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wav2vec_final_output

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wav2vec_final_output
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---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: wav2vec_final_output
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: speech_commands
      type: speech_commands
      config: v0.02
      split: test
      args: v0.02
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.901840490797546
---

<!-- 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. -->

# wav2vec_final_output

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4410
- Accuracy: 0.9018

## 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: 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: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4588        | 1.0   | 663  | 1.2309          | 0.8763   |
| 0.6109        | 2.0   | 1326 | 0.5745          | 0.8920   |
| 0.4153        | 3.0   | 1989 | 0.4884          | 0.8953   |
| 0.3227        | 4.0   | 2652 | 0.4574          | 0.8980   |
| 0.2806        | 5.0   | 3315 | 0.4412          | 0.8994   |
| 0.207         | 6.0   | 3978 | 0.4403          | 0.9014   |
| 0.2226        | 7.0   | 4641 | 0.4479          | 0.8998   |
| 0.2577        | 8.0   | 5304 | 0.4421          | 0.9014   |
| 0.2188        | 9.0   | 5967 | 0.4408          | 0.9016   |
| 0.2082        | 10.0  | 6630 | 0.4410          | 0.9018   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1