mirror of https://github.com/coqui-ai/TTS.git
.models.json and synthesize.py update for interfacing with model manager
This commit is contained in:
root
parent
435943ba39
commit
3d30dae8f3
|
|
@ -0,0 +1,77 @@
|
|||
{
|
||||
"tts_models":{
|
||||
"en":{
|
||||
"ljspeech":{
|
||||
"glow-tts":{
|
||||
"description": "",
|
||||
"model_file": "1NFsfhH8W8AgcfJ-BsL8CYAwQfZ5k4T-n",
|
||||
"config_file": "1IAROF3yy9qTK43vG_-R67y3Py9yYbD6t",
|
||||
"stats_file": null,
|
||||
"commit": ""
|
||||
},
|
||||
"tacotron2-DCA": {
|
||||
"description": "",
|
||||
"model_file": "1CFoPDQBnhfBFu2Gc0TBSJn8o-TuNKQn7",
|
||||
"config_file": "1lWSscNfKet1zZSJCNirOn7v9bigUZ8C1",
|
||||
"stats_file": "1qevpGRVHPmzfiRBNuugLMX62x1k7B5vK",
|
||||
"commit": ""
|
||||
},
|
||||
"speedy-speech-wn":{
|
||||
"description": "Speedy Speech model with wavenet decoder.",
|
||||
"model_file": "1VXAwiq6N-Viq3rsSXlf43bdoi0jSvMAJ",
|
||||
"config_file": "1KvZilhsNP3EumVggDcD46yd834eO5hR3",
|
||||
"stats_file": "1Ju7apZ5JlgsVECcETL-GEx3DRoNzWfkR",
|
||||
"commit": "77b6145"
|
||||
}
|
||||
}
|
||||
},
|
||||
"es":{
|
||||
"mai":{
|
||||
"tacotron2-DDC":{
|
||||
"model_file": "1jZ4HvYcAXI5ZClke2iGA7qFQQJBXIovw",
|
||||
"config_file": "1s7g4n-B73ChCB48AQ88_DV_8oyLth8r0",
|
||||
"stats_file": "13st0CZ743v6Br5R5Qw_lH1OPQOr3M-Jv",
|
||||
"commit": ""
|
||||
}
|
||||
}
|
||||
},
|
||||
"fr":{
|
||||
"mai":{
|
||||
"tacotron2-DDC":{
|
||||
"model_file": "1qyxrrCyoXUvBG2lqVd0KqAlHj-2nZCgS",
|
||||
"config_file": "1yECKeP2LI7tNv4E8yVNx1yLmCfTCpkqG",
|
||||
"stats_file": "13st0CZ743v6Br5R5Qw_lH1OPQOr3M-Jv",
|
||||
"commit": ""
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"vocoder_models":{
|
||||
"universal":{
|
||||
"libri-tts":{
|
||||
"wavegrad":{
|
||||
"model_file": "1r2g90JaZsfCj9dJkI9ioIU6JCFMPRqi6",
|
||||
"config_file": "1POrrLf5YEpZyjvWyMccj1nGCVc94mR6s",
|
||||
"stats_file": "1Vwbv4t-N1i3jXqI0bgKAhShAEO097sK0",
|
||||
"commit": "ea976b0"
|
||||
},
|
||||
"fullband-melgan":{
|
||||
"model_file": "1Ty5DZdOc0F7OTGj9oJThYbL5iVu_2G0K",
|
||||
"config_file": "1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu",
|
||||
"stats_file": "11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU",
|
||||
"commit": "4132240"
|
||||
}
|
||||
}
|
||||
},
|
||||
"en": {
|
||||
"ljspeech":{
|
||||
"mulitband-melgan":{
|
||||
"model_file": "1Ty5DZdOc0F7OTGj9oJThYbL5iVu_2G0K",
|
||||
"config_file": "1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu",
|
||||
"stats_file": "11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU",
|
||||
"commit": "ea976b0"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -3,50 +3,140 @@
|
|||
|
||||
import argparse
|
||||
import json
|
||||
# pylint: disable=redefined-outer-name, unused-argument
|
||||
import os
|
||||
import sys
|
||||
import string
|
||||
import time
|
||||
from argparse import RawTextHelpFormatter
|
||||
# pylint: disable=redefined-outer-name, unused-argument
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
from TTS.tts.utils.generic_utils import setup_model, is_tacotron
|
||||
import torch
|
||||
from TTS.tts.utils.generic_utils import is_tacotron, setup_model
|
||||
