---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- DewiBrynJones/commonvoice_18_0_cy
metrics:
- wer
model-index:
- name: whisper-large-v3-ft-cv-cy
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: DewiBrynJones/commonvoice_18_0_cy default
      type: DewiBrynJones/commonvoice_18_0_cy
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.2026720744363593
---

<!-- 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-cv-cy

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the DewiBrynJones/commonvoice_18_0_cy default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4426
- Wer: 0.2027

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0611        | 4.0161  | 1000 | 0.3231          | 0.2272 |
| 0.0088        | 8.0321  | 2000 | 0.3693          | 0.2145 |
| 0.0008        | 12.0482 | 3000 | 0.4037          | 0.2050 |
| 0.0005        | 16.0643 | 4000 | 0.4351          | 0.2050 |
| 0.0003        | 20.0803 | 5000 | 0.4426          | 0.2027 |


### Framework versions

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