load(self.utt2mel_path[utt]) - assert mel.shape[0] == self.cfg.preprocess.n_mel # [n_mels, T] - if self.cfg.preprocess.use_min_max_norm_mel: - # do mel norm - mel = cal_normalized_mel(mel, utt_info["Dataset"], self.cfg.preprocess) - - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = mel.shape[1] - single_feature["mel"] = mel.T # [T, n_mels] - - if self.cfg.preprocess.use_linear: - linear = np.load(self.utt2linear_path[utt]) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = linear.shape[1] - single_feature["linear"] = linear.T # [T, n_linear] - - if self.cfg.preprocess.use_frame_pitch: - frame_pitch_path = self.utt2frame_pitch_path[utt] - frame_pitch = np.load(frame_pitch_path) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_pitch) - aligned_frame_pitch = align_length( - frame_pitch, single_feature["target_len"] - ) - single_feature["frame_pitch"] = aligned_frame_pitch - - if self.cfg.preprocess.use_uv: - frame_uv_path = self.utt2uv_path[utt] - frame_uv = np.load(frame_uv_path) - aligned_frame_uv = align_length(frame_uv, single_feature["target_len"]) - aligned_frame_uv = [ - 0 if frame_uv else 1 for frame_uv in aligned_frame_uv - ] - aligned_frame_uv = np.array(aligned_frame_uv) - single_feature["frame_uv"] = aligned_frame_uv - - if self.cfg.preprocess.use_frame_energy: - frame_energy_path = self.utt2frame_energy_path[utt] - frame_energy = np.load(frame_energy_path) - if "target_len" not in single_feature.keys(): - single_feature["target_len"] = len(frame_energy) - aligned_frame_energy = align_length( - frame_energy, single_feature["target_len"] - ) - single_feature["frame_energy"] = aligned_frame_energy - - if self.cfg.preprocess.use_audio: - audio = np.load(self.utt2audio_path[utt]) - single_feature["audio"] = audio - single_feature["audio_len"] = audio.shape[0] - - if self.cfg.preprocess.use_phone or self.cfg.preprocess.use_text: - single_feature["phone_seq"] = np.array(self.utt2seq[utt]) - single_feature["phone_len"] = len(self.utt2seq[utt]) - - return single_feature - - def __len__(self): - return len(self.metadata) - - -class BaseCollator(object): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - self.cfg = cfg - - def __call__(self, batch): - packed_batch_features = dict() - - # mel: [b, T, n_mels] - # frame_pitch, frame_energy: [1, T] - # target_len: [1] - # spk_id: [b, 1] - # mask: [b, T, 1] - - for key in batch[0].keys(): - if key == "target_len": - packed_batch_features["target_len"] = torch.LongTensor( - [b["target_len"] for b in batch] - ) - masks = [ - torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch - ] - packed_batch_features["mask"] = pad_sequence( - masks, batch_first=True, padding_value=0 - ) - elif key == "phone_len": - packed_batch_features["phone_len"] = torch.LongTensor( - [b["phone_len"] for b in batch] - ) - masks = [ - torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch - ] - packed_batch_features["phn_mask"] = pad_sequence( - masks, batch_first=True, padding_value=0 - ) - elif key == "audio_len": - packed_batch_features["audio_len"] = torch.LongTensor( - [b["audio_len"] for b in batch] - ) - masks = [ - torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch - ] - else: - values = [torch.from_numpy(b[key]) for b in batch] - packed_batch_features[key] = pad_sequence( - values, batch_first=True, padding_value=0 - ) - return packed_batch_features - - -class BaseTestDataset(torch.utils.data.Dataset): - def __init__(self, cfg, args): - raise NotImplementedError - - def get_metadata(self): - raise NotImplementedError - - def __getitem__(self, index): - raise NotImplementedError - - def __len__(self): - return len(self.metadata) - - -class BaseTestCollator(object): - """Zero-pads model inputs and targets based on number of frames per step""" - - def __init__(self, cfg): - raise NotImplementedError - - def __call__(self, batch): - raise NotImplementedError \ No newline at end of file diff --git a/models/tts/naturalspeech2/ns2_trainer.py b/models/tts/naturalspeech2/ns2_trainer.py index 63c4353e..8ede54e8 100644 --- a/models/tts/naturalspeech2/ns2_trainer.py +++ b/models/tts/naturalspeech2/ns2_trainer.py @@ -433,26 +433,18 @@ def _train_step(self, batch): total_loss += dur_loss train_losses["dur_loss"] = dur_loss - x0 = self.model.module.code_to_latent(code) - if self.cfg.model.diffusion.diffusion_type == "diffusion": - # diff loss x0 - diff_loss_x0 = diff_loss(diff_out["x0_pred"], x0, mask=mask) - total_loss += diff_loss_x0 - train_losses["diff_loss_x0"] = diff_loss_x0 - - # diff loss noise - diff_loss_noise = diff_loss( - diff_out["noise_pred"], diff_out["noise"], mask=mask - ) - total_loss += diff_loss_noise * self.cfg.train.diff_noise_loss_lambda - train_losses["diff_loss_noise"] = diff_loss_noise - - elif self.cfg.model.diffusion.diffusion_type == "flow": - # diff flow matching loss - flow_gt = diff_out["noise"] - x0 - diff_loss_flow = diff_loss(diff_out["flow_pred"], flow_gt, mask=mask) - total_loss += diff_loss_flow - train_losses["diff_loss_flow"] = diff_loss_flow + x0 = self.model.module.code_to_latent(code) + # diff loss x0 + diff_loss_x0 = diff_loss(diff_out["x0_pred"], x0, mask=mask) + total_loss += diff_loss_x0 + train_losses["diff_loss_x0"] = diff_loss_x0 + + # diff loss noise + diff_loss_noise = diff_loss( + diff_out["noise_pred"], diff_out["noise"], mask=mask + ) + total_loss += diff_loss_noise * self.cfg.train.diff_noise_loss_lambda + train_losses["diff_loss_noise"] = diff_loss_noise # diff loss ce @@ -534,26 +526,17 @@ def _valid_step(self, batch): valid_losses["dur_loss"] = dur_loss x0 = self.model.module.code_to_latent(code) - if self.cfg.model.diffusion.diffusion_type == "diffusion": - # diff loss x0 - diff_loss_x0 = diff_loss(diff_out["x0_pred"], x0, mask=mask) - total_loss += diff_loss_x0 - valid_losses["diff_loss_x0"] = diff_loss_x0 - - # diff loss noise - diff_loss_noise = diff_loss( - diff_out["noise_pred"], diff_out["noise"], mask=mask - ) - total_loss += diff_loss_noise * self.cfg.train.diff_noise_loss_lambda - valid_losses["diff_loss_noise"] = diff_loss_noise - - elif self.cfg.model.diffusion.diffusion_type == "flow": - # diff flow matching loss - flow_gt = diff_out["noise"] - x0 - diff_loss_flow = diff_loss(diff_out["flow_pred"], flow_gt, mask=mask) - total_loss += diff_loss_flow - valid_losses["diff_loss_flow"] = diff_loss_flow - + # diff loss x0 + diff_loss_x0 = diff_loss(diff_out["x0_pred"], x0, mask=mask) + total_loss += diff_loss_x0 + valid_losses["diff_loss_x0"] = diff_loss_x0 + + # diff loss noise + diff_loss_noise = diff_loss( + diff_out["noise_pred"], diff_out["noise"], mask=mask + ) + total_loss += diff_loss_noise * self.cfg.train.diff_noise_loss_lambda + valid_losses["diff_loss_noise"] = diff_loss_noise # diff loss ce # (nq, B, T); (nq, B, T, 1024) diff --git a/models/vc/hubert_kmeans.py b/models/vc/Noro/hubert_kmeans.py similarity index 86% rename from models/vc/hubert_kmeans.py rename to models/vc/Noro/hubert_kmeans.py index 70df5d7a..c58f16ab 100644 --- a/models/vc/hubert_kmeans.py +++ b/models/vc/Noro/hubert_kmeans.py @@ -1,15 +1,10 @@ from pathlib import Path import torch -from torch import nn, einsum +from torch import nn from torchaudio.functional import resample -from einops import rearrange, repeat, pack, unpack +from einops import rearrange, repeat import torch.nn.functional as F -# from audiolm_pytorch.utils import curtail_to_multiple - -# suppress a few warnings - - def noop(*args, **kwargs): pass @@ -21,14 +16,9 @@ def noop(*args, **kwargs): warnings.warn = noop -# import fairseq and joblib for hubert - import joblib import fairseq -# helper functions - - def exists(val): return val is not None @@ -49,6 +39,7 @@ def curtail_to_multiple(t, mult, from_left=False): ) return t[..., seq_slice] +# TODO: modify according to xueyao's comments class HubertWithKmeans(nn.Module): """ @@ -103,7 +94,6 @@ def codebook_size(self): @property def downsample_factor(self): - # todo: double check return 320 @torch.inference_mode() @@ -121,17 +111,12 @@ def forward(self, wav_input, flatten=True, input_sample_hz=None): wav_input, features_only=True, mask=False, # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code - output_layer=self.output_layer, - )["x"] - - embed = embed.permute((0, 2, 1)) - embed = F.interpolate(embed, scale_factor=8, mode="nearest") - embed = F.interpolate(embed, scale_factor=0.2, mode="nearest") - embed = embed.permute((0, 2, 1)) - + ) + batched_cluster_centers = repeat( self.cluster_centers, "c d -> b c d", b=embed.shape[0] ) + dists = -torch.cdist(embed, batched_cluster_centers, p=2) clusters = dists.argmax(dim=-1) # (batch, seq_len) quantize = F.embedding(clusters, self.cluster_centers) diff --git a/models/vc/Noro/noro_base_trainer.py b/models/vc/Noro/noro_base_trainer.py new file mode 100644 index 00000000..bc343ed1 --- /dev/null +++ b/models/vc/Noro/noro_base_trainer.py @@ -0,0 +1,280 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import json +import os +import torch +import time +from pathlib import Path +import torch +import accelerate +from accelerate.logging import get_logger +from models.base.new_trainer import BaseTrainer + + +class Noro_base_Trainer(BaseTrainer): + r"""The base trainer for all TTS models. It inherits from BaseTrainer and implements + ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this + class, and implement ``_build_model``, ``_forward_step``. + """ + + def __init__(self, args=None, cfg=None): + self.args = args + self.cfg = cfg + + cfg.exp_name = args.exp_name + + # init with accelerate + self._init_accelerator() + self.accelerator.wait_for_everyone() + + with self.accelerator.main_process_first(): + self.logger = get_logger(args.exp_name, log_level="INFO") + + # Log some info + self.logger.info("=" * 56) + self.logger.info("||\t\t" + "New training process started." + "\t\t||") + self.logger.info("=" * 56) + self.logger.info("\n") + self.logger.debug(f"Using {args.log_level.upper()} logging level.") + self.logger.info(f"Experiment name: {args.exp_name}") + self.logger.info(f"Experiment directory: {self.exp_dir}") + self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") + if self.accelerator.is_main_process: + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") + + # init counts + self.batch_count: int = 0 + self.step: int = 0 + self.epoch: int = 0 + self.max_epoch = ( + self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") + ) + self.logger.info( + "Max epoch: {}".format( + self.max_epoch if self.max_epoch < float("inf") else "Unlimited" + ) + ) + + # Check values + if self.accelerator.is_main_process: + self.