def setup(self):
# load configs
self.TTS_CONFIG = load_config(self.TTS_CONFIG)
self.VOCODER_CONFIG = load_config(self.VOCODER_CONFIG)
# load the audio processor
self.ap = AudioProcessor(**self.TTS_CONFIG.audio)
# load the model
num_chars = len(phonemes) if self.TTS_CONFIG.use_phonemes else len(
symbols)
self.model = setup_model(num_chars, len(self.speakers),
self.TTS_CONFIG)
self.model, _ = load_checkpoint(self.model,
self.TTS_MODEL,
use_cuda=self.use_cuda)
self.model.eval()
# LOAD VOCODER MODEL
self.vocoder_model = setup_generator(self.VOCODER_CONFIG)
self.vocoder_model, _ = load_vocoder_checkpoint(
self.vocoder_model, checkpoint_path=self.VOCODER_MODEL)
self.vocoder_model.remove_weight_norm()
self.vocoder_model.inference_padding = 0
self.ap_vocoder = AudioProcessor(**self.VOCODER_CONFIG['audio'])
if self.use_cuda:
self.vocoder_model.cuda()
self.vocoder_model.eval()
def load_vocoder(self, model_file, model_config, use_cuda):
self.vocoder_config = load_config(model_config)
self.vocoder_ap = AudioProcessor(**self.vocoder_config['audio'])
self.vocoder_model = setup_generator(self.vocoder_config)
self.vocoder_model.load_checkpoint(self.vocoder_config, model_file, eval=True)
if use_cuda:
self.vocoder_model.cuda()
def load_tts(self, model_path, model_file, model_config, use_cuda):
tts_config = os.path.join(model_path, model_config)
self.model_file = os.path.join(model_path, model_file)
print(" > Loading TTS model ...")
print(" | > model config: ", tts_config)
print(" | > model file: ", model_file)
self.tts_config = load_config(tts_config)
self.use_phonemes = self.tts_config.use_phonemes
self.ap = AudioProcessor(**self.tts_config.audio)
if self.use_phonemes:
self.input_size = len(phonemes)
else:
self.input_size = len(symbols)
# load speakers
if self.config.tts_speakers is not None:
self.tts_speakers = load_speaker_mapping(
os.path.join(model_path, self.config.tts_speakers))
num_speakers = len(self.tts_speakers)
else:
num_speakers = 0
self.tts_model = setup_model(self.input_size,
num_speakers=num_speakers,
c=self.tts_config)
# load model state
cp = torch.load(self.model_file)
# load the model
self.tts_model.load_state_dict(cp['model'])
if use_cuda:
self.tts_model.cuda()
self.tts_model.eval()
self.tts_model.decoder.max_decoder_steps = 3000
if 'r' in cp and self.tts_config.model in ["Tacotron", "TacotronGST"]:
self.tts_model.decoder.set_r(cp['r'])
def load_tts(self, tts_checkpoint, tts_config, use_cuda):
print(" > Loading TTS model ...")
print(" | > model config: ", tts_config)
print(" | > checkpoint file: ", tts_checkpoint)
self.tts_config = load_config(tts_config)
self.use_phonemes = self.tts_config.use_phonemes
self.ap = AudioProcessor(**self.tts_config.audio)
if self.use_phonemes:
self.input_size = len(phonemes)
else:
self.input_size = len(symbols)
# TODO: fix this for multi-speaker model - load speakers
if self.config.tts_speakers is not None:
self.tts_speakers = load_speaker_mapping(self.config.tts_speakers)
num_speakers = len(self.tts_speakers)
else:
num_speakers = 0
self.tts_model = setup_model(self.input_size,
num_speakers=num_speakers,
c=self.tts_config)
# load model state
cp = torch.load(tts_checkpoint, map_location=torch.device('cpu'))
# load the model
self.tts_model.load_state_dict(cp['model'])
if use_cuda:
self.tts_model.cuda()
self.tts_model.eval()
self.tts_model.decoder.max_decoder_steps = 3000
if 'r' in cp:
self.tts_model.decoder.set_r(cp['r'])
def say(self, text, output):
# load the model
model = Tacotron(self.CONFIG.embedding_size, self.CONFIG.num_freq,
self.CONFIG.num_mels, self.CONFIG.r)
# load the audio processor
ap = AudioProcessor(self.CONFIG.sample_rate, self.CONFIG.num_mels,
self.CONFIG.min_level_db,
self.CONFIG.frame_shift_ms,
self.CONFIG.frame_length_ms,
self.CONFIG.ref_level_db, self.CONFIG.num_freq,
self.CONFIG.power, self.CONFIG.preemphasis, 60)
# load model state
if self.use_cuda:
cp = torch.load(self.MODEL_PATH)
else:
cp = torch.load(self.MODEL_PATH,
map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if self.use_cuda:
model.cuda()
model.eval()
model.decoder.max_decoder_steps = 400
wavs = self.text2audio(text, model, self.CONFIG, self.use_cuda, ap)
audio = np.concatenate(wavs)
ap.save_wav(audio, output)
return
def __init__(self,
csv_file,
root_dir,
outputs_per_step,
sample_rate,
text_cleaner,
num_mels,
min_level_db,
frame_shift_ms,
frame_length_ms,
preemphasis,
ref_level_db,
num_freq,
power,
min_seq_len=0):
with open(csv_file, "r") as f:
self.frames = [line.split('\t') for line in f]
self.root_dir = root_dir
self.outputs_per_step = outputs_per_step
self.sample_rate = sample_rate
self.cleaners = text_cleaner
self.min_seq_len = min_seq_len
self.ap = AudioProcessor(sample_rate, num_mels, min_level_db,
frame_shift_ms, frame_length_ms, preemphasis,
ref_level_db, num_freq, power)
print(" > Reading TWEB from - {}".format(root_dir))
print(" | > Number of instances : {}".format(len(self.frames)))
self._sort_frames()
def test_speaker_embedding():
# load config
config = load_config(encoder_config_path)
config.audio.resample = True
# create a dummy speaker encoder
model = setup_speaker_encoder_model(config)
save_checkpoint(model, None, None, get_tests_input_path(), 0)
# load audio processor and speaker encoder
ap = AudioProcessor(**config.audio)
manager = SpeakerManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)
# load a sample audio and compute embedding
waveform = ap.load_wav(sample_wav_path)
mel = ap.melspectrogram(waveform)
d_vector = manager.compute_d_vector(mel)
assert d_vector.shape[1] == 256
# compute d_vector directly from an input file
d_vector = manager.compute_d_vector_from_clip(sample_wav_path)
d_vector2 = manager.compute_d_vector_from_clip(sample_wav_path)
d_vector = torch.FloatTensor(d_vector)
d_vector2 = torch.FloatTensor(d_vector2)
assert d_vector.shape[0] == 256
assert (d_vector - d_vector2).sum() == 0.0
# compute d_vector from a list of wav files.
d_vector3 = manager.compute_d_vector_from_clip([sample_wav_path, sample_wav_path2])
d_vector3 = torch.FloatTensor(d_vector3)
assert d_vector3.shape[0] == 256
assert (d_vector - d_vector3).sum() != 0.0
# remove dummy model
os.remove(encoder_model_path)
def _load_vocoder(self, model_file: str, model_config: str,
use_cuda: bool) -> None:
"""Load the vocoder model.
Args:
model_file (str): path to the model checkpoint.
model_config (str): path to the model config file.
use_cuda (bool): enable/disable CUDA use.
"""
self.vocoder_config = load_config(model_config)
# Patch stats_path
stats_path = self.vocoder_config["audio"].get("stats_path", "")
if stats_path and (not os.path.isfile(stats_path)):
stats_path = os.path.join(os.path.dirname(model_file),
os.path.split(stats_path)[1])
self.vocoder_config["audio"]["stats_path"] = stats_path
self.vocoder_ap = AudioProcessor(verbose=False,
**self.vocoder_config.audio)
self.vocoder_model = setup_vocoder_model(self.vocoder_config)
self.vocoder_model.load_checkpoint(self.vocoder_config,
model_file,
eval=True)
if use_cuda:
self.vocoder_model.cuda()
def load_model(self, model_path, model_name, model_config, use_cuda):
model_config = os.path.join(model_path, model_config)
self.model_file = os.path.join(model_path, model_name)
print(" > Loading model ...")
