---
language:
- en
base_model: whisper
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: 'IA4GOOD '
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 1
      type: mozilla-foundation/common_voice_13_0
      config: fr
      split: test
      args: fr
    metrics:
    - name: Wer
      type: wer
      value: 16.940544564986173
---

<!-- 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. -->

# IA4GOOD 

This model is a fine-tuned version of [whisper](https://huggingface.co/whisper) on the Common Voice 1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3040
- Wer Ortho: 27.6287
- Wer: 16.9405

## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.3182        | 0.29  | 500  | 0.3040          | 27.6287   | 16.9405 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2