Merge pull request #74 from jnordberg/improved-cli

Add CLI tool

scripts/tortoise_tts.py

@@ -0,0 +1,259 @@
+#!/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'))

tortoise/api.py

@@ -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,
+        settings = {'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
-                       'top_p': .8,
+                    'top_p': .8,
-                       'cond_free_k': 2.0, 'diffusion_temperature': 1.0})
+                    '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])
+        settings.update(presets[preset])
-        return self.tts(text, **kwargs)
+        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,

tortoise/models/arch_util.py

@@ -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

tortoise/utils/audio.py

@@ -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:

tortoise/utils/tokenizer.py

@@ -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)