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internal.wav2vec2-base-superb-ks-int8-structured79

Audio Classification
Transformers
PyTorch
OpenVINO
wav2vec2
generated_from_trainer
Inference Endpoints
Model card Files Community
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  1. README.md +80 -0
  2. config.json +146 -0
  3. openvino_config.json +84 -0
  4. openvino_model.bin +3 -0
  5. openvino_model.xml +0 -0
  6. preprocessor_config.json +9 -0
  7. pytorch_model.bin +3 -0
  8. structured_sparsity.csv +73 -0
  9. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - audio-classification
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+ - generated_from_trainer
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+ datasets:
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+ - superb
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: w2v2-ks-jpqd-finetuned-student
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # w2v2-ks-jpqd-finetuned-student
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+
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+ This model is a fine-tuned version of [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) on the superb dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0641
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+ - Accuracy: 0.9815
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 15.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.4606 | 1.0 | 399 | 0.1543 | 0.9723 |
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+ | 14.8746 | 2.0 | 798 | 14.9490 | 0.9681 |
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+ | 24.7043 | 3.0 | 1197 | 24.6662 | 0.9706 |
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+ | 30.626 | 4.0 | 1596 | 30.4279 | 0.9732 |
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+ | 33.4796 | 5.0 | 1995 | 33.3182 | 0.9750 |
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+ | 34.4405 | 6.0 | 2394 | 34.2327 | 0.9744 |
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+ | 34.1743 | 7.0 | 2793 | 34.0161 | 0.9741 |
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+ | 33.47 | 8.0 | 3192 | 33.2669 | 0.9748 |
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+ | 0.2278 | 9.0 | 3591 | 0.1125 | 0.9757 |
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+ | 0.2259 | 10.0 | 3990 | 0.0848 | 0.9778 |
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+ | 0.1629 | 11.0 | 4389 | 0.0734 | 0.9788 |
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+ | 0.1658 | 12.0 | 4788 | 0.0736 | 0.9803 |
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+ | 0.2264 | 13.0 | 5187 | 0.0658 | 0.9803 |
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+ | 0.1564 | 14.0 | 5586 | 0.0677 | 0.9819 |
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+ | 0.1716 | 15.0 | 5985 | 0.0641 | 0.9815 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.0
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+ - Pytorch 1.13.1+cu116
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2
config.json ADDED
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+ }
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25
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28
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29
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30
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31
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32
+ 30,10,MHSA,nncf_module.wav2vec2.encoder.layers.5.attention.q_proj,"(768, 768)","(128, 768)","(768,)","(128,)","[9, 10]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2Attention[attention]/NNCFLinear[q_proj]/linear_0
33
+ 31,10,MHSA,nncf_module.wav2vec2.encoder.layers.5.attention.k_proj,"(768, 768)","(128, 768)","(768,)","(128,)","[9, 10]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2Attention[attention]/NNCFLinear[k_proj]/linear_0
34
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35
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36
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37
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39
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40
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42
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43
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44
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45
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48
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51
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62
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63
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72
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