From 8139afd0e5e3a3bd41675d6a212394d7cda1c94f Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Tue, 17 May 2022 12:21:25 -0600
Subject: [PATCH] Remove CVVP
After training a similar model for a different purpose, I realized that
this model is faulty: the contrastive loss it uses only pays attention
to high-frequency details which do not contribute meaningfully to
output quality. I validated this by comparing a no-CVVP output with
a baseline using tts-scores and found no differences.
---
tortoise/api.py | 36 +++--------
tortoise/do_tts.py | 6 +-
tortoise/models/cvvp.py | 133 ----------------------------------------
tortoise/read.py | 6 +-
4 files changed, 9 insertions(+), 172 deletions(-)
delete mode 100644 tortoise/models/cvvp.py
diff --git a/tortoise/api.py b/tortoise/api.py
index 5abcb95..a724ae6 100644
--- a/tortoise/api.py
+++ b/tortoise/api.py
@@ -10,7 +10,6 @@ import progressbar
import torchaudio
from tortoise.models.classifier import AudioMiniEncoderWithClassifierHead
-from tortoise.models.cvvp import CVVP
from tortoise.models.diffusion_decoder import DiffusionTts
from tortoise.models.autoregressive import UnifiedVoice
from tqdm import tqdm
@@ -35,7 +34,6 @@ def download_models(specific_models=None):
'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',
'clvp2.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/clvp2.pth',
- 'cvvp.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/cvvp.pth',
'diffusion_decoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/diffusion_decoder.pth',
'vocoder.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/vocoder.pth',
'rlg_auto.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/rlg_auto.pth',
@@ -223,10 +221,6 @@ class TextToSpeech:
use_xformers=True).cpu().eval()
self.clvp.load_state_dict(torch.load(f'{models_dir}/clvp2.pth'))
- self.cvvp = CVVP(model_dim=512, transformer_heads=8, dropout=0, mel_codes=8192, conditioning_enc_depth=8, cond_mask_percentage=0,
- speech_enc_depth=8, speech_mask_percentage=0, latent_multiplier=1).cpu().eval()
- self.cvvp.load_state_dict(torch.load(f'{models_dir}/cvvp.pth'))
-
self.vocoder = UnivNetGenerator().cpu()
self.vocoder.load_state_dict(torch.load(f'{models_dir}/vocoder.pth')['model_g'])
self.vocoder.eval(inference=True)
@@ -309,8 +303,6 @@ class TextToSpeech:
return_deterministic_state=False,
# autoregressive generation parameters follow
num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
- # CLVP & CVVP parameters
- clvp_cvvp_slider=.5,
# diffusion generation parameters follow
diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,
**hf_generate_kwargs):
@@ -321,10 +313,10 @@ class TextToSpeech:
:param conditioning_latents: A tuple of (autoregressive_conditioning_latent, diffusion_conditioning_latent), which
can be provided in lieu of voice_samples. This is ignored unless voice_samples=None.
Conditioning latents can be retrieved via get_conditioning_latents().
- :param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP and CVVP models) clips are returned.
+ :param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned.
:param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
~~AUTOREGRESSIVE KNOBS~~
- :param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP+CVVP.
+ :param num_autoregressive_samples: 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".
:param temperature: The softmax temperature of the autoregressive model.
:param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
@@ -336,11 +328,6 @@ class TextToSpeech:
I was interested in the premise, but the results were not as good as I was hoping. This is off by default, but
could use some tuning.
:param typical_mass: The typical_mass parameter from the typical_sampling algorithm.
- ~~CLVP-CVVP KNOBS~~
- :param clvp_cvvp_slider: Controls the influence of the CLVP and CVVP models in selecting the best output from the autoregressive model.
- [0,1]. Values closer to 1 will cause Tortoise to emit clips that follow the text more. Values closer to
- 0 will cause Tortoise to emit clips that more closely follow the reference clip (e.g. the voice sounds more
- similar).
~~DIFFUSION KNOBS~~
:param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. 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,
@@ -402,28 +389,19 @@ class TextToSpeech:
samples.append(codes)
self.autoregressive = self.autoregressive.cpu()
- clip_results = []
+ clvp_results = []
self.clvp = self.clvp.cuda()
- self.cvvp = self.cvvp.cuda()
if verbose:
- print("Computing best candidates using CLVP and CVVP")
+ print("Computing best candidates using CLVP")
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_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)
+ clvp_results.append(clvp)
+ clvp_results = torch.cat(clvp_results, dim=0)
samples = torch.cat(samples, dim=0)
- best_results = samples[torch.topk(clip_results, k=k).indices]
+ best_results = samples[torch.topk(clvp_results, k=k).indices]
self.clvp = self.clvp.cpu()
- self.cvvp = self.cvvp.cpu()
del samples
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
diff --git a/tortoise/do_tts.py b/tortoise/do_tts.py
index eb5af04..0807c69 100644
--- a/tortoise/do_tts.py
+++ b/tortoise/do_tts.py
@@ -13,9 +13,6 @@ if __name__ == '__main__':
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='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('--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')
@@ -31,8 +28,7 @@ if __name__ == '__main__':
for k, voice in enumerate(selected_voices):
voice_samples, conditioning_latents = load_voice(voice)
gen, dbg_state = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
- preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider,
- use_deterministic_seed=args.seed, return_deterministic_state=True)
+ preset=args.preset, use_deterministic_seed=args.seed, return_deterministic_state=True)
if isinstance(gen, list):
for j, g in enumerate(gen):
torchaudio.save(os.path.join(args.output_path, f'{voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000)
diff --git a/tortoise/models/cvvp.py b/tortoise/models/cvvp.py
deleted file mode 100644
index d094649..0000000
--- a/tortoise/models/cvvp.py
+++ /dev/null
@@ -1,133 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import einsum
-from torch.utils.checkpoint import checkpoint
-
-from tortoise.models.arch_util import AttentionBlock
-from tortoise.models.xtransformers import ContinuousTransformerWrapper, Encoder
-
-
-def exists(val):
- return val is not None
-
-
-def masked_mean(t, mask):
- t = t.masked_fill(~mask, 0.)
