def setup_model(config):
print(" > Using model: {}".format(config.model))
# fetch the right model implementation.
if "base_model" in config and config["base_model"] is not None:
MyModel = find_module("TTS.tts.models", config.base_model.lower())
else:
MyModel = find_module("TTS.tts.models", config.model.lower())
# define set of characters used by the model
if config.characters is not None:
# set characters from config
if hasattr(MyModel, "make_symbols"):
symbols = MyModel.make_symbols(config)
else:
symbols, phonemes = make_symbols(**config.characters)
else:
from TTS.tts.utils.text.symbols import ( # pylint: disable=import-outside-toplevel
phonemes, symbols,
)
if config.use_phonemes:
symbols = phonemes # noqa: F811
# use default characters and assign them to config
config.characters = parse_symbols()
# consider special `blank` character if `add_blank` is set True
num_chars = len(symbols) + getattr(config, "add_blank", False)
config.num_chars = num_chars
# compatibility fix
if "model_params" in config:
config.model_params.num_chars = num_chars
if "model_args" in config:
config.model_args.num_chars = num_chars
model = MyModel(config)
return model
def setup_model(config: "Coqpit",
samples: Union[List[List], List[Dict]] = None) -> "BaseTTS":
print(" > Using model: {}".format(config.model))
# fetch the right model implementation.
if "base_model" in config and config["base_model"] is not None:
MyModel = find_module("TTS.tts.models", config.base_model.lower())
else:
MyModel = find_module("TTS.tts.models", config.model.lower())
model = MyModel.init_from_config(config, samples)
return model
def _search_configs(model_name):
config_class = None
paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.speaker_encoder"]
for path in paths:
try:
config_class = find_module(path, model_name + "_config")
except ModuleNotFoundError:
pass
if config_class is None:
raise ModuleNotFoundError(
f" [!] Config for {model_name} cannot be found.")
return config_class
def register_config(model_name: str) -> Coqpit:
"""Find the right config for the given model name.
Args:
model_name (str): Model name.
Raises:
ModuleNotFoundError: No matching config for the model name.
Returns:
Coqpit: config class.
"""
config_class = None
paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.speaker_encoder"]
for path in paths:
try:
config_class = find_module(path, model_name + "_config")
except ModuleNotFoundError:
pass
if config_class is None:
raise ModuleNotFoundError(
f" [!] Config for {model_name} cannot be found.")
return config_class
def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
print(" > Using model: {}".format(c.model))
MyModel = find_module("TTS.tts.models", c.model.lower())
if c.model.lower() in "tacotron":
model = MyModel(
num_chars=num_chars + getattr(c, "add_blank", False),
num_speakers=num_speakers,
r=c.r,
postnet_output_dim=int(c.audio["fft_size"] / 2 + 1),
decoder_output_dim=c.audio["num_mels"],
use_gst=c.use_gst,
gst=c.gst,
memory_size=c.memory_size,
attn_type=c.attention_type,
attn_win=c.windowing,
attn_norm=c.attention_norm,
prenet_type=c.prenet_type,
prenet_dropout=c.prenet_dropout,
prenet_dropout_at_inference=c.prenet_dropout_at_inference,
forward_attn=c.use_forward_attn,
trans_agent=c.transition_agent,
forward_attn_mask=c.forward_attn_mask,
location_attn=c.location_attn,
attn_K=c.attention_heads,
separate_stopnet=c.separate_stopnet,
bidirectional_decoder=c.bidirectional_decoder,
double_decoder_consistency=c.double_decoder_consistency,
ddc_r=c.ddc_r,
speaker_embedding_dim=speaker_embedding_dim,
)
elif c.model.lower() == "tacotron2":
model = MyModel(
num_chars=num_chars + getattr(c, "add_blank", False),
num_speakers=num_speakers,
r=c.r,
postnet_output_dim=c.audio["num_mels"],
decoder_output_dim=c.audio["num_mels"],
use_gst=c.use_gst,
gst=c.gst,
attn_type=c.attention_type,
attn_win=c.windowing,
attn_norm=c.attention_norm,
prenet_type=c.prenet_type,
prenet_dropout=c.prenet_dropout,
prenet_dropout_at_inference=c.prenet_dropout_at_inference,
forward_attn=c.use_forward_attn,
trans_agent=c.transition_agent,
forward_attn_mask=c.forward_attn_mask,
location_attn=c.location_attn,
attn_K=c.attention_heads,
separate_stopnet=c.separate_stopnet,
bidirectional_decoder=c.bidirectional_decoder,
double_decoder_consistency=c.double_decoder_consistency,
ddc_r=c.ddc_r,
speaker_embedding_dim=speaker_embedding_dim,
)
elif c.model.lower() == "glow_tts":
model = MyModel(
num_chars=num_chars + getattr(c, "add_blank", False),
hidden_channels_enc=c["hidden_channels_encoder"],
hidden_channels_dec=c["hidden_channels_decoder"],
hidden_channels_dp=c["hidden_channels_duration_predictor"],
out_channels=c.audio["num_mels"],
encoder_type=c.encoder_type,
encoder_params=c.encoder_params,
use_encoder_prenet=c["use_encoder_prenet"],
inference_noise_scale=c.inference_noise_scale,
num_flow_blocks_dec=12,
kernel_size_dec=5,
dilation_rate=1,
num_block_layers=4,
dropout_p_dec=0.05,
num_speakers=num_speakers,
c_in_channels=0,
num_splits=4,
num_squeeze=2,
sigmoid_scale=False,
mean_only=True,
speaker_embedding_dim=speaker_embedding_dim,
)
elif c.model.lower() == "speedy_speech":
model = MyModel(
num_chars=num_chars + getattr(c, "add_blank", False),
out_channels=c.audio["num_mels"],
hidden_channels=c["hidden_channels"],
positional_encoding=c["positional_encoding"],
encoder_type=c["encoder_type"],
encoder_params=c["encoder_params"],
decoder_type=c["decoder_type"],
decoder_params=c["decoder_params"],
c_in_channels=0,
)
elif c.model.lower() == "align_tts":
model = MyModel(
num_chars=num_chars + getattr(c, "add_blank", False),
out_channels=c.audio["num_mels"],
hidden_channels=c["hidden_channels"],
hidden_channels_dp=c["hidden_channels_dp"],
encoder_type=c["encoder_type"],
encoder_params=c["encoder_params"],
decoder_type=c["decoder_type"],
decoder_params=c["decoder_params"],
c_in_channels=0,
)
return model