forked from mrq/DL-Art-School
UnifiedGptVoice!
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James Betker
parent
5bc9772cb0
commit
8b19c37409
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@ -99,7 +99,7 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
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'paired_text_tokens': snt['padded_text'],
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'paired_file': snt['filenames'],
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'speech_audio': snt['wav'],
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'speech_lengths': snt['wav_lengths'],
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'speech_audio_lengths': snt['wav_lengths'],
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'speech_file': snt['filenames'],
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'text_text': snt['real_text'],
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'text_tokens': snt['padded_text'],
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@ -114,7 +114,7 @@ class GrandConjoinedDataset(torch.utils.data.Dataset):
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'paired_text_tokens': snt['padded_text'],
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'paired_file': snt['filenames'],
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'speech_audio': sp['clip'],
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'speech_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length),
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'speech_audio_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length),
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'speech_file': sp['path'],
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'text_text': txt,
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'text_tokens': txt_tok,
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@ -38,7 +38,15 @@ class ConditioningEncoder(nn.Module):
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return h[:, :, 0]
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class GptTtsHf(nn.Module):
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class UnifiedGptVoice(nn.Module):
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"""
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Derived from GptTtsHf, but offers multiple modes of operation:
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- Text only
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- Voice only
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- Text conditioned on voice
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- Voice conditioned on text
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"""
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NUMBER_TEXT_TOKENS = 10000 # The number of tokens produced by our bespoke BPE tokenizer.
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START_TEXT_TOKEN = 9999
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STOP_TEXT_TOKEN = 0
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@ -79,87 +87,120 @@ class GptTtsHf(nn.Module):
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tar = F.pad(input, (0,1), value=stop_token)
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return inp, tar
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def get_logits(self, text_inputs, cond_input, mel_inputs, get_attns=False):
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text_emb = self.text_embedding(text_inputs)
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cond = self.conditioning_encoder(cond_input).unsqueeze(1)
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mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
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emb = torch.cat([text_emb, cond, mel_emb], dim=1)
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gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state
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text_logits = self.final_norm(enc[:, :text_emb.shape[1]])
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text_logits = self.text_head(text_logits)
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text_logits = text_logits.permute(0,2,1)
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mel_logits = self.final_norm(enc[:, -mel_emb.shape[1]:])
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mel_logits = self.mel_head(mel_logits)
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mel_logits = mel_logits.permute(0,2,1)
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return text_logits, mel_logits
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def forward(self, text_inputs, cond_input, mel_targets, wav_lengths, return_attentions=False):
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def set_mel_padding(self, mel_input_tokens, wav_lengths):
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"""
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Forward pass
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text_inputs: long tensor, (b,t)
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cond_inputs: MEL float tensor, (b,c,80,s)
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mel_targets: long tensor, (b,m)
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mel_lengths: long tensor, (b,)
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Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
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that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
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preformatting to create a working TTS model.
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"""
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# Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
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mel_lengths = wav_lengths // self.mel_length_compression
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for b in range(len(mel_lengths)):
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if mel_lengths[b] < mel_targets.shape[-1]:
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mel_targets[b, mel_lengths[b]:] = self.STOP_MEL_TOKEN
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actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
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if actual_end < mel_input_tokens.shape[-1]:
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mel_input_tokens[b, actual_end:] = self.STOP_MEL_TOKEN
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return mel_input_tokens
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# Randomly permute the conditioning spectrogram, to destroy any structure present.
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cond_input = cond_input[:,:,torch.randperm(cond_input.shape[-1])]
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def randomly_permute_conditioning_input(self, speech_conditioning_input):
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"""
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Randomly permute the conditioning spectrogram, to destroy any structure present. Note that since the
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conditioning input is derived from a discrete spectrogram, it does actually retain structure, but only a little
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bit (actually: exactly how much we want; enough to discriminate different vocal qualities, but nothing about
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what is being said).
