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  1. examples/audio-classification/ac/README.md +88 -0
  2. examples/audio-classification/ac/all_results.json +13 -0
  3. examples/audio-classification/ac/config.json +147 -0
  4. examples/audio-classification/ac/eval_results.json +8 -0
  5. examples/audio-classification/ac/model.safetensors +3 -0
  6. examples/audio-classification/ac/preprocessor_config.json +9 -0
  7. examples/audio-classification/ac/runs/May15_03-06-03_cs-Precision-7960-Tower/events.out.tfevents.1747292768.cs-Precision-7960-Tower.146737.0 +3 -0
  8. examples/audio-classification/ac/runs/May15_03-06-03_cs-Precision-7960-Tower/events.out.tfevents.1747293535.cs-Precision-7960-Tower.146737.1 +3 -0
  9. examples/audio-classification/ac/train_results.json +8 -0
  10. examples/audio-classification/ac/trainer_state.json +1598 -0
  11. examples/audio-classification/ac/training_args.bin +3 -0
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-base
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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-ft-keyword-spotting
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+ results:
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+ - task:
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+ name: Audio Classification
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+ type: audio-classification
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+ dataset:
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+ name: superb
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+ type: superb
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+ config: ks
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+ split: validation
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+ args: ks
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9826419535157399
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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-ft-keyword-spotting
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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: 0.0954
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+ - Accuracy: 0.9826
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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: 3e-05
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+ - train_batch_size: 48
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+ - eval_batch_size: 32
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+ - seed: 0
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 192
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 8.0
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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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+ | 1.3624 | 1.0 | 267 | 1.1959 | 0.6546 |
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+ | 0.3854 | 2.0 | 534 | 0.2675 | 0.9734 |
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+ | 0.2473 | 3.0 | 801 | 0.1461 | 0.9768 |
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+ | 0.1997 | 4.0 | 1068 | 0.1088 | 0.9804 |
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+ | 0.1723 | 5.0 | 1335 | 0.0954 | 0.9826 |
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+ | 0.1442 | 6.0 | 1602 | 0.0927 | 0.9813 |
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+ | 0.1397 | 7.0 | 1869 | 0.0892 | 0.9812 |
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+ | 0.1368 | 7.9728 | 2128 | 0.0896 | 0.9812 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0
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+ - Pytorch 2.6.0+cu118
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+ - Datasets 3.3.1
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+ - Tokenizers 0.21.0
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+ "train_steps_per_second": 2.814
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