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Update VCTK recipes

This commit is contained in:
Eren Gölge 2021-12-08 15:15:56 +00:00
parent 730f7c0df4
commit df0d58bf09
9 changed files with 192 additions and 116 deletions
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2
TTS/tts/datasets/formatters.py
View File

@ -289,7 +289,7 @@ def brspeech(root_path, meta_file, ignored_speakers=None):
return items
def vctk(root_path, meta_files=None, wavs_path="wav22", mic="mic2", ignored_speakers=None):
def vctk(root_path, meta_files=None, wavs_path="wav48_silence_trimmed", mic="mic2", ignored_speakers=None):
"""https://datashare.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip"""
file_ext = 'flac'
test_speakers = meta_files

6
recipes/ljspeech/speedy_speech/train_speedy_speech.py
View File

@ -68,12 +68,6 @@ tokenizer, config = TTSTokenizer.init_from_config(config)
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init model
model = ForwardTTS(config, ap, tokenizer)

43
recipes/vctk/fast_pitch/train_fast_pitch.py
View File

@ -6,6 +6,7 @@ from TTS.tts.configs.fast_pitch_config import FastPitchConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
@ -32,6 +33,7 @@ config = FastPitchConfig(
num_loader_workers=8,
num_eval_loader_workers=4,
compute_input_seq_cache=True,
precompute_num_workers=4,
compute_f0=True,
f0_cache_path=os.path.join(output_path, "f0_cache"),
run_eval=True,
@ -39,23 +41,35 @@ config = FastPitchConfig(
epochs=1000,
text_cleaner="english_cleaners",
use_phonemes=True,
use_espeak_phonemes=False,
phoneme_language="en-us",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
print_step=50,
print_eval=False,
mixed_precision=False,
sort_by_audio_len=True,
max_seq_len=500000,
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=500000,
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True,
)
# init audio processor
ap = AudioProcessor(**config.audio)
# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -65,16 +79,15 @@ speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
config.model_args.num_speakers = speaker_manager.num_speakers
# init model
model = ForwardTTS(config, speaker_manager)
model = ForwardTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()

48
recipes/vctk/fast_speech/train_fast_speech.py
View File

@ -6,6 +6,7 @@ from TTS.tts.configs.fast_speech_config import FastSpeechConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
@ -25,37 +26,48 @@ audio_config = BaseAudioConfig(
)
config = FastSpeechConfig(
run_name="fast_pitch_ljspeech",
run_name="fast_speech_vctk",
audio=audio_config,
batch_size=32,
eval_batch_size=16,
num_loader_workers=8,
num_eval_loader_workers=4,
compute_input_seq_cache=True,
compute_f0=True,
f0_cache_path=os.path.join(output_path, "f0_cache"),
precompute_num_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
text_cleaner="english_cleaners",
use_phonemes=True,
use_espeak_phonemes=False,
phoneme_language="en-us",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
print_step=50,
print_eval=False,
mixed_precision=False,
sort_by_audio_len=True,
max_seq_len=500000,
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=500000,
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True,
)
# init audio processor
ap = AudioProcessor(**config.audio)
## INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -65,16 +77,14 @@ speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
config.model_args.num_speakers = speaker_manager.num_speakers
# init model
model = ForwardTTS(config, speaker_manager)
model = ForwardTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
trainer.fit()
# AND... 3,2,1... 🚀
trainer.fit()

41
recipes/vctk/glow_tts/train_glow_tts.py
View File

@ -7,6 +7,7 @@ from TTS.tts.configs.shared_configs import BaseDatasetConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.glow_tts import GlowTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
# set experiment paths
@ -32,6 +33,7 @@ config = GlowTTSConfig(
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
precompute_num_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
@ -45,12 +47,27 @@ config = GlowTTSConfig(
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True,
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=500000,
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -60,16 +77,14 @@ speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
config.num_speakers = speaker_manager.num_speakers
# init model
model = GlowTTS(config, speaker_manager)
model = GlowTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
trainer.fit()
# AND... 3,2,1... 🚀
trainer.fit()

