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

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: test
    metrics:
    - name: Wer
      type: wer
      value: 0.6165
---

<!-- 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-v2

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: 0.26
- Wer: 0.6165

## 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
```