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internal.wav2vec2-base-superb-ks-int8-structured64-quantize-inputs

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 +85 -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-quant-all-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-quant-all-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: 0.0933
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+ - Accuracy: 0.9769
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+
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+ This model is quantized. The input is also quantized.
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+ Structured Sparsity in transformer block linear layers is 64%.
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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: 7e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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: 0.5
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+ - num_epochs: 12.0
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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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+ | 0.4481 | 1.0 | 399 | 0.2105 | 0.9469 |
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+ | 5.6584 | 2.0 | 798 | 5.5480 | 0.9428 |
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+ | 8.7915 | 3.0 | 1197 | 8.6634 | 0.9601 |
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+ | 10.4775 | 4.0 | 1596 | 10.2819 | 0.9553 |
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+ | 10.9142 | 5.0 | 1995 | 10.7770 | 0.9657 |
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+ | 10.9478 | 6.0 | 2394 | 10.7637 | 0.9660 |
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+ | 0.2765 | 7.0 | 2793 | 0.1335 | 0.9678 |
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+ | 0.2532 | 8.0 | 3192 | 0.1075 | 0.9732 |
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+ | 0.2837 | 9.0 | 3591 | 0.1109 | 0.9700 |
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+ | 0.2 | 10.0 | 3990 | 0.1006 | 0.9765 |
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+ | 0.1742 | 11.0 | 4389 | 0.0930 | 0.9776 |
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+ | 0.1718 | 12.0 | 4788 | 0.0933 | 0.9769 |
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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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24
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25
+ 23,7,FF,nncf_module.wav2vec2.encoder.layers.3.feed_forward.output_dense,"(768, 3072)","(768, 1503)","(768,)","(768,)",[1503 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[3]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[output_dense]/linear_0
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27
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28
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29
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30
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31
+ 29,9,FF,nncf_module.wav2vec2.encoder.layers.4.feed_forward.output_dense,"(768, 3072)","(768, 1333)","(768,)","(768,)",[1333 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[4]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[output_dense]/linear_0
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
+ 32,10,MHSA,nncf_module.wav2vec2.encoder.layers.5.attention.v_proj,"(768, 768)","(128, 768)","(768,)","(128,)","[9, 10]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2Attention[attention]/NNCFLinear[v_proj]/linear_0
35
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36
+ 34,11,FF,nncf_module.wav2vec2.encoder.layers.5.feed_forward.intermediate_dense,"(3072, 768)","(1083, 768)","(3072,)","(1083,)",[1083 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[intermediate_dense]/linear_0
37
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38
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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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47
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48
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63
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68
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70
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72
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