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# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import torch
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import towhee
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from PIL import Image as PILImage
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from transformers import Data2VecAudioModel, Wav2Vec2Processor
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from towhee.operator.base import NNOperator
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class Data2VecAudio(NNOperator):
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def __init__(self, model_name = "facebook/data2vec-audio-base-960h"):
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self.model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base-960h")
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self.processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
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def __call__(self, data):
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audio = np.hstack(data).reshape(1, -1)
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audio = audio.astype(np.float32, order='C') / 32768.0
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sampling_rate = data[0]._sample_rate
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inputs = self.processor(audio.flatten(), sampling_rate=sampling_rate, return_tensors="pt")
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with torch.no_grad():
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outputs = self.model(**inputs)
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last_hidden_states = outputs.last_hidden_state
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feat = last_hidden_states[:,-1,:].flatten().detach().cpu().numpy()
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return feat
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