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Merge pull request #74 from jnordberg/improved-cli

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Add CLI tool
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James Betker 2022-05-28 21:33:53 -06:00 committed by GitHub
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scripts/tortoise_tts.py Executable file
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#!/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.')
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)
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.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'))

14
tortoise/api.py
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@ -26,7 +26,8 @@ from tortoise.utils.wav2vec_alignment import Wav2VecAlignment
pbar = None
MODELS_DIR = os.environ.get('TORTOISE_MODELS_DIR', '.models')
DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser('~'), '.cache', 'tortoise', 'models')
MODELS_DIR = os.environ.get('TORTOISE_MODELS_DIR', DEFAULT_MODELS_DIR)
MODELS = {
'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth',
'classifier.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/classifier.pth',
@ -309,9 +310,9 @@ class TextToSpeech:
'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
"""
# Use generally found best tuning knobs for generation.
kwargs.update({'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
'top_p': .8,
'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
settings = {'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
'top_p': .8,
'cond_free_k': 2.0, 'diffusion_temperature': 1.0}
# Presets are defined here.
presets = {
'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
@ -319,8 +320,9 @@ class TextToSpeech:
'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
'high_quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
}
kwargs.update(presets[preset])
return self.tts(text, **kwargs)
settings.update(presets[preset])
settings.update(kwargs) # allow overriding of preset settings with kwargs
return self.tts(text, **settings)
def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True, use_deterministic_seed=None,
return_deterministic_state=False,

6
tortoise/models/arch_util.py
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@ -1,3 +1,4 @@
import os
import functools
import math
@ -288,9 +289,12 @@ class AudioMiniEncoder(nn.Module):
return h[:, :, 0]
DEFAULT_MEL_NORM_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/mel_norms.pth')
class TorchMelSpectrogram(nn.Module):
def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000,
sampling_rate=22050, normalize=False, mel_norm_file='tortoise/data/mel_norms.pth'):
sampling_rate=22050, normalize=False, mel_norm_file=DEFAULT_MEL_NORM_FILE):
super().__init__()
# These are the default tacotron values for the MEL spectrogram.
self.filter_length = filter_length

8
tortoise/utils/audio.py
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@ -10,6 +10,9 @@ from scipy.io.wavfile import read
from tortoise.utils.stft import STFT
BUILTIN_VOICES_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../voices')
def load_wav_to_torch(full_path):
sampling_rate, data = read(full_path)
if data.dtype == np.int32:
@ -83,7 +86,7 @@ def dynamic_range_decompression(x, C=1):
def get_voices(extra_voice_dirs=[]):
dirs = ['tortoise/voices'] + extra_voice_dirs
dirs = [BUILTIN_VOICES_DIR] + extra_voice_dirs
voices = {}
for d in dirs:
subs = os.listdir(d)
@ -115,7 +118,8 @@ def load_voices(voices, extra_voice_dirs=[]):
clips = []
for voice in voices:
if voice == 'random':
print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
if len(voices) > 1:
print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
return None, None
clip, latent = load_voice(voice, extra_voice_dirs)
if latent is None:

6
tortoise/utils/tokenizer.py
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@ -1,3 +1,4 @@
import os
import re
import inflect
@ -165,8 +166,11 @@ def lev_distance(s1, s2):
return distances[-1]
DEFAULT_VOCAB_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/tokenizer.json')
class VoiceBpeTokenizer:
def __init__(self, vocab_file='tortoise/data/tokenizer.json'):
def __init__(self, vocab_file=DEFAULT_VOCAB_FILE):
if vocab_file is not None:
self.tokenizer = Tokenizer.from_file(vocab_file)