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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| class PromptPreparer: | |
| def prepare_prompts(self, y, y_lens, codes, nar_stage, y_prompts_codes): | |
| if self.prefix_mode == 0: | |
| y_emb, prefix_len = self._handle_prefix_mode_0(y, codes, nar_stage) | |
| elif self.prefix_mode == 1: | |
| y_emb, prefix_len = self._handle_prefix_mode_1(y, y_lens, codes, nar_stage) | |
| elif self.prefix_mode in [2, 4]: | |
| y_emb, prefix_len = self._handle_prefix_mode_2_4(y, y_lens, codes, nar_stage, y_prompts_codes) | |
| else: | |
| raise ValueError("Invalid prefix mode") | |
| return y_emb, prefix_len | |
| def _handle_prefix_mode_0(self, y, codes, nar_stage): | |
| prefix_len = 0 | |
| y_emb = self.nar_audio_embeddings[0](y) | |
| for j in range(1, nar_stage): | |
| y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j]) | |
| return y_emb, 0 | |
| def _handle_prefix_mode_1(self, y, y_lens, codes, nar_stage): | |
| int_low = (0.25 * y_lens.min()).type(torch.int64).item() | |
| prefix_len = torch.randint(int_low, int_low * 2, size=()).item() | |
| prefix_len = min(prefix_len, 225) | |
| y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len]) | |
| y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:]) | |
| for j in range(1, self.num_quantizers): | |
| y_prompts += self.nar_audio_embeddings[j]( | |
| codes[:, :prefix_len, j] | |
| ) | |
| if j < nar_stage: | |
| y_emb += self.nar_audio_embeddings[j]( | |
| codes[:, prefix_len:, j] | |
| ) | |
| y_emb = torch.concat([y_prompts, y_emb], axis=1) | |
| return y_emb, prefix_len | |
| def _handle_prefix_mode_2_4(self, y, y_lens, codes, nar_stage, y_prompts_codes): | |
| if self.prefix_mode == 2: | |
| prefix_len = min(225, int(0.25 * y_lens.min().item())) | |
| y_prompts_codes = [] | |
| for b in range(codes.shape[0]): | |
| start = self.rng.randint(0, y_lens[b].item() - prefix_len) | |
| y_prompts_codes.append( | |
| torch.clone(codes[b, start : start + prefix_len]) | |
| ) | |
| codes[ | |
| b, start : start + prefix_len, nar_stage | |
| ] = self.audio_token_num | |
| y_prompts_codes = torch.stack(y_prompts_codes, dim=0) | |
| else: | |
| prefix_len = y_prompts_codes.shape[1] | |
| y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0]) | |
| y_emb = self.nar_audio_embeddings[0](y) | |
| for j in range(1, self.num_quantizers): | |
| y_prompts += self.nar_audio_embeddings[j]( | |
| y_prompts_codes[..., j] | |
| ) | |
| if j < nar_stage: | |
| y_emb += self.nar_audio_embeddings[j](codes[..., j]) | |
| y_emb = torch.concat([y_prompts, y_emb], axis=1) | |
| return y_emb, prefix_len | |