README.md

---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- Kiniu/go_dataset
metrics:
- wer
model-index:
- name: whisper_go
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: go_dataset
      type: Kiniu/go_dataset
      config: go_dataset
      split: train
      args: 'split: test'
    metrics:
    - name: Wer
      type: wer
      value: 190.5437352245863
---

<!-- 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. -->

# whisper_go

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the go_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Wer: 190.5437

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0369        | 4.71  | 1000 | 0.2629          | 76.9137  |
| 0.0015        | 9.41  | 2000 | 0.3291          | 110.8992 |
| 0.0018        | 14.12 | 3000 | 0.3530          | 181.1038 |
| 0.0006        | 18.82 | 4000 | 0.3608          | 190.5437 |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2