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Super Chiba 2023-11-23 12:17:32 +08:00
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tortoise/do_tts.py
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import argparse import os import torch import torchaudio from api import TextToSpeech, MODELS_DIR from utils.audio import load_voices if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--text', type=str, help='Text to speak.', default="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.") parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) ' 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='random') parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='fast', choices=['high_quality' , 'standard', 'fast', 'ultra_fast']) parser.add_argument('--use_deepspeed', type=str, help='Which voice preset to use. Default to False', default='False', choices=['True', 'False']) parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output. Default to True', default='True', choices=['True', 'False']) parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram. Default to True", default='True', choices=['True', 'False']) parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/') parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this' 'should only be specified if you have custom checkpoints.', default=MODELS_DIR) parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice. Default to 3', default=3) parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None) parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default='True', choices=['True', 'False']) parser.add_argument('--cvvp_amount', type=float, help='How much the CVVP model should influence the output.' 'Increasing this can in some cases reduce the likelihood of multiple speakers. Defaults to 0 (disabled)', default=.0) parser.add_argument('--batch_size', type=int, help='(Optional) If you want to specify the batch size to use for autoregression. Usually, VRAM-2GB if half=True, VRAM/2 if half=False') args = parser.parse_args() if torch.cuda.is_available(): args.use_deepspeed = True else: args.use_deepspeed = False os.makedirs(args.output_path, exist_ok=True) if args.batch_size is not None: tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half, autoregressive_batch_size=args.batch_size) else: tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half) selected_voices = args.voice.split(',') for k, selected_voice in enumerate(selected_voices): if '&' in selected_voice: voice_sel = selected_voice.split('&') else: voice_sel = [selected_voice] voice_samples, conditioning_latents = load_voices(voice_sel) gen, dbg_state = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents, preset=args.preset, use_deterministic_seed=args.seed, return_deterministic_state=True, cvvp_amount=args.cvvp_amount) if isinstance(gen, list): for j, g in enumerate(gen): torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000) else: torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000) if args.produce_debug_state: os.makedirs('debug_states', exist_ok=True) torch.save(dbg_state, f'debug_states/do_tts_debug_{selected_voice}.pth')
import argparse
import os
import torch
import torchaudio
from api import TextToSpeech, MODELS_DIR
from utils.audio import load_voices
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--text', type=str, help='Text to speak.', default="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.")
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='random')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='fast', choices=['high_quality' , 'standard', 'fast', 'ultra_fast'])
parser.add_argument('--use_deepspeed', type=str, help='Which voice preset to use. Default to False', default='False', choices=['True', 'False'])
parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output. Default to True', default='True', choices=['True', 'False'])
parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram. Default to True", default='True', choices=['True', 'False'])
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice. Default to 3', default=3)
parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default='True', choices=['True', 'False'])
parser.add_argument('--cvvp_amount', type=float, help='How much the CVVP model should influence the output.'
'Increasing this can in some cases reduce the likelihood of multiple speakers. Defaults to 0 (disabled)', default=.0)
parser.add_argument('--batch_size', type=int, help='(Optional) If you want to specify the batch size to use for autoregression. Usually, VRAM-2GB if half=True, VRAM/2 if half=False')
args = parser.parse_args()
if torch.cuda.is_available():
args.use_deepspeed = True
else:
args.use_deepspeed = False
os.makedirs(args.output_path, exist_ok=True)
if args.batch_size is not None:
tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache,
half=args.half, autoregressive_batch_size=args.batch_size)
else:
tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache,
half=args.half)
selected_voices = args.voice.split(',')
for k, selected_voice in enumerate(selected_voices):
if '&' in selected_voice:
voice_sel = selected_voice.split('&')
else:
voice_sel = [selected_voice]
voice_samples, conditioning_latents = load_voices(voice_sel)
gen, dbg_state = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
preset=args.preset, use_deterministic_seed=args.seed, return_deterministic_state=True, cvvp_amount=args.cvvp_amount)
if isinstance(gen, list):
for j, g in enumerate(gen):
torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000)
else:
torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000)
if args.produce_debug_state:
os.makedirs('debug_states', exist_ok=True)
torch.save(dbg_state, f'debug_states/do_tts_debug_{selected_voice}.pth')