__check_basic_configs() + # Set runtime configs + self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride + self.checkpoints_path = [ + [] for _ in range(len(self.save_checkpoint_stride)) + ] + self.keep_last = [ + i if i > 0 else float("inf") for i in self.cfg.train.keep_last + ] + self.run_eval = self.cfg.train.run_eval + + # set random seed + with self.accelerator.main_process_first(): + # start = time.monotonic_ns() + self._set_random_seed(self.cfg.train.random_seed) + end = time.monotonic_ns() + # self.logger.debug( + # f"Setting random seed done in {(end - start) / 1e6:.2f}ms" + # ) + self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") + + # setup data_loader + with self.accelerator.main_process_first(): + self.logger.info("Building dataset...") + start = time.monotonic_ns() + self.train_dataloader, self.valid_dataloader = self._build_dataloader() + end = time.monotonic_ns() + self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") + + # # save phone table to exp dir. Should be done before building model due to loading phone table in model + # if cfg.preprocess.use_phone and cfg.preprocess.phone_extractor != "lexicon": + # self._save_phone_symbols_file_to_exp_path() + + # setup model + with self.accelerator.main_process_first(): + self.logger.info("Building model...") + start = time.monotonic_ns() + self.model = self._build_model() + end = time.monotonic_ns() + self.logger.debug(self.model) + self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") + self.logger.info( + f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" + ) + + # optimizer & scheduler + with self.accelerator.main_process_first(): + self.logger.info("Building optimizer and scheduler...") + start = time.monotonic_ns() + self.optimizer = self._build_optimizer() + self.scheduler = self._build_scheduler() + end = time.monotonic_ns() + self.logger.info( + f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" + ) + + # create criterion + with self.accelerator.main_process_first(): + self.logger.info("Building criterion...") + start = time.monotonic_ns() + self.criterion = self._build_criterion() + end = time.monotonic_ns() + self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") + + # Resume or Finetune + with self.accelerator.main_process_first(): + self._check_resume() + + # accelerate prepare + self.logger.info("Initializing accelerate...") + start = time.monotonic_ns() + self._accelerator_prepare() + end = time.monotonic_ns() + self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") + + # save config file path + self.config_save_path = os.path.join(self.exp_dir, "args.json") + self.device = self.accelerator.device + + if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training: + self.speakers = self._build_speaker_lut() + self.utt2spk_dict = self._build_utt2spk_dict() + + # Only for TTS tasks + self.task_type = "TTS" + self.logger.info("Task type: {}".format(self.task_type)) + + def _check_resume(self): + # if args.resume: + if self.args.resume or ( + self.cfg.model_type == "VALLE" and self.args.train_stage == 2 + ): + if self.cfg.model_type == "VALLE" and self.args.train_stage == 2: + self.args.resume_type = "finetune" + + self.logger.info("Resuming from checkpoint...") + self.ckpt_path = self._load_model( + self.checkpoint_dir, self.args.checkpoint_path, self.args.resume_type + ) + self.checkpoints_path = json.load( + open(os.path.join(self.ckpt_path, "ckpts.json"), "r") + ) + + + def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): + """Load model from checkpoint. If a folder is given, it will + load the latest checkpoint in checkpoint_dir. If a path is given + it will load the checkpoint specified by checkpoint_path. + **Only use this method after** ``accelerator.prepare()``. + """ + if checkpoint_path is None or checkpoint_path == "": + ls = [str(i) for i in Path(checkpoint_dir).glob("*")] + # example path epoch-0000_step-0017000_loss-1.972191, 找step最大的 + checkpoint_path = max(ls, key=lambda x: int(x.split("_")[-2].split("-")[-1])) + + if self.accelerator.is_main_process: + self.logger.info("Load model from {}".format(checkpoint_path)) + print("Load model from {}".format(checkpoint_path)) + + if resume_type == "resume": + self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) + self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + if isinstance(self.model, dict): + for idx, sub_model in enumerate(self.model.keys()): + try: + if idx == 0: + ckpt_name = "pytorch_model.bin" + else: + ckpt_name = "pytorch_model_{}.bin".format(idx) + + self.model[sub_model].load_state_dict( + torch.load(os.path.join(checkpoint_path, ckpt_name)) + ) + except: + if idx == 0: + ckpt_name = "model.safetensors" + else: + ckpt_name = "model_{}.safetensors".format(idx) + + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model[sub_model]), + os.path.join(checkpoint_path, ckpt_name), + ) + + self.model[sub_model].cuda(self.accelerator.device) + else: + try: + self.model.load_state_dict( + torch.load(os.path.join(checkpoint_path, "pytorch_model.bin")) + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'pytorch_model.bin' for resume") + except: + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "model.safetensors"), + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'model.safetensors' for resume") + self.model.cuda(self.accelerator.device) + if self.accelerator.is_main_process: + self.logger.info("Load model weights SUCCESS!") + elif resume_type == "finetune": + if isinstance(self.model, dict): + for idx, sub_model in enumerate(self.model.keys()): + try: + if idx == 0: + ckpt_name = "pytorch_model.bin" + else: + ckpt_name = "pytorch_model_{}.bin".format(idx) + + self.model[sub_model].load_state_dict( + torch.load(os.path.join(checkpoint_path, ckpt_name)) + ) + except: + if idx == 0: + ckpt_name = "model.safetensors" + else: + ckpt_name = "model_{}.safetensors".format(idx) + + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model[sub_model]), + os.path.join(checkpoint_path, ckpt_name), + ) + + self.model[sub_model].cuda(self.accelerator.device) + else: + try: + self.model.load_state_dict( + torch.load(os.path.join(checkpoint_path, "pytorch_model.bin")) + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'pytorch_model.bin' for finetune") + except: + accelerate.load_checkpoint_and_dispatch( + self.accelerator.unwrap_model(self.model), + os.path.join(checkpoint_path, "model.safetensors"), + ) + if self.accelerator.is_main_process: + self.logger.info("Loaded 'model.safetensors' for finetune") + self.model.cuda(self.accelerator.device) + if self.accelerator.is_main_process: + self.logger.info("Load model weights for finetune SUCCESS!") + else: + raise ValueError("Unsupported resume type: {}".format(resume_type)) + return checkpoint_path + + def _check_basic_configs(self): + if self.cfg.train.gradient_accumulation_step <= 0: + self.logger.fatal("Invalid gradient_accumulation_step value!") + self.logger.error( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) + self.accelerator.end_training() + raise ValueError( + f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." + ) diff --git a/models/vc/vc_dataset.py b/models/vc/Noro/noro_dataset.py similarity index 79% rename from models/vc/vc_dataset.py rename to models/vc/Noro/noro_dataset.py index f20ff145..8c858788 100644 --- a/models/vc/vc_dataset.py +++ b/models/vc/Noro/noro_dataset.py @@ -1,3 +1,8 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + import os import numpy as np import librosa @@ -5,13 +10,9 @@ from torch.utils.data import Dataset from torch.nn.utils.rnn import pad_sequence from utils.data_utils import * -from models.base.vc_dataset import ( - BaseCollator, -) from multiprocessing import Pool, Lock import random import torchaudio -import rir_generator as rir NUM_WORKERS = 64 @@ -49,14 +50,8 @@ def __init__(self, args, TRAIN_MODE=True): directory_list = args.test_directory_list random.shuffle(directory_list) - # 配置噪声和说话人使用 - self.use_source_noise = args.use_source_noise self.use_ref_noise = args.use_ref_noise - self.use_speaker = args.use_speaker - - print(f"use_source_noise: {self.use_source_noise}") print(f"use_ref_noise: {self.use_ref_noise}") - print(f"use_speaker: {self.use_speaker}") # number of workers print(f"Using {NUM_WORKERS} workers") @@ -112,12 +107,11 @@ def __init__(self, args, TRAIN_MODE=True): random.shuffle(self.files) # Shuffle the files. self.filtered_files, self.all_num_frames, index2numframes, index2speakerid = self.filter_files() - #只有3-30s的语音才会被保留 print(f"Loaded {len(self.filtered_files)} files") - self.index2numframes = index2numframes#index to 每条utt的长度 - self.index2speaker = index2speakerid #index to 每条utt的speaker - self.speaker2id = self.create_speaker2id() #每条utt的speaker to 每条utt的speaker_id + self.index2numframes = index2numframes + self.index2speaker = index2speakerid + self.speaker2id = self.create_speaker2id() self.num_frame_sorted = np.array(sorted(self.all_num_frames)) self.num_frame_indices = np.array( sorted( @@ -126,7 +120,7 @@ def __init__(self, args, TRAIN_MODE=True): ) del self.meta_data_cache, self.speaker_cache - if self.use_ref_noise or self.use_source_noise: + if self.use_ref_noise: if TRAIN_MODE: self.noise_filenames = self.get_all_flac(args.noise_dir) else: @@ -200,12 +194,12 @@ def filter_files(self): def create_speaker2id(self): speaker2id = {} - unique_id = 0 # 开始的唯一 ID + unique_id = 0 print(f"Creating speaker2id from {len(self.index2speaker)} utterences") for _, speaker in tqdm(self.index2speaker.items()): if speaker not in speaker2id: speaker2id[speaker] = unique_id - unique_id += 1 # 为下一个唯一 speaker 增加 ID + unique_id += 1 print(f"Created speaker2id with {len(speaker2id)} speakers") return speaker2id @@ -235,46 +229,19 @@ def add_noise(self, clean): noise, _ = librosa.load(self.noise_filenames[random_idx], sr=SAMPLE_RATE) clean = clean.cpu().numpy() if len(noise)>=len(clean): - noise = noise[0:len(clean)] #截取噪声的长度 + noise = noise[0:len(clean)] else: - while len(noise)<=len(clean): #如果噪声的长度小于语音的长度 - random_idx = (random_idx + 1)%len(self.noise_filenames) #随机读一个噪声 + while len(noise)<=len(clean): + random_idx = (random_idx + 1)%len(self.noise_filenames) newnoise, fs = librosa.load(self.noise_filenames[random_idx], sr=SAMPLE_RATE) - noiseconcat = np.append(noise, np.zeros(int(fs * 0.2)))#在噪声后面加上0.2静音 - noise = np.append(noiseconcat, newnoise)#拼接噪声 - noise = noise[0:len(clean)] #截取噪声的长度 - #随机sample一个小于20大于0的随机数 + noiseconcat = np.append(noise, np.zeros(int(fs * 0.2))) + noise = np.append(noiseconcat, newnoise) + noise = noise[0:len(clean)] snr = random.uniform(0.0,20.0) - noisyspeech = self.snr_mixer(clean=clean, noise=noise, snr=snr) #根据随机的SNR级别，混合生成带噪音频 + noisyspeech = self.snr_mixer(clean=clean, noise=noise, snr=snr) del noise return noisyspeech - def add_reverb(self, speech): - room_dim = [np.random.uniform(1, 12) for _ in range(3)] # [length, width, height] - mic_pos = [np.random.uniform(0, dim) for dim in room_dim] # 随机选择麦克风位置 - distance = np.random.normal(2, 4) # 确定声源与麦克风的距离 - while distance <= 0 or distance > 5: - distance = np.random.normal(2, 4) - source_pos = [mic_pos[0] + distance, mic_pos[1], mic_pos[2]] # 随机选择声源位置，确保它在以麦克风为中心的球内 - rt60 = np.random.uniform(0.05, 1.0) # 随机选择RT60值 - try: - rir_filter = rir.generate( - c=340, # 声速 - fs=SAMPLE_RATE, - r=[mic_pos], # 麦克风位置 - s=source_pos, # 声源位置 - L=room_dim, # 房间尺寸 - reverberation_time=rt60,# RT60值 - nsample=4096, # IR长度 - ) - # 应用混响 - speech_reverb = np.convolve(speech.cpu().numpy(), rir_filter[:, 0], mode='same') - speech = torch.tensor(speech_reverb, dtype=torch.float32) - return speech - except: - return speech #如果遇到ValueError: s is outside the room，直接返回没加混响的声音 - - def __len__(self): return len(self.files) @@ -293,7 +260,7 @@ def __getitem__(self, idx): def _get_reference_vc(self, speech, hop_length): pad_size = 1600 - speech.shape[0] % 1600 - speech = torch.nn.functional.pad(speech, (0, pad_size)) # 保证语音长度是1600的倍数 + speech = torch.nn.functional.pad(speech, (0, pad_size)) #hop_size frame_nums = speech.shape[0] // hop_length @@ -306,33 +273,71 @@ def _get_reference_vc(self, speech, hop_length): ref_mask = torch.ones(len(ref_speech) // hop_length) mask = torch.ones(len(new_speech) // hop_length) - - if not (self.use_source_noise or self.use_ref_noise): - # 不使用噪声 + if not self.use_ref_noise: + # not use noise return {"speech": new_speech, "ref_speech": ref_speech, "ref_mask": ref_mask, "mask": mask} - elif self.use_source_noise and self.use_ref_noise: - # 使用噪声 - noisy_ref_speech = self.add_noise(ref_speech) # 添加噪声 - nosiy_speech = self.add_noise(new_speech) # 添加噪声 - return {"speech": new_speech, "noisy_speech":nosiy_speech, "ref_speech": ref_speech, "noisy_ref_speech": noisy_ref_speech, "ref_mask": ref_mask, "mask": mask} - elif self.use_source_noise and not self.use_ref_noise: - # 只使用源噪声 - noisy_speech = self.add_noise(new_speech) - return {"speech": new_speech, "noisy_speech": noisy_speech, "ref_speech": ref_speech, "ref_mask": ref_mask, "mask": mask} - elif self.use_ref_noise and not self.use_source_noise: - # 只使用参考噪声 + else: + # use reference noise noisy_ref_speech = self.add_noise(ref_speech) return {"speech": new_speech, "ref_speech": ref_speech, "noisy_ref_speech": noisy_ref_speech, "ref_mask": ref_mask, "mask": mask} + +class BaseCollator(object): + """Zero-pads model inputs and targets based on number of frames per step""" + + def __init__(self, cfg): + self.cfg = cfg + + def __call__(self, batch): + packed_batch_features = dict() + + # mel: [b, T, n_mels] + # frame_pitch, frame_energy: [1, T] + # target_len: [1] + # spk_id: [b, 1] + # mask: [b, T, 1] + + for key in batch[0].keys(): + if key == "target_len": + packed_batch_features["target_len"] = torch.LongTensor( + [b["target_len"] for b in batch] + ) + masks = [ + torch.ones((b["target_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "phone_len": + packed_batch_features["phone_len"] = torch.LongTensor( + [b["phone_len"] for b in batch] + ) + masks = [ + torch.ones((b["phone_len"], 1), dtype=torch.long) for b in batch + ] + packed_batch_features["phn_mask"] = pad_sequence( + masks, batch_first=True, padding_value=0 + ) + elif key == "audio_len": + packed_batch_features["audio_len"] = torch.LongTensor( + [b["audio_len"] for b in batch] + ) + masks = [ + torch.ones((b["audio_len"], 1), dtype=torch.long) for b in batch + ] + else: + values = [torch.from_numpy(b[key]) for b in batch] + packed_batch_features[key] = pad_sequence( + values, batch_first=True, padding_value=0 + ) + return packed_batch_features + class VCCollator(BaseCollator): def __init__(self, cfg): BaseCollator.