---
license: apache-2.0
base_model: honzapucalek/p6_commonvoice_16_1
tags:
- generated_from_trainer
datasets:
- honzapucalek/p6_all
metrics:
- wer
model-index:
- name: p6_commonvoice_all
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: honzapucalek/p6_all cs
      type: honzapucalek/p6_all
      config: cs
      split: test
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 0.4069800980674935
---

<!-- 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_all

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_all cs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2821
- Wer: 0.4070

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0153        | 7.35  | 1000 | 0.9578          | 0.4361 |
| 0.0015        | 14.71 | 2000 | 1.1251          | 0.4246 |
| 0.0003        | 22.06 | 3000 | 1.1868          | 0.4280 |
| 0.0001        | 29.41 | 4000 | 1.2511          | 0.4087 |
| 0.0001        | 36.76 | 5000 | 1.2821          | 0.4070 |


### Framework versions

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