---
library_name: transformers
license: mit
base_model: Sunbird/asr-whisper-large-v3-salt
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: cdli-whisper-en-ug-sunbird-encoder-a40
  results: []
---

<!-- 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. -->

# cdli-whisper-en-ug-sunbird-encoder-a40

This model is a fine-tuned version of [Sunbird/asr-whisper-large-v3-salt](https://huggingface.co/Sunbird/asr-whisper-large-v3-salt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9443
- Wer: 0.3339
- Cer: 0.2373

## Test Results 

- epoch                   =     7.7177
- test_cer                =     0.1415
- test_loss               =     0.6851
- test_runtime            = 0:10:08.37
- test_samples_per_second =      1.665
- test_steps_per_second   =      0.418
- test_wer                =     0.2246

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- training_steps: 2500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 0.7206        | 0.7734 | 250  | 0.9708          | 0.3777 | 0.2721 |
| 0.5798        | 1.5445 | 500  | 0.9283          | 0.3481 | 0.2501 |
| 0.5444        | 2.3155 | 750  | 0.9325          | 0.3467 | 0.2508 |
| 0.5114        | 3.0866 | 1000 | 0.9377          | 0.3461 | 0.2485 |
| 0.4933        | 3.8600 | 1250 | 0.9307          | 0.3341 | 0.2398 |
| 0.4684        | 4.6311 | 1500 | 0.9362          | 0.3433 | 0.2463 |
| 0.4699        | 5.4022 | 1750 | 0.9397          | 0.3278 | 0.2327 |
| 0.4224        | 6.1732 | 2000 | 0.9451          | 0.3331 | 0.2363 |
| 0.4961        | 6.9466 | 2250 | 0.9438          | 0.3339 | 0.2372 |
| 0.4316        | 7.7177 | 2500 | 0.9443          | 0.3339 | 0.2373 |


### Framework versions

- Transformers 4.52.0
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.4