__init__(self, cfg) #self.use_noise = cfg.trans_exp.use_noise - self.use_source_noise = self.cfg.trans_exp.use_source_noise self.use_ref_noise = self.cfg.trans_exp.use_ref_noise - - print(f"use_source_noise: {self.use_source_noise}") print(f"use_ref_noise: {self.use_ref_noise}") @@ -365,10 +370,6 @@ def process_tensor(data, dtype=torch.float32): # Process 'speaker_id' data speaker_ids = [process_tensor(b['speaker_id'], dtype=torch.int64) for b in batch] packed_batch_features['speaker_id'] = torch.stack(speaker_ids, dim=0) - if self.use_source_noise: - # Process 'noisy_speech' data - noisy_speeches = [process_tensor(b['noisy_speech']) for b in batch] - packed_batch_features['noisy_speech'] = pad_sequence(noisy_speeches, batch_first=True, padding_value=0) if self.use_ref_noise: # Process 'noisy_ref_speech' data noisy_ref_speeches = [process_tensor(b['noisy_ref_speech']) for b in batch] @@ -376,6 +377,7 @@ def process_tensor(data, dtype=torch.float32): return packed_batch_features + def _is_batch_full(batch, num_tokens, max_tokens, max_sentences): if len(batch) == 0: return 0 diff --git a/models/vc/noro_inference.py b/models/vc/Noro/noro_inference.py similarity index 77% rename from models/vc/noro_inference.py rename to models/vc/Noro/noro_inference.py index 8eb62dcb..c4eab1e2 100644 --- a/models/vc/noro_inference.py +++ b/models/vc/Noro/noro_inference.py @@ -1,3 +1,8 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + import argparse import torch import numpy as np @@ -5,14 +10,15 @@ from safetensors.torch import load_model import os from utils.util import load_config -from models.vc.vc_trainer import VCTrainer -from models.vc.ns2_uniamphion import UniAmphionVC -from models.vc.hubert_kmeans import HubertWithKmeans -from models.vc.vc_utils import mel_spectrogram, extract_world_f0 +from models.vc.Noro.noro_trainer import NoroTrainer +from models.vc.Noro.noro_model import Noro_VCmodel +from processors.content_extractor import HubertExtractor +from utils.mel import mel_spectrogram_torch +from utils.f0 import get_f0_features_using_dio, interpolate def build_trainer(args, cfg): supported_trainer = { - "VC": VCTrainer, + "VC": NoroTrainer, } trainer_class = supported_trainer[cfg.model_type] trainer = trainer_class(args, cfg) @@ -70,11 +76,11 @@ def main(): torch.cuda.empty_cache() ckpt_path = args.checkpoint_path - w2v = HubertWithKmeans() + w2v = HubertExtractor(cfg) w2v = w2v.to(device=args.local_rank) w2v.eval() - model = UniAmphionVC(cfg=cfg.model) + model = Noro_VCmodel(cfg=cfg.model) print("Loading model") load_model(model, ckpt_path) @@ -96,14 +102,18 @@ def main(): ref_audio = ref_audio[None, :] with torch.no_grad(): - ref_mel = mel_spectrogram(ref_audio) + ref_mel = mel_spectrogram_torch(ref_audio, cfg) ref_mel = ref_mel.transpose(1, 2).to(device=args.local_rank) ref_mask = torch.ones(ref_mel.shape[0], ref_mel.shape[1]).to(args.local_rank).bool() - _, content_feature = w2v(audio) + _, content_feature = w2v.extract_content_features(audio) content_feature = content_feature.to(device=args.local_rank) - pitch_raw = extract_world_f0(audio) + wav = audio.cpu().numpy() + pitch_raw = get_f0_features_using_dio(wav, cfg) + pitch_raw, _ = interpolate(pitch_raw) + frame_num = len(wav) // cfg.preprocess.hop_size + pitch_raw = torch.from_numpy(pitch_raw[:frame_num]).float() pitch = (pitch_raw - pitch_raw.mean(dim=1, keepdim=True)) / ( pitch_raw.std(dim=1, keepdim=True) + 1e-6 ) diff --git a/models/vc/Noro/noro_loss.py b/models/vc/Noro/noro_loss.py new file mode 100644 index 00000000..257c47e8 --- /dev/null +++ b/models/vc/Noro/noro_loss.py @@ -0,0 +1,50 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +def cross_entropy_loss(preds, targets, reduction='none'): + log_softmax = nn.LogSoftmax(dim=-1) + loss = (-targets * log_softmax(preds)).sum(1) + if reduction == "none": + return loss + elif reduction == "mean": + return loss.mean() + +class ConstractiveSpeakerLoss(nn.Module): + def __init__(self, temperature=1.): + super(ConstractiveSpeakerLoss, self).__init__() + self.temperature = temperature + + def forward(self, x, speaker_ids): + # x : B, H + # speaker_ids: B 3 4 3 + speaker_ids = speaker_ids.reshape(-1) + speaker_ids_expand = torch.zeros(len(speaker_ids),len(speaker_ids)).to(speaker_ids.device) + speaker_ids_expand = (speaker_ids.view(-1,1) == speaker_ids).float() + x_t = x.transpose(0,1) # B, C --> C,B + logits = (x @ x_t) / self.temperature # B, H * H, B --> B, B + targets = F.softmax(speaker_ids_expand / self.temperature, dim=-1) + loss = cross_entropy_loss(logits, targets, reduction='none') + return loss.mean() + +def diff_loss(pred, target, mask, loss_type="l1"): + # pred: (B, T, d) + # target: (B, T, d) + # mask: (B, T) + if loss_type == "l1": + loss = F.l1_loss(pred, target, reduction="none").float() * ( + mask.to(pred.dtype).unsqueeze(-1) + ) + elif loss_type == "l2": + loss = F.mse_loss(pred, target, reduction="none").float() * ( + mask.to(pred.dtype).unsqueeze(-1) + ) + else: + raise NotImplementedError() + loss = (torch.mean(loss, dim=-1)).sum() / (mask.to(pred.dtype).sum()) + return loss \ No newline at end of file diff --git a/models/vc/ns2_uniamphion.py b/models/vc/Noro/noro_model.py similarity index 95% rename from models/vc/ns2_uniamphion.py rename to models/vc/Noro/noro_model.py index b6a699d8..0b0bbbf4 100644 --- a/models/vc/ns2_uniamphion.py +++ b/models/vc/Noro/noro_model.py @@ -1,3 +1,8 @@ +# Copyright (c) 2023 Amphion. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + import torch import torch.nn as nn import numpy as np @@ -733,13 +738,6 @@ def forward( # condition_embedding: from condition adapter, shape is (B, T, d_c) # reference_embedding: from reference encoder, shape is (B, N, d_r), or (B, 1, d_r), or (B, d_r) - # TODO: How to add condition emedding? concatenate then linear (or FilM?) or cross attention? - # concatenate then linear - # TODO: How to add reference embedding to the model? use style adaptive norm or cross attention? - # use style adaptive norm - # TODO: How to add diffusion step embedding? add a timestep token? add timestep embedding in each layers? use style adaptive norm? - # choose: (add_diff_step) add timestep embedding in each layers followed by a linear layer / (cat_diff_step) cat a timestep token before the first tokens - if self.in_linear != None: x = self.in_linear(x) condition_embedding = self.cond_project(condition_embedding) @@ -1201,19 +1199,13 @@ def __repr__(self): return self.__dict__.__repr__() -class UniAmphionVC(nn.Module): - def __init__(self, cfg, use_source_noise = False, use_ref_noise = False): +class Noro_VCmodel(nn.Module): + def __init__(self, cfg, use_ref_noise = False): super().__init__() self.cfg = cfg - self.use_source_noise = use_source_noise self.use_ref_noise = use_ref_noise self.reference_encoder = ReferenceEncoder(cfg=cfg.reference_encoder) - if cfg.diffusion.diff_model_type == "Transformer": - self.diffusion = Diffusion( - cfg=cfg.diffusion, - diff_model=DiffTransformer(cfg=cfg.diffusion.diff_transformer), - ) - elif cfg.diffusion.diff_model_type == "WaveNet": + if cfg.diffusion.diff_model_type == "WaveNet": self.diffusion = Diffusion( cfg=cfg.diffusion, diff_model=DiffWaveNet(cfg=cfg.diffusion.diff_wavenet), @@ -1236,7 +1228,6 @@ def __init__(self, cfg, use_source_noise = False, use_ref_noise = False): ) self.reset_parameters() - def forward( self, x=None, @@ -1245,9 +1236,7 @@ def forward( x_ref=None, x_mask=None, x_ref_mask=None, - noisy_x_ref=None, - noisy_content_feature=None, - noisy_pitch=None, + noisy_x_ref=None ): noisy_reference_embedding = None noisy_condition_embedding = None @@ -1256,18 +1245,14 @@ def forward( x_ref=x_ref, key_padding_mask=x_ref_mask ) - # print(f'reference_embedding shape: {reference_embedding.shape}') # content_feature: B x T x D # pitch: B x T x 1 # B x t x D+1 # 2B x T condition_embedding = torch.cat([content_feature, pitch[:, :, None]], dim=-1) condition_embedding = self.content_f0_enc(condition_embedding) - - # print(f'condition_embedding shape: {condition_embedding.shape}') # 2B x T x D - if self.use_ref_noise: # noisy_reference noisy_reference_embedding, _ = self.reference_encoder( @@ -1276,14 +1261,8 @@ def forward( combined_reference_embedding = (noisy_reference_embedding + reference_embedding) / 2 else: combined_reference_embedding = reference_embedding - - if self.use_source_noise: - # condition_embedding - noisy_condition_embedding = torch.cat([noisy_content_feature, noisy_pitch[:, :, None]], dim=-1) - noisy_condition_embedding = self.content_f0_enc(noisy_condition_embedding) - combined_condition_embedding = condition_embedding - else: - combined_condition_embedding = condition_embedding + + combined_condition_embedding = condition_embedding diff_out = self.diffusion( x=x, @@ -1311,14 +1290,7 @@ def inference( condition_embedding = self.content_f0_enc(condition_embedding) bsz, l, _ = condition_embedding.shape - if self.cfg.diffusion.diff_model_type == "Transformer": - z = ( - torch.randn(bsz, l, self.cfg.diffusion.diff_transformer.in_dim).to( - condition_embedding.device - ) - / sigma - ) - elif self.cfg.diffusion.diff_model_type == "WaveNet": + if self.cfg.diffusion.diff_model_type == "WaveNet": z = ( torch.randn(bsz, l, self.cfg.diffusion.diff_wavenet.input_size).to( condition_embedding.device diff --git a/models/vc/vc_trainer.py b/models/vc/Noro/noro_trainer.py similarity index 82% rename from models/vc/vc_trainer.py rename to models/vc/Noro/noro_trainer.py index 80daca4a..79de602a 100644 --- a/models/vc/vc_trainer.py +++ b/models/vc/Noro/noro_trainer.py @@ -12,26 +12,24 @@ from tqdm import tqdm from utils.util import ValueWindow from torch.utils.data import DataLoader -from models.vc.base_trainer import TTSTrainer +from models.vc.Noro.noro_base_trainer import Noro_base_Trainer from torch.nn import functional as F from models.base.base_sampler import VariableSampler from diffusers import get_scheduler - import accelerate from accelerate.logging import get_logger from accelerate.utils import ProjectConfiguration +from models.vc.Noro.noro_model import Noro_VCmodel +from models.vc.Noro.noro_dataset import VCCollator, VCDataset, batch_by_size +from processors.content_extractor import HubertExtractor +from models.vc.Noro.noro_loss import diff_loss, ConstractiveSpeakerLoss +from utils.mel import mel_spectrogram_torch +from utils.f0 import get_f0_features_using_dio, interpolate +from torch.nn.utils.rnn import pad_sequence -from models.vc.ns2_uniamphion import UniAmphionVC -from models.vc.vc_dataset import VCCollator, VCDataset, batch_by_size -from models.vc.hubert_kmeans import HubertWithKmeans -from models.vc.vc_loss import diff_loss, ConstractiveSpeakerLoss -from models.vc.vc_utils import mel_spectrogram, extract_world_f0 - -# import descript_audio_codec.dac -# from audiotools import AudioSignal -class VCTrainer(TTSTrainer): +class NoroTrainer(Noro_base_Trainer): def __init__(self, args, cfg): self.args = args self.cfg = cfg @@ -47,15 +45,11 @@ def __init__(self, args, cfg): self.logger = get_logger(args.exp_name, log_level="INFO") # Configure noise and speaker usage - self.use_source_noise = self.cfg.trans_exp.use_source_noise self.use_ref_noise = self.cfg.trans_exp.use_ref_noise - self.use_speaker = self.cfg.trans_exp.use_speaker # Log configuration on the main process if self.accelerator.is_main_process: - self.logger.info(f"use_source_noise: {self.use_source_noise}") self.logger.info(f"use_ref_noise: {self.use_ref_noise}") - self.logger.info(f"use_speaker: {self.use_speaker}") # Initialize