---
language:
- multilingual
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
base_model: openai/whisper-large-v3
model-index:
- name: Whisper large-v3 nan-tw
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 16.0
      type: mozilla-foundation/common_voice_16_0
      config: nan-tw
      split: test
      args: 'config: nan-tw, split: test'
    metrics:
    - type: wer
      value: 280.9248554913295
      name: Wer
---

<!-- 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 large-v3 nan-tw

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0601
- Wer: 280.9249

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer       |
|:-------------:|:-----:|:----:|:---------------:|:---------:|
| 0.2485        | 3.05  | 1000 | 0.9971          | 538.5505  |
| 0.0154        | 6.1   | 2000 | 1.0482          | 1460.5158 |
| 0.0024        | 9.15  | 3000 | 1.0330          | 261.3161  |
| 0.0014        | 12.2  | 4000 | 1.0554          | 300.3112  |
| 0.0003        | 15.24 | 5000 | 1.0601          | 280.9249  |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1