---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- fsicoli/cv16-fleurs
metrics:
- wer
model-index:
- name: whisper-small-pt-cv16-fleurs
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fsicoli/cv16-fleurs default
      type: fsicoli/cv16-fleurs
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.14422069232381535
---

<!-- 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. -->

# whisper-small-pt-cv16-fleurs

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fsicoli/cv16-fleurs default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2154
- Wer: 0.1442

## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3915        | 0.47  | 1000 | 0.2978          | 0.1858 |
| 0.2942        | 0.93  | 2000 | 0.2500          | 0.1626 |
| 0.2877        | 1.4   | 3000 | 0.2336          | 0.1536 |
| 0.2303        | 1.87  | 4000 | 0.2225          | 0.1482 |
| 0.2192        | 2.33  | 5000 | 0.2154          | 0.1442 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2