a time window for monitoring metrics self.time_window = ValueWindow(50) @@ -175,14 +169,10 @@ def _init_accelerator(self): self.accelerator.init_trackers(self.args.exp_name) def _build_model(self): - w2v = HubertWithKmeans() - self.cfg.model.vc_feature.content_feature_dim = 768 - model = UniAmphionVC(cfg=self.cfg.model, use_ref_noise=self.use_ref_noise, use_source_noise=self.use_source_noise) + w2v = HubertExtractor(self.cfg) + model = Noro_VCmodel(cfg=self.cfg.model, use_ref_noise=self.use_ref_noise) return model, w2v - def _build_dataset(self): - return VCDataset, VCCollator - def _build_dataloader(self): np.random.seed(int(time.time())) if self.accelerator.is_main_process: @@ -234,15 +224,6 @@ def _build_criterion(self): criterion = torch.nn.L1Loss(reduction="mean") return criterion - def _count_parameters(self, model): - model_param = 0.0 - if isinstance(model, dict): - for key, _ in model.items(): - model_param += sum(p.numel() for p in model[key].parameters()) - else: - model_param = sum(p.numel() for p in model.parameters()) - return model_param - def _dump_cfg(self, path): os.makedirs(os.path.dirname(path), exist_ok=True) json5.dump( @@ -254,17 +235,6 @@ def _dump_cfg(self, path): quote_keys=True, ) - def get_state_dict(self): - state_dict = { - "model": self.model.state_dict(), - "optimizer": self.optimizer.state_dict(), - "scheduler": self.scheduler.state_dict(), - "step": self.step, - "epoch": self.epoch, - "batch_size": self.cfg.train.batch_size, - } - return state_dict - def load_model(self, checkpoint): self.step = checkpoint["step"] self.epoch = checkpoint["epoch"] @@ -293,37 +263,30 @@ def _train_step(self, batch): with torch.set_grad_enabled(False): # Extract features and spectrograms - mel = mel_spectrogram(speech).transpose(1, 2) - ref_mel = mel_spectrogram(ref_speech).transpose(1, 2) + mel = mel_spectrogram_torch(speech, self.cfg).transpose(1, 2) + ref_mel = mel_spectrogram_torch(ref_speech,self.cfg).transpose(1, 2) mask = batch["mask"] ref_mask = batch["ref_mask"] # Extract pitch and content features - if not self.use_source_noise: - pitch = extract_world_f0(speech) - pitch = (pitch - pitch.mean(dim=1, keepdim=True)) / (pitch.std(dim=1, keepdim=True) + 1e-6) # Normalize pitch (B,T) - _, content_feature = self.w2v(speech) # semantic (B, T, 768) + audio = speech.cpu().numpy() + f0s = [] + for i in range(audio.shape[0]): + wav = audio[i] + f0 = get_f0_features_using_dio(wav, self.cfg.preprocess) + f0, _ = interpolate(f0) + frame_num = len(wav) // self.cfg.preprocess.hop_size + f0 = torch.from_numpy(f0[:frame_num]).to(speech.device) + f0s.append(f0) + + pitch = pad_sequence(f0s, batch_first=True, padding_value=0).float() + pitch = (pitch - pitch.mean(dim=1, keepdim=True)) / (pitch.std(dim=1, keepdim=True) + 1e-6) # Normalize pitch (B,T) + _, content_feature = self.w2v.extract_content_features(speech) # semantic (B, T, 768) if self.use_ref_noise: - noisy_ref_mel = mel_spectrogram(batch["noisy_ref_speech"]).transpose(1, 2) - - if self.use_source_noise: - combined_speech = torch.cat((speech, batch["noisy_speech"]), dim=0) - _, combined_features = self.w2v(combined_speech) - content_feature, noisy_content_feature = torch.split(combined_features, speech.shape[0], dim=0) - combined_pitch = extract_world_f0(combined_speech) - clean_pitch, noisy_pitch = torch.split(combined_pitch, speech.shape[0], dim=0) - pitch = (clean_pitch - clean_pitch.mean(dim=1, keepdim=True)) / (clean_pitch.std(dim=1, keepdim=True) + 1e-6) - noisy_pitch = (noisy_pitch - noisy_pitch.mean(dim=1, keepdim=True)) / (noisy_pitch.std(dim=1, keepdim=True) + 1e-6) - - # Forward pass through the model - if self.use_ref_noise and self.use_source_noise: - diff_out, (ref_emb, noisy_ref_emb), (cond_emb, noisy_cond_emb) = self.model( - x=mel, content_feature=content_feature, pitch=pitch, x_ref=ref_mel, - x_mask=mask, x_ref_mask=ref_mask, noisy_x_ref=noisy_ref_mel, - noisy_content_feature=noisy_content_feature, noisy_pitch=noisy_pitch - ) - elif self.use_ref_noise: + noisy_ref_mel = mel_spectrogram_torch(batch["noisy_ref_speech"], self.cfg).transpose(1, 2) + + if self.use_ref_noise: diff_out, (ref_emb, noisy_ref_emb), (cond_emb, _) = self.model( x=mel, content_feature=content_feature, pitch=pitch, x_ref=ref_mel, x_mask=mask, x_ref_mask=ref_mask, noisy_x_ref=noisy_ref_mel @@ -344,12 +307,6 @@ def _train_step(self, batch): total_loss += cs_loss train_losses["ref_loss"] = cs_loss - if self.use_source_noise: - # B x T x D - diff_loss_cond = F.l1_loss(noisy_cond_emb, cond_emb, reduction="mean") * 2.0 - total_loss += diff_loss_cond - train_losses["source_loss"] = diff_loss_cond - diff_loss_x0 = diff_loss(diff_out["x0_pred"], mel, mask=mask) total_loss += diff_loss_x0 train_losses["diff_loss_x0"] = diff_loss_x0 @@ -499,4 +456,3 @@ def save_checkpoint(self): self.accelerator.save_state(path) self.logger.info("Finished saving state.") self.accelerator.wait_for_everyone() - diff --git a/models/vc/base_trainer.py b/models/vc/base_trainer.py deleted file mode 100644 index 5a34a04d..00000000 --- a/models/vc/base_trainer.py +++ /dev/null @@ -1,762 +0,0 @@ -# Copyright (c) 2023 Amphion. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import json -import os -import shutil -import torch -import time -from pathlib import Path -import torch -from tqdm import tqdm -import re -import json5 -import accelerate -from accelerate.logging import get_logger -from accelerate.utils import ProjectConfiguration -from torch.utils.data import ConcatDataset, DataLoader -from accelerate import DistributedDataParallelKwargs -from schedulers.scheduler import Eden -from models.base.base_sampler import build_samplers -from models.base.new_trainer import BaseTrainer - - -class TTSTrainer(BaseTrainer): - r"""The base trainer for all TTS models. It inherits from BaseTrainer and implements - ``build_criterion``, ``_build_dataset`` and ``_build_singer_lut`` methods. You can inherit from this - class, and implement ``_build_model``, ``_forward_step``. - """ - - def __init__(self, args=None, cfg=None): - self.args = args - self.cfg = cfg - - cfg.exp_name = args.exp_name - - # init with accelerate - self._init_accelerator() - self.accelerator.wait_for_everyone() - - with self.accelerator.main_process_first(): - self.logger = get_logger(args.exp_name, log_level="INFO") - - # Log some info - self.logger.info("=" * 56) - self.logger.info("||\t\t" + "New training process started." + "\t\t||") - self.logger.info("=" * 56) - self.logger.info("\n") - self.logger.debug(f"Using {args.log_level.upper()} logging level.") - self.logger.info(f"Experiment name: {args.exp_name}") - self.logger.info(f"Experiment directory: {self.exp_dir}") - self.checkpoint_dir = os.path.join(self.exp_dir, "checkpoint") - if self.accelerator.is_main_process: - os.makedirs(self.checkpoint_dir, exist_ok=True) - self.logger.debug(f"Checkpoint directory: {self.checkpoint_dir}") - - # init counts - self.batch_count: int = 0 - self.step: int = 0 - self.epoch: int = 0 - self.max_epoch = ( - self.cfg.train.max_epoch if self.cfg.train.max_epoch > 0 else float("inf") - ) - self.logger.info( - "Max epoch: {}".format( - self.max_epoch if self.max_epoch < float("inf") else "Unlimited" - ) - ) - - # Check values - if self.accelerator.is_main_process: - self.__check_basic_configs() - # Set runtime configs - self.save_checkpoint_stride = self.cfg.train.save_checkpoint_stride - self.checkpoints_path = [ - [] for _ in range(len(self.save_checkpoint_stride)) - ] - self.keep_last = [ - i if i > 0 else float("inf") for i in self.cfg.train.keep_last - ] - self.run_eval = self.cfg.train.run_eval - - # set random seed - with self.accelerator.main_process_first(): - # start = time.monotonic_ns() - self._set_random_seed(self.cfg.train.random_seed) - end = time.monotonic_ns() - # self.logger.debug( - # f"Setting random seed done in {(end - start) / 1e6:.2f}ms" - # ) - self.logger.debug(f"Random seed: {self.cfg.train.random_seed}") - - # setup data_loader - with self.accelerator.main_process_first(): - self.logger.info("Building dataset...") - start = time.monotonic_ns() - self.train_dataloader, self.valid_dataloader = self._build_dataloader() - end = time.monotonic_ns() - self.logger.info(f"Building dataset done in {(end - start) / 1e6:.2f}ms") - - # # save phone table to exp dir. Should be done before building model due to loading phone table in model - # if cfg.preprocess.use_phone and cfg.preprocess.phone_extractor != "lexicon": - # self._save_phone_symbols_file_to_exp_path() - - # setup model - with self.accelerator.main_process_first(): - self.logger.info("Building model...") - start = time.monotonic_ns() - self.model = self._build_model() - end = time.monotonic_ns() - self.logger.debug(self.model) - self.logger.info(f"Building model done in {(end - start) / 1e6:.2f}ms") - self.logger.info( - f"Model parameters: {self.__count_parameters(self.model)/1e6:.2f}M" - ) - - # optimizer & scheduler - with self.accelerator.main_process_first(): - self.logger.info("Building optimizer and scheduler...") - start = time.monotonic_ns() - self.optimizer = self._build_optimizer() - self.scheduler = self._build_scheduler() - end = time.monotonic_ns() - self.logger.info( - f"Building optimizer and scheduler done in {(end - start) / 1e6:.2f}ms" - ) - - # create criterion - with self.accelerator.main_process_first(): - self.logger.info("Building criterion...") - start = time.monotonic_ns() - self.criterion = self._build_criterion() - end = time.monotonic_ns() - self.logger.info(f"Building criterion done in {(end - start) / 1e6:.2f}ms") - - # Resume or Finetune - with self.accelerator.main_process_first(): - self._check_resume() - - # accelerate prepare - self.logger.info("Initializing accelerate...") - start = time.monotonic_ns() - self._accelerator_prepare() - end = time.monotonic_ns() - self.logger.info(f"Initializing accelerate done in {(end - start) / 1e6:.2f}ms") - - # save config file path - self.config_save_path = os.path.join(self.exp_dir, "args.json") - self.device = self.accelerator.device - - if cfg.preprocess.use_spkid and cfg.train.multi_speaker_training: - self.speakers = self._build_speaker_lut() - self.utt2spk_dict = self._build_utt2spk_dict() - - # Only for TTS tasks - self.task_type = "TTS" - self.logger.info("Task type: {}".format(self.task_type)) - - def _check_resume(self): - # if args.resume: - if self.args.resume or ( - self.cfg.model_type == "VALLE" and self.args.train_stage == 2 - ): - if self.cfg.model_type == "VALLE" and self.args.train_stage == 2: - self.args.resume_type = "finetune" - - self.logger.info("Resuming from checkpoint...") - self.ckpt_path = self._load_model( - self.checkpoint_dir, self.args.checkpoint_path, self.args.resume_type - ) - self.checkpoints_path = json.load( - open(os.path.join(self.ckpt_path, "ckpts.json"), "r") - ) - - def _init_accelerator(self): - self.exp_dir = os.path.join( - os.path.abspath(self.cfg.log_dir), self.args.exp_name - ) - project_config = ProjectConfiguration( - project_dir=self.exp_dir, - logging_dir=os.path.join(self.exp_dir, "log"), - ) - kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) - self.accelerator = accelerate.Accelerator( - gradient_accumulation_steps=self.cfg.train.gradient_accumulation_step, - log_with=self.cfg.train.tracker, - project_config=project_config, - kwargs_handlers=[kwargs], - ) - if self.accelerator.is_main_process: - os.makedirs(project_config.project_dir, exist_ok=True) - os.makedirs(project_config.logging_dir, exist_ok=True) - with self.accelerator.main_process_first(): - self.accelerator.init_trackers(self.args.exp_name) - - def _accelerator_prepare(self): - ( - self.train_dataloader, - self.valid_dataloader, - ) = self.accelerator.prepare( - self.train_dataloader, - self.valid_dataloader, - ) - - if isinstance(self.model, dict): - for key in self.model.keys(): - self.model[key] = self.accelerator.prepare(self.model[key]) - else: - self.model = self.accelerator.prepare(self.model) - - if isinstance(self.optimizer, dict): - for key in self.optimizer.keys(): - self.optimizer[key] = self.accelerator.prepare(self.optimizer[key]) - else: - self.optimizer = self.accelerator.prepare(self.optimizer) - - if isinstance(self.scheduler, dict): - for