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| # Copyright (c) 2024 Alibaba Inc | |
| # | |
| # 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 functools import partial | |
| import torch | |
| from typing import Callable | |
| import re | |
| import inflect | |
| from inspiremusic.cli.model import InspireMusicModel | |
| from inspiremusic.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph | |
| from inspiremusic.wavtokenizer.decoder.pretrained import WavTokenizer | |
| class InspireMusicFrontEnd: | |
| def __init__(self, | |
| configs: Callable, | |
| get_tokenizer: Callable, | |
| llm_model: str, | |
| flow_model: str, | |
| music_tokenizer_dir: str, | |
| audio_tokenizer_dir: str, | |
| instruct: bool = False, | |
| dtype: str = "fp16", | |
| fast: bool = False, | |
| fp16: bool = True, | |
| allowed_special: str = 'all'): | |
| self.tokenizer = get_tokenizer() | |
| self.audio_tokenizer_dir = audio_tokenizer_dir | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| self.bandwidth_id = torch.tensor([0]).to(self.device) | |
| self.wavtokenizer = WavTokenizer.from_pretrained_feat(f"{audio_tokenizer_dir}/config.yaml", f"{audio_tokenizer_dir}/model.pt").to(self.device) | |
| self.model = InspireMusicModel(configs['llm'], configs['flow'], configs['hift'], configs['wavtokenizer'], dtype, fast, fp16) | |
| self.model = self.model.load(llm_model, flow_model, music_tokenizer_dir, audio_tokenizer_dir) | |
| self.instruct = instruct | |
| self.allowed_special = allowed_special | |
| self.inflect_parser = inflect.engine() | |
| def _extract_text_token(self, text): | |
| text_token = self.tokenizer.encode(text, allowed_special=self.allowed_special) | |
| text_token = torch.tensor([text_token], dtype=torch.int32).to(self.device) | |
| text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.int32).to(self.device) | |
| return text_token, text_token_len | |
| def _extract_audio_token(self, audio, sample_rate=24000): | |
| audio = torch.tensor(audio, dtype=torch.float32, device=self.device) | |
| _, audio_token = self.wavtokenizer.encode_infer(audio, bandwidth_id=self.bandwidth_id) | |
| audio_token = audio_token.squeeze(0) | |
| audio_token_len = torch.tensor([audio_token.shape[1]], dtype=torch.int32, device=self.device) | |
| return audio_token, audio_token_len | |
| def text_normalize(self, text, split=True): | |
| text = text.strip() | |
| if contains_chinese(text): | |
| text = text.replace("\n", "") | |
| text = replace_blank(text) | |
| text = replace_corner_mark(text) | |
| text = text.replace(".", "、") | |
| text = text.replace(" - ", "，") | |
| text = remove_bracket(text) | |
| text = re.sub(r'[，,]+$', '。', text) | |
| texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80, token_min_n=60, merge_len=20, comma_split=False)) | |
| else: | |
| text = spell_out_number(text, self.inflect_parser) | |
| texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80, token_min_n=60, merge_len=20, comma_split=False)) | |
| if split is False: | |
| return text | |
| return texts | |
| def frontend_text_to_music(self, text, time_start, time_end, chorus): | |
| text_token, text_token_len = self._extract_text_token(text) | |
| model_input = {"text": text, "audio_token": None, "audio_token_len": None, | |
| "text_token": text_token, "text_token_len": text_token_len, | |
| "embeddings": [time_start, time_end, chorus], "raw_text":text} | |
| return model_input | |
| def frontend_continuation(self, text, audio, time_start, time_end, chorus, target_sr=24000): | |
| if text is None: | |
| text_token = None | |
| text_token_len = None | |
| else: | |
| text_token, text_token_len = self._extract_text_token(text) | |
| audio_token, audio_token_len = self._extract_audio_token(audio, target_sr) | |
| model_input = {"text": text, "audio_token": audio_token, "audio_token_len": audio_token_len, | |
| "text_token": text_token, "text_token_len": text_token_len, | |
| "embeddings": [time_start, time_end, chorus], "raw_text":text} | |
| return model_input | |