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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_fast 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='lj') |
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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('--output_name', type=str, help='How to name the output file', default='combined.wav') |
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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('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None) |
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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('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=False) |
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parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output', default=True) |
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parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram", default=True) |
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args = parser.parse_args() |
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if torch.backends.mps.is_available(): |
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args.use_deepspeed = False |
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tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half) |
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outpath = args.output_path |
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outname = args.output_name |
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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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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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start_time = time() |
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gen = tts.tts(text, voice_samples=voice_samples, use_deterministic_seed=seed) |
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end_time = time() |
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audio_ = gen.squeeze(0).cpu() |
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print("Time taken to generate the audio: ", end_time - start_time, "seconds") |
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print("RTF: ", (end_time - start_time) / (audio_.shape[1] / 24000)) |
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torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), audio_, 24000) |
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all_parts.append(audio_) |
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full_audio = torch.cat(all_parts, dim=-1) |
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torchaudio.save(os.path.join(voice_outpath, f"{outname}.wav"), full_audio, 24000) |
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