Medix-AI/coqui-tts
Medix-AI
/
coqui-tts
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Fix train_tts.py and uncomment code () 

* Fix SE loading and language embedding logic

* remove trailing white space

* Uncomment resmapling code for SCL
This commit is contained in:
WeberJulian 2022-01-03 17:44:57 +01:00 committed by GitHub
parent 58c38de58d
commit e1accb6e28
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 19 additions and 19 deletions
TTS
bin
train_tts.py
tts/models
vits.py

16
TTS/bin/train_tts.py
View File

@ -1,4 +1,5 @@
import os import os
import torch
from TTS.config import check_config_and_model_args, get_from_config_or_model_args, load_config, register_config from TTS.config import check_config_and_model_args, get_from_config_or_model_args, load_config, register_config
from TTS.trainer import Trainer, TrainingArgs from TTS.trainer import Trainer, TrainingArgs
@ -53,15 +54,22 @@ def main():
else: else:
config.num_speakers = speaker_manager.num_speakers config.num_speakers = speaker_manager.num_speakers
elif check_config_and_model_args(config, "use_d_vector_file", True): elif check_config_and_model_args(config, "use_d_vector_file", True):
speaker_manager = SpeakerManager(d_vectors_file_path=get_from_config_or_model_args(config, "d_vector_file")) if check_config_and_model_args(config, "use_speaker_encoder_as_loss", True):
speaker_manager = SpeakerManager(
d_vectors_file_path=config.model_args.d_vector_file,
encoder_model_path=config.model_args.speaker_encoder_model_path,
encoder_config_path=config.model_args.speaker_encoder_config_path,
use_cuda=torch.cuda.is_available(),
)
else:
speaker_manager = SpeakerManager(d_vectors_file_path=get_from_config_or_model_args(config, "d_vector_file"))
config.num_speakers = speaker_manager.num_speakers
if hasattr(config, "model_args"): if hasattr(config, "model_args"):
config.model_args.num_speakers = speaker_manager.num_speakers config.model_args.num_speakers = speaker_manager.num_speakers
else:
config.num_speakers = speaker_manager.num_speakers
else: else:
speaker_manager = None speaker_manager = None
if hasattr(config, "use_language_embedding") and config.use_language_embedding: if check_config_and_model_args(config, "use_language_embedding", True):
language_manager = LanguageManager(config=config) language_manager = LanguageManager(config=config)
if hasattr(config, "model_args"): if hasattr(config, "model_args"):
config.model_args.num_languages = language_manager.num_languages config.model_args.num_languages = language_manager.num_languages

22
TTS/tts/models/vits.py
View File

@ -5,7 +5,7 @@ from typing import Dict, List, Tuple
import torch import torch
# import torchaudio import torchaudio
from coqpit import Coqpit from coqpit import Coqpit
from torch import nn from torch import nn
from torch.cuda.amp.autocast_mode import autocast from torch.cuda.amp.autocast_mode import autocast
@ -419,21 +419,12 @@ class Vits(BaseTTS):
hasattr(self.speaker_manager.speaker_encoder, "audio_config") hasattr(self.speaker_manager.speaker_encoder, "audio_config")
and self.config.audio["sample_rate"] != self.speaker_manager.speaker_encoder.audio_config["sample_rate"] and self.config.audio["sample_rate"] != self.speaker_manager.speaker_encoder.audio_config["sample_rate"]
): ):
# TODO: change this with torchaudio Resample self.audio_transform = torchaudio.transforms.Resample(
raise RuntimeError(
" [!] To use the speaker consistency loss (SCL) you need to have matching sample rates between the TTS model ({}) and the speaker encoder ({})!".format(
self.config.audio["sample_rate"],
self.speaker_manager.speaker_encoder.audio_config["sample_rate"],
)
)
# pylint: disable=W0101,W0105
""" self.audio_transform = torchaudio.transforms.Resample(
orig_freq=self.audio_config["sample_rate"], orig_freq=self.audio_config["sample_rate"],
new_freq=self.speaker_manager.speaker_encoder.audio_config["sample_rate"], new_freq=self.speaker_manager.speaker_encoder.audio_config["sample_rate"],
) )
else: else:
self.audio_transform = None self.audio_transform = None
"""
def _init_speaker_embedding(self): def _init_speaker_embedding(self):
# pylint: disable=attribute-defined-outside-init # pylint: disable=attribute-defined-outside-init
@ -458,6 +449,7 @@ class Vits(BaseTTS):
self.language_manager = LanguageManager(language_ids_file_path=config.language_ids_file) self.language_manager = LanguageManager(language_ids_file_path=config.language_ids_file)
if self.args.use_language_embedding and self.language_manager: if self.args.use_language_embedding and self.language_manager:
print(" > initialization of language-embedding layers.")
self.num_languages = self.language_manager.num_languages self.num_languages = self.language_manager.num_languages
self.embedded_language_dim = self.args.embedded_language_dim self.embedded_language_dim = self.args.embedded_language_dim
self.emb_l = nn.Embedding(self.num_languages, self.embedded_language_dim) self.emb_l = nn.Embedding(self.num_languages, self.embedded_language_dim)
@ -643,8 +635,8 @@ class Vits(BaseTTS):
# resample audio to speaker encoder sample_rate # resample audio to speaker encoder sample_rate
# pylint: disable=W0105 # pylint: disable=W0105
"""if self.audio_transform is not None: if self.audio_transform is not None:
wavs_batch = self.audio_transform(wavs_batch)""" wavs_batch = self.audio_transform(wavs_batch)
pred_embs = self.speaker_manager.speaker_encoder.forward(wavs_batch, l2_norm=True) pred_embs = self.speaker_manager.speaker_encoder.forward(wavs_batch, l2_norm=True)