---
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: 'whisper small trained on 5000 en de en'
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: xbilek25/xbilek25/train_set_5000_en_de_en
      type: mozilla-foundation/common_voice_11_0
      args: 'config: ende, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 6.089633937735203
---

<!-- 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. -->

# basic_train_basic_test 1000 similar params: per_device_train_batch_size=32, # bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the xbilek25/xbilek25/train_set_5000_en_de_en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1263
- Wer: 6.0896

## 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: 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: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1048        | 1.05  | 500  | 0.1559          | 9.2371 |
| 0.0147        | 3.02  | 1000 | 0.1256          | 7.3212 |
| 0.004         | 4.06  | 1500 | 0.1147          | 6.7054 |
| 0.0013        | 6.03  | 2000 | 0.1188          | 6.1238 |
| 0.0011        | 7.08  | 2500 | 0.1216          | 6.1923 |
| 0.0012        | 9.05  | 3000 | 0.1234          | 5.9528 |
| 0.0007        | 11.01 | 3500 | 0.1258          | 6.0896 |
| 0.0007        | 12.06 | 4000 | 0.1263          | 6.0896 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2