Jarod/tortoise-tts
1
1
Fork 0
forked from mrq/tortoise-tts
Code Pull Requests Activity

support latents into the diffusion decoder

Browse Source
This commit is contained in:
James Betker 2022-04-12 20:53:09 -06:00
parent 5988aa34eb
commit 732deaa212
4 changed files with 55 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
api.py
View File

@ -117,7 +117,7 @@ def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_
cond_mels.append(cond_mel)
cond_mels = torch.stack(cond_mels, dim=1)
output_seq_len = mel_codes.shape[-1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
output_seq_len = mel_codes.shape[1]*4*24000//22050 # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
output_shape = (mel_codes.shape[0], 100, output_seq_len)
precomputed_embeddings = diffusion_model.timestep_independent(mel_codes, cond_mels, output_seq_len, False)
@ -151,11 +151,6 @@ class TextToSpeech:
layer_drop=0, unconditioned_percentage=0).cpu().eval()
self.diffusion.load_state_dict(torch.load('.models/diffusion.pth'))
self.diffusion_next = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
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_next.load_state_dict(torch.load('.models/diffusion_next.pth'))
self.vocoder = UnivNetGenerator().cpu()
self.vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g'])
self.vocoder.eval(inference=True)
@ -223,12 +218,22 @@ class TextToSpeech:
self.clip = self.clip.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.cuda()
best_latents = self.autoregressive(conds, 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),
return_latent=True, clip_inputs=False)
self.autoregressive = self.autoregressive.cpu()
print("Performing vocoding..")
wav_candidates = []
self.diffusion = self.diffusion.cuda()
self.vocoder = self.vocoder.cuda()
for b in range(best_results.shape[0]):
codes = best_results[b].unsqueeze(0)
latents = best_latents[b].unsqueeze(0)
# Find the first occurrence of the "calm" token and trim the codes to that.
ctokens = 0
@ -238,10 +243,10 @@ class TextToSpeech:
else:
ctokens = 0
if ctokens > 8: # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
codes = codes[:, :k]
latents = latents[:, :k]
break
mel = do_spectrogram_diffusion(self.diffusion, diffuser, codes, voice_samples, temperature=diffusion_temperature)
mel = do_spectrogram_diffusion(self.diffusion, diffuser, latents, voice_samples, temperature=diffusion_temperature)
wav = self.vocoder.inference(mel)
wav_candidates.append(wav.cpu())
self.diffusion = self.diffusion.cpu()

2
eval_multiple.py
View File

@ -7,7 +7,7 @@ from utils.audio import load_audio
if __name__ == '__main__':
fname = 'Y:\\libritts\\test-clean\\transcribed-brief-w2v.tsv'
outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_auto_256_samp_100_di_4'
outpath = 'D:\\tmp\\tortoise-tts-eval\\diverse_new_decoder_1'
outpath_real = 'D:\\tmp\\tortoise-tts-eval\\real'
os.makedirs(outpath, exist_ok=True)

44
models/autoregressive.py
View File

@ -362,7 +362,7 @@ class UnifiedVoice(nn.Module):
mel_input_tokens[b, actual_end:] = self.stop_mel_token
return mel_input_tokens
def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
if second_inputs is not None:
emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
else:
@ -374,6 +374,10 @@ class UnifiedVoice(nn.Module):
enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
enc = self.final_norm(enc)
if return_latent:
return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
first_logits = enc[:, :first_inputs.shape[1]]
first_logits = first_head(first_logits)
first_logits = first_logits.permute(0,2,1)
@ -385,7 +389,8 @@ class UnifiedVoice(nn.Module):
else:
return first_logits
def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False):
def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, 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`).
@ -396,19 +401,23 @@ class UnifiedVoice(nn.Module):
mel_inputs: long tensor, (b,m)
wav_lengths: long tensor, (b,)
raw_mels: MEL float tensor (b,80,s)
"""
assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}'
assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}'
# 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.
max_text_len = text_lengths.max()
text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token)
max_mel_len = wav_lengths.max() // self.mel_length_compression
mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token)
if raw_mels is not None:
raw_mels = raw_mels[:, :, :max_mel_len*4]
If return_attentions is specified, only logits are returned.
If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
"""
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.
max_text_len = text_lengths.max()
text_inputs = text_inputs[:, :max_text_len]
max_mel_len = wav_lengths.max() // self.mel_length_compression
mel_codes = mel_codes[:, :max_mel_len]
if raw_mels is not None:
raw_mels = raw_mels[:, :, :max_mel_len*4]
mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
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)
speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input
conds = []
@ -427,10 +436,15 @@ class UnifiedVoice(nn.Module):
mel_inp = mel_codes
mel_emb = self.mel_embedding(mel_inp)
mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
if text_first:
text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
if return_latent:
return mel_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
else:
mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions)
mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
if return_latent:
return text_logits[:, :-2] # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
if return_attentions:
return mel_logits

17
models/diffusion_decoder.py
View File

@ -176,7 +176,13 @@ class DiffusionTts(nn.Module):
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
self.code_norm = normalization(model_channels)
self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
self.latent_conditioner = nn.Sequential(
nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
)
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
@ -190,6 +196,7 @@ class DiffusionTts(nn.Module):
DiffusionLayer(model_channels, dropout, num_heads),
DiffusionLayer(model_channels, dropout, num_heads),
)
self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
@ -206,7 +213,7 @@ class DiffusionTts(nn.Module):
groups = {
'minicoder': list(self.contextual_embedder.parameters()),
'layers': list(self.layers.parameters()),
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()),
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()),
'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
@ -227,7 +234,7 @@ class DiffusionTts(nn.Module):
cond_emb = conds.mean(dim=-1)
cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
if is_latent(aligned_conditioning):
code_emb = self.autoregressive_latent_converter(aligned_conditioning)
code_emb = self.latent_conditioner(aligned_conditioning)
else:
code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
code_emb = self.code_converter(code_emb)
@ -269,7 +276,7 @@ class DiffusionTts(nn.Module):
if conditioning_free:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
unused_params.extend(list(self.latent_converter.parameters()))
unused_params.extend(list(self.latent_conditioner.parameters()))
else:
if precomputed_aligned_embeddings is not None:
code_emb = precomputed_aligned_embeddings
@ -278,7 +285,7 @@ class DiffusionTts(nn.Module):
if is_latent(aligned_conditioning):
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
else:
unused_params.extend(list(self.latent_converter.parameters()))
unused_params.extend(list(self.latent_conditioner.parameters()))
unused_params.append(self.unconditioned_embedding)