Add improvements and fixes to upstream

.gitignore

@@ -161,4 +161,10 @@ cython_debug/
 
 *.ckpt
 *.wav
-wandb/*
+wandb/*
+
+# Dataset folders and outputs
+/outputs
+/pre_encoded*
+/rawfiles*
+*.zip

dataset_config.json

@@ -0,0 +1,8 @@
+{
+    "dataset_type": "pre_encoded",
+    "datasets": [{
+        "id": "audio_pre_encoded",
+        "path": "pre_encoded",
+        "custom_metadata_module": "paths_md.py"
+    }]
+}

paths_md.py

@@ -0,0 +1,17 @@
+import os
+import re
+
+
+def get_custom_metadata(info, audio):
+    # Get filename without extension
+    file_name = os.path.basename(info["relpath"])
+    file_name_without_extension = os.path.splitext(file_name)[0]
+
+    # Replace non-alphanumeric characters with spaces, and remove leading/trailing spaces
+    cleaned_file_name = re.sub('[^0-9a-zA-Z]+', ' ', file_name_without_extension).strip()
+    #cleaned_file_name = re.match('', cleaned_file_name).groups()[0]
+
+    # Sanity check
+    print(f'{info["relpath"]} => {cleaned_file_name}')
+
+    return {"prompt": cleaned_file_name}

paths_md_pre_encode.py

@@ -0,0 +1,17 @@
+import os
+import re
+
+
+def get_custom_metadata(info, audio):
+    # Get filename without extension
+    file_name = os.path.basename(info["relpath"])
+    file_name_without_extension = os.path.splitext(file_name)[0]
+
+    # Replace non-alphanumeric characters with spaces, and remove leading/trailing spaces
+    cleaned_file_name = re.sub('[^0-9a-zA-Z]+', ' ', file_name_without_extension).strip()
+    #cleaned_file_name = re.match('', cleaned_file_name).groups()[0]
+
+    # Sanity check
+    print(f'{info["relpath"]} => {cleaned_file_name}')
+
+    return {"prompt": cleaned_file_name}

pe_dataset_config.json

@@ -0,0 +1,11 @@
+{
+    "dataset_type": "audio_dir",
+    "datasets": [{
+        "id": "audio",
+        "path": "./rawfiles",
+        "custom_metadata_module": "./paths_md_pre_encode.py",
+        "drop_last": false
+    }],
+    "drop_last": false,
+    "random_crop": false
+}

pre-encode.bat

@@ -0,0 +1,9 @@
+python ./pre_encode.py ^
+    --ckpt-path ./vae_model.ckpt ^
+    --model-config ./vae_model_config.json ^
+    --batch-size 8 ^
+    --dataset-config pe_dataset_config.json ^
+    --output-path ./pre_encoded ^
+    --model-half ^
+    --sample-size 131072 ^
+

setup.py

@@ -10,7 +10,9 @@
     install_requires=[
         'alias-free-torch==0.0.6',
         'auraloss==0.4.0',
+        'bitsandbytes==0.47.0',
         'descript-audio-codec==1.0.0',
+        'dill==0.4.0',
         'einops',
         'einops-exts',
         'ema-pytorch==0.2.3',
@@ -23,7 +25,8 @@
         'local-attention==1.8.6',
         'pandas==2.0.2',
         'prefigure==0.0.9',
-        'pytorch_lightning==2.1.0',
+        'pytorch_lightning==2.4.0',
+        'pytorch_optimizer==3.1.2',
         'PyWavelets==1.4.1',
         'safetensors',
         'sentencepiece==0.1.99',

stable_audio_tools/configs/model_configs/txt2audio/stable_audio_small_adamw8bit_cawr_base_model_config.json

@@ -0,0 +1,132 @@
+{
+    "model_type": "diffusion_cond",
+    "sample_size": 524288,
+    "sample_rate": 44100,
+    "audio_channels": 2,
+    "model": {
+        "pretransform": {
+            "type": "autoencoder",
+            "iterate_batch": true,
+            "model_half": true,
+            "chunked": true,
+            "config": {
+                "encoder": {
+                    "type": "oobleck",
+                    "requires_grad": false,
+                    "config": {
+                        "in_channels": 2,
+                        "channels": 128,
+                        "c_mults": [1, 2, 4, 8, 16],
+                        "strides": [2, 4, 4, 8, 8],
+                        "latent_dim": 128,
+                        "use_snake": true
+                    }
+                },
+                "decoder": {
+                    "type": "oobleck",
+                    "config": {
+                        "out_channels": 2,
+                        "channels": 128,
+                        "c_mults": [1, 2, 4, 8, 16],
+                        "strides": [2, 4, 4, 8, 8],
+                        "latent_dim": 64,
+                        "use_snake": true,
+                        "final_tanh": false
+                    }
+                },
+                "bottleneck": {
+                    "type": "vae"
+                },
+                "latent_dim": 64,
+                "downsampling_ratio": 2048,
+                "io_channels": 2
+            }
+        },
+        "conditioning": {
+            "configs": [
+                {
+                    "id": "prompt",
+                    "type": "t5",
+                    "config": {
+                        "t5_model_name": "google/t5gemma-b-b-ul2",
+                        "max_length": 128
+                    }
+                },
+                {
+                    "id": "seconds_total",
+                    "type": "number",
+                    "config": {
+                        "min_val": 0,
+                        "max_val": 256
+                    }
+                }
+            ],
+            "cond_dim": 768
+        },
+        "diffusion": {
+            "cross_attention_cond_ids": ["prompt", "seconds_total"],
+            "global_cond_ids": ["seconds_total"],
+            "diffusion_objective": "rectified_flow",
+            "distribution_shift_options": {
+                "min_length": 256,
+                "max_length": 4096
+            },
+            "type": "dit",
+            "config": {
+                "io_channels": 64,
+                "embed_dim": 1024,
+                "depth": 16,
+                "num_heads": 8,
+                "cond_token_dim": 768,
+                "global_cond_dim": 768,
+                "transformer_type": "continuous_transformer",
+                "attn_kwargs": {
+                    "qk_norm": "ln"
+                }
+            }
+        },
+        "io_channels": 64
+    },
+    "training": {
+        "use_ema": true,
+        "log_loss_info": false,
+        "pre_encoded": true,
+        "timestep_sampler": "trunc_logit_normal",
+        "optimizer_configs": {
+            "diffusion": {
+                "optimizer": {
+                    "type": "AdamW8bit",
+                    "config": {
+                        "lr": 1e-5,
+                        "betas": [0.9, 0.999],
+                        "eps": 1e-8,
+                        "weight_decay": 1e-2,
+                        "block_wise": true
+                    }
+                },
+                "scheduler": {
+                    "type": "CosineAnnealingWarmRestarts",
+                    "config": {
+                        "T_0": 10,
+                        "T_mult": 2
+                    }
+                }
+            }
+        },
+        "demo": {
+            "demo_every": 512,
+            "demo_steps": 100,
+            "num_demos": 7,
+            "demo_cond": [
+                {"prompt": "kick", "seconds_total": 2},
+                {"prompt": "bass", "seconds_total": 2},
+                {"prompt": "drum breaks 174 BPM", "seconds_total": 6},
+                {"prompt": "A short, beautiful piano riff in C minor", "seconds_total": 6},
+                {"prompt": "Tight Snare Drum", "seconds_total": 1},
+                {"prompt": "Glitchy bass design, I used Serum for this", "seconds_total": 4},
+                {"prompt": "Synth pluck arp with reverb and delay, 128 BPM", "seconds_total": 6}
+            ],
+            "demo_cfg_scales": [0.5, 1, 1.5, 8]
+        }
+    }
+}

stable_audio_tools/data/dataset.py

@@ -1,3 +1,4 @@
+import dill
 import importlib
 import numpy as np
 import io
@@ -19,7 +20,10 @@
 
 from .utils import Stereo, Mono, PhaseFlipper, PadCrop_Normalized_T, VolumeNorm
 
-AUDIO_KEYS = ("flac", "wav", "mp3", "m4a", "ogg", "opus")
+from torchdata.stateful_dataloader import StatefulDataLoader
+
+
+AUDIO_KEYS = ("flac", "wav", "mp3", "m4a", "ogg", "opus", "aiff", "aif")
 
 # fast_scandir implementation by Scott Hawley originally in https://github.com/zqevans/audio-diffusion/blob/main/dataset/dataset.py
 
@@ -94,7 +98,7 @@ def keyword_scandir(
 def get_audio_filenames(
     paths: list,  # directories in which to search
     keywords=None,
-    exts=['.wav', '.mp3', '.flac', '.ogg', '.aif', '.opus']
+    exts=['.wav', '.mp3', '.flac', '.ogg', '.aif', '.opus', '.aif', '.aiff']
 ):
     "recursively get a list of audio filenames"
     filenames = []
@@ -178,7 +182,7 @@ def __init__(
             self.root_paths.append(config.path)
             self.filenames.extend(get_audio_filenames(config.path, keywords))
             if config.custom_metadata_fn is not None:
-                self.custom_metadata_fns[config.path] = config.custom_metadata_fn
+                self.custom_metadata_fns[config.path] = dill.dumps(config.custom_metadata_fn)
 
         print(f'Found {len(self.filenames)} files')
 
@@ -238,8 +242,8 @@ def __getitem__(self, idx):
 
             for custom_md_path in self.custom_metadata_fns.keys():
                 if custom_md_path in audio_filename:
-                    custom_metadata_fn = self.custom_metadata_fns[custom_md_path]
-                    custom_metadata = custom_metadata_fn(info, audio)
+                    custom_metadata_fn_deserialized = dill.loads(self.custom_metadata_fns[custom_md_path])
+                    custom_metadata = custom_metadata_fn_deserialized(info, audio)
                     info.update(custom_metadata)
 
                 if "__reject__" in info and info["__reject__"]:
@@ -282,7 +286,7 @@ def __init__(
         for config in configs:
             self.filenames.extend(get_latent_filenames(config.path, [latent_extension]))
             if config.custom_metadata_fn is not None:
-                self.custom_metadata_fns[config.path] = config.custom_metadata_fn
+                self.custom_metadata_fns[config.path] = dill.dumps(config.custom_metadata_fn)
 
         self.latent_crop_length = latent_crop_length
         self.random_crop = random_crop
@@ -339,8 +343,9 @@ def __getitem__(self, idx):
 
             for custom_md_path in self.custom_metadata_fns.keys():
                 if custom_md_path in latent_filename:
-                    custom_metadata_fn = self.custom_metadata_fns[custom_md_path]
-                    custom_metadata = custom_metadata_fn(info, None)
+
+                    custom_metadata_fn_deserialized = dill.loads(self.custom_metadata_fns[custom_md_path])
+                    custom_metadata = custom_metadata_fn_deserialized(info, None)
                     info.update(custom_metadata)
 
                 if "__reject__" in info and info["__reject__"]:
@@ -849,8 +854,14 @@ def create_dataloader_from_config(dataset_config, batch_size, sample_size, sampl
             force_channels=force_channels
         )
 
-        return torch.utils.data.DataLoader(train_set, batch_size, shuffle=shuffle,
-                                num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=dataset_config.get("drop_last", True), collate_fn=collation_fn)
+        # https://docs.pytorch.org/docs/stable/notes/randomness.html#dataloader
+        g = torch.Generator()
+        g.manual_seed(0)
+
+        #return torch.utils.data.DataLoader(train_set, batch_size, shuffle=shuffle,
+        #                        num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=dataset_config.get("drop_last", True), collate_fn=collation_fn, generator=g)
+        return StatefulDataLoader(train_set, batch_size, shuffle=shuffle,
+                                num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=dataset_config.get("drop_last", True), collate_fn=collation_fn, generator=g)
 
     elif dataset_type == "pre_encoded":
 
@@ -899,8 +910,14 @@ def create_dataloader_from_config(dataset_config, batch_size, sample_size, sampl
             latent_extension=latent_extension
         )
 
-        return torch.utils.data.DataLoader(train_set, batch_size, shuffle=shuffle,
-                                num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=dataset_config.get("drop_last", True), collate_fn=collation_fn)
+        # https://docs.pytorch.org/docs/stable/notes/randomness.html#dataloader
+        g = torch.Generator()
+        g.manual_seed(0)
+
+        #return torch.utils.data.DataLoader(train_set, batch_size, shuffle=shuffle,
+        #                        num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=dataset_config.get("drop_last", True), collate_fn=collation_fn, generator=g)
+        return StatefulDataLoader(train_set, batch_size, shuffle=shuffle,
+                                num_workers=num_workers, persistent_workers=True, pin_memory=True, drop_last=dataset_config.get("drop_last", True), collate_fn=collation_fn, generator=g)
 
     elif dataset_type in ["s3", "wds"]: # Support "s3" type for backwards compatibility
         wds_configs = []