whisper-tiny-khmer-mlx-fp32
This model was converted to MLX format from openai-whisper-tiny, then fine-tined to Khmer language using three datasets:
- seanghay/khmer_mpwt_speech
- seanghay/km-speech-corpus
trainsplit of openslr/openslrSLR42
It achieves the following word error rate (wer) on 2 popular datasets:
- 77.3% on
testsplit of google/fleurskm-kh
NOTE MLX format is usable for M-chip series of Apple.
Use with mlx
pip install mlx-whisper
Write a python script, example.py, as the following
import mlx_whisper
result = mlx_whisper.transcribe(
SPEECH_FILE_NAME,
path_or_hf_repo="mlx-community/whisper-tiny-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
mlx_whisper --model mlx-community/whisper-tiny-khmer-mlx-fp32 --task transcribe SPEECH_FILE_NAME --fp16 False
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Dataset used to train mlx-community/whisper-tiny-khmer-mlx-fp32
Space using mlx-community/whisper-tiny-khmer-mlx-fp32 1
Evaluation results
- test on test split of "km_kh" in google/fleursself-reported77.3%