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GptTtsHf: Make the input/target placement easier to reason about

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This commit is contained in:
James Betker 2021-12-17 10:24:14 -07:00
parent 2fb4213a3e
commit 9b9f7ea61b
1 changed files with 18 additions and 22 deletions
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40
codes/models/gpt_voice/gpt_tts_hf.py
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@ -52,15 +52,14 @@ class GptTtsHf(nn.Module):
self.mel_head = nn.Linear(model_dim, self.NUMBER_MEL_CODES) self.mel_head = nn.Linear(model_dim, self.NUMBER_MEL_CODES)
def get_logits(self, text_inputs, cond_inputs, mel_targets, get_attns=False): def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
assert text_inputs.shape[1] <= self.max_symbols_per_phrase inp = F.pad(input, (1,0), value=start_token)
assert cond_inputs.shape[1] <= self.max_conditioning_inputs tar = F.pad(input, (0,1), value=stop_token)
assert mel_targets.shape[1] <= self.max_mel_tokens return inp, tar
def get_logits(self, text_inputs, cond_inputs, mel_inputs, get_attns=False):
text_targets = F.pad(text_inputs, (1,0), value=self.START_TEXT_TOKEN) text_emb = self.text_embedding(text_inputs)
text_emb = self.text_embedding(text_targets) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_inputs.device))
text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
conds = [] conds = []
for k in range(cond_inputs.shape[1]): for k in range(cond_inputs.shape[1]):
@ -70,9 +69,8 @@ class GptTtsHf(nn.Module):
conds = torch.stack(conds, dim=1) conds = torch.stack(conds, dim=1)
conds = conds + self.conditioning_embedding conds = conds + self.conditioning_embedding
mel_targets = F.pad(mel_targets, (1,0), value=self.START_MEL_TOKEN) mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
mel_emb = self.gpt.get_input_embeddings()(mel_targets) mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_inputs.device))
mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_targets.device))
emb = torch.cat([text_emb, conds, mel_emb], dim=1) emb = torch.cat([text_emb, conds, mel_emb], dim=1)
gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns) gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
@ -80,10 +78,10 @@ class GptTtsHf(nn.Module):
return gpt_out.attentions return gpt_out.attentions
enc = gpt_out.last_hidden_state enc = gpt_out.last_hidden_state
text_logits = self.final_norm(enc[:, :self.max_symbols_per_phrase+1]) text_logits = self.final_norm(enc[:, :text_emb.shape[1]])
text_logits = self.text_head(text_logits) text_logits = self.text_head(text_logits)
text_logits = text_logits.permute(0,2,1) text_logits = text_logits.permute(0,2,1)
mel_logits = self.final_norm(enc[:, -(self.max_mel_tokens+1):]) mel_logits = self.final_norm(enc[:, -mel_emb.shape[1]:])
mel_logits = self.mel_head(mel_logits) mel_logits = self.mel_head(mel_logits)
mel_logits = mel_logits.permute(0,2,1) mel_logits = mel_logits.permute(0,2,1)
@ -103,13 +101,12 @@ class GptTtsHf(nn.Module):
if mel_lengths[b] < mel_targets.shape[-1]: if mel_lengths[b] < mel_targets.shape[-1]:
mel_targets[b, mel_lengths[b]:] = self.STOP_MEL_TOKEN mel_targets[b, mel_lengths[b]:] = self.STOP_MEL_TOKEN
text_logits, mel_logits = self.get_logits(text_inputs, cond_inputs, mel_targets, get_attns=return_attentions) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN)
mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_targets, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN)
text_logits, mel_logits = self.get_logits(text_inputs, cond_inputs, mel_inputs, get_attns=return_attentions)
if return_attentions: if return_attentions:
return mel_logits return mel_logits
text_targets = F.pad(text_inputs, (0,self.max_symbols_per_phrase-text_inputs.shape[1]+1), value=self.STOP_TEXT_TOKEN)
loss_text = F.cross_entropy(text_logits, text_targets.long()) loss_text = F.cross_entropy(text_logits, text_targets.long())
mel_targets = F.pad(mel_targets, (0,self.max_mel_tokens-mel_targets.shape[1]+1), value=self.STOP_MEL_TOKEN)
loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
return loss_text.mean(), loss_mel.mean(), mel_logits return loss_text.mean(), loss_mel.mean(), mel_logits
@ -117,10 +114,9 @@ class GptTtsHf(nn.Module):
if not hasattr(self, 'inference_model'): if not hasattr(self, 'inference_model'):
self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.text_pos_embedding, self.final_norm, self.text_head) self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, self.text_pos_embedding, self.final_norm, self.text_head)
text_targets = F.pad(text_inputs, (1,0), value=self.START_TEXT_TOKEN) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN)
text_targets = F.pad(text_targets, (0,1), value=self.STOP_TEXT_TOKEN) text_emb = self.text_embedding(text_inputs)
text_emb = self.text_embedding(text_targets) text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_inputs.device))
text_emb = text_emb + self.text_pos_embedding(torch.arange(text_emb.shape[1], device=text_targets.device))
conds = [] conds = []
for k in range(cond_inputs.shape[1]): for k in range(cond_inputs.shape[1]):
@ -133,7 +129,7 @@ class GptTtsHf(nn.Module):
emb = torch.cat([text_emb, conds], dim=1) emb = torch.cat([text_emb, conds], dim=1)
self.inference_model.store_mel_emb(emb) self.inference_model.store_mel_emb(emb)
fake_inputs = torch.full((text_inputs.shape[0],emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device) fake_inputs = torch.full((emb.shape[0],emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device)
fake_inputs[:,-1] = self.START_MEL_TOKEN fake_inputs[:,-1] = self.START_MEL_TOKEN
gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN, gen = self.inference_model.generate(fake_inputs, do_sample=do_sample, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN,