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inference.py
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inference.py
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# Adapted from
# https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/FastPitch
# *****************************************************************************
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the NVIDIA CORPORATION nor the
# names of its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# *****************************************************************************
"""Inference -- voice synthesis script.
"""
import time
import sys
import warnings
import argparse
from argparse import ArgumentParser
from pathlib import Path
import torch
from torch.nn.utils.rnn import pad_sequence
import numpy as np
from scipy.stats import norm
from librosa.output import write_wav
import models
import dllogger as DLLogger
from dllogger import StdOutBackend, JSONStreamBackend, Verbosity
from common import utils
from common.log_helper import unique_dllogger_fpath
from common.text import text_to_sequence
from waveglow import model as glow
from waveglow.denoiser import Denoiser
sys.modules['glow'] = glow
def parse_args(parser) -> ArgumentParser:
"""Parse command line arguments.
Args:
parser ([type]): [description]
Retu25fe4d385680d1c633f0c711e22a7bb9a0ecae78rns:
ArgumentParser: [description]
"""
parser.add_argument('-i', '--input', type=str, required=True,
help='Full path to the input text (phareses separated by newlines)')
parser.add_argument('-o', '--output', default=None,
help='Output folder to save audio (file per phrase)')
parser.add_argument('--log-file', type=str, default=None,
help='Path to a DLLogger log file')
parser.add_argument('--cuda', action='store_true',
help='Run inference on a GPU using CUDA')
parser.add_argument('--fastpitch', type=str,
help='Full path to the generator checkpoint file (skip to use ground truth mels)')
parser.add_argument('--waveglow', type=str,
help='Full path to the WaveGlow model checkpoint file (skip to only generate mels)')
parser.add_argument('-s', '--sigma-infer', default=0.9, type=float,
help='WaveGlow sigma')
parser.add_argument('-d', '--denoising-strength', default=0.01, type=float,
help='WaveGlow denoising')
parser.add_argument('-sr', '--sampling-rate', default=22050, type=int,
help='Sampling rate')
parser.add_argument('--stft-hop-length', type=int, default=256,
help='STFT hop length for estimating audio length from mel size')
parser.add_argument('--amp', action='store_true',
help='Inference with AMP')
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--include-warmup', action='store_true',
help='Include warmup')
parser.add_argument('--repeats', type=int, default=1,
help='Repeat inference for benchmarking')
parser.add_argument('--torchscript', action='store_true',
help='Apply TorchScript')
parser.add_argument('--ema', action='store_true',
help='Use EMA averaged model (if saved in checkpoints)')
parser.add_argument('--dataset-path', type=str,
help='Path to dataset (for loading extra data fields)')
transform = parser.add_argument_group('transform')
transform.add_argument('--fade-out', type=int, default=5,
help='Number of fadeout frames at the end')
transform.add_argument('--pace', type=float, default=1.0,
help='Adjust the pace of speech')
transform.add_argument('--pitch-transform-flatten', action='store_true',
help='Flatten the pitch')
transform.add_argument('--pitch-transform-invert', action='store_true',
help='Invert the pitch wrt mean value')
transform.add_argument('--pitch-transform-amplify', action='store_true',
help='Amplify the pitch variability')
transform.add_argument('--pitch-transform-shift', type=float, default=0.0,
help='Raise/lower the pitch by <hz>')
return parser
def load_and_setup_model(model_name, parser, checkpoint, amp: bool, device: torch.device,
unk_args=[], forward_is_infer=False, ema=True,
jitable=False):
"""Load from `checkpoint`, set to `device` and return a `model_name` for inference.
Args:
model_name ([type]): [description]
parser ([type]): [description]
checkpoint (str): Saved checkpoint to load.
amp (bool): Auto Mixed Precision.
device (torch.device): Device to load the model to.
unk_args (list, optional): [description]. Defaults to [].
forward_is_infer (bool, optional): [description]. Defaults to False.
ema (bool, optional): [description]. Defaults to True.
jitable (bool, optional): [description]. Defaults to False.
Returns:
[type]: A model
"""
model_parser = models.parse_model_args(model_name, parser, add_help=False)
model_args, model_unk_args = model_parser.parse_known_args()
unk_args[:] = list(set(unk_args) & set(model_unk_args))
model_config = models.get_model_config(model_name, model_args)
model = models.get_model(model_name, model_config, device,
forward_is_infer=forward_is_infer,
jitable=jitable)
if checkpoint is not None:
checkpoint_data = torch.load(checkpoint)
status = ''
if 'state_dict' in checkpoint_data:
sd = checkpoint_data['state_dict']
if ema and 'ema_state_dict' in checkpoint_data:
sd = checkpoint_data['ema_state_dict']
status += ' (EMA)'
elif ema and 'ema_state_dict' not in checkpoint_data:
print(f'WARNING: EMA weights missing for {model_name}')
if any(key.startswith('module.') for key in sd):
sd = {k.replace('module.', ''): v for k, v in sd.items()}
status += ' ' + str(model.load_state_dict(sd, strict=False))
else:
model = checkpoint_data['model']
print(f'Loaded {model_name}{status}')
if model_name == "WaveGlow":
model = model.remove_weightnorm(model)
if amp:
model.half()
model.eval()
return model.to(device)
def load_fields(fpath: str):
"""Return a dict of fields from `fpath`
Args:
fpath (str): [description]
Returns:
dict: [description]
"""
lines = [l.strip() for l in open(fpath, encoding='utf-8')]
if fpath.endswith('.tsv'):
columns = lines[0].split('\t')
fields = list(zip(*[t.split('\t') for t in lines[1:]]))
else:
columns = ['text']
fields = [lines]
return {c:f for c, f in zip(columns, fields)}
def prepare_input_sequence(fields: dict, device: torch.device, batch_size: int = 128,
dataset=None, load_mels=False, load_pitch=False) -> list:
"""[summary]
Args:
fields (dict): [description]
device (torch.device): [description]
batch_size (int, optional): [description]. Defaults to 128.
dataset ([type], optional): [description]. Defaults to None.
load_mels (bool, optional): [description]. Defaults to False.
load_pitch (bool, optional): [description]. Defaults to False.
Returns:
list: batches
"""
fields['text'] = [torch.LongTensor(text_to_sequence(t, ['english_cleaners']))
for t in fields['text']]
order = np.argsort([-t.size(0) for t in fields['text']])
fields['text'] = [fields['text'][i] for i in order]
fields['text_lens'] = torch.LongTensor([t.size(0) for t in fields['text']])
if load_mels:
assert 'mel' in fields
fields['mel'] = [
torch.load(Path(dataset, fields['mel'][i])).t() for i in order]
fields['mel_lens'] = torch.LongTensor([t.size(0) for t in fields['mel']])
if load_pitch:
assert 'pitch' in fields
fields['pitch'] = [
torch.load(Path(dataset, fields['pitch'][i])) for i in order]
fields['pitch_lens'] = torch.LongTensor([t.size(0) for t in fields['pitch']])
if 'output' in fields:
fields['output'] = [fields['output'][i] for i in order]
# cut into batches & pad
batches = []
for b in range(0, len(order), batch_size):
batch = {f: values[b:b+batch_size] for f, values in fields.items()}
for f in batch:
if f == 'text':
batch[f] = pad_sequence(batch[f], batch_first=True)
elif f == 'mel' and load_mels:
batch[f] = pad_sequence(batch[f], batch_first=True).permute(0, 2, 1)
elif f == 'pitch' and load_pitch:
batch[f] = pad_sequence(batch[f], batch_first=True)
if isinstance(batch[f], torch.Tensor):
batch[f] = batch[f].to(device)
batches.append(batch)
return batches
def build_pitch_transformation(args):
"""[summary]
Args:
args ([type]): [description]
Returns:
[type]: [description]
"""
fun = 'pitch'
if args.pitch_transform_flatten:
fun = f'({fun}) * 0.0'
if args.pitch_transform_invert:
fun = f'({fun}) * -1.0'
if args.pitch_transform_amplify:
fun = f'({fun}) * 2.0'
if args.pitch_transform_shift != 0.0:
hz = args.pitch_transform_shift
fun = f'({fun}) + {hz} / std'
# FIXME: eliminate usage of eval
return eval(f'lambda pitch, mean, std: {fun}')
# TODO: Figure out how it is intended to work
class MeasureTime(list):
"""[summary]
Base type:
list:
"""
def __enter__(self):
"""Save time in seconds in float, upon entrance into with."""
