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wav2vec2-base-superb-ks-jpqd-ov

Audio Classification
Transformers
OpenVINO
wav2vec2
Generated from Trainer
Inference Endpoints
Model card Files Community
helenai commited on
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Add JPQD model

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Files changed (15) hide show
  1. README.md +70 -0
  2. all_results.json +12 -0
  3. compressed_graph.dot +0 -0
  4. config.json +146 -0
  5. eval_results.json +8 -0
  6. nncf_output.log +0 -0
  7. openvino_config.json +82 -0
  8. openvino_model.bin +3 -0
  9. openvino_model.xml +0 -0
  10. original_graph.dot +0 -0
  11. preprocessor_config.json +9 -0
  12. structured_sparsity.csv +73 -0
  13. train_results.json +7 -0
  14. trainer_state.json +0 -0
  15. 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: wav2vec2-base-superb-ks-jpqd-ov
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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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+ # wav2vec2-base-superb-ks-jpqd-ov
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
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+
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+ It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the
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+ JPQD image classification example.
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+
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0996
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+ - Accuracy: 0.9734
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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: 0
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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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+ | 2.5323 | 1.0 | 399 | 2.7354 | 0.6209 |
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+ | 2.716 | 2.0 | 798 | 2.7349 | 0.6209 |
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+ | 2.6712 | 3.0 | 1197 | 2.7327 | 0.6209 |
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+ | 13.204 | 4.0 | 1596 | 13.0685 | 0.8610 |
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+ | 19.3024 | 5.0 | 1995 | 19.1352 | 0.9335 |
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+ | 21.9148 | 6.0 | 2394 | 21.6996 | 0.9547 |
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+ | 22.3788 | 7.0 | 2793 | 22.1079 | 0.9620 |
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+ | 21.8158 | 8.0 | 3192 | 21.6228 | 0.9634 |
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+ | 0.3194 | 9.0 | 3591 | 0.1420 | 0.9657 |
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+ | 0.253 | 10.0 | 3990 | 0.1238 | 0.9693 |
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+ | 0.2921 | 11.0 | 4389 | 0.1159 | 0.9687 |
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+ | 0.2229 | 12.0 | 4788 | 0.1096 | 0.9704 |
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+ | 0.2492 | 13.0 | 5187 | 0.1028 | 0.9712 |
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+ | 0.2658 | 14.0 | 5586 | 0.1026 | 0.9734 |
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+ | 0.213 | 15.0 | 5985 | 0.0996 | 0.9734 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 1.13.1+cu117
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+ - Datasets 2.8.0
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+ - Tokenizers 0.13.2
all_results.json ADDED
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+ {
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+ "epoch": 15.0,
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+ "eval_accuracy": 0.9733745219182113,
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+ "eval_loss": 0.09959075599908829,
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+ "eval_runtime": 29.3442,
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+ "eval_samples_per_second": 231.664,
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+ "eval_steps_per_second": 3.646,
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+ "train_loss": 6.759114137568271,
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+ "train_runtime": 10160.2494,
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+ "train_samples_per_second": 75.432,
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+ "train_steps_per_second": 0.589
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+ }
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+ "model_type": "wav2vec2",
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+ "num_codevectors_per_group": 320,
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+ "vocab_size": 32,
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+ "xvector_output_dim": 512
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+ }
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+ "eval_steps_per_second": 3.646
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21
+ 19,6,MHSA,nncf_module.wav2vec2.encoder.layers.3.attention.k_proj,"(768, 768)","(128, 768)","(768,)","(128,)","[5, 8]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[3]/Wav2Vec2Attention[attention]/NNCFLinear[k_proj]/linear_0
22
+ 20,6,MHSA,nncf_module.wav2vec2.encoder.layers.3.attention.v_proj,"(768, 768)","(128, 768)","(768,)","(128,)","[5, 8]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[3]/Wav2Vec2Attention[attention]/NNCFLinear[v_proj]/linear_0
23
+ 21,6,MHSA,nncf_module.wav2vec2.encoder.layers.3.attention.out_proj,"(768, 768)","(768, 128)","(768,)","(768,)","[5, 8]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[3]/Wav2Vec2Attention[attention]/NNCFLinear[out_proj]/linear_0
24
+ 22,7,FF,nncf_module.wav2vec2.encoder.layers.3.feed_forward.intermediate_dense,"(3072, 768)","(82, 768)","(3072,)","(82,)",[82 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[3]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[intermediate_dense]/linear_0
25
+ 23,7,FF,nncf_module.wav2vec2.encoder.layers.3.feed_forward.output_dense,"(768, 3072)","(768, 82)","(768,)","(768,)",[82 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[3]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[output_dense]/linear_0
26
+ 24,8,MHSA,nncf_module.wav2vec2.encoder.layers.4.attention.q_proj,"(768, 768)","(64, 768)","(768,)","(64,)",[3],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[4]/Wav2Vec2Attention[attention]/NNCFLinear[q_proj]/linear_0
27
+ 25,8,MHSA,nncf_module.wav2vec2.encoder.layers.4.attention.k_proj,"(768, 768)","(64, 768)","(768,)","(64,)",[3],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[4]/Wav2Vec2Attention[attention]/NNCFLinear[k_proj]/linear_0
28
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29
+ 27,8,MHSA,nncf_module.wav2vec2.encoder.layers.4.attention.out_proj,"(768, 768)","(768, 64)","(768,)","(768,)",[3],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[4]/Wav2Vec2Attention[attention]/NNCFLinear[out_proj]/linear_0
30
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31
+ 29,9,FF,nncf_module.wav2vec2.encoder.layers.4.feed_forward.output_dense,"(768, 3072)","(768, 78)","(768,)","(768,)",[78 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)","(64, 768)","(768,)","(64,)",[1],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)","(64, 768)","(768,)","(64,)",[1],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)","(64, 768)","(768,)","(64,)",[1],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2Attention[attention]/NNCFLinear[v_proj]/linear_0
35
+ 33,10,MHSA,nncf_module.wav2vec2.encoder.layers.5.attention.out_proj,"(768, 768)","(768, 64)","(768,)","(768,)",[1],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2Attention[attention]/NNCFLinear[out_proj]/linear_0
36
+ 34,11,FF,nncf_module.wav2vec2.encoder.layers.5.feed_forward.intermediate_dense,"(3072, 768)","(74, 768)","(3072,)","(74,)",[74 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[intermediate_dense]/linear_0
37
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38
+ 36,12,MHSA,nncf_module.wav2vec2.encoder.layers.6.attention.q_proj,"(768, 768)","(64, 768)","(768,)","(64,)",[9],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[6]/Wav2Vec2Attention[attention]/NNCFLinear[q_proj]/linear_0
39
+ 37,12,MHSA,nncf_module.wav2vec2.encoder.layers.6.attention.k_proj,"(768, 768)","(64, 768)","(768,)","(64,)",[9],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[6]/Wav2Vec2Attention[attention]/NNCFLinear[k_proj]/linear_0
40
+ 38,12,MHSA,nncf_module.wav2vec2.encoder.layers.6.attention.v_proj,"(768, 768)","(64, 768)","(768,)","(64,)",[9],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[6]/Wav2Vec2Attention[attention]/NNCFLinear[v_proj]/linear_0
41
+ 39,12,MHSA,nncf_module.wav2vec2.encoder.layers.6.attention.out_proj,"(768, 768)","(768, 64)","(768,)","(768,)",[9],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[6]/Wav2Vec2Attention[attention]/NNCFLinear[out_proj]/linear_0
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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