key in self.scheduler.keys(): - self.scheduler[key] = self.accelerator.prepare(self.scheduler[key]) - else: - self.scheduler = self.accelerator.prepare(self.scheduler) - - ### Following are methods only for TTS tasks ### - def _build_dataset(self): - pass - - def _build_criterion(self): - pass - - def _build_model(self): - pass - - def _build_dataloader(self): - """Build dataloader which merges a series of datasets.""" - # Build dataset instance for each dataset and combine them by ConcatDataset - Dataset, Collator = self._build_dataset() - - # Build train set - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=False) - datasets_list.append(subdataset) - train_dataset = ConcatDataset(datasets_list) - train_collate = Collator(self.cfg) - _, batch_sampler = build_samplers(train_dataset, self.cfg, self.logger, "train") - train_loader = DataLoader( - train_dataset, - collate_fn=train_collate, - batch_sampler=batch_sampler, - num_workers=self.cfg.train.dataloader.num_worker, - pin_memory=self.cfg.train.dataloader.pin_memory, - ) - - # Build test set - datasets_list = [] - for dataset in self.cfg.dataset: - subdataset = Dataset(self.cfg, dataset, is_valid=True) - datasets_list.append(subdataset) - valid_dataset = ConcatDataset(datasets_list) - valid_collate = Collator(self.cfg) - _, batch_sampler = build_samplers(valid_dataset, self.cfg, self.logger, "valid") - valid_loader = DataLoader( - valid_dataset, - collate_fn=valid_collate, - batch_sampler=batch_sampler, - num_workers=self.cfg.train.dataloader.num_worker, - pin_memory=self.cfg.train.dataloader.pin_memory, - ) - return train_loader, valid_loader - - def _build_optimizer(self): - pass - - def _build_scheduler(self): - pass - - def _load_model(self, checkpoint_dir, checkpoint_path=None, resume_type="resume"): - """Load model from checkpoint. If a folder is given, it will - load the latest checkpoint in checkpoint_dir. If a path is given - it will load the checkpoint specified by checkpoint_path. - **Only use this method after** ``accelerator.prepare()``. - """ - if checkpoint_path is None or checkpoint_path == "": - ls = [str(i) for i in Path(checkpoint_dir).glob("*")] - # example path epoch-0000_step-0017000_loss-1.972191, 找step最大的 - checkpoint_path = max(ls, key=lambda x: int(x.split("_")[-2].split("-")[-1])) - - if self.accelerator.is_main_process: - self.logger.info("Load model from {}".format(checkpoint_path)) - print("Load model from {}".format(checkpoint_path)) - # if resume_type == "resume": - # self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) - # self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) + 1 - # self.accelerator.load_state(checkpoint_path) - if resume_type == "resume": - self.epoch = int(checkpoint_path.split("_")[-3].split("-")[-1]) - self.step = int(checkpoint_path.split("_")[-2].split("-")[-1]) - if isinstance(self.model, dict): - for idx, sub_model in enumerate(self.model.keys()): - try: - if idx == 0: - ckpt_name = "pytorch_model.bin" - else: - ckpt_name = "pytorch_model_{}.bin".format(idx) - - self.model[sub_model].load_state_dict( - torch.load(os.path.join(checkpoint_path, ckpt_name)) - ) - except: - if idx == 0: - ckpt_name = "model.safetensors" - else: - ckpt_name = "model_{}.safetensors".format(idx) - - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model[sub_model]), - os.path.join(checkpoint_path, ckpt_name), - ) - - self.model[sub_model].cuda(self.accelerator.device) - else: - try: - self.model.load_state_dict( - torch.load(os.path.join(checkpoint_path, "pytorch_model.bin")) - ) - if self.accelerator.is_main_process: - self.logger.info("Loaded 'pytorch_model.bin' for resume") - except: - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model), - os.path.join(checkpoint_path, "model.safetensors"), - ) - if self.accelerator.is_main_process: - self.logger.info("Loaded 'model.safetensors' for resume") - self.model.cuda(self.accelerator.device) - if self.accelerator.is_main_process: - self.logger.info("Load model weights SUCCESS!") - elif resume_type == "finetune": - if isinstance(self.model, dict): - for idx, sub_model in enumerate(self.model.keys()): - try: - if idx == 0: - ckpt_name = "pytorch_model.bin" - else: - ckpt_name = "pytorch_model_{}.bin".format(idx) - - self.model[sub_model].load_state_dict( - torch.load(os.path.join(checkpoint_path, ckpt_name)) - ) - except: - if idx == 0: - ckpt_name = "model.safetensors" - else: - ckpt_name = "model_{}.safetensors".format(idx) - - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model[sub_model]), - os.path.join(checkpoint_path, ckpt_name), - ) - - self.model[sub_model].cuda(self.accelerator.device) - else: - try: - self.model.load_state_dict( - torch.load(os.path.join(checkpoint_path, "pytorch_model.bin")) - ) - if self.accelerator.is_main_process: - self.logger.info("Loaded 'pytorch_model.bin' for finetune") - except: - accelerate.load_checkpoint_and_dispatch( - self.accelerator.unwrap_model(self.model), - os.path.join(checkpoint_path, "model.safetensors"), - ) - if self.accelerator.is_main_process: - self.logger.info("Loaded 'model.safetensors' for finetune") - self.model.cuda(self.accelerator.device) - if self.accelerator.is_main_process: - self.logger.info("Load model weights for finetune SUCCESS!") - else: - raise ValueError("Unsupported resume type: {}".format(resume_type)) - return checkpoint_path - - ### THIS IS MAIN ENTRY ### - def train_loop(self): - r"""Training loop. The public entry of training process.""" - # Wait everyone to prepare before we move on - self.accelerator.wait_for_everyone() - # dump config file - if self.accelerator.is_main_process: - self.__dump_cfg(self.config_save_path) - self.accelerator.wait_for_everyone() - while self.epoch < self.max_epoch: - self.logger.info("\n") - self.logger.info("-" * 32) - self.logger.info("Epoch {}: ".format(self.epoch)) - - # Do training & validating epoch - train_total_loss, train_losses = self._train_epoch() - if isinstance(train_losses, dict): - for key, loss in train_losses.items(): - self.logger.info(" |- Train/{} Loss: {:.6f}".format(key, loss)) - self.accelerator.log( - {"Epoch/Train {} Loss".format(key): loss}, - step=self.epoch, - ) - - valid_total_loss, valid_losses = self._valid_epoch() - if isinstance(valid_losses, dict): - for key, loss in valid_losses.items(): - self.logger.info(" |- Valid/{} Loss: {:.6f}".format(key, loss)) - self.accelerator.log( - {"Epoch/Train {} Loss".format(key): loss}, - step=self.epoch, - ) - - self.logger.info(" |- Train/Loss: {:.6f}".format(train_total_loss)) - self.logger.info(" |- Valid/Loss: {:.6f}".format(valid_total_loss)) - self.accelerator.log( - { - "Epoch/Train Loss": train_total_loss, - "Epoch/Valid Loss": valid_total_loss, - }, - step=self.epoch, - ) - - self.accelerator.wait_for_everyone() - - # Check if hit save_checkpoint_stride and run_eval - run_eval = False - if self.accelerator.is_main_process: - save_checkpoint = False - hit_dix = [] - for i, num in enumerate(self.save_checkpoint_stride): - if self.epoch % num == 0: - save_checkpoint = True - hit_dix.append(i) - run_eval |= self.run_eval[i] - - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process and save_checkpoint: - path = os.path.join( - self.checkpoint_dir, - "epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, train_total_loss - ), - ) - self.accelerator.save_state(path) - - json.dump( - self.checkpoints_path, - open(os.path.join(path, "ckpts.json"), "w"), - ensure_ascii=False, - indent=4, - ) - - # Remove old checkpoints - to_remove = [] - for idx in hit_dix: - self.checkpoints_path[idx].append(path) - while len(self.checkpoints_path[idx]) > self.keep_last[idx]: - to_remove.append((idx, self.checkpoints_path[idx].pop(0))) - - # Search conflicts - total = set() - for i in self.checkpoints_path: - total |= set(i) - do_remove = set() - for idx, path in to_remove[::-1]: - if path in total: - self.checkpoints_path[idx].insert(0, path) - else: - do_remove.add(path) - - # Remove old checkpoints - for path in do_remove: - shutil.rmtree(path, ignore_errors=True) - self.logger.debug(f"Remove old checkpoint: {path}") - - self.accelerator.wait_for_everyone() - if run_eval: - # TODO: run evaluation - pass - - # Update info for each epoch - self.epoch += 1 - - # Finish training and save final checkpoint - self.accelerator.wait_for_everyone() - if self.accelerator.is_main_process: - path = os.path.join( - self.checkpoint_dir, - "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, valid_total_loss - ), - ) - self.accelerator.save_state( - os.path.join( - self.checkpoint_dir, - "final_epoch-{:04d}_step-{:07d}_loss-{:.6f}".format( - self.epoch, self.step, valid_total_loss - ), - ) - ) - - json.dump( - self.checkpoints_path, - open(os.path.join(path, "ckpts.json"), "w"), - ensure_ascii=False, - indent=4, - ) - - self.accelerator.end_training() - - ### Following are methods that can be used directly in child classes ### - def _train_epoch(self): - r"""Training epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - if isinstance(self.model, dict): - for key in self.model.keys(): - self.model[key].train() - else: - self.model.train() - - epoch_sum_loss: float = 0.0 - epoch_losses: dict = {} - epoch_step: int = 0 - for batch in tqdm( - self.train_dataloader, - desc=f"Training Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - # Do training step and BP - with self.accelerator.accumulate(self.model): - total_loss, train_losses, _ = self._train_step(batch) - self.batch_count += 1 - - # Update info for each step - # TODO: step means BP counts or batch counts? - if self.batch_count % self.cfg.train.gradient_accumulation_step == 0: - if isinstance(self.scheduler, dict): - for key in self.scheduler.keys(): - self.scheduler[key].step() - else: - if isinstance(self.scheduler, Eden): - self.scheduler.step_batch(self.step) - else: - self.scheduler.step() - - epoch_sum_loss += total_loss - - if isinstance(train_losses, dict): - for key, value in train_losses.items(): - epoch_losses[key] += value - - if isinstance(train_losses, dict): - for key, loss in train_losses.items(): - self.accelerator.log( - {"Epoch/Train {} Loss".format(key): loss}, - step=self.step, - ) - - self.step += 1 - epoch_step += 1 - - self.accelerator.wait_for_everyone() - - epoch_sum_loss = ( - epoch_sum_loss - / len(self.train_dataloader) - * self.cfg.train.gradient_accumulation_step - ) - - for key in epoch_losses.keys(): - epoch_losses[key] = ( - epoch_losses[key] - / len(self.train_dataloader) - * self.cfg.train.gradient_accumulation_step - ) - return epoch_sum_loss, epoch_losses - - @torch.inference_mode() - def _valid_epoch(self): - r"""Testing epoch. Should return average loss of a batch (sample) over - one epoch. See ``train_loop`` for usage. - """ - if isinstance(self.model, dict): - for key in self.model.keys(): - self.model[key].eval() - else: - self.model.eval() - - epoch_sum_loss = 0.0 - epoch_losses = dict() - for batch in tqdm( - self.valid_dataloader, - desc=f"Validating Epoch {self.epoch}", - unit="batch", - colour="GREEN", - leave=False, - dynamic_ncols=True, - smoothing=0.04, - disable=not self.accelerator.is_main_process, - ): - total_loss, valid_losses, valid_stats = self._valid_step(batch) - epoch_sum_loss += total_loss - if isinstance(valid_losses, dict): - for key, value in valid_losses.items(): - if key not in epoch_losses.keys(): - epoch_losses[key] = value - else: - epoch_losses[key] += value - - epoch_sum_loss = epoch_sum_loss / len(self.valid_dataloader) - for key in epoch_losses.keys(): - epoch_losses[key] = epoch_losses[key] / len(self.valid_dataloader) - - self.accelerator.wait_for_everyone() - - return epoch_sum_loss, epoch_losses - - def _train_step(self): - pass - - def _valid_step(self, batch): - pass - - def _inference(self): - pass - - def _is_valid_pattern(self, directory_name): - directory_name = str(directory_name) - pattern = r"^epoch-\d{4}_step-\d{7}_loss-\d{1}\.