---
language:
- km
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- openslr
- google/fleurs

metrics:
- wer

model-index:
- name: Whisper Small Khmer Spaced - Seanghay Yath
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Google FLEURS
      type: google/fleurs
      config: km_kh
      split: all
    metrics:
    - name: Wer
      type: wer
      value: 0.6464
---

<!-- 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4657
- Wer: 0.6464

## Model description

This model is fine-tuned with Google FLEURS & OpenSLR (SLR42) dataset.

- [ggml-model.bin](https://huggingface.co/seanghay/whisper-small-khmer/blob/main/ggml-model.bin)
- [model.onnx](https://huggingface.co/seanghay/whisper-small-khmer/blob/main/model.onnx)

```python
from transformers import pipeline

pipe = pipeline(
    task="automatic-speech-recognition",
    model="seanghay/whisper-small-khmer",
)

result = pipe("audio.wav",
  generate_kwargs={
    "language":"<|km|>",
    "task":"transcribe"},
    batch_size=16
)

print(result["text"])
```


## whisper.cpp


### 1. Transcode the input audio to 16kHz PCM

```shell
ffmpeg -i audio.ogg -ar 16000 -ac 1 -c:a pcm_s16le output.wav
```

### 2. Transcribe with whisper.cpp

```shell
./main -m ggml-model.bin -f output.wav --print-colors --language km
```


## Training and evaluation data

- `training` = google/fleurs['train+validation'] + openslr['train']
- `eval` = google/fleurs['test']

## Training procedure

This model was trained based on the project on [GitHub](https://github.com/seanghay/whisper-tiny-khmer) with an NVIDIA A10 24GB.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6.25e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2065        | 3.37  | 1000 | 0.3403          | 0.7929 |
| 0.0446        | 6.73  | 2000 | 0.2911          | 0.6961 |
| 0.008         | 10.1  | 3000 | 0.3578          | 0.6627 |
| 0.003         | 13.47 | 4000 | 0.3982          | 0.6564 |
| 0.0012        | 16.84 | 5000 | 0.4287          | 0.6512 |
| 0.0004        | 20.2  | 6000 | 0.4499          | 0.6419 |
| 0.0001        | 23.57 | 7000 | 0.4614          | 0.6469 |
| 0.0001        | 26.94 | 8000 | 0.4657          | 0.6464 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.1.dev0
- Tokenizers 0.13.3