---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- DewiBrynJones/oscar-cy-tts
metrics:
- wer
model-index:
- name: whisper-large-v3-ft-tts-cy
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: DewiBrynJones/oscar-cy-tts default
      type: DewiBrynJones/oscar-cy-tts
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.10755268881722042
---

<!-- 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. -->

# whisper-large-v3-ft-tts-cy

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the DewiBrynJones/oscar-cy-tts default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- Wer: 0.1076

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.2469        | 0.3232 | 1000 | 0.2313          | 0.1733 |
| 0.1578        | 0.6464 | 2000 | 0.1785          | 0.1373 |
| 0.1491        | 0.9696 | 3000 | 0.1531          | 0.1213 |
| 0.099         | 1.2928 | 4000 | 0.1434          | 0.1129 |
| 0.0874        | 1.6160 | 5000 | 0.1365          | 0.1076 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1