forked from mrq/tortoise-tts
95 lines
5.1 KiB
Python
95 lines
5.1 KiB
Python
import argparse
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import os
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from time import time
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import torch
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import torchaudio
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from api import TextToSpeech, MODELS_DIR
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from utils.audio import load_audio, load_voices
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from utils.text import split_and_recombine_text
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="tortoise/data/riding_hood.txt")
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parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
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'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
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parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
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parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
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parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=True)
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parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
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parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice. Only the first candidate is actually used in the final product, the others can be used manually.', default=1)
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parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
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'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
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parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
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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)
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args = parser.parse_args()
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tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed)
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outpath = args.output_path
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selected_voices = args.voice.split(',')
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regenerate = args.regenerate
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if regenerate is not None:
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regenerate = [int(e) for e in regenerate.split(',')]
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# Process text
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with open(args.textfile, 'r', encoding='utf-8') as f:
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text = ' '.join([l for l in f.readlines()])
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if '|' in text:
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print("Found the '|' character in your text, which I will use as a cue for where to split it up. If this was not"
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"your intent, please remove all '|' characters from the input.")
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texts = text.split('|')
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else:
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texts = split_and_recombine_text(text)
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seed = int(time()) if args.seed is None else args.seed
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for selected_voice in selected_voices:
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voice_outpath = os.path.join(outpath, selected_voice)
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os.makedirs(voice_outpath, exist_ok=True)
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if '&' in selected_voice:
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voice_sel = selected_voice.split('&')
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else:
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voice_sel = [selected_voice]
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voice_samples, conditioning_latents = load_voices(voice_sel)
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all_parts = []
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for j, text in enumerate(texts):
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if regenerate is not None and j not in regenerate:
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all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
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continue
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gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
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preset=args.preset, k=args.candidates, use_deterministic_seed=seed)
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if args.candidates == 1:
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gen = gen.squeeze(0).cpu()
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torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000)
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else:
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candidate_dir = os.path.join(voice_outpath, str(j))
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os.makedirs(candidate_dir, exist_ok=True)
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for k, g in enumerate(gen):
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torchaudio.save(os.path.join(candidate_dir, f'{k}.wav'), g.squeeze(0).cpu(), 24000)
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gen = gen[0].squeeze(0).cpu()
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all_parts.append(gen)
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if args.candidates == 1:
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full_audio = torch.cat(all_parts, dim=-1)
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torchaudio.save(os.path.join(voice_outpath, 'combined.wav'), full_audio, 24000)
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if args.produce_debug_state:
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os.makedirs('debug_states', exist_ok=True)
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dbg_state = (seed, texts, voice_samples, conditioning_latents)
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torch.save(dbg_state, f'debug_states/read_debug_{selected_voice}.pth')
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# Combine each candidate's audio clips.
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if args.candidates > 1:
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audio_clips = []
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for candidate in range(args.candidates):
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for line in range(len(texts)):
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wav_file = os.path.join(voice_outpath, str(line), f"{candidate}.wav")
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audio_clips.append(load_audio(wav_file, 24000))
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audio_clips = torch.cat(audio_clips, dim=-1)
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torchaudio.save(os.path.join(voice_outpath, f"combined_{candidate:02d}.wav"), audio_clips, 24000)
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audio_clips = []
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