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"""
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cond_input = speech_conditioning_input[:,:,torch.randperm(speech_conditioning_input.shape[-1])]
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if cond_input.shape[-1] > self.max_conditioning_length:
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cond_input = cond_input[:,:,:self.max_conditioning_length]
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return cond_input
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def get_logits(self, speech_conditioning_input, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False):
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if second_inputs is not None:
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emb = torch.cat([speech_conditioning_input, first_inputs, second_inputs], dim=1)
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else:
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emb = torch.cat([speech_conditioning_input, first_inputs], dim=1)
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gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
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if get_attns:
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return gpt_out.attentions
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enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input
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first_logits = self.final_norm(enc[:, :first_inputs.shape[1]])
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first_logits = first_head(first_logits)
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first_logits = first_logits.permute(0,2,1)
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if second_inputs is not None:
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second_logits = self.final_norm(enc[:, -second_inputs.shape[1]:])
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second_logits = second_head(second_logits)
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second_logits = second_logits.permute(0,2,1)
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return first_logits, second_logits
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else:
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return first_logits
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def forward(self, speech_conditioning_input, text_inputs, mel_inputs, wav_lengths, text_first=True, return_attentions=False):
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"""
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Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
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(actuated by `text_first`).
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speech_conditioning_input: MEL float tensor, (b,80,s)
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text_inputs: long tensor, (b,t)
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mel_inputs: long tensor, (b,m)
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wav_lengths: long tensor, (b,)
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"""
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mel_inputs = self.set_mel_padding(mel_inputs, wav_lengths)
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN)
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mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_targets, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN)
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text_logits, mel_logits = self.get_logits(text_inputs, cond_input, mel_inputs, get_attns=return_attentions)
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text_emb = self.text_embedding(text_inputs)
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mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_inputs, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN)
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mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
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if text_first:
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text_logits, mel_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions)
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else:
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mel_logits, text_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions)
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if return_attentions:
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return mel_logits
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
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return loss_text.mean(), loss_mel.mean(), mel_logits
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def inference(self, text_inputs, cond_input, **hf_generate_kwargs):
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if not hasattr(self, 'inference_model'):
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self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, None, self.final_norm, self.mel_head)
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def text_forward(self, speech_conditioning_input, text_inputs):
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"""
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Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the
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model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided).
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"""
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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text_inputs = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN)
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text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN)
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text_emb = self.text_embedding(text_inputs)
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text_logits = self.get_logits(speech_conditioning_input, text_emb, self.text_head)
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loss_text = F.cross_entropy(text_logits, text_targets.long())
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return loss_text.mean()
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# Randomly permute the conditioning spectrogram, to destroy any structure present.
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cond_input = cond_input[:,:,torch.randperm(cond_input.shape[-1])]
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if cond_input.shape[-1] > self.max_conditioning_length:
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cond_input = cond_input[:,:,:self.max_conditioning_length]
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cond = self.conditioning_encoder(cond_input).unsqueeze(1)
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def speech_forward(self, speech_conditioning_input, mel_inputs, wav_lengths):
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"""
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Performs autoregressive modeling on only speech data.
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"""
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mel_inputs = self.set_mel_padding(mel_inputs, wav_lengths)
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speech_conditioning_input = self.randomly_permute_conditioning_input(speech_conditioning_input)
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speech_conditioning_input = self.conditioning_encoder(speech_conditioning_input).unsqueeze(1)
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emb = torch.cat([text_emb, cond], dim=1)
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self.inference_model.store_mel_emb(emb)
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fake_inputs = torch.full((emb.shape[0],emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device)
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fake_inputs[:,-1] = self.START_MEL_TOKEN
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gen = self.inference_model.generate(fake_inputs, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN,
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max_length=emb.shape[1]+self.max_mel_tokens, **hf_generate_kwargs)
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return gen[:, fake_inputs.shape[1]:]
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mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_inputs, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN)
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mel_emb = self.gpt.get_input_embeddings()(mel_inputs)
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mel_logits = self.get_logits(speech_conditioning_input, mel_emb, self.mel_head)
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loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
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return loss_mel.mean()
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@register_model
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def register_gpt_tts_hf(opt_net, opt):
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return GptTtsHf(**opt_get(opt_net, ['kwargs'], {}))
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def register_unified_gpt_voice(opt_net, opt):
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return UnifiedGptVoice(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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gpt = GptTtsHf(model_dim=1024, heads=16)
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l = gpt(torch.randint(high=len(symbols), size=(2,200)),
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torch.arange(0, 80, 1, dtype=torch.float).view(1,80,1).repeat(2,1,800),
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gpt = UnifiedGptVoice(model_dim=256, heads=4)
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l = gpt(torch.randn(2, 80, 800),
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torch.randint(high=len(symbols), size=(2,80)),
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torch.randint(high=8192, size=(2,250)),
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torch.tensor([150*256,195*256]))
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@ -286,7 +286,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_tts.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_unified_voice.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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