print(" | > model config: ", model_config)
print(" | > model file: ", self.model_file)
config = load_config(model_config)
self.config = config
self.use_cuda = use_cuda
self.model = Tacotron(config.embedding_size, config.num_freq,
config.num_mels, config.r)
self.ap = AudioProcessor(config.sample_rate,
config.num_mels,
config.min_level_db,
config.frame_shift_ms,
config.frame_length_ms,
config.preemphasis,
config.ref_level_db,
config.num_freq,
config.power,
griffin_lim_iters=60)
# load model state
if use_cuda:
cp = torch.load(self.model_file)
else:
cp = torch.load(self.model_file,
map_location=lambda storage, loc: storage)
# load the model
self.model.load_state_dict(cp['model'])
if use_cuda:
self.model.cuda()
self.model.eval()
def load(self, t_checkpoint_path, v_checkpoint_path,
t_config_path=None, v_config_path=None, model_name='tacotron'):
if t_checkpoint_path.endswith('.pt'):
self.model_name = 'nvidia'
print('Constructing model: %s' % self.model_name)
# set-up params
hparams = create_hparams()
# load model from checkpoint
self.model = Tacotron2(hparams)
self.model.load_state_dict(torch.load(t_checkpoint_path,
map_location='cpu')['state_dict'])
_ = self.model.eval()
else: # elif t_checkpoint_path.endswith('.pth.tar'):
self.model_name = 'coqui'
print('Constructing model: %s' % self.model_name)
# load tts config and audio processor
self.tts_config = load_config(t_config_path)
self.tts_model = setup_tts_model(config=self.tts_config)
self.tts_model.load_checkpoint(self.tts_config,
t_checkpoint_path, eval=True)
self.ap = AudioProcessor(verbose=False, **self.tts_config.audio)
# load vocoder config and audio processor
vocoder_config = load_config(v_config_path)
self.vocoder_ap = AudioProcessor(verbose=False, **vocoder_config.audio)
# Load neurips MelGAN for mel2audio synthesis
self.vocoder = torch.hub.load('descriptinc/melgan-neurips', 'load_melgan')
melgan_ckpt = torch.load(v_checkpoint_path, map_location='cpu')
self.vocoder.mel2wav.load_state_dict(melgan_ckpt)
def init_speaker_encoder(self, model_path: str, config_path: str) -> None:
self.speaker_encoder_config = load_config(config_path)
self.speaker_encoder = setup_model(self.speaker_encoder_config)
self.speaker_encoder.load_checkpoint(config_path, model_path, True)
self.speaker_encoder_ap = AudioProcessor(**self.speaker_encoder_config.audio)
# normalize the input audio level and trim silences
self.speaker_encoder_ap.do_sound_norm = True
self.speaker_encoder_ap.do_trim_silence = True
def init_speaker_encoder(self, model_path: str, config_path: str) -> None:
"""Initialize a speaker encoder model.
Args:
model_path (str): Model file path.
config_path (str): Model config file path.
