# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Dict, Optional, Callable, List, Generator import torch from torch import nn import torch.nn.functional as F from torch.nn.utils.rnn import pad_sequence, unpad_sequence from cosyvoice.utils.common import IGNORE_ID from cosyvoice.transformer.label_smoothing_loss import LabelSmoothingLoss from cosyvoice.utils.common import th_accuracy class TransformerLM(torch.nn.Module): def __init__( self, text_encoder_input_size: int, llm_input_size: int, llm_output_size: int, text_token_size: int, speech_token_size: int, text_encoder: torch.nn.Module, llm: torch.nn.Module, sampling: Callable, length_normalized_loss: bool = True, lsm_weight: float = 0.0, spk_embed_dim: int = 192, ): super().__init__() self.llm_input_size = llm_input_size self.speech_token_size = speech_token_size # 1. build text token inputs related modules self.text_embedding = torch.nn.Embedding(text_token_size, text_encoder_input_size) self.text_encoder = text_encoder self.text_encoder_affine_layer = nn.Linear( self.text_encoder.output_size(), llm_input_size ) # 2. build speech token language model related modules self.sos_eos = 0 self.task_id = 1 self.llm_embedding = torch.nn.Embedding(2, llm_input_size) self.llm = llm self.llm_decoder = nn.Linear(llm_output_size, speech_token_size + 1) self.criterion_ce = LabelSmoothingLoss( size=speech_token_size + 1, padding_idx=IGNORE_ID, smoothing=lsm_weight, normalize_length=length_normalized_loss, ) # 3. [Optional] build speech token related modules self.speech_embedding = torch.nn.Embedding(speech_token_size, llm_input_size) self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, llm_input_size) # 4. sampling method self.sampling = sampling def encode( self, text: torch.Tensor, text_lengths: torch.Tensor, ): encoder_out, encoder_mask = self.text_encoder(text, text_lengths, decoding_chunk_size=1, num_decoding_left_chunks=-1) encoder_out_lens = encoder_mask.squeeze(1).sum(1) encoder_out = self.text_encoder_affine_layer(encoder_out) return encoder_out, encoder_out_lens def pad_unpad_sequence(self, sos_eos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len): text_token = unpad_sequence(text_token, text_token_len.cpu(), batch_first=True) speech_token = unpad_sequence(speech_token, speech_token_len.cpu(), batch_first=True) lm_input = [torch.concat([sos_eos_emb.squeeze(dim=0), embedding[i], text_token[i], task_id_emb.squeeze(dim=0), speech_token[i]], dim=0) for i in range(len(text_token))] lm_input_len = torch.tensor([i.size(0) for i in lm_input], dtype=torch.int32) lm_input = pad_sequence(lm_input, batch_first=True, padding_value=IGNORE_ID) return lm_input, lm_input_len def forward( self, batch: dict, device: torch.device, ) -> Dict[str, Optional[torch.Tensor]]: """ Args: text: (B, L, D) text_lengths: (B,) audio: (B, T, N) or (B, T) audio_lengths: (B,) """ text_token = batch['text_token'].to(device) text_token_len = batch['text_token_len'].to(device) speech_token = batch['speech_token'].to(device) speech_token_len = batch['speech_token_len'].to(device) embedding = batch['embedding'].to(device) # 1. prepare llm_target lm_target = [torch.tensor([IGNORE_ID] * (2 + text_token_len[i]) + speech_token[i, :speech_token_len[i]].tolist() + [self.speech_token_size]) for i in range(text_token.size(0))] lm_target = pad_sequence(lm_target, batch_first=True, padding_value=IGNORE_ID).to(device) # 1. encode text_token text_token = self.text_embedding(text_token) text_token, text_token_len = self.encode(text_token, text_token_len) # 2. embedding projection embedding = F.normalize(embedding, dim=1) embedding = self.spk_embed_affine_layer(embedding) embedding = embedding.unsqueeze(1) # 3. eos and task_id sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1) task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) # 4. encode speech_token speech_token = self.speech_embedding(speech_token) # 5. unpad and pad lm_input, lm_input_len = self.pad_unpad_sequence(sos_eos_emb, embedding, text_token, text_token_len, task_id_emb, speech_token, speech_token_len) # 6. run lm forward lm_output, lm_output_mask = self.llm(lm_input, lm_input_len.to(device)) logits = self.llm_decoder(lm_output) loss = self.criterion_ce(logits, lm_target) acc = th_accuracy(logits.view(-1, self.speech_token_size + 1), lm_target, ignore_label=IGNORE_ID) return {'loss': loss, 'acc': acc} def sampling_ids( self, weighted_scores: torch.Tensor, decoded_tokens: List, sampling: int, ignore_eos: bool = True, ): while True: top_ids = self.sampling(weighted_scores, decoded_tokens, sampling) if (not ignore_eos) or (self.speech_token_size not in top_ids): break return top_ids @torch.inference_mode() def inference( self, text: torch.Tensor, text_len: torch.Tensor, prompt_text: torch.Tensor, prompt_text_len: torch.Tensor, prompt_speech_token: torch.Tensor, prompt_speech_token_len: torch.Tensor, embedding: torch.Tensor, sampling: int = 25, max_token_text_ratio: float = 20, min_token_text_ratio: float = 2, ) -> Generator[torch.Tensor, None, None]: device = text.device text = torch.concat([prompt_text, text], dim=1) text_len += prompt_text_len text = self.text_embedding(text) # 1. encode text text, text_len = self.encode(text, text_len) # 2. encode embedding if embedding.shape[0] != 0: embedding = F.normalize(embedding, dim=1) embedding = self.spk_embed_affine_layer(embedding) embedding = embedding.unsqueeze(dim=1) else: embedding = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) # 3. concat llm_input sos_eos_emb = self.llm_embedding.weight[self.sos_eos].reshape(1, 1, -1) task_id_emb = self.llm_embedding.weight[self.task_id].reshape(1, 1, -1) if prompt_speech_token_len != 0: prompt_speech_token_emb = self.speech_embedding(prompt_speech_token) else: prompt_speech_token_emb = torch.zeros(1, 0, self.llm_input_size, dtype=text.dtype).to(device) lm_input = torch.concat([sos_eos_emb, embedding, text, task_id_emb, prompt_speech_token_emb], dim=1) # 4. cal min/max_length min_len = int((text_len - prompt_text_len) * min_token_text_ratio) max_len = int((text_len - prompt_text_len) * max_token_text_ratio) # 5. step by step decode out_tokens = [] offset = 0 att_cache, cnn_cache = torch.zeros((0, 0, 0, 0), device=lm_input.device), torch.zeros((0, 0, 0, 0), device=lm_input.device) for i in range(max_len): y_pred, att_cache, cnn_cache = self.llm.forward_chunk(lm_input, offset=0, required_cache_size=-1, att_cache=att_cache, cnn_cache=cnn_cache, att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]), device=lm_input.device)).to(torch.bool)) logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1) top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False).item() if top_ids == self.speech_token_size: break # in stream mode, yield token one by one yield torch.tensor([[top_ids]], dtype=torch.int64, device=device) out_tokens.append(top_ids) offset += lm_input.size(1) lm_input = self.speech_embedding.weight[top_ids].reshape(1, 1, -1)