---
license: apache-2.0
tags:
- generated_from_trainer
- hf-asr-leaderboard
- whisper-event
metrics:
- wer
model-index:
- name: openai/whisper-medium
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 ca
      type: mozilla-foundation/common_voice_11_0
      args: 'config: ml, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 16.15101446793939
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs ca
      type: google/fleurs
      args: 'config: ml, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 20.4
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: projecte-aina/parlament_parla clean
      type: projecte-aina/parlament_parla
      args: 'config: ml, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 21.14
---

<!-- 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. -->

# openai/whisper-base

This is an automatic speech recognition model that also does punctuation and casing. This model is for research only, **we do not recommend using this model on production environments**. See our [learnings](https://huggingface.co/softcatala/whisper-small-ca/blob/main/TRAINING.md) when training these models.

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_11_0 ca dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3608
- Wer: 16.1510

## 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: 2
- eval_batch_size: 1
- 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: 40000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.4841        | 0.1   | 4000  | 0.5078          | 26.7974 |
| 0.3116        | 0.2   | 8000  | 0.4524          | 22.9455 |
| 0.3971        | 0.3   | 12000 | 0.4281          | 21.5427 |
| 0.2965        | 0.4   | 16000 | 0.4037          | 20.3082 |
| 0.2634        | 1.09  | 20000 | 0.3875          | 18.7980 |
| 0.2163        | 1.19  | 24000 | 0.3754          | 17.8170 |
| 0.3182        | 1.29  | 28000 | 0.3695          | 16.8587 |
| 0.2201        | 1.39  | 32000 | 0.3613          | 16.5785 |
| 0.155         | 2.08  | 36000 | 0.3633          | 16.3959 |
| 0.0904        | 2.18  | 40000 | 0.3608          | 16.1510 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.10.0+cu102
- Datasets 2.8.0
- Tokenizers 0.13.2