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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.
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
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