---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_9_0
metrics:
- wer
model-index:
- name: cv9-special-batch8-small-concat-Fleur-Norm
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_9_0
      type: common_voice_9_0
      config: id
      split: test
      args: id
    metrics:
    - name: Wer
      type: wer
      value: 12.003680699332874
---

<!-- 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. -->

# cv9-special-batch8-small-concat-Fleur-Norm

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_9_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2567
- Wer: 12.0037

## 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: 8
- eval_batch_size: 4
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2878        | 0.72  | 1000 | 0.2612          | 14.8930 |
| 0.1133        | 1.43  | 2000 | 0.2420          | 12.9101 |
| 0.0512        | 2.15  | 3000 | 0.2399          | 12.2107 |
| 0.0389        | 2.86  | 4000 | 0.2449          | 11.9669 |
| 0.0161        | 3.58  | 5000 | 0.2567          | 12.0037 |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3