44
recipes/vctk/speedy_speech/train_speedy_speech.py
View File

@ -6,6 +6,7 @@ from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.forward_tts import ForwardTTS
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
@ -32,30 +33,41 @@ config = SpeedySpeechConfig(
num_loader_workers=8,
num_eval_loader_workers=4,
compute_input_seq_cache=True,
compute_f0=True,
f0_cache_path=os.path.join(output_path, "f0_cache"),
precompute_num_workers=4,
run_eval=True,
test_delay_epochs=-1,
epochs=1000,
text_cleaner="english_cleaners",
use_phonemes=True,
use_espeak_phonemes=False,
phoneme_language="en-us",
phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
print_step=50,
print_eval=False,
mixed_precision=False,
sort_by_audio_len=True,
max_seq_len=500000,
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=500000,
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True,
)
# init audio processor
ap = AudioProcessor(**config.audio)
# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -65,16 +77,14 @@ speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
config.model_args.num_speakers = speaker_manager.num_speakers
# init model
model = ForwardTTS(config, speaker_manager)
model = ForwardTTS(config, ap, tokenizer, speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()

42
recipes/vctk/tacotron-DDC/train_tacotron-DDC.py
View File

@ -7,6 +7,7 @@ from TTS.tts.configs.tacotron_config import TacotronConfig
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron import Tacotron
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
@ -32,6 +33,7 @@ config = TacotronConfig( # This is the config that is saved for the future use
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
precompute_num_workers=4,
run_eval=True,
test_delay_epochs=-1,
r=6,
@ -45,18 +47,30 @@ config = TacotronConfig( # This is the config that is saved for the future use
print_step=25,
print_eval=False,
mixed_precision=True,
sort_by_audio_len=True,
min_seq_len=0,
max_seq_len=44000 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=44000 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True, # set this to enable multi-sepeaker training
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
## INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -65,16 +79,14 @@ speaker_manager = SpeakerManager()
speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
# init model
model = Tacotron(config, speaker_manager)
model = Tacotron(config, ap, tokenizer, speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()

41
recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py
View File

@ -7,6 +7,7 @@ from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
@ -44,9 +45,10 @@ config = Tacotron2Config( # This is the config that is saved for the future use
print_step=150,
print_eval=False,
mixed_precision=True,
sort_by_audio_len=True,
min_seq_len=14800,
max_seq_len=22050 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=44000 * 10,
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True, # set this to enable multi-sepeaker training
@ -60,10 +62,21 @@ config = Tacotron2Config( # This is the config that is saved for the future use
lr=3e-5,
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -72,16 +85,14 @@ speaker_manager = SpeakerManager()
speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
# init model
model = Tacotron2(config, speaker_manager)
model = Tacotron2(config, ap, tokenizer, speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()

41
recipes/vctk/tacotron2/train_tacotron2.py
View File

@ -7,6 +7,7 @@ from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
@ -44,9 +45,10 @@ config = Tacotron2Config( # This is the config that is saved for the future use
print_step=150,
print_eval=False,
mixed_precision=True,
sort_by_audio_len=True,
min_seq_len=14800,
max_seq_len=22050 * 10, # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
min_text_len=0,
max_text_len=500,
min_audio_len=0,
max_audio_len=44000 * 10,
output_path=output_path,
datasets=[dataset_config],
use_speaker_embedding=True, # set this to enable multi-sepeaker training
@ -60,10 +62,21 @@ config = Tacotron2Config( # This is the config that is saved for the future use
lr=3e-5,
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
## INITIALIZE THE AUDIO PROCESSOR
# Audio processor is used for feature extraction and audio I/O.
# It mainly serves to the dataloader and the training loggers.
ap = AudioProcessor.init_from_config(config)
# load training samples
# INITIALIZE THE TOKENIZER
# Tokenizer is used to convert text to sequences of token IDs.
# If characters are not defined in the config, default characters are passed to the config
tokenizer, config = TTSTokenizer.init_from_config(config)
# LOAD DATA SAMPLES
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# init speaker manager for multi-speaker training
@ -72,16 +85,14 @@ speaker_manager = SpeakerManager()
speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
# init model
model = Tacotron2(config, speaker_manager)
model = Tacotron2(config, ap, tokenizer, speaker_manager)
# init the trainer and 🚀
# INITIALIZE THE TRAINER
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training, etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
TrainingArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
)
# AND... 3,2,1... 🚀
trainer.fit()