"""
self.speaker_encoder_config = load_config(config_path)
self.speaker_encoder = setup_speaker_encoder_model(self.speaker_encoder_config)
self.speaker_encoder.load_checkpoint(config_path, model_path, eval=True, use_cuda=self.use_cuda)
self.speaker_encoder_ap = AudioProcessor(**self.speaker_encoder_config.audio)
def setup():
use_cuda = True
# model paths
TTS_MODEL = "tts_model.pth.tar"
TTS_CONFIG = "config.json"
VOCODER_MODEL = "vocoder_model.pth.tar"
VOCODER_CONFIG = "config_vocoder.json"
# Load configs
TTS_CONFIG = load_config(TTS_CONFIG)
VOCODER_CONFIG = load_config(VOCODER_CONFIG)
ap = AudioProcessor(**TTS_CONFIG.audio)
# LOAD TTS MODEL
# multi speaker
speaker_id = None
speakers = []
# load the model
num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)
model = setup_model(num_chars, len(speakers), TTS_CONFIG)
# load model state
cp = torch.load(TTS_MODEL, map_location=torch.device('cpu'))
# load the model
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
model.eval()
# set model stepsize
if 'r' in cp:
model.decoder.set_r(cp['r'])
from TTS.vocoder.utils.generic_utils import setup_generator
# LOAD VOCODER MODEL
vocoder_model = setup_generator(VOCODER_CONFIG)
vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location="cpu")["model"])
vocoder_model.remove_weight_norm()
vocoder_model.inference_padding = 0
ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio'])
if use_cuda:
vocoder_model.cuda()
vocoder_model.eval()
return model, vocoder_model, speaker_id, TTS_CONFIG, use_cuda, ap
def __init__(self, csv_file, root_dir, outputs_per_step, sample_rate,
text_cleaner, num_mels, min_level_db, frame_shift_ms,
frame_length_ms, preemphasis, ref_level_db, num_freq, power):
self.frames = pd.read_csv(csv_file, sep='|', header=None)
self.root_dir = root_dir
self.outputs_per_step = outputs_per_step
self.sample_rate = sample_rate
self.cleaners = text_cleaner
self.ap = AudioProcessor(sample_rate, num_mels, min_level_db,
frame_shift_ms, frame_length_ms, preemphasis,
ref_level_db, num_freq, power)
print(" > Reading LJSpeech from - {}".format(root_dir))
print(" | > Number of instances : {}".format(len(self.frames)))
def load_tts_model():
MODEL_PATH = dirpath + '/tts_model/best_model.pth.tar'
CONFIG_PATH = dirpath + '/tts_model/config.json'
CONFIG = load_config(CONFIG_PATH)
use_cuda = False
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars, CONFIG.embedding_size, CONFIG.audio['num_freq'], CONFIG.audio['num_mels'], CONFIG.r, attn_windowing=False)
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars, CONFIG.embedding_size, CONFIG.audio['num_freq'], CONFIG.audio['num_mels'], CONFIG.r, attn_windowing=False)
# load the audio processor
# CONFIG.audio["power"] = 1.3
CONFIG.audio["preemphasis"] = 0.97
ap = AudioProcessor(**CONFIG.audio)
# load model state
if use_cuda:
cp = torch.load(MODEL_PATH)
else:
cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
#model.eval()
model.decoder.max_decoder_steps = 1000
return model, ap, MODEL_PATH, CONFIG, use_cuda
def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use
alignments = outputs["alignments"]
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_input = batch["mel_input"]
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
# model runs reverse flow to predict spectrograms
pred_outputs = self.inference(
text_input[:1],
aux_input={
"x_lengths": text_lengths[:1],
"d_vectors": d_vectors,
"speaker_ids": speaker_ids
},
)
model_outputs = pred_outputs["model_outputs"]
pred_spec = model_outputs[0].data.cpu().numpy()
gt_spec = mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
"alignment": plot_alignment(align_img, output_fig=False),
}
# Sample audio
train_audio = ap.inv_melspectrogram(pred_spec.T)
return figures, {"audio": train_audio}
def load_tacotron2(use_cuda):
"""
Loads the Tacotron2 model
Parameters
----------
use_cuda : bool
whether to use the gpu
Returns
-------
model, audio processor, model config
"""
TTS_MODEL = model_path / 'model.pth.tar'
TTS_CONFIG = model_path / 'config.json'
TTS_CONFIG = load_config(TTS_CONFIG)
TTS_CONFIG.audio['stats_path'] = str(model_path / 'scale_stats.npy')
ap = AudioProcessor(**TTS_CONFIG.audio)
num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)
model = setup_model(num_chars, 0, TTS_CONFIG)
cp = torch.load(TTS_MODEL, map_location=torch.device('cpu'))
