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internal.wav2vec2-base-superb-ks-int8-structured83

Audio Classification
Transformers
PyTorch
Safetensors
OpenVINO
wav2vec2
Generated from Trainer
Model card Files Community
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Files changed (12) hide show
  1. README.md +80 -0
  2. all_results.json +12 -0
  3. config.json +146 -0
  4. eval_results.json +8 -0
  5. openvino_config.json +82 -0
  6. openvino_model.bin +3 -0
  7. openvino_model.xml +0 -0
  8. preprocessor_config.json +9 -0
  9. pytorch_model.bin +3 -0
  10. structured_sparsity.csv +73 -0
  11. trainer_state.json +0 -0
  12. 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-lr1e-4
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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-lr1e-4
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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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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1228
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+ - Accuracy: 0.9695
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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: 0.0001
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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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+ | 2.5357 | 1.0 | 399 | 2.7821 | 0.6209 |
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+ | 2.7107 | 2.0 | 798 | 2.7331 | 0.6209 |
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+ | 2.671 | 3.0 | 1197 | 2.7330 | 0.6209 |
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+ | 14.208 | 4.0 | 1596 | 14.2660 | 0.7139 |
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+ | 21.0916 | 5.0 | 1995 | 21.0315 | 0.8104 |
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+ | 24.4471 | 6.0 | 2394 | 24.2357 | 0.9073 |
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+ | 25.366 | 7.0 | 2793 | 25.0893 | 0.9273 |
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+ | 25.1369 | 8.0 | 3192 | 24.8976 | 0.9394 |
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+ | 0.4678 | 9.0 | 3591 | 0.2528 | 0.9435 |
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+ | 0.3576 | 10.0 | 3990 | 0.1873 | 0.9613 |
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+ | 0.3622 | 11.0 | 4389 | 0.1583 | 0.9645 |
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+ | 0.2796 | 12.0 | 4788 | 0.1419 | 0.9666 |
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+ | 0.3157 | 13.0 | 5187 | 0.1327 | 0.9693 |
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+ | 0.2997 | 14.0 | 5586 | 0.1263 | 0.9694 |
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+ | 0.2667 | 15.0 | 5985 | 0.1228 | 0.9695 |
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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
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+ "epoch": 15.0,
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+ "eval_loss": 0.1227947399020195,
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+ "eval_samples_per_second": 181.864,
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+ "eval_steps_per_second": 2.863,
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+ "train_loss": 7.481976795455467,
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+ "train_runtime": 12266.9504,
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+ "train_samples_per_second": 62.478,
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+ "train_steps_per_second": 0.488
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+ }
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21
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22
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23
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24
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25
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26
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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, 94)","(768,)","(768,)",[94 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)","(192, 768)","(768,)","(192,)","[1, 6, 7]",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)","(192, 768)","(768,)","(192,)","[1, 6, 7]",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)","(192, 768)","(768,)","(192,)","[1, 6, 7]",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, 192)","(768,)","(768,)","[1, 6, 7]",Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2Attention[attention]/NNCFLinear[out_proj]/linear_0
36
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37
+ 35,11,FF,nncf_module.wav2vec2.encoder.layers.5.feed_forward.output_dense,"(768, 3072)","(768, 97)","(768,)","(768,)",[97 items],Wav2Vec2ForSequenceClassification/Wav2Vec2Model[wav2vec2]/Wav2Vec2Encoder[encoder]/ModuleList[layers]/Wav2Vec2EncoderLayer[5]/Wav2Vec2FeedForward[feed_forward]/NNCFLinear[output_dense]/linear_0
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
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41
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42
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43
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44
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45
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46
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47
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48
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49
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50
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51
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52
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53
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54
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56
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57
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59
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60
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61
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62
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63
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68
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69
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70
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71
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72
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