---
license: apache-2.0
base_model: honzapucalek/p6_commonvoice_16_1
tags:
- generated_from_trainer
datasets:
- honzapucalek/p6_severe
metrics:
- wer
model-index:
- name: p6_commonvoice_severe
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: honzapucalek/p6_severe cs
      type: honzapucalek/p6_severe
      config: cs
      split: test
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 0.47937698298240555
---

<!-- 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. -->

# p6_commonvoice_severe

This model is a fine-tuned version of [honzapucalek/p6_commonvoice_16_1](https://huggingface.co./honzapucalek/p6_commonvoice_16_1) on the honzapucalek/p6_severe cs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6996
- Wer: 0.4794

## 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0001        | 95.24  | 1000 | 1.4954          | 0.5013 |
| 0.0           | 190.48 | 2000 | 1.6008          | 0.4817 |
| 0.0           | 285.71 | 3000 | 1.6537          | 0.4817 |
| 0.0           | 380.95 | 4000 | 1.6881          | 0.4802 |
| 0.0           | 476.19 | 5000 | 1.6996          | 0.4794 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1