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
if 'r' in cp:
model.decoder.set_r(cp['r'])
model.eval()
return model, ap, TTS_CONFIG
def load_vocoder(use_cuda):
"""
Loads the Vocoder model
Parameters
----------
use_cuda : bool
whether to use the gpu
Returns
-------
model, audio processor, model config
"""
VOCODER_MODEL = model_path / 'vocoder_model.pth.tar'
VOCODER_CONFIG = model_path / 'vocoder_config.json'
VOCODER_CONFIG = load_config(VOCODER_CONFIG)
VOCODER_CONFIG.audio['stats_path'] = str(model_path /
'vocoder_scale_stats.npy')
ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio'])
vocoder_model = setup_generator(VOCODER_CONFIG)
cp = torch.load(VOCODER_MODEL, map_location=torch.device('cpu'))
vocoder_model.load_state_dict(cp['model'])
vocoder_model.remove_weight_norm()
vocoder_model.inference_padding = 0
if use_cuda:
vocoder_model.cuda()
vocoder_model.eval()
return vocoder_model, ap_vocoder, VOCODER_CONFIG
def main(args): # pylint: disable=redefined-outer-name
# pylint: disable=global-variable-undefined
global meta_data_train
global meta_data_eval
ap = AudioProcessor(**c.audio)
model = setup_model(c)
optimizer = RAdam(model.parameters(), lr=c.lr)
# pylint: disable=redefined-outer-name
meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=False)
data_loader, num_speakers = setup_loader(ap, is_val=False, verbose=True)
if c.loss == "ge2e":
criterion = GE2ELoss(loss_method="softmax")
elif c.loss == "angleproto":
criterion = AngleProtoLoss()
elif c.loss == "softmaxproto":
criterion = SoftmaxAngleProtoLoss(c.model["proj_dim"], num_speakers)
else:
raise Exception("The %s not is a loss supported" % c.loss)
if args.restore_path:
checkpoint = torch.load(args.restore_path)
try:
model.load_state_dict(checkpoint["model"])
if "criterion" in checkpoint:
criterion.load_state_dict(checkpoint["criterion"])
except (KeyError, RuntimeError):
print(" > Partial model initialization.")
model_dict = model.state_dict()
model_dict = set_init_dict(model_dict, checkpoint["model"], c)
model.load_state_dict(model_dict)
del model_dict
for group in optimizer.param_groups:
group["lr"] = c.lr
print(" > Model restored from step %d" % checkpoint["step"], flush=True)
args.restore_step = checkpoint["step"]
else:
args.restore_step = 0
if c.lr_decay:
scheduler = NoamLR(optimizer, warmup_steps=c.warmup_steps, last_epoch=args.restore_step - 1)
else:
scheduler = None
num_params = count_parameters(model)
print("\n > Model has {} parameters".format(num_params), flush=True)
if use_cuda:
model = model.cuda()
criterion.cuda()
global_step = args.restore_step
_, global_step = train(model, optimizer, scheduler, criterion, data_loader, global_step)
def load_model(self, MODEL_PATH, sentence, CONFIG, use_cuda, OUT_FILE):
# load the model
num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)
model = Tacotron(num_chars,
CONFIG.embedding_size,
CONFIG.audio['num_freq'],
CONFIG.audio['num_mels'],
CONFIG.r,
attn_windowing=False)
# load the audio processor
# CONFIG.audio["power"] = 1.3
CONFIG.audio["preemphasis"] = 0.97
ap = AudioProcessor(**CONFIG.audio)
# load model state
if use_cuda:
cp = torch.load(MODEL_PATH)
else:
cp = torch.load(MODEL_PATH,
map_location=lambda storage, loc: storage)
# load the model
model.load_state_dict(cp['model'])
if use_cuda:
model.cuda()
model.eval()
model.decoder.max_decoder_steps = 1000
align, spec, stop_tokens, wav_norm = self.tts(model, sentence, CONFIG,
use_cuda, ap, OUT_FILE)
return wav_norm
def main(args): # pylint: disable=redefined-outer-name
# pylint: disable=global-variable-undefined
global meta_data_train
global meta_data_eval
ap = AudioProcessor(**c.audio)
model = SpeakerEncoder(
input_dim=c.model["input_dim"],
proj_dim=c.model["proj_dim"],
lstm_dim=c.model["lstm_dim"],
num_lstm_layers=c.model["num_lstm_layers"],
)
optimizer = RAdam(model.parameters(), lr=c.lr)
if c.loss == "ge2e":
criterion = GE2ELoss(loss_method="softmax")
elif c.loss == "angleproto":
criterion = AngleProtoLoss()
else:
raise Exception("The %s not is a loss supported" % c.loss)
if args.restore_path:
checkpoint = torch.load(args.restore_path)
try:
# TODO: fix optimizer init, model.cuda() needs to be called before
# optimizer restore
# optimizer.load_state_dict(checkpoint['optimizer'])
if c.reinit_layers:
raise RuntimeError
model.load_state_dict(checkpoint["model"])
except KeyError:
print(" > Partial model initialization.")
