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wav2vec2-base-finetuned-ks

Audio Classification
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
wav2vec2-base-finetuned-ks
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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
- recall
- f1
model-index:
- name: wav2vec2-base-finetuned-ks
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: superb
      type: superb
      config: ks
      split: validation
      args: ks
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9832303618711385
    - name: Recall
      type: recall
      value: 0.9664413018718482
    - name: F1
      type: f1
      value: 0.9719648106690262
---

<!-- 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. -->

# wav2vec2-base-finetuned-ks

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0711
- Accuracy: 0.9832
- Recall: 0.9664
- F1: 0.9720

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| 0.4397        | 1.0   | 798  | 0.2810          | 0.9651   | 0.9289 | 0.9361 |
| 0.2067        | 2.0   | 1597 | 0.1142          | 0.9769   | 0.9536 | 0.9593 |
| 0.1881        | 3.0   | 2395 | 0.0829          | 0.9821   | 0.9644 | 0.9693 |
| 0.1167        | 4.0   | 3194 | 0.0752          | 0.9831   | 0.9644 | 0.9726 |
| 0.13          | 5.0   | 3990 | 0.0711          | 0.9832   | 0.9664 | 0.9720 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1