from TTS.tts.utils.synthesis import synthesis
|
||||
from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
|
||||
from TTS.tts.utils.io import load_checkpoint
|
||||
from TTS.utils.audio import AudioProcessor
|
||||
from TTS.utils.io import load_config
|
||||
from TTS.vocoder.utils.generic_utils import setup_generator
|
||||
from TTS.utils.manage import ModelManager
|
||||
from TTS.vocoder.utils.generic_utils import setup_generator, interpolate_vocoder_input
|
||||
|
||||
|
||||
def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None, gst_style=None):
|
||||
def str2bool(v):
|
||||
if isinstance(v, bool):
|
||||
return v
|
||||
if v.lower() in ('yes', 'true', 't', 'y', '1'):
|
||||
return True
|
||||
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
|
||||
return False
|
||||
else:
|
||||
raise argparse.ArgumentTypeError('Boolean value expected.')
|
||||
|
||||
|
||||
def load_tts_model(model_path, config_path, use_cuda, speakers_json=None, speaker_idx=None):
|
||||
global phonemes
|
||||
global symbols
|
||||
|
||||
# load the config
|
||||
model_config = load_config(config_path)
|
||||
|
||||
# load the audio processor
|
||||
ap = AudioProcessor(**model_config.audio)
|
||||
|
||||
# if the vocabulary was passed, replace the default
|
||||
if 'characters' in model_config.keys():
|
||||
symbols, phonemes = make_symbols(**model_config.characters)
|
||||
|
||||
# load speakers
|
||||
speaker_embedding = None
|
||||
speaker_embedding_dim = None
|
||||
num_speakers = 0
|
||||
if speakers_json is not None:
|
||||
speaker_mapping = json.load(open(speakers_json, 'r'))
|
||||
num_speakers = len(speaker_mapping)
|
||||
if model_config.use_external_speaker_embedding_file:
|
||||
if speaker_idx is not None:
|
||||
speaker_embedding = speaker_mapping[speaker_idx]['embedding']
|
||||
else: # if speaker_idx is not specificated use the first sample in speakers.json
|
||||
speaker_embedding = speaker_mapping[list(speaker_mapping.keys())[0]]['embedding']
|
||||
speaker_embedding_dim = len(speaker_embedding)
|
||||
|
||||
# load tts model
|
||||
num_chars = len(phonemes) if model_config.use_phonemes else len(symbols)
|
||||
model = setup_model(num_chars, num_speakers, model_config, speaker_embedding_dim)
|
||||
model.load_checkpoint(model_config, model_path, eval=True)
|
||||
if use_cuda:
|
||||
model.cuda()
|
||||
return model, model_config, ap, speaker_embedding
|
||||
|
||||
|
||||
def load_vocoder_model(model_path, config_path, use_cuda):
|
||||
vocoder_config = load_config(vocoder_config_path)
|
||||
vocoder_ap = AudioProcessor(**vocoder_config['audio'])
|
||||
vocoder_model = setup_generator(vocoder_config)
|
||||
vocoder_model.load_checkpoint(vocoder_config, model_path, eval=True)
|
||||
if use_cuda:
|
||||
vocoder_model.cuda()
|
||||
return vocoder_model, vocoder_config, vocoder_ap
|
||||
|
||||
|
||||
def tts(model,
|
||||
vocoder_model,
|
||||
text,
|
||||
model_config,
|
||||
vocoder_config,
|
||||
use_cuda,
|
||||
ap,
|
||||
vocoder_ap,
|
||||
use_gl,
|
||||
speaker_fileid,
|
||||
speaker_embedding=None,
|
||||
gst_style=None):
|
||||
t_1 = time.time()
|
||||
waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, gst_style, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)
|
||||
waveform, _, _, mel_postnet_spec, _, _ = synthesis(
|
||||
model,
|
||||
text,
|
||||
model_config,
|
||||
use_cuda,
|
||||
ap,
|
||||
speaker_fileid,
|
||||
gst_style,
|
||||
False,
|
||||
model_config.enable_eos_bos_chars,
|
||||
use_gl,
|
||||
speaker_embedding=speaker_embedding)
|
||||
|
||||
# grab spectrogram (thx to the nice guys at mozilla discourse for codesnipplet)
|
||||
# grab spectrogram (thx to the nice guys at mozilla discourse for codesnippet)
|
||||
if args.save_spectogram:
|
||||
spec_file_name = args.text.replace(" ", "_")[0:10]
|
||||
spec_file_name = spec_file_name.translate(
|
||||
str.maketrans('', '', string.punctuation.replace('_', ''))) + '.npy'
|
||||
spec_file_name = os.path.join(args.out_path, spec_file_name)
|
||||
spectrogram = torch.FloatTensor(mel_postnet_spec.T)
|
||||
spectrogram = spectrogram.unsqueeze(0)
|
||||
spectrogram = mel_postnet_spec.T
|
||||
spectrogram = spectrogram[0]
|
||||
np.save(spec_file_name, spectrogram)
|
||||
print(" > Saving raw spectogram to " + spec_file_name)
|
||||
|
||||
if CONFIG.model == "Tacotron" and not use_gl:
|
||||
mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T
|
||||
# convert linear spectrogram to melspectrogram for tacotron
|
||||
if model_config.model == "Tacotron" and not use_gl:
|
||||
mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T)
|
||||
# run vocoder_model
|
||||
if not use_gl:
|
||||
# Use if not computed noise schedule with tune_wavegrad
|
||||
beta = np.linspace(1e-6, 0.01, 50)
|
||||
vocoder_model.compute_noise_level(beta)
|
||||
|
||||
# Use alternative when using output npy file from tune_wavegrad
|
||||
# beta = np.load("output-tune-wavegrad.npy", allow_pickle=True).item()
|
||||
# vocoder_model.compute_noise_level(beta['beta'])
|
||||
|
||||
# denormalize tts output based on tts audio config
|
||||
mel_postnet_spec = ap._denormalize(mel_postnet_spec.T).T
|
||||
device_type = "cuda" if use_cuda else "cpu"
|
||||
waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).to(device_type).unsqueeze(0))
|
||||
# renormalize spectrogram based on vocoder config
|
||||
vocoder_input = vocoder_ap._normalize(mel_postnet_spec.T)
|
||||
# compute scale factor for possible sample rate mismatch
|
||||
scale_factor = [1, vocoder_config['audio']['sample_rate'] / ap.sample_rate]
|
||||
if scale_factor[1] != 1:
|
||||
print(" > interpolating tts model output.")
|
||||
vocoder_input = interpolate_vocoder_input(scale_factor, vocoder_input)
|
||||
else:
|
||||
vocoder_input = torch.tensor(vocoder_input).unsqueeze(0)
|
||||
# run vocoder model
|
||||
# [1, T, C]
|
||||
waveform = vocoder_model.inference(vocoder_input.to(device_type))
|
||||
if use_cuda and not use_gl:
|
||||
waveform = waveform.cpu()
|
||||
if not use_gl:
|
||||
|
|
@ -62,54 +152,115 @@ def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid
|
|||
|
||||
if __name__ == "__main__":
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('text', type=str, help='Text to generate speech.')
|
||||
parser.add_argument('config_path',
|
||||
type=str,
|
||||
help='Path to model config file.')
