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| # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) | |
| # | |
| # 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 json | |
| import onnxruntime | |
| import torch | |
| import numpy as np | |
| import whisper | |
| from typing import Callable | |
| import torchaudio.compliance.kaldi as kaldi | |
| import torchaudio | |
| import os | |
| import re | |
| import inflect | |
| try: | |
| import ttsfrd | |
| use_ttsfrd = True | |
| except ImportError: | |
| print("failed to import ttsfrd, use WeTextProcessing instead") | |
| from tn.chinese.normalizer import Normalizer as ZhNormalizer | |
| from tn.english.normalizer import Normalizer as EnNormalizer | |
| use_ttsfrd = False | |
| from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph | |
| class CosyVoiceFrontEnd: | |
| def __init__(self, | |
| get_tokenizer: Callable, | |
| feat_extractor: Callable, | |
| campplus_model: str, | |
| speech_tokenizer_model: str, | |
| spk2info: str = '', | |
| instruct: bool = False, | |
| allowed_special: str = 'all'): | |
| self.tokenizer = get_tokenizer() | |
| self.feat_extractor = feat_extractor | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| option = onnxruntime.SessionOptions() | |
| option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL | |
| option.intra_op_num_threads = 1 | |
| self.campplus_session = onnxruntime.InferenceSession(campplus_model, sess_options=option, providers=["CPUExecutionProvider"]) | |
| self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, | |
| providers=["CUDAExecutionProvider" if torch.cuda.is_available() else | |
| "CPUExecutionProvider"]) | |
| if os.path.exists(spk2info): | |
| self.spk2info = torch.load(spk2info, map_location=self.device) | |
| else: | |
| self.spk2info = {} | |
| self.instruct = instruct | |
| self.allowed_special = allowed_special | |
| self.inflect_parser = inflect.engine() | |
| self.use_ttsfrd = use_ttsfrd | |
| if self.use_ttsfrd: | |
| self.frd = ttsfrd.TtsFrontendEngine() | |
| ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) | |
| assert self.frd.initialize('{}/../../pretrained_models/CosyVoice-ttsfrd/resource'.format(ROOT_DIR)) is True, \ | |
| 'failed to initialize ttsfrd resource' | |
| self.frd.set_lang_type('pinyinvg') | |
| else: | |
| self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False) | |
| self.en_tn_model = EnNormalizer() | |
| 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_speech_token(self, speech): | |
| assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s' | |
| feat = whisper.log_mel_spectrogram(speech, n_mels=128) | |
| speech_token = self.speech_tokenizer_session.run(None, | |
| {self.speech_tokenizer_session.get_inputs()[0].name: | |
| feat.detach().cpu().numpy(), | |
| self.speech_tokenizer_session.get_inputs()[1].name: | |
| np.array([feat.shape[2]], dtype=np.int32)})[0].flatten().tolist() | |
| speech_token = torch.tensor([speech_token], dtype=torch.int32).to(self.device) | |
| speech_token_len = torch.tensor([speech_token.shape[1]], dtype=torch.int32).to(self.device) | |
| return speech_token, speech_token_len | |
| def _extract_spk_embedding(self, speech): | |
| feat = kaldi.fbank(speech, | |
| num_mel_bins=80, | |
| dither=0, | |
| sample_frequency=16000) | |
| feat = feat - feat.mean(dim=0, keepdim=True) | |
| embedding = self.campplus_session.run(None, | |
| {self.campplus_session.get_inputs()[0].name: feat.unsqueeze(dim=0).cpu().numpy()})[0].flatten().tolist() | |
| embedding = torch.tensor([embedding]).to(self.device) | |
| return embedding | |
| def _extract_speech_feat(self, speech): | |
| speech_feat = self.feat_extractor(speech).squeeze(dim=0).transpose(0, 1).to(self.device) | |
| speech_feat = speech_feat.unsqueeze(dim=0) | |
| speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device) | |
| return speech_feat, speech_feat_len | |
| def text_normalize(self, text, split=True): | |
| text = text.strip() | |
| if contains_chinese(text): | |
| if self.use_ttsfrd: | |
| texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]] | |
| text = ''.join(texts) | |
| else: | |
| text = self.zh_tn_model.normalize(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: | |
| if self.use_ttsfrd: | |
| texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]] | |
| text = ''.join(texts) | |
| else: | |
| text = self.en_tn_model.normalize(text) | |
| 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_sft(self, tts_text, spk_id): | |
| tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) | |
| embedding = self.spk2info[spk_id]['embedding'] | |
| model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding} | |
| return model_input | |
| def frontend_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, resample_rate): | |
| tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) | |
| prompt_text_token, prompt_text_token_len = self._extract_text_token(prompt_text) | |
| prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k) | |
| speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_resample) | |
| speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k) | |
| if resample_rate == 24000: | |
| # cosyvoice2, force speech_feat % speech_token = 2 | |
| token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1]) | |
| speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2* token_len | |
| speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len | |
| embedding = self._extract_spk_embedding(prompt_speech_16k) | |
| model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, | |
| 'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len, | |
| 'llm_prompt_speech_token': speech_token, 'llm_prompt_speech_token_len': speech_token_len, | |
| 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len, | |
| 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len, | |
| 'llm_embedding': embedding, 'flow_embedding': embedding} | |
| return model_input | |
| def frontend_instruct2(self, tts_text, instruct_text, prompt_speech_16k, resample_rate): | |
| tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) | |
| prompt_text_token, prompt_text_token_len = self._extract_text_token(instruct_text + '<|endofprompt|>') | |
| prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k) | |
| speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_resample) | |
| speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k) | |
| if resample_rate == 24000: | |
| # cosyvoice2, force speech_feat % speech_token = 2 | |
| token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1]) | |
| speech_feat, speech_feat_len[:] = speech_feat[:, :2 * token_len], 2* token_len | |
| speech_token, speech_token_len[:] = speech_token[:, :token_len], token_len | |
| embedding = self._extract_spk_embedding(prompt_speech_16k) | |
| model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, | |
| 'prompt_text': prompt_text_token, 'prompt_text_len': prompt_text_token_len, | |
| 'flow_prompt_speech_token': speech_token, 'flow_prompt_speech_token_len': speech_token_len, | |
| 'prompt_speech_feat': speech_feat, 'prompt_speech_feat_len': speech_feat_len, | |
| 'llm_embedding': embedding, 'flow_embedding': embedding} | |
| return model_input | |
| def frontend_cross_lingual(self, tts_text, prompt_speech_16k, resample_rate): | |
| model_input = self.frontend_zero_shot(tts_text, '', prompt_speech_16k, resample_rate) | |
| # in cross lingual mode, we remove prompt in llm | |
| del model_input['prompt_text'] | |
| del model_input['prompt_text_len'] | |
| del model_input['llm_prompt_speech_token'] | |
| del model_input['llm_prompt_speech_token_len'] | |
| return model_input | |
| def frontend_instruct(self, tts_text, spk_id, instruct_text): | |
| model_input = self.frontend_sft(tts_text, spk_id) | |
| # in instruct mode, we remove spk_embedding in llm due to information leakage | |
| del model_input['llm_embedding'] | |
| instruct_text_token, instruct_text_token_len = self._extract_text_token(instruct_text + '<endofprompt>') | |
| model_input['prompt_text'] = instruct_text_token | |
| model_input['prompt_text_len'] = instruct_text_token_len | |
| return model_input | |
| def frontend_vc(self, source_speech_16k, prompt_speech_16k, resample_rate): | |
| prompt_speech_token, prompt_speech_token_len = self._extract_speech_token(prompt_speech_16k) | |
| prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k) | |
| prompt_speech_feat, prompt_speech_feat_len = self._extract_speech_feat(prompt_speech_resample) | |
| embedding = self._extract_spk_embedding(prompt_speech_16k) | |
| source_speech_token, source_speech_token_len = self._extract_speech_token(source_speech_16k) | |
| model_input = {'source_speech_token': source_speech_token, 'source_speech_token_len': source_speech_token_len, | |
| 'flow_prompt_speech_token': prompt_speech_token, 'flow_prompt_speech_token_len': prompt_speech_token_len, | |
| 'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len, | |
| 'flow_embedding': embedding} | |
| return model_input | |