---
language:
- eu
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Small Basque
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_13_0 eu
      type: mozilla-foundation/common_voice_13_0
      config: eu
      split: test
      args: eu
    metrics:
    - type: wer
      value: 12.839726193851513
      name: Wer
---

# Whisper Small Basque

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_13_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2287
- Wer: 12.8397

If you need to use this model with [whisper.cpp](https://github.com/ggerganov/whisper.cpp), you can download the ggml file: [ggml-medium-eu.bin](https://huggingface.co/xezpeleta/whisper-medium-eu/blob/main/ggml-medium.eu.bin)

## 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: 4
- eval_batch_size: 8
- 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: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4415        | 0.06  | 500  | 0.5092          | 36.9699 |
| 0.4206        | 0.12  | 1000 | 0.4144          | 28.3365 |
| 0.272         | 0.19  | 1500 | 0.3554          | 24.7438 |
| 0.2681        | 0.25  | 2000 | 0.3271          | 22.1414 |
| 0.2099        | 0.31  | 2500 | 0.2973          | 19.5350 |
| 0.2283        | 0.38  | 3000 | 0.2760          | 18.5042 |
| 0.1477        | 1.03  | 3500 | 0.2637          | 17.1493 |
| 0.1008        | 1.09  | 4000 | 0.2592          | 16.3939 |
| 0.0866        | 1.15  | 4500 | 0.2561          | 15.8066 |
| 0.0915        | 1.21  | 5000 | 0.2411          | 15.0310 |
| 0.0803        | 1.28  | 5500 | 0.2330          | 14.7616 |
| 0.0674        | 1.34  | 6000 | 0.2325          | 13.8462 |
| 0.0679        | 1.4   | 6500 | 0.2299          | 13.5809 |
| 0.027         | 2.05  | 7000 | 0.2304          | 13.3805 |
| 0.0231        | 2.11  | 7500 | 0.2287          | 12.8397 |
| 0.0285        | 2.18  | 8000 | 0.2304          | 12.8883 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2