---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- honzapucalek/hc_impaired_all_v3
metrics:
- wer
model-index:
- name: hc-impaired-all-v3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: honzapucalek/hc_impaired_all_v3 cs
      type: honzapucalek/hc_impaired_all_v3
      config: cs
      split: test
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 0.11072981011210249
---

<!-- 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. -->

# hc-impaired-all-v3

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the honzapucalek/hc_impaired_all_v3 cs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3837
- Wer: 0.1107

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0275        | 6.87  | 1000 | 0.2212          | 0.1163 |
| 0.0021        | 13.75 | 2000 | 0.3051          | 0.1123 |
| 0.0004        | 20.62 | 3000 | 0.3517          | 0.1113 |
| 0.0001        | 27.49 | 4000 | 0.3760          | 0.1104 |
| 0.0001        | 34.36 | 5000 | 0.3837          | 0.1107 |


### Framework versions

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