transformers pytorch whisper automatic-speech-recognition whisper-event generated_from_trainer ca dataset:mozilla-foundation/common_voice_13_0 base_model:openai/whisper-large license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ca
license: apache-2.0
base_model: openai/whisper-large
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Large Catalan
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 ca
      type: mozilla-foundation/common_voice_13_0
      config: ca
      split: test
      args: ca
    metrics:
    - name: Wer
      type: wer
      value: 5.070020005715919
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Large Catalan

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the mozilla-foundation/common_voice_13_0 ca dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1458
- Wer: 5.0700

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.1059        | 1.02  | 1000  | 0.1744          | 7.6342 |
| 0.0159        | 3.02  | 2000  | 0.1943          | 7.3850 |
| 0.0526        | 5.02  | 3000  | 0.1899          | 6.8522 |
| 0.058         | 7.02  | 4000  | 0.1782          | 6.7802 |
| 0.0161        | 9.02  | 5000  | 0.1995          | 6.6339 |
| 0.065         | 11.02 | 6000  | 0.1563          | 6.4544 |
| 0.082         | 13.02 | 7000  | 0.1789          | 6.0309 |
| 0.0339        | 15.02 | 8000  | 0.1509          | 5.7554 |
| 0.0581        | 17.01 | 9000  | 0.1573          | 6.0446 |
| 0.0181        | 19.01 | 10000 | 0.1838          | 5.5913 |
| 0.0188        | 21.01 | 11000 | 0.1610          | 5.4804 |
| 0.0134        | 23.01 | 12000 | 0.1821          | 5.3953 |
| 0.008         | 25.01 | 13000 | 0.1748          | 5.3804 |
| 0.0071        | 27.01 | 14000 | 0.1858          | 5.4701 |
| 0.0371        | 29.01 | 15000 | 0.1610          | 5.6599 |
| 0.0076        | 31.01 | 16000 | 0.1571          | 5.1655 |
| 0.0181        | 33.01 | 17000 | 0.1449          | 5.4558 |
| 0.0522        | 35.0  | 18000 | 0.1340          | 5.8388 |
| 0.0356        | 37.0  | 19000 | 0.1458          | 5.0700 |
| 0.0132        | 39.0  | 20000 | 0.1310          | 5.1941 |


### Framework versions

- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
