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---
library_name: mlx
license: apache-2.0
language:
- km
pipeline_tag: automatic-speech-recognition
datasets:
- seanghay/km-speech-corpus
- seanghay/khmer_mwpt_speech
tags:
- Khmer
- mlx
base_model: openai-whisper-small
model-index:
- name: whisper-small-khmer-mlx-fp32 by Kimang KHUN
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: test split of "km_kh" in google/fleurs
type: google/fleurs
metrics:
- type: wer
value: 52.3%
name: test
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
name: train split of "SLR42" in openslr/openslr
type: openslr/openslr
metrics:
- type: wer
value: 51.2%
name: test
---
# whisper-small-khmer-mlx-fp32
This model was converted to MLX format from [`openai-whisper-small`](https://github.com/openai/whisper), then fine-tined to Khmer language using two datasets:
- [seanghay/khmer_mpwt_speech](https://huggingface.com/datasets/seanghay/khmer_mpwt_speech)
- [seanghay/km-speech-corpus](https://huggingface.com/datasets/seanghay/km-speech-corpus)
It achieves the following __word error rate__ (`wer`) on 2 popular datasets:
- 52.3% on `test` split of [google/fleurs](https://huggingface.co/datasets/google/fleurs) `km-kh`
- 51.2% on `train` split of [openslr/openslr](https://huggingface.co/datasets/openslr/openslr) `SLR42`
__NOTE__ MLX format is usable for M-chip series of Apple.
## Use with mlx
```bash
pip install mlx-whisper
```
Write a python script, `example.py`, as the following
```python
import mlx_whisper
result = mlx_whisper.transcribe(
SPEECH_FILE_NAME,
path_or_hf_repo="Kimang18/whisper-small-khmer-mlx-fp32",
fp16=False
)
print(result['text'])
```
Then execute this script `example.py` to see the result.
You can also use command line in terminal
```bash
mlx_whisper --model Kimang18/whisper-small-khmer-mlx-fp32 --task transcribe SPEECH_FILE_NAME --fp16 False
```
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