model_dict = model.state_dict()
model_dict = set_init_dict(model_dict, checkpoint, c)
model.load_state_dict(model_dict)
del model_dict
for group in optimizer.param_groups:
group["lr"] = c.lr
print(" > Model restored from step %d" % checkpoint["step"], flush=True)
args.restore_step = checkpoint["step"]
else:
args.restore_step = 0
if use_cuda:
model = model.cuda()
criterion.cuda()
if c.lr_decay:
scheduler = NoamLR(optimizer, warmup_steps=c.warmup_steps, last_epoch=args.restore_step - 1)
else:
scheduler = None
num_params = count_parameters(model)
print("\n > Model has {} parameters".format(num_params), flush=True)
# pylint: disable=redefined-outer-name
meta_data_train, meta_data_eval = load_meta_data(c.datasets)
global_step = args.restore_step
_, global_step = train(model, criterion, optimizer, scheduler, ap, global_step)
def main():
"""Run `tts` model training directly by a `config.json` file."""
# init trainer args
train_args = TrainVocoderArgs()
parser = train_args.init_argparse(arg_prefix="")
# override trainer args from comman-line args
args, config_overrides = parser.parse_known_args()
train_args.parse_args(args)
# load config.json and register
if args.config_path or args.continue_path:
if args.config_path:
# init from a file
config = load_config(args.config_path)
if len(config_overrides) > 0:
config.parse_known_args(config_overrides, relaxed_parser=True)
elif args.continue_path:
# continue from a prev experiment
config = load_config(
os.path.join(args.continue_path, "config.json"))
if len(config_overrides) > 0:
config.parse_known_args(config_overrides, relaxed_parser=True)
else:
# init from console args
from TTS.config.shared_configs import BaseTrainingConfig # pylint: disable=import-outside-toplevel
config_base = BaseTrainingConfig()
config_base.parse_known_args(config_overrides)
config = register_config(config_base.model)()
# load training samples
if "feature_path" in config and config.feature_path:
# load pre-computed features
print(f" > Loading features from: {config.feature_path}")
eval_samples, train_samples = load_wav_feat_data(
config.data_path, config.feature_path, config.eval_split_size)
else:
# load data raw wav files
eval_samples, train_samples = load_wav_data(config.data_path,
config.eval_split_size)
# setup audio processor
ap = AudioProcessor(**config.audio)
# init the model from config
model = setup_model(config)
# init the trainer and 🚀
trainer = Trainer(
train_args,
config,
config.output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
parse_command_line_args=False,
)
trainer.fit()
def init_encoder(self,
model_path: str,
config_path: str,
use_cuda=False) -> None:
"""Initialize a speaker encoder model.
Args:
model_path (str): Model file path.
config_path (str): Model config file path.
use_cuda (bool, optional): Use CUDA. Defaults to False.
"""
self.use_cuda = use_cuda
self.encoder_config = load_config(config_path)
self.encoder = setup_encoder_model(self.encoder_config)
self.encoder_criterion = self.encoder.load_checkpoint(
self.encoder_config, model_path, eval=True, use_cuda=use_cuda)
self.encoder_ap = AudioProcessor(**self.encoder_config.audio)
def init_from_config(config: Coqpit):
"""Initialize model from config."""
from TTS.utils.audio import AudioProcessor
ap = AudioProcessor.init_from_config(config)
tokenizer = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config)
return BaseTacotron(config, ap, tokenizer, speaker_manager)
class Synthesizer(object):
def load_model(self, model_path, model_name, model_config, use_cuda):
model_config = os.path.join(model_path, model_config)
self.model_file = os.path.join(model_path, model_name)
print(" > Loading model ...")