torch.cuda.synchronize() # Wait for all kernels in all streams on a CUDA device to complete
self.t0 = time.perf_counter()
def __exit__(self, exc_type, exc_value, exc_traceback):
"""Save time in seconds in float, upon exit from with."""
torch.cuda.synchronize() # Wait for all kernels in all streams on a CUDA device to complete
self.append(time.perf_counter() - self.t0)
def __add__(self, other):
assert len(self) == len(other)
return MeasureTime(sum(ab) for ab in zip(self, other))
def main():
"""
Launches text to speech inference.
Inference is executed on a single GPU.
"""
# Enable benchmark mode in cudnn
# https://discuss.pytorch.org/t/pytorch-performance/3079/7
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser(description='PyTorch FastPitch Inference',
allow_abbrev=False)
parser = parse_args(parser)
args, unk_args = parser.parse_known_args()
if args.output is not None:
Path(args.output).mkdir(parents=False, exist_ok=True)
log_fpath = args.log_file or str(Path(args.output, 'nvlog_infer.json'))
log_fpath = unique_dllogger_fpath(log_fpath)
DLLogger.init(backends=[JSONStreamBackend(Verbosity.DEFAULT, log_fpath),
StdOutBackend(Verbosity.VERBOSE)])
for k, v in vars(args).items():
DLLogger.log("PARAMETER", {k:v})
device = torch.device('cuda' if args.cuda else 'cpu')
if args.fastpitch is not None:
generator = load_and_setup_model(
'FastPitch', parser, args.fastpitch, args.amp, device,
unk_args=unk_args, forward_is_infer=True, ema=args.ema,
jitable=args.torchscript)
if args.torchscript:
generator = torch.jit.script(generator)
else:
generator = None
if args.waveglow is not None:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
waveglow = load_and_setup_model(
'WaveGlow', parser, args.waveglow, args.amp, device,
unk_args=unk_args, forward_is_infer=True, ema=args.ema)
denoiser = Denoiser(waveglow).to(device)
waveglow = getattr(waveglow, 'infer', waveglow)
else:
waveglow = None
if len(unk_args) > 0:
raise ValueError(f'Invalid options {unk_args}')
fields = load_fields(args.input)
batches = prepare_input_sequence(
fields, device, args.batch_size, args.dataset_path,
load_mels=(generator is None))
if args.include_warmup:
# Use real data rather than synthetic - FastPitch predicts len
for i in range(3):
with torch.no_grad():
if generator is not None:
b = batches[0]
mel, *_ = generator(b['text'], b['text_lens'])
if waveglow is not None:
audios = waveglow(mel, sigma=args.sigma_infer).float()
_ = denoiser(audios, strength=args.denoising_strength)
gen_measures = MeasureTime()
waveglow_measures = MeasureTime()
gen_kw = {'pace': args.pace,
'pitch_tgt': None,
'pitch_transform': build_pitch_transformation(args)}
if args.torchscript:
gen_kw.pop('pitch_transform')
all_utterances = 0
all_samples = 0
all_letters = 0
all_frames = 0
reps = args.repeats
log_enabled = True # reps == 1
log = lambda s, d: DLLogger.log(step=s, data=d) if log_enabled else None
# for repeat in (tqdm.tqdm(range(reps)) if reps > 1 else range(reps)):
for rep in range(reps):
for b in batches:
if generator is None:
log(rep, {'Synthesizing from ground truth mels'})
mel, mel_lens = b['mel'], b['mel_lens']
else:
