---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- PhanithLIM/ams-speech-dataset
- openslr/openslr
- google/fleurs
- PhanithLIM/kh-wmc
- PhanithLIM/wmc-international-news
- PhanithLIM/rfi-news-dataset
- PhanithLIM/aakanee-kh
- rinabuoy/khm-asr-open
- seanghay/khmer_grkpp_speech
- seanghay/khmer_mpwt_speech
- seanghay/km-speech-corpus
metrics:
- wer
- cer
model-index:
- name: whisper-medium-aug-05-june
  results: []
language:
- km
---

<!-- 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-medium-aug-05-june

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0721
- Wer: 78.5554

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer     |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.1867        | 1.0   | 2847  | 0.0867          | 78.8824 |
| 0.0689        | 2.0   | 5694  | 0.0720          | 75.8348 |
| 0.0485        | 3.0   | 8541  | 0.0706          | 77.7656 |
| 0.0362        | 4.0   | 11388 | 0.0690          | 77.5133 |
| 0.0274        | 5.0   | 14235 | 0.0721          | 78.5554 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1