107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
# 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.
|
|
import onnxruntime
|
|
import torch
|
|
import numpy as np
|
|
import whisper
|
|
from typing import Callable
|
|
import torchaudio.compliance.kaldi as kaldi
|
|
|
|
|
|
class CosyVoiceFrontEnd:
|
|
|
|
def __init__(
|
|
self,
|
|
feat_extractor: Callable,
|
|
campplus_model: str,
|
|
speech_tokenizer_model: str,
|
|
):
|
|
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"
|
|
)
|
|
],
|
|
)
|
|
|
|
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
|