- return t.sum(dim = 1) / mask.sum(dim = 1)
-
-
-class CollapsingTransformer(nn.Module):
- def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
- super().__init__()
- self.transformer = ContinuousTransformerWrapper(
- max_seq_len=-1,
- use_pos_emb=False,
- attn_layers=Encoder(
- dim=model_dim,
- depth=depth,
- heads=heads,
- ff_dropout=dropout,
- ff_mult=1,
- attn_dropout=dropout,
- use_rmsnorm=True,
- ff_glu=True,
- rotary_pos_emb=True,
- **encoder_kwargs,
- ))
- self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
- AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False),
- nn.Conv1d(output_dims, output_dims, 1))
- self.mask_percentage = mask_percentage
-
- def forward(self, x, **transformer_kwargs):
- h = self.transformer(x, **transformer_kwargs)
- h = h.permute(0,2,1)
- h = checkpoint(self.pre_combiner, h).permute(0,2,1)
- if self.training:
- mask = torch.rand_like(h.float()) > self.mask_percentage
- else:
- mask = torch.ones_like(h.float()).bool()
- return masked_mean(h, mask)
-
-
-class ConvFormatEmbedding(nn.Module):
- def __init__(self, *args, **kwargs):
- super().__init__()
- self.emb = nn.Embedding(*args, **kwargs)
-
- def forward(self, x):
- y = self.emb(x)
- return y.permute(0,2,1)
-
-
-class CVVP(nn.Module):
- def __init__(
- self,
- model_dim=512,
- transformer_heads=8,
- dropout=.1,
- conditioning_enc_depth=8,
- cond_mask_percentage=0,
- mel_channels=80,
- mel_codes=None,
- speech_enc_depth=8,
- speech_mask_percentage=0,
- latent_multiplier=1,
- ):
- super().__init__()
- latent_dim = latent_multiplier*model_dim
- self.temperature = nn.Parameter(torch.tensor(1.))
-
- self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2),
- nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
- self.conditioning_transformer = CollapsingTransformer(model_dim, model_dim, transformer_heads, dropout, conditioning_enc_depth, cond_mask_percentage)
- self.to_conditioning_latent = nn.Linear(latent_dim, latent_dim, bias=False)
-
- if mel_codes is None:
- self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2)
- else:
- self.speech_emb = ConvFormatEmbedding(mel_codes, model_dim)
- self.speech_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage)
- self.to_speech_latent = nn.Linear(latent_dim, latent_dim, bias=False)
-
- def get_grad_norm_parameter_groups(self):
- return {
- 'conditioning': list(self.conditioning_transformer.parameters()),
- 'speech': list(self.speech_transformer.parameters()),
- }
-
- def forward(
- self,
- mel_cond,
- mel_input,
- return_loss=False
- ):
- cond_emb = self.cond_emb(mel_cond).permute(0,2,1)
- enc_cond = self.conditioning_transformer(cond_emb)
- cond_latents = self.to_conditioning_latent(enc_cond)
-
- speech_emb = self.speech_emb(mel_input).permute(0,2,1)
- enc_speech = self.speech_transformer(speech_emb)
- speech_latents = self.to_speech_latent(enc_speech)
-
-
- cond_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (cond_latents, speech_latents))
- temp = self.temperature.exp()
-
- if not return_loss:
- sim = einsum('n d, n d -> n', cond_latents, speech_latents) * temp
- return sim
-
- sim = einsum('i d, j d -> i j', cond_latents, speech_latents) * temp
- labels = torch.arange(cond_latents.shape[0], device=mel_input.device)
- loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
-
- return loss
-
-
-if __name__ == '__main__':
- clvp = CVVP()
- clvp(torch.randn(2,80,100),
- torch.randn(2,80,95),
- return_loss=True)
\ No newline at end of file
diff --git a/tortoise/read.py b/tortoise/read.py
index ae68202..33332b6 100644
--- a/tortoise/read.py
+++ b/tortoise/read.py
@@ -18,9 +18,6 @@ if __name__ == '__main__':
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,
- 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('--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')
parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
@@ -62,8 +59,7 @@ if __name__ == '__main__':
all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
continue
gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
- preset=args.preset, clvp_cvvp_slider=args.voice_diversity_intelligibility_slider,
- use_deterministic_seed=seed)
+ preset=args.preset, use_deterministic_seed=seed)
gen = gen.squeeze(0).cpu()
torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), gen, 24000)
all_parts.append(gen)