---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: whisper-base-common-voice-16-pt-v8
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: pt
      split: test
      args: pt
    metrics:
    - name: Wer
      type: wer
      value: 26.192630898513254
---

<!-- 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-base-common-voice-16-pt-v8

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4574
- Wer: 26.1926
- Wer Normalized: 20.0029

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 16
- 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: 7000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Wer Normalized |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.4883        | 0.74  | 1000 | 0.3803          | 28.0317 | 21.8327        |
| 0.2659        | 1.48  | 2000 | 0.3677          | 26.3688 | 20.1666        |
| 0.1251        | 2.22  | 3000 | 0.3730          | 26.3752 | 20.4620        |
| 0.1071        | 2.96  | 4000 | 0.3867          | 25.5026 | 19.5470        |
| 0.0523        | 3.7   | 5000 | 0.4148          | 25.7094 | 19.6851        |
| 0.02          | 4.44  | 6000 | 0.4491          | 25.6803 | 19.5759        |
| 0.0134        | 5.18  | 7000 | 0.4574          | 26.1926 | 20.0029        |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.0