# 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 + '') 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