---
license: mit
base_model: distil-whisper/distil-large-v3
tags:
- generated_from_trainer
datasets:
- ravnursson_asr
metrics:
- wer
model-index:
- name: distil-whisper-large-fo-100h-5k-steps
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: ravnursson_asr
      type: ravnursson_asr
      config: ravnursson_asr
      split: test
      args: ravnursson_asr
    metrics:
    - name: Wer
      type: wer
      value: 13.55445943225287
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/setur/huggingface/runs/z2f3h1m3)
# distil-whisper-large-fo-100h-5k-steps

This model is a fine-tuned version of [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) on the ravnursson_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2075
- Wer: 13.5545

## 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4469        | 0.2320 | 1000 | 0.5128          | 30.3201 |
| 0.2995        | 0.4640 | 2000 | 0.3383          | 21.1295 |
| 0.2338        | 0.6961 | 3000 | 0.2666          | 17.4351 |
| 0.2009        | 0.9281 | 4000 | 0.2270          | 14.9940 |
| 0.0963        | 1.1601 | 5000 | 0.2075          | 13.5545 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1