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Support totally random voices (and make fixes to previous changes)

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This commit is contained in:
James Betker 2022-05-02 15:40:03 -06:00
parent a57fcaf814
commit ee24d3ee4b
8 changed files with 125 additions and 34 deletions
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3
.gitignore vendored
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@ -129,6 +129,7 @@ dmypy.json
.pyre/
.idea/*
.models/*
tortoise/.models/*
tortoise/random_voices/*
.custom/*
results/*

67
tortoise/api.py
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@ -1,5 +1,6 @@
import os
import random
import uuid
from urllib import request
import torch
@ -15,6 +16,7 @@ from tqdm import tqdm
from tortoise.models.arch_util import TorchMelSpectrogram
from tortoise.models.clvp import CLVP
from tortoise.models.random_latent_generator import RandomLatentConverter
from tortoise.models.vocoder import UnivNetGenerator
from tortoise.utils.audio import wav_to_univnet_mel, denormalize_tacotron_mel
from tortoise.utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
@ -161,7 +163,8 @@ class TextToSpeech:
Main entry point into Tortoise.
"""
def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True):
def __init__(self, autoregressive_batch_size=16, models_dir='.models', enable_redaction=True,
save_random_voices=False):
"""
Constructor
:param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
@ -170,11 +173,15 @@ class TextToSpeech:
models, otherwise use the defaults.
:param enable_redaction: When true, 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.
Default is true.
:param save_random_voices: When true, voices that are randomly generated are saved to the `random_voices`
directory. Default is false.
"""
self.autoregressive_batch_size = autoregressive_batch_size
self.enable_redaction = enable_redaction
if self.enable_redaction:
self.aligner = Wav2VecAlignment()
self.save_random_voices = save_random_voices
self.tokenizer = VoiceBpeTokenizer()
download_models()
@ -210,6 +217,10 @@ class TextToSpeech:
self.vocoder.load_state_dict(torch.load(f'{models_dir}/vocoder.pth')['model_g'])
self.vocoder.eval(inference=True)
# Random latent generators (RLGs) are loaded lazily.
self.rlg_auto = None
self.rlg_diffusion = None
def tts_with_preset(self, text, preset='fast', **kwargs):
"""
Calls TTS with one of a set of preset generation parameters. Options:
@ -265,7 +276,21 @@ class TextToSpeech:
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
self.diffusion = self.diffusion.cpu()
return auto_latent, diffusion_latent
return auto_latent, diffusion_latent, auto_conds
def get_random_conditioning_latents(self):
# Lazy-load the RLG models.
if self.rlg_auto is None:
self.rlg_auto = RandomLatentConverter(1024).eval()
self.rlg_auto.load_state_dict(torch.load('.models/rlg_auto.pth', map_location=torch.device('cpu')))
self.rlg_diffusion = RandomLatentConverter(2048).eval()
self.rlg_diffusion.load_state_dict(torch.load('.models/rlg_diffuser.pth', map_location=torch.device('cpu')))
with torch.no_grad():
latents = self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
if self.save_random_voices:
os.makedirs('random_voices', exist_ok=True)
torch.save(latents, f'random_voices/{str(uuid.uuid4())}.pth')
return latents
def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True,
# autoregressive generation parameters follow
@ -323,14 +348,19 @@ class TextToSpeech:
:return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
Sample rate is 24kHz.
"""
text = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
text = F.pad(text, (0, 1)) # This may not be necessary.
assert text.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).cuda()
text_tokens = F.pad(text_tokens, (0, 1)) # This may not be necessary.
assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
auto_conds = None
if voice_samples is not None:
auto_conditioning, diffusion_conditioning = self.get_conditioning_latents(voice_samples)
else:
auto_conditioning, diffusion_conditioning, auto_conds = self.get_conditioning_latents(voice_samples)
elif conditioning_latents is not None:
auto_conditioning, diffusion_conditioning = conditioning_latents
else:
auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
auto_conditioning = auto_conditioning.cuda()
diffusion_conditioning = diffusion_conditioning.cuda()
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
@ -343,7 +373,7 @@ class TextToSpeech:
if verbose:
print("Generating autoregressive samples..")
for b in tqdm(range(num_batches), disable=not verbose):
codes = self.autoregressive.inference_speech(auto_conditioning, text,
codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
do_sample=True,
top_p=top_p,
temperature=temperature,
@ -365,12 +395,15 @@ class TextToSpeech:
for batch in tqdm(samples, disable=not verbose):
for i in range(batch.shape[0]):
batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
clvp = self.clvp(text.repeat(batch.shape[0], 1), batch, return_loss=False)
cvvp_accumulator = 0
for cl in range(conds.shape[1]):
cvvp_accumulator = cvvp_accumulator + self.cvvp(conds[:, cl].repeat(batch.shape[0], 1, 1), batch, return_loss=False )
cvvp = cvvp_accumulator / conds.shape[1]
clip_results.append(clvp * clvp_cvvp_slider + cvvp * (1-clvp_cvvp_slider))
clvp = self.clvp(text_tokens.repeat(batch.shape[0], 1), batch, return_loss=False)
if auto_conds is not None:
cvvp_accumulator = 0
for cl in range(auto_conds.shape[1]):
cvvp_accumulator = cvvp_accumulator + self.cvvp(auto_conds[:, cl].repeat(batch.shape[0], 1, 1), batch, return_loss=False)
cvvp = cvvp_accumulator / auto_conds.shape[1]
clip_results.append(clvp * clvp_cvvp_slider + cvvp * (1-clvp_cvvp_slider))
else:
clip_results.append(clvp)
clip_results = torch.cat(clip_results, dim=0)
samples = torch.cat(samples, dim=0)
best_results = samples[torch.topk(clip_results, k=k).indices]
@ -382,8 +415,8 @@ class TextToSpeech:
# inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
# results, but will increase memory usage.
self.autoregressive = self.autoregressive.cuda()
best_latents = self.autoregressive(auto_conditioning, text, torch.tensor([text.shape[-1]], device=conds.device), best_results,
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=conds.device),
best_latents = self.autoregressive(auto_conditioning, text_tokens, torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), best_results,
torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
return_latent=True, clip_inputs=False)
self.autoregressive = self.autoregressive.cpu()
del auto_conditioning
@ -415,7 +448,7 @@ class TextToSpeech:
self.diffusion = self.diffusion.cpu()
self.vocoder = self.vocoder.cpu()
def potentially_redact(self, clip, text):
def potentially_redact(clip, text):
if self.enable_redaction:
return self.aligner.redact(clip, text)
return clip

