---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: w2v2-ks-jpqd-quant-all-finetuned-student
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v2-ks-jpqd-quant-all-finetuned-student

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.
It achieves the following results on the evaluation set:
- Loss: 0.0933
- Accuracy: 0.9769

This model is quantized. The input is also quantized.
Structured Sparsity in transformer block linear layers is 64%. 

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 12.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4481        | 1.0   | 399  | 0.2105          | 0.9469   |
| 5.6584        | 2.0   | 798  | 5.5480          | 0.9428   |
| 8.7915        | 3.0   | 1197 | 8.6634          | 0.9601   |
| 10.4775       | 4.0   | 1596 | 10.2819         | 0.9553   |
| 10.9142       | 5.0   | 1995 | 10.7770         | 0.9657   |
| 10.9478       | 6.0   | 2394 | 10.7637         | 0.9660   |
| 0.2765        | 7.0   | 2793 | 0.1335          | 0.9678   |
| 0.2532        | 8.0   | 3192 | 0.1075          | 0.9732   |
| 0.2837        | 9.0   | 3591 | 0.1109          | 0.9700   |
| 0.2           | 10.0  | 3990 | 0.1006          | 0.9765   |
| 0.1742        | 11.0  | 4389 | 0.0930          | 0.9776   |
| 0.1718        | 12.0  | 4788 | 0.0933          | 0.9769   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2