---
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper_small_Khmer
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: google/fleurs km_kh
      type: google/fleurs
      config: km_kh
      split: test
    metrics:
    - name: Wer
      type: wer
      value: 84.941730294506
---

<!-- 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_small_Khmer

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs km_kh dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9221
- Wer: 84.9417

## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5571        | 40.0  | 400  | 1.2022          | 89.9564 |
| 0.0088        | 80.0  | 800  | 1.7980          | 86.6669 |
| 0.0023        | 120.0 | 1200 | 1.9221          | 84.9417 |
| 0.0002        | 160.0 | 1600 | 2.0559          | 85.4326 |
| 0.0002        | 200.0 | 2000 | 2.0787          | 85.6536 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2