12
tortoise/do_tts.py
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@ -10,23 +10,23 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.")
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='random')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='fast')
parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
help='How to balance vocal diversity with the quality/intelligibility of the spoken text. 0 means highly diverse voice (not recommended), 1 means maximize intellibility',
default=.5)
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='../results/')
parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
'should only be specified if you have custom checkpoints.', default='.models')
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
tts = TextToSpeech(models_dir=args.model_dir)
tts = TextToSpeech(models_dir=args.model_dir, save_random_voices=True)
selected_voices = args.voice.split(',')
for voice in selected_voices:
for k, voice in enumerate(selected_voices):
voice_samples, conditioning_latents = load_voice(voice)
gen = tts.tts_with_preset(args.text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider)
torchaudio.save(os.path.join(args.output_path, f'{voice}.wav'), gen.squeeze(0).cpu(), 24000)
torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000)

10
tortoise/models/autoregressive.py
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@ -401,13 +401,13 @@ class UnifiedVoice(nn.Module):
conds = conds.mean(dim=1).unsqueeze(1)
return conds
def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
return_latent=False, clip_inputs=True):
"""
Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
(actuated by `text_first`).
speech_conditioning_input: MEL float tensor, (b,80,s)
speech_conditioning_input: MEL float tensor, (b,1024)
text_inputs: long tensor, (b,t)
text_lengths: long tensor, (b,)
mel_inputs: long tensor, (b,m)
@ -421,7 +421,7 @@ class UnifiedVoice(nn.Module):
# Types are expressed by expanding the text embedding space.
if types is not None:
text_inputs = text_inputs * (1+types).unsqueeze(-1)
if clip_inputs:
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
# chopping the inputs by the maximum actual length.
@ -435,7 +435,7 @@ class UnifiedVoice(nn.Module):
text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
conds = self.get_conditioning(speech_conditioning_input)
conds = speech_conditioning_latent.unsqueeze(1)
text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
@ -540,7 +540,7 @@ class UnifiedVoice(nn.Module):
text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
conds = speech_conditioning_latent
conds = speech_conditioning_latent.unsqueeze(1)
emb = torch.cat([conds, text_emb], dim=1)
self.inference_model.store_mel_emb(emb)

5
tortoise/models/diffusion_decoder.py
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@ -226,6 +226,7 @@ class DiffusionTts(nn.Module):
for j in range(speech_conditioning_input.shape[1]):
conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
conds = torch.cat(conds, dim=-1)
conds = conds.mean(dim=-1)
return conds
def timestep_independent(self, aligned_conditioning, conditioning_latent, expected_seq_len, return_code_pred):
@ -233,9 +234,7 @@ class DiffusionTts(nn.Module):
if is_latent(aligned_conditioning):
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
conds = conditioning_latent
cond_emb = conds.mean(dim=-1)
cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
cond_scale, cond_shift = torch.chunk(conditioning_latent, 2, dim=1)
if is_latent(aligned_conditioning):
code_emb = self.latent_conditioner(aligned_conditioning)
else:

55
tortoise/models/random_latent_generator.py Normal file
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@ -0,0 +1,55 @@
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
if bias is not None:
rest_dim = [1] * (input.ndim - bias.ndim - 1)
return (
F.leaky_relu(
input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
)
* scale
)
else:
return F.leaky_relu(input, negative_slope=0.2) * scale
class EqualLinear(nn.Module):
def __init__(
self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
if bias:
self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
else:
self.bias = None
self.scale = (1 / math.sqrt(in_dim)) * lr_mul
self.lr_mul = lr_mul
def forward(self, input):
out = F.linear(input, self.weight * self.scale)
out = fused_leaky_relu(out, self.bias * self.lr_mul)
return out
class RandomLatentConverter(nn.Module):
def __init__(self, channels):
super().__init__()
self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
nn.Linear(channels, channels))
self.channels = channels
def forward(self, ref):
r = torch.randn(ref.shape[0], self.channels, device=ref.device)
y = self.layers(r)
return y
if __name__ == '__main__':
model = RandomLatentConverter(512)
model(torch.randn(5,512))

4
tortoise/read.py
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@ -31,7 +31,7 @@ if __name__ == '__main__':
parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="data/riding_hood.txt")
parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='../results/longform/')
parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
parser.add_argument('--voice_diversity_intelligibility_slider', type=float,
@ -40,7 +40,7 @@ if __name__ == '__main__':
parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
'should only be specified if you have custom checkpoints.', default='.models')
args = parser.parse_args()
tts = TextToSpeech(models_dir=args.model_dir)
tts = TextToSpeech(models_dir=args.model_dir, save_random_voices=True)
outpath = args.output_path
selected_voices = args.voice.split(',')

3
tortoise/utils/audio.py
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@ -92,6 +92,9 @@ def get_voices():
def load_voice(voice):
if voice == 'random':
return None, None
voices = get_voices()
paths = voices[voice]
if len(paths) == 1 and paths[0].endswith('.pth'):