\d{6}" - return re.match(pattern, directory_name) is not None - - def _check_basic_configs(self): - if self.cfg.train.gradient_accumulation_step <= 0: - self.logger.fatal("Invalid gradient_accumulation_step value!") - self.logger.error( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - self.accelerator.end_training() - raise ValueError( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - - def __dump_cfg(self, path): - os.makedirs(os.path.dirname(path), exist_ok=True) - json5.dump( - self.cfg, - open(path, "w"), - indent=4, - sort_keys=True, - ensure_ascii=False, - quote_keys=True, - ) - - def __check_basic_configs(self): - if self.cfg.train.gradient_accumulation_step <= 0: - self.logger.fatal("Invalid gradient_accumulation_step value!") - self.logger.error( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - self.accelerator.end_training() - raise ValueError( - f"Invalid gradient_accumulation_step value: {self.cfg.train.gradient_accumulation_step}. It should be positive." - ) - # TODO: check other values - - @staticmethod - def __count_parameters(model): - model_param = 0.0 - if isinstance(model, dict): - for key, value in model.items(): - model_param += sum(p.numel() for p in model[key].parameters()) - else: - model_param = sum(p.numel() for p in model.parameters()) - return model_param - - def _build_speaker_lut(self): - # combine speakers - if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.spk2id)): - speakers = {} - else: - with open( - os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "r" - ) as speaker_file: - speakers = json.load(speaker_file) - for dataset in self.cfg.dataset: - speaker_lut_path = os.path.join( - self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.spk2id - ) - with open(speaker_lut_path, "r") as speaker_lut_path: - singer_lut = json.load(speaker_lut_path) - for singer in singer_lut.keys(): - if singer not in speakers: - speakers[singer] = len(speakers) - with open( - os.path.join(self.exp_dir, self.cfg.preprocess.spk2id), "w" - ) as speaker_file: - json.dump(speakers, speaker_file, indent=4, ensure_ascii=False) - print( - "speakers have been dumped to {}".format( - os.path.join(self.exp_dir, self.cfg.preprocess.spk2id) - ) - ) - return speakers - - def _build_utt2spk_dict(self): - # combine speakers - utt2spk = {} - if not os.path.exists(os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk)): - utt2spk = {} - else: - with open( - os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "r" - ) as utt2spk_file: - for line in utt2spk_file.readlines(): - utt, spk = line.strip().split("\t") - utt2spk[utt] = spk - for dataset in self.cfg.dataset: - utt2spk_dict_path = os.path.join( - self.cfg.preprocess.processed_dir, dataset, self.cfg.preprocess.utt2spk - ) - with open(utt2spk_dict_path, "r") as utt2spk_dict: - for line in utt2spk_dict.readlines(): - utt, spk = line.strip().split("\t") - if utt not in utt2spk.keys(): - utt2spk[utt] = spk - with open( - os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk), "w" - ) as utt2spk_file: - for utt, spk in utt2spk.items(): - utt2spk_file.write(utt + "\t" + spk + "\n") - print( - "utterance and speaker mapper have been dumped to {}".format( - os.path.join(self.exp_dir, self.cfg.preprocess.utt2spk) - ) - ) - return utt2spk - - def _save_phone_symbols_file_to_exp_path(self): - phone_symbols_file = os.path.join( - self.cfg.preprocess.processed_dir, - self.cfg.dataset[0], - self.cfg.preprocess.symbols_dict, - ) - phone_symbols_file_to_exp_path = os.path.join( - self.exp_dir, self.cfg.preprocess.symbols_dict - ) - shutil.copy(phone_symbols_file, phone_symbols_file_to_exp_path) - print( - "phone symbols been dumped to {}".format( - os.path.join(self.exp_dir, self.cfg.preprocess.symbols_dict) - ) - ) \ No newline at end of file diff --git a/models/vc/vc_loss.py b/models/vc/vc_loss.py deleted file mode 100644 index d2f9356f..00000000 --- a/models/vc/vc_loss.py +++ /dev/null @@ -1,89 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -cosine_loss = nn.CosineEmbeddingLoss() - -class AMSoftmaxLoss(nn.Module): - def __init__(self, nOut, nClasses, margin=0.3, scale=30): - super(AMSoftmaxLoss, self).__init__() - self.m = margin - self.s = scale - self.in_feats = nOut # input feature size - self.nClasses = nClasses # number of classes (speaker numbers) - self.W = nn.Parameter(torch.randn(nOut, nClasses), requires_grad=True) - nn.init.xavier_normal_(self.W, gain=1) - self.ce = nn.CrossEntropyLoss(reduction='mean') - print('Initialised AMSoftmax m=%.3f s=%.3f'%(self.m,self.s)) - - def forward(self, x, label=None): - # x : B, H - # label: B - assert x.size()[0] == label.size()[0] - assert x.size()[1] == self.in_feats - label = label.reshape(-1) - x_norm = torch.norm(x, p=2, dim=1, keepdim=True).clamp(min=1e-12) - x_norm = torch.div(x, x_norm) - w_norm = torch.norm(self.W, p=2, dim=0, keepdim=True).clamp(min=1e-12) - w_norm = torch.div(self.W, w_norm) - costh = torch.mm(x_norm, w_norm) - label_view = label.view(-1, 1) - if label_view.is_cuda: - label_view = label_view.cpu() - delt_costh = torch.zeros(costh.size()).scatter_(1, label_view, self.m) - if x.is_cuda: - delt_costh = delt_costh.to(x.device) - costh_m = costh - delt_costh - costh_m_s = self.s * costh_m - loss = self.ce(costh_m_s, label) - return loss - - -def noise_clean_similarity_loss(ref_emb, noisy_ref_emb): - target = torch.tensor([1], dtype=torch.float).to(ref_emb.device) - ref_emb = ref_emb.reshape(ref_emb.size(0), -1) # (B, 32, 512) --> (B, 32 * 512) - noisy_ref_emb = noisy_ref_emb.reshape(noisy_ref_emb.size(0), -1) # (B, 32, 512) --> (B, 32 * 512) - loss = cosine_loss(ref_emb, noisy_ref_emb, target) + 1e-6 - return loss - -def cross_entropy_loss(preds, targets, reduction='none'): - log_softmax = nn.LogSoftmax(dim=-1) - loss = (-targets * log_softmax(preds)).sum(1) - if reduction == "none": - return loss - elif reduction == "mean": - return loss.mean() - -class ConstractiveSpeakerLoss(nn.Module): - def __init__(self, temperature=1.): - super(ConstractiveSpeakerLoss, self).__init__() - self.temperature = temperature - - def forward(self, x, speaker_ids): - # x : B, H - # speaker_ids: B 3 4 3 - speaker_ids = speaker_ids.reshape(-1) - speaker_ids_expand = torch.zeros(len(speaker_ids),len(speaker_ids)).to(speaker_ids.device) - speaker_ids_expand = (speaker_ids.view(-1,1) == speaker_ids).float() #形成一个mask - x_t = x.transpose(0,1) # B, C --> C,B - logits = (x @ x_t) / self.temperature # B, H * H, B --> B, B - targets = F.softmax(speaker_ids_expand / self.temperature, dim=-1) - loss = cross_entropy_loss(logits, targets, reduction='none') - return loss.mean() - -def diff_loss(pred, target, mask, loss_type="l1"): - # pred: (B, T, d) - # target: (B, T, d) - # mask: (B, T) - if loss_type == "l1": - loss = F.l1_loss(pred, target, reduction="none").float() * ( - mask.to(pred.dtype).unsqueeze(-1) - ) - elif loss_type == "l2": - loss = F.mse_loss(pred, target, reduction="none").float() * ( - mask.to(pred.dtype).unsqueeze(-1) - ) - else: - raise NotImplementedError() - loss = (torch.mean(loss, dim=-1)).sum() / (mask.to(pred.dtype).sum()) - return loss \ No newline at end of file diff --git a/models/vc/vc_utils.py b/models/vc/vc_utils.py deleted file mode 100644 index 6fb0e948..00000000 --- a/models/vc/vc_utils.py +++ /dev/null @@ -1,94 +0,0 @@ -import torch -import pyworld as pw -import numpy as np -from librosa.filters import mel as librosa_mel_fn -from torchaudio.functional import detect_pitch_frequency -from torchaudio.functional import pitch_shift - -mel_basis = {} -hann_window = {} -init_mel_and_hann = False - -# online feature extraction -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - return torch.log(torch.clamp(x, min=clip_val) * C) - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def mel_spectrogram( - y, n_fft=1024, num_mels=80, sampling_rate=16000, hop_size=200, win_size=800, fmin=0, fmax=8000, center=False -): - global mel_basis, hann_window, init_mel_and_hann - device = y.device - if not init_mel_and_hann: - mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax) - mel_basis[str(fmax) + "_" + str(y.device)] = (torch.from_numpy(mel).float().to(device)) - hann_window[str(device)] = torch.hann_window(win_size).to(device) - init_mel_and_hann = True - y = torch.nn.functional.pad( - y.unsqueeze(1), - (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)), - mode="reflect", - ) - y = y.squeeze(1) - spec = torch.stft( - y, - n_fft, - hop_length=hop_size, - win_length=win_size, - window=hann_window[str(y.device)], - center=center, - pad_mode="reflect", - normalized=False, - onesided=True, - return_complex=True, - ) - spec = torch.view_as_real(spec) - spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9)) - spec = torch.matmul(mel_basis[str(fmax) + "_" + str(y.device)], spec) - spec = spectral_normalize_torch(spec) - return spec - -def interpolate(f0): - uv = f0 == 0 - if len(f0[~uv]) > 0: - # interpolate the unvoiced f0 - f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv]) - uv = uv.astype("float") - uv = np.min(np.array([uv[:-2], uv[1:-1], uv[2:]]), axis=0) - uv = np.pad(uv, (1, 1)) - return f0, uv - -def extract_world_f0(speech): - audio = speech.cpu().numpy() - f0s = [] - for i in range(audio.shape[0]): - wav = audio[i] - frame_num = len(wav) // 200 - f0, t = pw.dio(wav.astype(np.float64), 16000, frame_period=12.5) - f0 = pw.stonemask(wav.astype(np.float64), f0, t, 16000) - f0, _ = interpolate(f0) - f0 = torch.from_numpy(f0).to(speech.device) - f0s.append(f0[:frame_num]) - f0s = torch.stack(f0s, dim=0).float() - return f0s - - -def get_pitch_shifted_speech(speech, sr = 16000): - # pitch shift - shifted_speech = torch.zeros_like(speech) - need_shift = False - for i in range(speech.shape[0]): - sub_speech = speech[i] - f0_mean = detect_pitch_frequency(sub_speech, sr).mean() - if f0_mean > 350: - need_shift = True - n_step = -12 * torch.log2(f0_mean / 300) - shifted_speech[i] = pitch_shift(sub_speech, sr, n_steps=n_step) - else: - shifted_speech[i] = sub_speech - shifted_speech = shifted_speech.to(speech.device) - return shifted_speech, need_shift \ No newline at end of file diff --git a/processors/audio_features_extractor.py b/processors/audio_features_extractor.py index 8e38bd5e..0018fcf6 100644 --- a/processors/audio_features_extractor.py +++ b/processors/audio_features_extractor.py @@ -25,6 +25,7 @@ WhisperExtractor, ContentvecExtractor, WenetExtractor, + HubertExtractor ) @@ -155,3 +156,20 @@ def get_wenet_features(self, wavs, target_frame_len, wav_lens=None): wenet_feats = self.wenet_extractor.extract_content_features(wavs, lens=wav_lens) wenet_feats = self.wenet_extractor.ReTrans(wenet_feats, target_frame_len) return wenet_feats + + def get_hubert_features(self, wavs): + """Get HuBERT Features + + Args: + wavs: Tensor whose shape is (B, T) + + Returns: + Tensor whose shape is (B, T, D) + """ + if not hasattr(self, "model"): + self.hubert_extractor = HubertExtractor(self.cfg) + + clusters, hubert_feats = self.hubert_extractor.extract_content_features(wavs) + + + return clusters, hubert_feats diff --git a/processors/content_extractor.py b/processors/content_extractor.py index 34b54917..c45e587a 100644 --- a/processors/content_extractor.py +++ b/processors/content_extractor.py @@ -14,6 +14,10 @@ from torch.utils.data import DataLoader from fairseq import checkpoint_utils from transformers import AutoModel, Wav2Vec2FeatureExtractor +import torch.nn as nn +import torch.nn.functional as F +import joblib +from einops import repeat from utils.io_optim import ( TorchaudioDataset, @@ -493,6 +497,60 @@ def extract_content_features(self, wavs): mert_features.append(feature) return mert_features + +class HubertExtractor(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.load_model() + + def load_model(self): + kmeans_model_path = self.cfg.preprocess.kmeans_model_path + hubert_model_path = self.cfg.preprocess.hubert_model_path + print("Load Hubert Model...") + checkpoint = torch.load(hubert_model_path) + load_model_input = {hubert_model_path: checkpoint} + model, *_ = checkpoint_utils.load_model_ensemble_and_task( + load_model_input + ) + self.model = model[0] + self.model.eval() + + # Load KMeans cluster centers + kmeans = joblib.load(kmeans_model_path) + self.kmeans = kmeans + + self.register_buffer( + "cluster_centers", torch.from_numpy(kmeans.cluster_centers_) + ) + + def extract_content_features(self, wav_input): + """ + Extract content features and quantize using KMeans clustering. + + Args: + audio_data: tensor (batch_size, T) + + Returns: + quantize: tensor (batch_size, T, 768) + """ + # Extract features using HuBERT + wav_input = F.pad(wav_input, (40, 40), "reflect") + embed = self.model( + wav_input, + features_only=True, + mask=False, + ) + + batched_cluster_centers = repeat( + self.cluster_centers, "c d -> b c d", b=embed.shape[0] + ) + + dists = -torch.cdist(embed, batched_cluster_centers, p=2) + clusters = dists.argmax(dim=-1) # (batch, seq_len) + quantize = F.embedding(clusters, self.cluster_centers) + + return clusters, quantize def extract_utt_content_features_dataloader(cfg, metadata, num_workers): From 5cf2a4a5ff42ce18429b9b6627aeb87f229fc8c8 Mon Sep 17 00:00:00 2001 From: kenxxxxx Date: Thu, 29 Aug 2024 01:23:17 +0800 Subject: [PATCH 07/16] add image --- imgs/vc/NoroVC.png | Bin 0 -> 1622675 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 imgs/vc/NoroVC.png diff --git a/imgs/vc/NoroVC.png b/imgs/vc/NoroVC.png new file mode 100644 index 0000000000000000000000000000000000000000..3ad477502180502f118874ca92078f742dcfc011 GIT binary patch literal 1622675 zcmeFY1y>wf&@Mc~0E26ACrE+^cTWOAgS)%CYX}}B5Znpw?lM6`un^oQxV!5&Ip^fa zeee4NzP0WwV0yZHcJE#FRMk^en{Y+>*XSt3C;$KeU0O;~2>`%u0sxSjkq}{L`meGv 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<144111552+kenxxxxx@users.noreply.github.com> Date: Thu, 29 Aug 2024 02:39:27 +0800 Subject: [PATCH 08/16] Update README.md --- egs/vc/Noro/README.md | 30 ++++++++++++++++++++++-------- 1 file changed, 22 insertions(+), 8 deletions(-) diff --git a/egs/vc/Noro/README.md b/egs/vc/Noro/README.md index ae3b00c0..55c1340f 100644 --- a/egs/vc/Noro/README.md +++ b/egs/vc/Noro/README.md @@ -1,5 +1,16 @@ # Noro: A Noise-Robust One-shot Voice Conversion System +
