# 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 onnxruntime import torch import numpy as np import whisper from typing import Callable import torchaudio.compliance.kaldi as kaldi import torchaudio import os import inflect import ttsfrd 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') self.device = '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"]) # use cpu provider for onnx to avoid zero gpu error self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option, providers=["CPUExecutionProvider"]) if os.path.exists(spk2info): self.spk2info = torch.load(spk2info, map_location=self.device) self.instruct = instruct self.allowed_special = allowed_special self.inflect_parser = inflect.engine() self.frd = ttsfrd.TtsFrontendEngine() ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) assert self.frd.initialize('{}/../../pretrained_models/speech_kantts_ttsfrd/resource'.format(ROOT_DIR)) is True, 'failed to initialize ttsfrd resource' self.frd.set_lang_type('pinyin') self.frd.enable_pinyin_mix(True) self.frd.set_breakmodel_index(1) 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): 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): text = self.frd.get_frd_extra_info(text, 'input').replace("\n", "") text = replace_blank(text) text = replace_corner_mark(text) text = text.replace(".", "、") text = text.replace(" - ", "，") text = remove_bracket(text) texts = [i for i in 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 = [i for i in 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): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') tts_text_token, tts_text_token_len = self._extract_text_token(tts_text) embedding = self.spk2info[spk_id]['embedding'].to(self.device) 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): self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 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_22050 = torchaudio.transforms.Resample(orig_freq=16000, new_freq=22050)(prompt_speech_16k) speech_feat, speech_feat_len = self._extract_speech_feat(prompt_speech_22050) speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k) 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_cross_lingual(self, tts_text, prompt_speech_16k): model_input = self.frontend_zero_shot(tts_text, '', prompt_speech_16k) # 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