|
||||
parser = argparse.ArgumentParser(description='''Synthesize speech on command line.\n\n'''
|
||||
|
||||
'''You can either use your trained model or choose a model from the provided list.\n'''
|
||||
|
||||
'''
|
||||
Example runs:
|
||||
|
||||
# list provided models
|
||||
./TTS/bin/synthesize.py --list_models
|
||||
|
||||
# run a model from the list
|
||||
./TTS/bin/synthesize.py --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>" --vocoder_name "<language>/<dataset>/<model_name>" --output_path
|
||||
|
||||
# run your own TTS model (Using Griffin-Lim Vocoder)
|
||||
./TTS/bin/synthesize.py --text "Text for TTS" --model_path path/to/model.pth.tar --config_path path/to/config.json --out_path output/path/speech.wav
|
||||
|
||||
# run your own TTS and Vocoder models
|
||||
./TTS/bin/synthesize.py --text "Text for TTS" --model_path path/to/config.json --config_path path/to/model.pth.tar --out_path output/path/speech.wav
|
||||
--vocoder_path path/to/vocoder.pth.tar --vocoder_config_path path/to/vocoder_config.json
|
||||
|
||||
''',
|
||||
formatter_class=RawTextHelpFormatter)
|
||||
|
||||
parser.add_argument(
|
||||
'model_path',
|
||||
'--list_models',
|
||||
type=str2bool,
|
||||
nargs='?',
|
||||
const=True,
|
||||
default=False,
|
||||
help='list available pre-trained tts and vocoder models.'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--text',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Text to generate speech.'
|
||||
)
|
||||
|
||||
# Args for running pre-trained TTS models.
|
||||
parser.add_argument(
|
||||
'--model_name',
|
||||
type=str,
|
||||
default=None,
|
||||
help=
|
||||
'Name of one of the pre-trained tts models in format <language>/<dataset>/<model_name>'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--vocoder_name',
|
||||
type=str,
|
||||
default=None,
|
||||
help=
|
||||
'Name of one of the pre-trained vocoder models in format <language>/<dataset>/<model_name>'
|
||||
)
|
||||
|
||||
# Args for running custom models
|
||||
parser.add_argument(
|
||||
'--config_path',
|
||||
default=None,
|
||||
type=str,
|
||||
help='Path to model config file.'
|
||||
)
|
||||
parser.add_argument(
|
||||
'--model_path',
|
||||
type=str,
|
||||
default=None,
|
||||
help='Path to model file.',
|
||||
)
|
||||
parser.add_argument(
|
||||
'out_path',
|
||||
'--out_path',
|
||||
type=str,
|
||||
help='Path to save final wav file. Wav file will be names as the text given.',
|
||||
default=Path(__file__).resolve().parent,
|
||||
help='Path to save final wav file. Wav file will be named as the given text.',
|
||||
)
|
||||
parser.add_argument('--use_cuda',
|
||||
type=bool,
|
||||
help='Run model on CUDA.',
|
||||
default=False)
|
||||
parser.add_argument(
|
||||
'--use_cuda',
|
||||
type=bool,
|
||||
help='Run model on CUDA.',
|
||||
default=False
|
||||
)
|
||||
parser.add_argument(
|
||||
'--vocoder_path',
|
||||
type=str,
|
||||
help=
|
||||
'Path to vocoder model file. If it is not defined, model uses GL as vocoder. Please make sure that you installed vocoder library before (WaveRNN).',
|
||||
default="",
|
||||
default=None,
|
||||
)
|
||||
parser.add_argument('--vocoder_config_path',
|
||||
type=str,
|
||||
help='Path to vocoder model config file.',
|
||||
default="")
|
||||
parser.add_argument(
|
||||
'--batched_vocoder',
|
||||
type=bool,
|
||||
help="If True, vocoder model uses faster batch processing.",
|
||||
default=True)
|
||||
parser.add_argument('--speakers_json',
|
||||
type=str,
|
||||
help="JSON file for multi-speaker model.",
|
