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Remove CVVP

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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.
This commit is contained in:
James Betker 2022-05-17 12:21:25 -06:00
parent 849db260ba
commit 8fdf516e62
3 changed files with 9 additions and 39 deletions
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36
tortoise/api.py
View File

@ -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

6
tortoise/do_tts.py
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@ -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)

6
tortoise/read.py
View File

@ -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)