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music2vec-v1

Feature Extraction

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a43992899 commited on Dec 10, 2022

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@@ -46,7 +46,7 @@ print(time_reduced_hidden_states.shape) # [13, 768]
 # you can even use a learnable weighted average representation
 aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1)
 weighted_avg_hidden_states = aggregator(time_reduced_hidden_states).squeeze()
-print(weighted_avg_hidden_states.shape)
 ```
 Our model is based on the [data2vec audio model](https://huggingface.co/docs/transformers/model_doc/data2vec#transformers.Data2VecAudioModel).

 # you can even use a learnable weighted average representation
 aggregator = nn.Conv1d(in_channels=13, out_channels=1, kernel_size=1)
 weighted_avg_hidden_states = aggregator(time_reduced_hidden_states).squeeze()
+print(weighted_avg_hidden_states.shape) # [768]
 ```
 Our model is based on the [data2vec audio model](https://huggingface.co/docs/transformers/model_doc/data2vec#transformers.Data2VecAudioModel).