||||
default="")
|
||||
parser.add_argument(
|
||||
'--speaker_fileid',
|
||||
'--vocoder_config_path',
|
||||
type=str,
|
||||
help="if CONFIG.use_external_speaker_embedding_file is true, name of speaker embedding reference file present in speakers.json, else target speaker_fileid if the model is multi-speaker.",
|
||||
help='Path to vocoder model config file.',
|
||||
default=None)
|
||||
|
||||
# args for multi-speaker synthesis
|
||||
parser.add_argument(
|
||||
'--speakers_json',
|
||||
type=str,
|
||||
help="JSON file for multi-speaker model.",
|
||||
default=None)
|
||||
parser.add_argument(
|
||||
'--speaker_idx',
|
||||
type=str,
|
||||
help="if the tts model is trained with x-vectors, then speaker_idx is a file present in speakers.json else speaker_idx is the speaker id corresponding to a speaker in the speaker embedding layer.",
|
||||
default=None)
|
||||
parser.add_argument(
|
||||
'--gst_style',
|
||||
help="Wav path file for GST stylereference.",
|
||||
default=None)
|
||||
|
||||
# aux args
|
||||
parser.add_argument(
|
||||
'--save_spectogram',
|
||||
type=bool,
|
||||
|
|
@ -118,86 +269,86 @@ if __name__ == "__main__":
|
|||
|
||||
args = parser.parse_args()
|
||||
|
||||
# load the config
|
||||
C = load_config(args.config_path)
|
||||
C.forward_attn_mask = True
|
||||
# load model manager
|
||||
path = Path(__file__).parent / "../../.models.json"
|
||||
manager = ModelManager(path)
|
||||
|
||||
# load the audio processor
|
||||
ap = AudioProcessor(**C.audio)
|
||||
model_path = None
|
||||
vocoder_path = None
|
||||
model = None
|
||||
vocoder_model = None
|
||||
vocoder_config = None
|
||||
vocoder_ap = None
|
||||
|
||||
# if the vocabulary was passed, replace the default
|
||||
if 'characters' in C.keys():
|
||||
symbols, phonemes = make_symbols(**C.characters)
|
||||
# CASE1: list pre-trained TTS models
|
||||
if args.list_models:
|
||||
manager.list_models()
|
||||
sys.exit()
|
||||
|
||||
speaker_embedding = None
|
||||
speaker_embedding_dim = None
|
||||
num_speakers = 0
|
||||
# CASE2: load pre-trained models
|
||||
if args.model_name is not None:
|
||||
model_path, config_path = manager.download_model(args.model_name)
|
||||
|
||||
# load speakers
|
||||
if args.speakers_json != '':
|
||||
speaker_mapping = json.load(open(args.speakers_json, 'r'))
|
||||
num_speakers = len(speaker_mapping)
|
||||
if C.use_external_speaker_embedding_file:
|
||||
if args.speaker_fileid is not None:
|
||||
speaker_embedding = speaker_mapping[args.speaker_fileid]['embedding']
|
||||
else: # if speaker_fileid is not specificated use the first sample in speakers.json
|
||||
speaker_embedding = speaker_mapping[list(speaker_mapping.keys())[0]]['embedding']
|
||||
speaker_embedding_dim = len(speaker_embedding)
|
||||
if args.vocoder_name is not None:
|
||||
vocoder_path, vocoder_config_path = manager.download_model(args.vocoder_name)
|
||||
|
||||
# load the model
|
||||
num_chars = len(phonemes) if C.use_phonemes else len(symbols)
|
||||
model = setup_model(num_chars, num_speakers, C, speaker_embedding_dim)
|
||||
cp = torch.load(args.model_path, map_location=torch.device('cpu'))
|
||||
model.load_state_dict(cp['model'])
|
||||
model.eval()
|
||||
if args.use_cuda:
|
||||
model.cuda()
|
||||
if is_tacotron(C):
|
||||
model.decoder.set_r(cp['r'])
|
||||
# CASE3: load custome models
|
||||
if args.model_path is not None:
|
||||
model_path = args.model_path
|
||||
config_path = args.config_path
|
||||
|
||||
# load vocoder model
|
||||
if args.vocoder_path != "":
|
||||
