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Merge pull request neonbjb#74 from jnordberg/improved-cli
Add CLI tool
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
|
|
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| import argparse | ||
| import os | ||
| import sys | ||
| import tempfile | ||
| import time | ||
|
|
||
| import torch | ||
| import torchaudio | ||
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| 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 | ||
|
|
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| 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.') | ||
|
|
||
| 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 likelyhood 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) | ||
|
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| extra_voice_dirs = args.voices_dir.split(',') if args.voices_dir else [] | ||
| all_voices = sorted(get_voices(extra_voice_dirs)) | ||
|
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| if args.list_voices: | ||
| for v in all_voices: | ||
| print(v) | ||
| sys.exit(0) | ||
|
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| 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.candiates > 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) | ||
| gen_settings = { | ||
| 'use_deterministic_seed': seed, | ||
| 'varbose': 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')) |
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