print(" | > model config: ", model_config)
print(" | > model file: ", self.model_file)
config = load_config(model_config)
self.config = config
self.use_cuda = use_cuda
self.model = Tacotron(config.embedding_size, config.num_freq, config.num_mels, config.r)
self.ap = AudioProcessor(config.sample_rate, config.num_mels, config.min_level_db,
config.frame_shift_ms, config.frame_length_ms, config.preemphasis,
config.ref_level_db, config.num_freq, config.power, griffin_lim_iters=60)
# load model state
if use_cuda:
cp = torch.load(self.model_file)
else:
cp = torch.load(self.model_file, map_location=lambda storage, loc: storage)
# load the model
self.model.load_state_dict(cp['model'])
if use_cuda:
self.model.cuda()
self.model.eval()
def save_wav(self, wav, path):
wav *= 32767 / max(1e-8, np.max(np.abs(wav)))
# sf.write(path, wav.astype(np.int32), self.config.sample_rate, format='wav')
# wav = librosa.util.normalize(wav.astype(np.float), norm=np.inf, axis=None)
# wav = wav / wav.max()
# sf.write(path, wav.astype('float'), self.config.sample_rate, format='ogg')
scipy.io.wavfile.write(path, self.config.sample_rate, wav.astype(np.int16))
# librosa.output.write_wav(path, wav.astype(np.int16), self.config.sample_rate, norm=True)
def tts(self, text):
text_cleaner = [self.config.text_cleaner]
wavs = []
for sen in text.split('.'):
if len(sen) < 3:
continue
sen = sen.strip()
sen +='.'
print(sen)
sen = sen.strip()
seq = np.array(text_to_sequence(text, text_cleaner))
chars_var = torch.from_numpy(seq).unsqueeze(0)
if self.use_cuda:
chars_var = chars_var.cuda()
mel_out, linear_out, alignments, stop_tokens = self.model.forward(chars_var)
linear_out = linear_out[0].data.cpu().numpy()
wav = self.ap.inv_spectrogram(linear_out.T)
# wav = wav[:self.ap.find_endpoint(wav)]
out = io.BytesIO()
wavs.append(wav)
wavs.append(np.zeros(10000))
self.save_wav(wav, out)
return out
def _load_vocoder(self, model_file: str, model_config: str,
use_cuda: bool) -> None:
"""Load the vocoder model.
Args:
model_file (str): path to the model checkpoint.
model_config (str): path to the model config file.
use_cuda (bool): enable/disable CUDA use.
"""
self.vocoder_config = load_config(model_config)
self.vocoder_ap = AudioProcessor(verbose=False,
**self.vocoder_config["audio"])
self.vocoder_model = setup_generator(self.vocoder_config)
self.vocoder_model.load_checkpoint(self.vocoder_config,
model_file,
eval=True)
if use_cuda:
self.vocoder_model.cuda()
def main(args): # pylint: disable=redefined-outer-name
# pylint: disable=global-variable-undefined
global meta_data_train
global meta_data_eval
global train_classes
ap = AudioProcessor(**c.audio)
model = setup_encoder_model(c)
optimizer = get_optimizer(c.optimizer, c.optimizer_params, c.lr, model)
# pylint: disable=redefined-outer-name
meta_data_train, meta_data_eval = load_tts_samples(c.datasets,
eval_split=True)
train_data_loader, train_classes, map_classid_to_classname = setup_loader(
ap, is_val=False, verbose=True)
if c.run_eval:
eval_data_loader, _, _ = setup_loader(ap, is_val=True, verbose=True)
else:
eval_data_loader = None
num_classes = len(train_classes)
criterion = model.get_criterion(c, num_classes)
if c.loss == "softmaxproto" and c.model != "speaker_encoder":
c.map_classid_to_classname = map_classid_to_classname
copy_model_files(c, OUT_PATH)
if args.restore_path:
criterion, args.restore_step = model.load_checkpoint(
c,
args.restore_path,
eval=False,
use_cuda=use_cuda,
criterion=criterion)
print(" > Model restored from step %d" % args.restore_step, flush=True)
