init

Signed-off-by: wxywb <xy.wang@zilliz.com>

__init__.py

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+# Copyright 2021 Zilliz. All rights reserved.
+#
+# 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 .data2vec_audio import Data2VecAudio
+
+def data2vec_audio(model_name="facebook/data2vec-audio-base-960h"):
+    return Data2VecAudio(model_name)

data2vec_audio.py

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+# Copyright 2021 Zilliz. All rights reserved.
+#
+# 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 numpy
+import torch
+import towhee
+
+from PIL import Image as PILImage
+
+from transformers import BeitFeatureExtractor, Data2VecVisionForImageClassification
+from towhee.operator.base import NNOperator
+
+class Data2VecAudio(NNOperator):
+    def __init__(self, model_name = "facebook/data2vec-audio-base-960h"):
+        self.model = Data2VecAudioModel.from_pretrained("facebook/data2vec-audio-base-960h")
+        self.processor = Wav2Vec2Processor.from_pretrained("facebook/data2vec-audio-base-960h")
+
+    def __call__(self, data):
+        audio = np.hstack(data).reshape(1, -1)
+        audio = audio.astype(np.float32, order='C') / 32768.0
+        inputs = processor(audio, sampling_rate=sampling_rate, return_tensors="pt")
+        with torch.no_grad():
+            outputs = model(**inputs)
+        last_hidden_states = outputs.last_hidden_state
+        return last_hidden_states
+
+        
+        
+