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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 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 + '<endofprompt>')
model_input['prompt_text'] = instruct_text_token
model_input['prompt_text_len'] = instruct_text_token_len
return model_input
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