scripts/tortoise_tts.py

#!/usr/bin/env python3

import argparse
import os
import sys
import tempfile
import time

import torch
import torchaudio

from tortoise.api import MODELS_DIR, TextToSpeech
from tortoise.utils.audio import get_voices, load_voices, load_audio
from tortoise.utils.text import split_and_recombine_text

parser = argparse.ArgumentParser(
    description='TorToiSe is a text-to-speech program that is capable of synthesizing speech '
                'in multiple voices with realistic prosody and intonation.')

parser.add_argument(
    'text', type=str, nargs='*',
    help='Text to speak. If omitted, text is read from stdin.')
parser.add_argument(
    '-v, --voice', type=str, default='random', metavar='VOICE', dest='voice',
    help='Selects the voice to use for generation. Use the & character to join two voices together. '
         'Use a comma to perform inference on multiple voices. Set to "all" to use all available voices. '
         'Note that multiple voices require the --output-dir option to be set.')
parser.add_argument(
    '-V, --voices-dir', metavar='VOICES_DIR', type=str, dest='voices_dir',
    help='Path to directory containing extra voices to be loaded. Use a comma to specify multiple directories.')
parser.add_argument(
    '-p, --preset', type=str, default='fast', choices=['ultra_fast', 'fast', 'standard', 'high_quality'], dest='preset',
    help='Which voice quality preset to use.')
parser.add_argument(
    '-q, --quiet', default=False, action='store_true', dest='quiet',
    help='Suppress all output.')

output_group = parser.add_mutually_exclusive_group(required=True)
output_group.add_argument(
    '-l, --list-voices', default=False, action='store_true', dest='list_voices',
    help='List available voices and exit.')
output_group.add_argument(
    '-P, --play', action='store_true', dest='play',
    help='Play the audio (requires pydub).')
output_group.add_argument(
    '-o, --output', type=str, metavar='OUTPUT', dest='output',
    help='Save the audio to a file.')
output_group.add_argument(
    '-O, --output-dir', type=str, metavar='OUTPUT_DIR', dest='output_dir',
    help='Save the audio to a directory as individual segments.')

multi_output_group = parser.add_argument_group('multi-output options (requires --output-dir)')
multi_output_group.add_argument(
    '--candidates', type=int, default=1,
    help='How many output candidates to produce per-voice. Note that only the first candidate is used in the combined output.')
multi_output_group.add_argument(
    '--regenerate', type=str, default=None,
    help='Comma-separated list of clip numbers to re-generate.')
multi_output_group.add_argument(
    '--skip-existing', action='store_true',
    help='Set to skip re-generating existing clips.')

advanced_group = parser.add_argument_group('advanced options')
advanced_group.add_argument(
    '--produce-debug-state', default=False, action='store_true',
    help='Whether or not to produce debug_states in current directory, which can aid in reproducing problems.')
advanced_group.add_argument(
    '--seed', type=int, default=None,
    help='Random seed which can be used to reproduce results.')
advanced_group.add_argument(
    '--models-dir', type=str, default=MODELS_DIR,
    help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to '
         '~/.cache/tortoise/.models, so this should only be specified if you have custom checkpoints.')
advanced_group.add_argument(
    '--text-split', type=str, default=None,
    help='How big chunks to split the text into, in the format <desired_length>,<max_length>.')
advanced_group.add_argument(
    '--disable-redaction', default=False, action='store_true',
    help='Normally text enclosed in brackets are automatically redacted from the spoken output '
         '(but are still rendered by the model), this can be used for prompt engineering. '
         'Set this to disable this behavior.')
advanced_group.add_argument(
    '--device', type=str, default=None,
    help='Device to use for inference.')
advanced_group.add_argument(
    '--batch-size', type=int, default=None,
    help='Batch size to use for inference. If omitted, the batch size is set based on available GPU memory.')

tuning_group = parser.add_argument_group('tuning options (overrides preset settings)')
tuning_group.add_argument(
    '--num-autoregressive-samples', type=int, default=None,
    help='Number of samples taken from the autoregressive model, all of which are filtered using CLVP. '
         'As TorToiSe is a probabilistic model, more samples means a higher probability of creating something "great".')
tuning_group.add_argument(
    '--temperature', type=float, default=None,
    help='The softmax temperature of the autoregressive model.')
tuning_group.add_argument(
    '--length-penalty', type=float, default=None,
    help='A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.')
tuning_group.add_argument(
    '--repetition-penalty', type=float, default=None,
    help='A penalty that prevents the autoregressive decoder from repeating itself during decoding. '
         'Can be used to reduce the incidence of long silences or "uhhhhhhs", etc.')
tuning_group.add_argument(
    '--top-p', type=float, default=None,
    help='P value used in nucleus sampling. 0 to 1. Lower values mean the decoder produces more "likely" (aka boring) outputs.')
tuning_group.add_argument(
    '--max-mel-tokens', type=int, default=None,
    help='Restricts the output length. 1 to 600. Each unit is 1/20 of a second.')
tuning_group.add_argument(
    '--cvvp-amount', type=float, default=None,
    help='How much the CVVP model should influence the output.'
    'Increasing this can in some cases reduce the likelihood of multiple speakers.')
tuning_group.add_argument(
    '--diffusion-iterations', type=int, default=None,
    help='Number of diffusion steps to perform.  More steps means the network has more chances to iteratively'
         'refine the output, which should theoretically mean a higher quality output. '
         'Generally a value above 250 is not noticeably better, however.')
tuning_group.add_argument(
    '--cond-free', type=bool, default=None,
    help='Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for '
         'each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output '
         'of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and '
         'dramatically improves realism.')
tuning_group.add_argument(
    '--cond-free-k', type=float, default=None,
    help='Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf]. '
         'As cond_free_k increases, the output becomes dominated by the conditioning-free signal. '
         'Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k')
tuning_group.add_argument(
    '--diffusion-temperature', type=float, default=None,
    help='Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0 '
         'are the "mean" prediction of the diffusion network and will sound bland and smeared. ')

usage_examples = f'''
Examples:

Read text using random voice and place it in a file:

    {parser.prog} -o hello.wav "Hello, how are you?"