with torch.no_grad(), gen_measures:
mel, mel_lens, *_ = generator(
b['text'], b['text_lens'], **gen_kw)
gen_infer_perf = mel.size(0) * mel.size(2) / gen_measures[-1]
all_letters += b['text_lens'].sum().item()
all_frames += mel.size(0) * mel.size(2)
log(rep, {"fastpitch_frames_per_sec": gen_infer_perf})
log(rep, {"fastpitch_latency": gen_measures[-1]})
if waveglow is not None:
with torch.no_grad(), waveglow_measures:
audios = waveglow(mel, sigma=args.sigma_infer)
audios = denoiser(audios.float(),
strength=args.denoising_strength
).squeeze(1)
all_utterances += len(audios)
all_samples += sum(audio.size(0) for audio in audios)
waveglow_infer_perf = (
audios.size(0) * audios.size(1) / waveglow_measures[-1])
log(rep, {"waveglow_samples_per_sec": waveglow_infer_perf})
log(rep, {"waveglow_latency": waveglow_measures[-1]})
if args.output is not None and reps == 1:
for i, audio in enumerate(audios):
audio = audio[:mel_lens[i].item() * args.stft_hop_length]
if args.fade_out:
fade_len = args.fade_out * args.stft_hop_length
fade_w = torch.linspace(1.0, 0.0, fade_len)
audio[-fade_len:] *= fade_w.to(audio.device)
audio = audio/torch.max(torch.abs(audio))
fname = b['output'][i] if 'output' in b else f'audio_{i}.wav'
audio_path = Path(args.output, fname)
write_wav(audio_path, audio.cpu().numpy(), args.sampling_rate)
if generator is not None and waveglow is not None:
log(rep, {"latency": (gen_measures[-1] + waveglow_measures[-1])})
log_enabled = True
if generator is not None:
gm = np.sort(np.asarray(gen_measures))
rtf = all_samples / (all_utterances * gm.mean() * args.sampling_rate)
log('avg', {"fastpitch letters/s": all_letters / gm.sum()})
log('avg', {"fastpitch_frames/s": all_frames / gm.sum()})
log('avg', {"fastpitch_latency": gm.mean()})
log('avg', {"fastpitch RTF": rtf})
log('90%', {"fastpitch_latency": gm.mean() + norm.ppf((1.0 + 0.90) / 2) * gm.std()})
log('95%', {"fastpitch_latency": gm.mean() + norm.ppf((1.0 + 0.95) / 2) * gm.std()})
log('99%', {"fastpitch_latency": gm.mean() + norm.ppf((1.0 + 0.99) / 2) * gm.std()})
if waveglow is not None:
wm = np.sort(np.asarray(waveglow_measures))
rtf = all_samples / (all_utterances * wm.mean() * args.sampling_rate)
log('avg', {"waveglow_samples/s": all_samples / wm.sum()})
log('avg', {"waveglow_latency": wm.mean()})
log('avg', {"waveglow RTF": rtf})
log('90%', {"waveglow_latency": wm.mean() + norm.ppf((1.0 + 0.90) / 2) * wm.std()})
log('95%', {"waveglow_latency": wm.mean() + norm.ppf((1.0 + 0.95) / 2) * wm.std()})
log('99%', {"waveglow_latency": wm.mean() + norm.ppf((1.0 + 0.99) / 2) * wm.std()})
if generator is not None and waveglow is not None:
m = gm + wm
rtf = all_samples / (all_utterances * m.mean() * args.sampling_rate)
log('avg', {"samples/s": all_samples / m.sum()})
log('avg', {"letters/s": all_letters / m.sum()})
log('avg', {"latency": m.mean()})
log('avg', {"RTF": rtf})
log('90%', {"latency": m.mean() + norm.ppf((1.0 + 0.90) / 2) * m.std()})
log('95%', {"latency": m.mean() + norm.ppf((1.0 + 0.95) / 2) * m.std()})
log('99%', {"latency": m.mean() + norm.ppf((1.0 + 0.99) / 2) * m.std()})
DLLogger.flush()
if __name__ == '__main__':
main()