else:
args.restore_step = 0
if c.lr_decay:
scheduler = NoamLR(optimizer,
warmup_steps=c.warmup_steps,
last_epoch=args.restore_step - 1)
else:
scheduler = None
num_params = count_parameters(model)
print("\n > Model has {} parameters".format(num_params), flush=True)
if use_cuda:
model = model.cuda()
criterion.cuda()
global_step = args.restore_step
_, global_step = train(model, optimizer, scheduler, criterion,
train_data_loader, eval_data_loader, global_step)
def __init__(self, use_cuda=False, verbose=False):
self.use_cuda = use_cuda
self.verbose = verbose
# load configs
self.TTS_CONFIG = load_config(TTS_CONFIG)
self.VOCODER_CONFIG = load_config(VOCODER_CONFIG)
# load the audio processor
self.ap = AudioProcessor(**self.TTS_CONFIG.audio)
# LOAD TTS MODEL
self.speaker_id = None
speakers = []
# load the model
num_chars = len(phonemes) if self.TTS_CONFIG.use_phonemes else len(symbols)
self.model = setup_model(num_chars, len(speakers), self.TTS_CONFIG)
# load model state
cp = torch.load(TTS_MODEL, map_location=torch.device('cpu'))
# load the model
self.model.load_state_dict(cp['model'])
if self.use_cuda:
self.model.cuda()
self.model.eval()
# set model stepsize
if 'r' in cp:
self.model.decoder.set_r(cp['r'])
# LOAD VOCODER MODEL
self.vocoder_model = setup_generator(self.VOCODER_CONFIG)
self.vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location="cpu")["model"])
self.vocoder_model.remove_weight_norm()
self.vocoder_model.inference_padding = 0
ap_vocoder = AudioProcessor(**self.VOCODER_CONFIG['audio'])
if self.use_cuda:
self.vocoder_model.cuda()
self.vocoder_model.eval()
def _load_vocoder(self, model_file: str, model_config: str, use_cuda: bool) -> None:
"""Load the vocoder model.
1. Load the vocoder config.
2. Init the AudioProcessor for the vocoder.
3. Init the vocoder model from the config.
4. Move the model to the GPU if CUDA is enabled.
Args:
model_file (str): path to the model checkpoint.
model_config (str): path to the model config file.
use_cuda (bool): enable/disable CUDA use.
"""
self.vocoder_config = load_config(model_config)
self.vocoder_ap = AudioProcessor(verbose=False, **self.vocoder_config.audio)
self.vocoder_model = setup_vocoder_model(self.vocoder_config)
self.vocoder_model.load_checkpoint(self.vocoder_config, model_file, eval=True)
if use_cuda:
self.vocoder_model.cuda()
def _load_tts(self, tts_checkpoint: str, tts_config_path: str,
use_cuda: bool) -> None:
"""Load the TTS model.
Args:
tts_checkpoint (str): path to the model checkpoint.
tts_config_path (str): path to the model config file.
use_cuda (bool): enable/disable CUDA use.
"""
# pylint: disable=global-statement
global symbols, phonemes
self.tts_config = load_config(tts_config_path)
self.use_phonemes = self.tts_config.use_phonemes
self.ap = AudioProcessor(verbose=False, **self.tts_config.audio)
if "characters" in self.tts_config.keys():
symbols, phonemes = make_symbols(**self.tts_config.characters)
if self.use_phonemes:
self.input_size = len(phonemes)
else:
self.input_size = len(symbols)
if self.tts_config.use_speaker_embedding is True:
self.tts_speakers_file = (
self.tts_speakers_file if self.tts_speakers_file else
self.tts_config["external_speaker_embedding_file"])
self._load_speakers(self.tts_speakers_file)
self.tts_model = setup_model(
self.input_size,
num_speakers=self.num_speakers,
c=self.tts_config,
speaker_embedding_dim=self.speaker_embedding_dim,
)
self.tts_model.load_checkpoint(self.tts_config,
tts_checkpoint,
eval=True)
if use_cuda:
self.tts_model.cuda()
