---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- p6_moderate
metrics:
- wer
model-index:
- name: p6_moderate
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: p6_moderate
      type: p6_moderate
      config: cs
      split: test
      args: cs
    metrics:
    - name: Wer
      type: wer
      value: 0.2500697155605131
---

<!-- 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_moderate

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the p6_moderate dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8973
- Wer: 0.2501

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0015        | 27.78  | 1000 | 0.7347          | 0.2520 |
| 0.0008        | 55.56  | 2000 | 0.7309          | 0.2523 |
| 0.0001        | 83.33  | 3000 | 0.8548          | 0.2531 |
| 0.0           | 111.11 | 4000 | 0.8869          | 0.2503 |
| 0.0           | 138.89 | 5000 | 0.8973          | 0.2501 |


### Framework versions

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