---
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: 'basic_train_basic_test 1000 similar params: per_device_train_batch_size=8,
    # bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000'
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: xbilek25/basic_train_set_en_last3cs_1000
      type: mozilla-foundation/common_voice_11_0
      args: 'config: csen, split: train'
    metrics:
    - name: Wer
      type: wer
      value: 15.800293685756243
---

<!-- 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=8, # 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/basic_train_set_en_last3cs_1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1368
- Wer: 15.8003

## 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: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0021        | 9.01  | 600  | 0.1419          | 16.5590 |
| 0.0002        | 19.0  | 1200 | 0.1322          | 15.3842 |
| 0.0001        | 28.02 | 1800 | 0.1351          | 15.2912 |
| 0.0001        | 38.01 | 2400 | 0.1362          | 15.8639 |
| 0.0001        | 47.02 | 3000 | 0.1368          | 15.8003 |


### Framework versions

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