Read text from stdin and play it using the tom voice:

    echo "Say it like you mean it!" | {parser.prog} -P -v tom

Read a text file using multiple voices and save the audio clips to a directory:

    {parser.prog} -O /tmp/tts-results -v tom,emma <textfile.txt
'''

try:
    args = parser.parse_args()
except SystemExit as e:
    if e.code == 0:
        print(usage_examples)
    sys.exit(e.code)

extra_voice_dirs = args.voices_dir.split(',') if args.voices_dir else []
all_voices = sorted(get_voices(extra_voice_dirs))

if args.list_voices:
    for v in all_voices:
        print(v)
    sys.exit(0)

selected_voices = all_voices if args.voice == 'all' else args.voice.split(',')
selected_voices = [v.split('&') if '&' in v else [v] for v in selected_voices]
for voices in selected_voices:
    for v in voices:
        if v != 'random' and v not in all_voices:
            parser.error(f'voice {v} not available, use --list-voices to see available voices.')

if len(args.text) == 0:
    text = ''
    for line in sys.stdin:
        text += line
else:
    text = ' '.join(args.text)
text = text.strip()
if args.text_split:
    desired_length, max_length = [int(x) for x in args.text_split.split(',')]
    if desired_length > max_length:
        parser.error(f'--text-split: desired_length ({desired_length}) must be <= max_length ({max_length})')
    texts = split_and_recombine_text(text, desired_length, max_length)
else:
    texts = split_and_recombine_text(text)
if len(texts) == 0:
    parser.error('no text provided')

if args.output_dir:
    os.makedirs(args.output_dir, exist_ok=True)
else:
    if len(selected_voices) > 1:
        parser.error('cannot have multiple voices without --output-dir"')
    if args.candidates > 1:
        parser.error('cannot have multiple candidates without --output-dir"')

# error out early if pydub isn't installed
if args.play:
    try:
        import pydub
        import pydub.playback
    except ImportError:
        parser.error('--play requires pydub to be installed, which can be done with "pip install pydub"')

seed = int(time.time()) if args.seed is None else args.seed
if not args.quiet:
    print('Loading tts...')
tts = TextToSpeech(models_dir=args.models_dir, enable_redaction=not args.disable_redaction,
                   device=args.device, autoregressive_batch_size=args.batch_size)
gen_settings = {
    'use_deterministic_seed': seed,
    'verbose': not args.quiet,
    'k': args.candidates,
    'preset': args.preset,
}
tuning_options = [
    'num_autoregressive_samples', 'temperature', 'length_penalty', 'repetition_penalty', 'top_p',
    'max_mel_tokens', 'cvvp_amount', 'diffusion_iterations', 'cond_free', 'cond_free_k', 'diffusion_temperature']
for option in tuning_options:
    if getattr(args, option) is not None:
        gen_settings[option] = getattr(args, option)
total_clips = len(texts) * len(selected_voices)
regenerate_clips = [int(x) for x in args.regenerate.split(',')] if args.regenerate else None
for voice_idx, voice in enumerate(selected_voices):
    audio_parts = []
    voice_samples, conditioning_latents = load_voices(voice, extra_voice_dirs)
    for text_idx, text in enumerate(texts):
        clip_name = f'{"-".join(voice)}_{text_idx:02d}'
        if args.output_dir:
            first_clip = os.path.join(args.output_dir, f'{clip_name}_00.wav')
            if (args.skip_existing or (regenerate_clips and text_idx not in regenerate_clips)) and os.path.exists(first_clip):
                audio_parts.append(load_audio(first_clip, 24000))
                if not args.quiet:
                    print(f'Skipping {clip_name}')
                continue
        if not args.quiet:
            print(f'Rendering {clip_name} ({(voice_idx * len(texts) + text_idx + 1)} of {total_clips})...')
            print('  ' + text)
        gen = tts.tts_with_preset(
            text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, **gen_settings)
        gen = gen if args.candidates > 1 else [gen]
        for candidate_idx, audio in enumerate(gen):
            audio = audio.squeeze(0).cpu()
            if candidate_idx == 0:
                audio_parts.append(audio)
            if args.output_dir:
                filename = f'{clip_name}_{candidate_idx:02d}.wav'
                torchaudio.save(os.path.join(args.output_dir, filename), audio, 24000)

    audio = torch.cat(audio_parts, dim=-1)
    if args.output_dir:
        filename = f'{"-".join(voice)}_combined.wav'
        torchaudio.save(os.path.join(args.output_dir, filename), audio, 24000)
    elif args.output:
        filename = args.output if args.output else os.tmp
        torchaudio.save(args.output, audio, 24000)
    elif args.play:
        f = tempfile.NamedTemporaryFile(suffix='.wav', delete=True)
        torchaudio.save(f.name, audio, 24000)
        pydub.playback.play(pydub.AudioSegment.from_wav(f.name))

    if args.produce_debug_state:
        os.makedirs('debug_states', exist_ok=True)
        dbg_state = (seed, texts, voice_samples, conditioning_latents, args)
        torch.save(dbg_state, os.path.join('debug_states', f'debug_{"-".join(voice)}.pth'))