VC = load_config(args.vocoder_config_path)
|
||||
vocoder_model = setup_generator(VC)
|
||||
vocoder_model.load_state_dict(torch.load(args.vocoder_path, map_location="cpu")["model"])
|
||||
vocoder_model.remove_weight_norm()
|
||||
if args.use_cuda:
|
||||
vocoder_model.cuda()
|
||||
vocoder_model.eval()
|
||||
else:
|
||||
vocoder_model = None
|
||||
VC = None
|
||||
if args.vocoder_path is not None:
|
||||
vocoder_path = args.vocoder_path
|
||||
vocoder_config_path = args.vocoder_config_path
|
||||
|
||||
# synthesize voice
|
||||
use_griffin_lim = args.vocoder_path == ""
|
||||
# RUN THE SYNTHESIS
|
||||
# load models
|
||||
model, model_config, ap, speaker_embedding = load_tts_model(model_path, config_path, args.use_cuda, args.speaker_idx)
|
||||
if vocoder_path is not None:
|
||||
vocoder_model, vocoder_config, vocoder_ap = load_vocoder_model(vocoder_path, vocoder_config_path, use_cuda=args.use_cuda)
|
||||
|
||||
use_griffin_lim = vocoder_path is None
|
||||
print(" > Text: {}".format(args.text))
|
||||
|
||||
if not C.use_external_speaker_embedding_file:
|
||||
if args.speaker_fileid.isdigit():
|
||||
args.speaker_fileid = int(args.speaker_fileid)
|
||||
# handle multi-speaker setting
|
||||
if not model_config.use_external_speaker_embedding_file and args.speaker_idx is not None:
|
||||
if args.speaker_idx.isdigit():
|
||||
args.speaker_idx = int(args.speaker_idx)
|
||||
else:
|
||||
args.speaker_fileid = None
|
||||
args.speaker_idx = None
|
||||
else:
|
||||
args.speaker_fileid = None
|
||||
args.speaker_idx = None
|
||||
|
||||
if args.gst_style is None:
|
||||
if is_tacotron(C):
|
||||
gst_style = C.gst['gst_style_input']
|
||||
if 'gst' in model_config.keys() and model_config.gst['gst_style_input'] is not None:
|
||||
gst_style = model_config.gst['gst_style_input']
|
||||
else:
|
||||
gst_style = None
|
||||
else:
|
||||
# check if gst_style string is a dict, if is dict convert else use string
|
||||
try:
|
||||
gst_style = json.loads(args.gst_style)
|
||||
if max(map(int, gst_style.keys())) >= C.gst['gst_style_tokens']:
|
||||
raise RuntimeError("The highest value of the gst_style dictionary key must be less than the number of GST Tokens, \n Highest dictionary key value: {} \n Number of GST tokens: {}".format(max(map(int, gst_style.keys())), C.gst['gst_style_tokens']))
|
||||
if max(map(int, gst_style.keys())) >= model_config.gst['gst_style_tokens']:
|
||||
raise RuntimeError("The highest value of the gst_style dictionary key must be less than the number of GST Tokens, \n Highest dictionary key value: {} \n Number of GST tokens: {}".format(max(map(int, gst_style.keys())), model_config.gst['gst_style_tokens']))
|
||||
except ValueError:
|
||||
gst_style = args.gst_style
|
||||
|
||||
wav = tts(model, vocoder_model, args.text, C, args.use_cuda, ap, use_griffin_lim, args.speaker_fileid, speaker_embedding=speaker_embedding, gst_style=gst_style)
|
||||
# kick it
|
||||
wav = tts(model,
|
||||
vocoder_model,
|
||||
args.text,
|
||||
model_config,
|
||||
vocoder_config,
|
||||
args.use_cuda,
|
||||
ap,
|
||||
vocoder_ap,
|
||||
use_griffin_lim,
|
||||
args.speaker_idx,
|
||||
speaker_embedding=speaker_embedding,
|
||||
gst_style=gst_style)
|
||||
|
||||
# save the results
|
||||
file_name = args.text.replace(" ", "_")[0:10]
|
||||
file_name = args.text.replace(" ", "_")[0:20]
|
||||
file_name = file_name.translate(
|
||||
str.maketrans('', '', string.punctuation.replace('_', ''))) + '.wav'
|
||||
out_path = os.path.join(args.out_path, file_name)
|
||||
|
|
|
|||
Loading…
Reference in New Issue