+

+
+ +This is the official implementation of the paper: NORO: A Noise-Robust One-Shot Voice Conversion System with Hidden Speaker Representation Capabilities. + +- The semantic extractor is from [Hubert](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert). +- The vocoder is [BigVGAN](https://github.com/NVIDIA/BigVGAN) architecture. + ## Project Overview Noro is a noise-robust one-shot voice conversion (VC) system designed to convert the timbre of speech from a source speaker to a target speaker using only a single reference speech sample, while preserving the semantic content of the original speech. Noro introduces innovative components tailored for VC using noisy reference speeches, including a dual-branch reference encoding module and a noise-agnostic contrastive speaker loss. @@ -15,7 +26,7 @@ Set up your environment as in Amphion README (you'll need a conda environment, a ### Prepare Hubert Model Humbert checkpoint and kmeans can be downloaded [here](https://github.com/facebookresearch/fairseq/tree/main/examples/hubert). -Set the downloded model path at `models/vc/hubert_kmeans.py`. +Set the downloded model path at `egs/vc/Noro/exp_config_base.json`. ## Usage @@ -25,7 +36,7 @@ You need to download our pretrained weights from [Google Drive](https://drive.go ### Inference 1. Configure inference parameters: - Modify the pretrained checkpoint path, source voice path and reference voice path at egs/vc/noro_inference.sh` file. + Modify the pretrained checkpoint path, source voice path and reference voice path at `egs/vc/Noro/noro_inference.sh` file. Currently it's at line 35. ``` checkpoint_path="path/to/checkpoint/model.safetensors" @@ -55,7 +66,7 @@ We use the LibriLight dataset for training and evaluation. You can download it u ### Training the Model with Clean Reference Voice Configure training parameters: -Our configuration file for training clean Noro model is at "egs/vc/exp_config_4gpu_clean.json", and Nosiy Noro model at "egs/vc/exp_config_4gpu_noisy.json". +Our configuration file for training clean Noro model is at "egs/vc/Noro/exp_config_clean.json", and Nosiy Noro model at "egs/vc/Noro/exp_config_noisy.json". To train your model, you need to modify the `dataset` variable in the json configurations. Currently it's at line 40, you should modify the "data_dir" to your dataset's root directory. @@ -66,18 +77,21 @@ Currently it's at line 40, you should modify the "data_dir" to your dataset's ro "path/to/your/training_data_directory3" ], ``` -You should also select a reasonable batch size at the "batch_size" entry (currently it's set at 8). -You can change other experiment settings in the `/egs/tts/VALLE_V2/exp_ar_libritts.json` such as the learning rate, optimizer and the dataset. +If you want to train for the noisy noro model, you also need to set the direction path for the noisy data at "egs/vc/Noro/exp_config_noisy.json". +``` + "noise_dir": "path/to/your/noise/train/directory", + "test_noise_dir": "path/to/your/noise/test/directory" +``` + +You can change other experiment settings in the config flies such as the learning rate, optimizer and the dataset. **Set smaller batch_size if you are out of memory😢😢** I used max_tokens = 3200000 to successfully run on a single card, if you'r out of memory, try smaller. ```json - "batch_size": 8, - "max_tokens": 3200000, - "max_sentences": 64, + "max_tokens": 3200000 ``` ### Resume from existing checkpoint Our framework supports resuming from existing checkpoint. From 868ef3172679953a7cf486056ce6a8261093d8c0 Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Thu, 29 Aug 2024 02:59:46 +0800 Subject: [PATCH 09/16] Create README.md --- egs/vc/README.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 egs/vc/README.md diff --git a/egs/vc/README.md b/egs/vc/README.md new file mode 100644 index 00000000..4dc17d70 --- /dev/null +++ b/egs/vc/README.md @@ -0,0 +1,20 @@ +# Amphion Singing Voice Cloning (VC) Recipe + +## Quick Start + +We provide a **[beginner recipe](Noro)** to demonstrate how to train a cutting edge SVC model. Specifically, it is an official implementation of the paper "NORO: A Noise-Robust One-Shot Voice Conversion System with Hidden Speaker Representation Capabilities". + +## Supported Model Architectures + +Until now, Amphion has supported a noise-robust VC model with the following architecture: + +
+
+ +
+
+ +It has the following features: +1. Noise-Robust Voice Conversion: Utilizes a dual-branch reference encoding module and noise-agnostic contrastive speaker loss to maintain high-quality voice conversion in noisy environments. +2. One-shot Voice Conversion: Achieves timbre conversion using only one reference speech sample. +3. Speaker Representation Learning: Explores the potential of the reference encoder as a self-supervised speaker encoder. From 9e0eba2213f2406773883fac45a1ecc6cbdcb8db Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Thu, 29 Aug 2024 03:01:05 +0800 Subject: [PATCH 10/16] Update README.md --- egs/vc/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/egs/vc/README.md b/egs/vc/README.md index 4dc17d70..13c10477 100644 --- a/egs/vc/README.md +++ b/egs/vc/README.md @@ -10,7 +10,7 @@ Until now, Amphion has supported a noise-robust VC model with the following arch
- +

From 0c34c6901a95ad26c0683fd33a5e42af174f0c14 Mon Sep 17 00:00:00 2001 From: kenxxxxx Date: Fri, 30 Aug 2024 15:03:39 +0800 Subject: [PATCH 11/16] code update --- egs/vc/Noro/exp_config_clean.json | 7 +- models/vc/Noro/hubert_kmeans.py | 127 ------------------------------ 2 files changed, 1 insertion(+), 133 deletions(-) delete mode 100644 models/vc/Noro/hubert_kmeans.py diff --git a/egs/vc/Noro/exp_config_clean.json b/egs/vc/Noro/exp_config_clean.json index 5656db3f..e0dbd367 100644 --- a/egs/vc/Noro/exp_config_clean.json +++ b/egs/vc/Noro/exp_config_clean.json @@ -33,11 +33,6 @@ "path/to/your/training_data_directory2", "path/to/your/training_data_directory3" ], - "use_ref_noise": false, - "snr_lower": 2, - "snr_upper": 42, - "total_snrlevels": 5, - "noise_dir": "path/to/your/noise/train/directory", - "test_noise_dir": "path/to/your/noise/test/directory" + "use_ref_noise": false } } \ No newline at end of file diff --git a/models/vc/Noro/hubert_kmeans.py b/models/vc/Noro/hubert_kmeans.py deleted file mode 100644 index c58f16ab..00000000 --- a/models/vc/Noro/hubert_kmeans.py +++ /dev/null @@ -1,127 +0,0 @@ -from pathlib import Path -import torch -from torch import nn -from torchaudio.functional import resample -from einops import rearrange, repeat -import torch.nn.functional as F - -def noop(*args, **kwargs): - pass - - -import warnings -import logging - -logging.root.setLevel(logging.ERROR) - -warnings.warn = noop - -import joblib -import fairseq - -def exists(val): - return val is not None - - -def default(val, d): - return val if exists(val) else d - - -def round_down_nearest_multiple(num, divisor): - return num // divisor * divisor - - -def curtail_to_multiple(t, mult, from_left=False): - data_len = t.shape[-1] - rounded_seq_len = round_down_nearest_multiple(data_len, mult) - seq_slice = ( - slice(None, rounded_seq_len) if not from_left else slice(-rounded_seq_len, None) - ) - return t[..., seq_slice] - -# TODO: modify according to xueyao's comments - -class HubertWithKmeans(nn.Module): - """ - checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert - or you can train your own - """ - - def __init__( - self, - checkpoint_path= 'path/to/your/mhubert_model/mhubert_base_vp_en_es_fr_it3.pt', - kmeans_path = 'path/to/your/mhubert/mhubert_base_vp_en_es_fr_it3_L11_km1000.bin', - target_sample_hz=16000, - seq_len_multiple_of=None, - output_layer=9, - ): - super().__init__() - - self.target_sample_hz = target_sample_hz - self.seq_len_multiple_of = seq_len_multiple_of - self.output_layer = output_layer - - model_path = Path(checkpoint_path) - kmeans_path = Path(kmeans_path) - - assert model_path.exists(), f"path {checkpoint_path} does not exist" - assert kmeans_path.exists(), f"path {kmeans_path} does not exist" - - checkpoint = torch.load(checkpoint_path) - load_model_input = {checkpoint_path: checkpoint} - model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task( - load_model_input - ) - - self.model = model[0] - self.model.eval() - - kmeans = joblib.load(kmeans_path) - - self.kmeans = kmeans - - self.register_buffer( - "cluster_centers", torch.from_numpy(kmeans.cluster_centers_) - ) - - @property - def groups(self): - return 1 - - @property - def codebook_size(self): - return self.kmeans.n_clusters - - @property - def downsample_factor(self): - return 320 - - @torch.inference_mode() - def forward(self, wav_input, flatten=True, input_sample_hz=None): - batch, device = wav_input.shape[0], wav_input.device - wav_input = F.pad(wav_input, (40, 40), "reflect") - - if exists(input_sample_hz): - wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz) - - if exists(self.seq_len_multiple_of): - wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of) - - embed = self.model( - wav_input, - features_only=True, - mask=False, # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code - ) - - batched_cluster_centers = repeat( - self.cluster_centers, "c d -> b c d", b=embed.shape[0] - ) - - dists = -torch.cdist(embed, batched_cluster_centers, p=2) - clusters = dists.argmax(dim=-1) # (batch, seq_len) - quantize = F.embedding(clusters, self.cluster_centers) - - if flatten: - return clusters, quantize - - return rearrange(clusters, "b ... -> b (...)"), quantize \ No newline at end of file From 546e3b7092e7bb8f008530d2825ef98d2af6e405 Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Wed, 25 Sep 2024 23:14:01 +0800 Subject: [PATCH 12/16] README.md conflict resolved --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 3e1f9d0e..95ca237e 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,11 @@ In addition to the specific generation tasks, Amphion includes several **vocoders** and **evaluation metrics**. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. Moreover, Amphion is dedicated to advancing audio generation in real-world applications, such as building **large-scale datasets** for speech synthesis. ## 🚀 News +- **2024/9/26**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system. The Noro model significantly improves performance in noisy environments by introducing a dual-branch reference encoding module and noise-agnostic contrastive speaker loss. It performs well in both clean and noisy conditions, making it suitable for real-world applications. Additionally, we explore the potential of the reference encoder as a self-supervised speaker encoder, showing competitive performance in speaker representation tasks. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) +- **2024/09/01**: [Amphion](https://arxiv.org/abs/2312.09911) and [Emilia](https://arxiv.org/abs/2407.05361) got accepted by IEEE SLT 2024! 🤗 +- **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.com/invite/ZxxREr3Y) to stay connected and engage with our community! +- **2024/08/20**: [SingVisio](https://arxiv.org/abs/2402.12660) got accepted by Computers & Graphics, [available here](https://www.sciencedirect.com/science/article/pii/S0097849324001936)! 🎉 +- **2024/08/27**: *The Emilia dataset is now publicly available!* Discover the most extensive and diverse speech generation dataset with 101k hours of in-the-wild speech data now at [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia-Dataset) or [![OpenDataLab](https://img.shields.io/badge/OpenDataLab-Dataset-blue)](https://opendatalab.com/Amphion/Emilia)! 👑👑👑 - **2024/7/17**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system. The Noro model significantly improves performance in noisy environments by introducing a dual-branch reference encoding module and noise-agnostic contrastive speaker loss. It performs well in both clean and noisy conditions, making it suitable for real-world applications. Additionally, we explore the potential of the reference encoder as a self-supervised speaker encoder, showing competitive performance in speaker representation tasks. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) - **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md) - **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/tts/VALLE_V2/README.md) From ff5bb748e45bddf83e98519209faae3d2b6d6580 Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Wed, 25 Sep 2024 23:16:48 +0800 Subject: [PATCH 13/16] README.md conflict --- README.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/README.md b/README.md index 95ca237e..95ac4da4 100644 --- a/README.md +++ b/README.md @@ -28,13 +28,11 @@ In addition to the specific generation tasks, Amphion includes several **vocoders** and **evaluation metrics**. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. Moreover, Amphion is dedicated to advancing audio generation in real-world applications, such as building **large-scale datasets** for speech synthesis. ## 🚀 News -- **2024/9/26**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system. The Noro model significantly improves performance in noisy environments by introducing a dual-branch reference encoding module and noise-agnostic contrastive speaker loss. It performs well in both clean and noisy conditions, making it suitable for real-world applications. Additionally, we explore the potential of the reference encoder as a self-supervised speaker encoder, showing competitive performance in speaker representation tasks. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) +- **2024/9/26**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system![![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) - **2024/09/01**: [Amphion](https://arxiv.org/abs/2312.09911) and [Emilia](https://arxiv.org/abs/2407.05361) got accepted by IEEE SLT 2024! 🤗 - **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.com/invite/ZxxREr3Y) to stay connected and engage with our community! - **2024/08/20**: [SingVisio](https://arxiv.org/abs/2402.12660) got accepted by Computers & Graphics, [available here](https://www.sciencedirect.com/science/article/pii/S0097849324001936)! 🎉 - **2024/08/27**: *The Emilia dataset is now publicly available!* Discover the most extensive and diverse speech generation dataset with 101k hours of in-the-wild speech data now at [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia-Dataset) or [![OpenDataLab](https://img.shields.io/badge/OpenDataLab-Dataset-blue)](https://opendatalab.com/Amphion/Emilia)! 👑👑👑 -- **2024/7/17**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system. The Noro model significantly improves performance in noisy environments by introducing a dual-branch reference encoding module and noise-agnostic contrastive speaker loss. It performs well in both clean and noisy conditions, making it suitable for real-world applications. Additionally, we explore the potential of the reference encoder as a self-supervised speaker encoder, showing competitive performance in speaker representation tasks. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) -- **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md) - **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/tts/VALLE_V2/README.md) - **2024/03/12**: Amphion now support **NaturalSpeech3 FACodec** and release pretrained checkpoints. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2403.03100) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/naturalspeech3_facodec) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/naturalspeech3_facodec) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](models/codec/ns3_codec/README.md) - **2024/02/22**: The first Amphion visualization tool, **SingVisio**, release. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2402.12660) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [![Video](https://img.shields.io/badge/Video-Demo-orange)](https://github.com/open-mmlab/Amphion/assets/33707885/0a6e39e8-d5f1-4288-b0f8-32da5a2d6e96) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/visualization/SingVisio/README.md) From 5d1bbdd27d98e989b9ffda5be7c912b90c13644a Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Wed, 25 Sep 2024 23:19:41 +0800 Subject: [PATCH 14/16] README.md conflict resolved --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 95ac4da4..87360869 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,7 @@ In addition to the specific generation tasks, Amphion includes several **vocoder - **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.com/invite/ZxxREr3Y) to stay connected and engage with our community! - **2024/08/20**: [SingVisio](https://arxiv.org/abs/2402.12660) got accepted by Computers & Graphics, [available here](https://www.sciencedirect.com/science/article/pii/S0097849324001936)! 🎉 - **2024/08/27**: *The Emilia dataset is now publicly available!* Discover the most extensive and diverse speech generation dataset with 101k hours of in-the-wild speech data now at [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia-Dataset) or [![OpenDataLab](https://img.shields.io/badge/OpenDataLab-Dataset-blue)](https://opendatalab.com/Amphion/Emilia)! 👑👑👑 +- **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md) - **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/tts/VALLE_V2/README.md) - **2024/03/12**: Amphion now support **NaturalSpeech3 FACodec** and release pretrained checkpoints. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2403.03100) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/naturalspeech3_facodec) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/naturalspeech3_facodec) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](models/codec/ns3_codec/README.md) - **2024/02/22**: The first Amphion visualization tool, **SingVisio**, release. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2402.12660) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [![Video](https://img.shields.io/badge/Video-Demo-orange)](https://github.com/open-mmlab/Amphion/assets/33707885/0a6e39e8-d5f1-4288-b0f8-32da5a2d6e96) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/visualization/SingVisio/README.md) From 799bec0eee25201e74a532e887d9b42357f5da2b Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Wed, 25 Sep 2024 23:20:13 +0800 Subject: [PATCH 15/16] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 87360869..666d089a 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ In addition to the specific generation tasks, Amphion includes several **vocoder - **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.com/invite/ZxxREr3Y) to stay connected and engage with our community! - **2024/08/20**: [SingVisio](https://arxiv.org/abs/2402.12660) got accepted by Computers & Graphics, [available here](https://www.sciencedirect.com/science/article/pii/S0097849324001936)! 🎉 - **2024/08/27**: *The Emilia dataset is now publicly available!* Discover the most extensive and diverse speech generation dataset with 101k hours of in-the-wild speech data now at [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia-Dataset) or [![OpenDataLab](https://img.shields.io/badge/OpenDataLab-Dataset-blue)](https://opendatalab.com/Amphion/Emilia)! 👑👑👑 -- **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md) +- **2024/07/01**: Amphion now releases **Emilia**, the first open-source multilingual in-the-wild dataset for speech generation with over 101k hours of speech data, and the **Emilia-Pipe**, the first open-source preprocessing pipeline designed to transform in-the-wild speech data into high-quality training data with annotations for speech generation! [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2407.05361) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/amphion/Emilia) [![demo](https://img.shields.io/badge/WebPage-Demo-red)](https://emilia-dataset.github.io/Emilia-Demo-Page/) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](preprocessors/Emilia/README.md) - **2024/06/17**: Amphion has a new release for its **VALL-E** model! It uses Llama as its underlying architecture and has better model performance, faster training speed, and more readable codes compared to our first version. [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/tts/VALLE_V2/README.md) - **2024/03/12**: Amphion now support **NaturalSpeech3 FACodec** and release pretrained checkpoints. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2403.03100) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-model-yellow)](https://huggingface.co/amphion/naturalspeech3_facodec) [![hf](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-demo-pink)](https://huggingface.co/spaces/amphion/naturalspeech3_facodec) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](models/codec/ns3_codec/README.md) - **2024/02/22**: The first Amphion visualization tool, **SingVisio**, release. [![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](https://arxiv.org/abs/2402.12660) [![openxlab](https://cdn-static.openxlab.org.cn/app-center/openxlab_app.svg)](https://openxlab.org.cn/apps/detail/Amphion/SingVisio) [![Video](https://img.shields.io/badge/Video-Demo-orange)](https://github.com/open-mmlab/Amphion/assets/33707885/0a6e39e8-d5f1-4288-b0f8-32da5a2d6e96) [![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/visualization/SingVisio/README.md) From 0d05a37df4027bd1b40cc3d8ee3f667ea022486f Mon Sep 17 00:00:00 2001 From: Yuchen Song <144111552+kenxxxxx@users.noreply.github.com> Date: Wed, 25 Sep 2024 23:21:26 +0800 Subject: [PATCH 16/16] Update README.md --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 666d089a..c288ca61 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,6 @@ In addition to the specific generation tasks, Amphion includes several **vocoders** and **evaluation metrics**. A vocoder is an important module for producing high-quality audio signals, while evaluation metrics are critical for ensuring consistent metrics in generation tasks. Moreover, Amphion is dedicated to advancing audio generation in real-world applications, such as building **large-scale datasets** for speech synthesis. ## 🚀 News -- **2024/9/26**: Amphion releases the Noro model, a noise-robust one-shot voice conversion system![![readme](https://img.shields.io/badge/README-Key%20Features-blue)](egs/vc/noro/README.md) - **2024/09/01**: [Amphion](https://arxiv.org/abs/2312.09911) and [Emilia](https://arxiv.org/abs/2407.05361) got accepted by IEEE SLT 2024! 🤗 - **2024/08/28**: Welcome to join Amphion's [Discord channel](https://discord.com/invite/ZxxREr3Y) to stay connected and engage with our community! - **2024/08/20**: [SingVisio](https://arxiv.org/abs/2402.12660) got accepted by Computers & Graphics, [available here](https://www.sciencedirect.com/science/article/pii/S0097849324001936)! 🎉

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