KwamaEggStew/tortoise-tts
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Reverted slight improvement patch, as it's just enough to OOM on GPUs with low VRAM

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mrq 2023-02-03 21:45:06 +00:00
parent 4f359bffa4
commit 061aa65ac4
1 changed files with 19 additions and 7 deletions
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22
tortoise/api.py
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@ -242,21 +242,17 @@ class TextToSpeech:
in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
layer_drop=0, unconditioned_percentage=0).cpu().eval()
self.diffusion.load_state_dict(torch.load(get_model_path('diffusion_decoder.pth', models_dir)))
self.autoregressive = self.autoregressive.to(self.device)
self.diffusion = self.diffusion.to(self.device)
self.clvp = CLVP(dim_text=768, dim_speech=768, dim_latent=768, num_text_tokens=256, text_enc_depth=20,
text_seq_len=350, text_heads=12,
num_speech_tokens=8192, speech_enc_depth=20, speech_heads=12, speech_seq_len=430,
use_xformers=True).cpu().eval()
self.clvp.load_state_dict(torch.load(get_model_path('clvp2.pth', models_dir)))
self.clvp = self.clvp.to(self.device)
self.cvvp = None # CVVP model is only loaded if used.
self.vocoder = UnivNetGenerator().cpu()
self.vocoder.load_state_dict(torch.load(get_model_path('vocoder.pth', models_dir), map_location=torch.device('cpu'))['model_g'])
self.vocoder.eval(inference=True)
self.vocoder = self.vocoder.to(self.device)
# Random latent generators (RLGs) are loaded lazily.
self.rlg_auto = None
@ -267,7 +263,6 @@ class TextToSpeech:
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(get_model_path('cvvp.pth', self.models_dir)))
self.cvvp = self.cvvp.to(self.device)
def get_conditioning_latents(self, voice_samples, return_mels=False):
"""
@ -285,7 +280,9 @@ class TextToSpeech:
for vs in voice_samples:
auto_conds.append(format_conditioning(vs, device=self.device))
auto_conds = torch.stack(auto_conds, dim=1)
self.autoregressive = self.autoregressive.to(self.device)
auto_latent = self.autoregressive.get_conditioning(auto_conds)
self.autoregressive = self.autoregressive.cpu()
diffusion_conds = []
for sample in voice_samples:
@ -296,7 +293,9 @@ class TextToSpeech:
diffusion_conds.append(cond_mel)
diffusion_conds = torch.stack(diffusion_conds, dim=1)
self.diffusion = self.diffusion.to(self.device)
diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
self.diffusion = self.diffusion.cpu()
if return_mels:
return auto_latent, diffusion_latent, auto_conds, diffusion_conds
@ -414,6 +413,7 @@ class TextToSpeech:
num_batches = num_autoregressive_samples // self.autoregressive_batch_size
stop_mel_token = self.autoregressive.stop_mel_token
calm_token = 83 # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
self.autoregressive = self.autoregressive.to(self.device)
for b in tqdm_override(range(num_batches), verbose=verbose, progress=progress, desc="Generating autoregressive samples"):
codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
@ -428,11 +428,14 @@ class TextToSpeech:
padding_needed = max_mel_tokens - codes.shape[1]
codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
samples.append(codes)
self.autoregressive = self.autoregressive.cpu()
clip_results = []
self.clvp = self.clvp.to(self.device)
if cvvp_amount > 0:
if self.cvvp is None:
self.load_cvvp()
self.cvvp = self.cvvp.to(self.device)
desc="Computing best candidates"
if verbose:
@ -460,18 +463,25 @@ class TextToSpeech:
clip_results = torch.cat(clip_results, dim=0)
samples = torch.cat(samples, dim=0)
best_results = samples[torch.topk(clip_results, k=k).indices]
self.clvp = self.clvp.cpu()
if self.cvvp is not None:
self.cvvp = self.cvvp.cpu()
del samples
# The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
# 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.to(self.device)
best_latents = self.autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
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
wav_candidates = []
self.diffusion = self.diffusion.to(self.device)
self.vocoder = self.vocoder.to(self.device)
for b in range(best_results.shape[0]):
codes = best_results[b].unsqueeze(0)
latents = best_latents[b].unsqueeze(0)
@ -491,6 +501,8 @@ class TextToSpeech:
temperature=diffusion_temperature, verbose=verbose, progress=progress, desc="Transforming autoregressive outputs into audio..")
wav = self.vocoder.inference(mel)
wav_candidates.append(wav.cpu())
self.diffusion = self.diffusion.cpu()
self.vocoder = self.vocoder.cpu()
def potentially_redact(clip, text):
if self.enable_redaction: