---
license: apache-2.0
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: jpqd-wav2vec2-base-ft-keyword-spotting
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# jpqd-wav2vec2-base-ft-keyword-spotting

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the
superb dataset, using [superb/wav2vec2-base-superb-ks](https://huggingface.co/superb/wav2vec2-base-superb-ks) as a teacher model

It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the 
JPQD image classification example.


It achieves the following results on the evaluation set:
- Loss: 0.5632
- Accuracy: 0.9756

## 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: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2245        | 1.0   | 399  | 2.2351          | 0.6209   |
| 6.9856        | 2.0   | 798  | 7.0597          | 0.7354   |
| 10.013        | 3.0   | 1197 | 9.8779          | 0.8069   |
| 11.3484       | 4.0   | 1596 | 11.1949         | 0.8719   |
| 11.6849       | 5.0   | 1995 | 11.5479         | 0.9014   |
| 11.5921       | 6.0   | 2394 | 11.4193         | 0.9495   |
| 0.8911        | 7.0   | 2793 | 0.7334          | 0.9500   |
| 0.8965        | 8.0   | 3192 | 0.6553          | 0.9685   |
| 0.7198        | 9.0   | 3591 | 0.6213          | 0.9669   |
| 0.7372        | 10.0  | 3990 | 0.5929          | 0.9675   |
| 0.7004        | 11.0  | 4389 | 0.5720          | 0.9721   |
| 0.6195        | 12.0  | 4788 | 0.5632          | 0.9756   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2