Update README.md

README.md

@@ -2,7 +2,7 @@
 library_name: mlx
 license: apache-2.0
 language:
-- kh
+- km
 pipeline_tag: automatic-speech-recognition
 datasets:
 - seanghay/km-speech-corpus
@@ -42,8 +42,8 @@ This model was converted to MLX format from [`openai-whisper-tiny`](https://gith
 - [seanghay/km-speech-corpus](https://huggingface.com/datasets/seanghay/km-speech-corpus)
 
 It achieves the following __word error rate__ (`wer`) on 2 popular datasets:
-- ??? on `test` split of [google/fleurs](https://huggingface.co/datasets/google/fleurs) `km-kh`
-- ??? on `train` split of [openslr/openslr](https://huggingface.co/datasets/openslr/openslr) `SLR42`
+- 93.8% on `test` split of [google/fleurs](https://huggingface.co/datasets/google/fleurs) `km-kh`
+- 69.7% on `train` split of [openslr/openslr](https://huggingface.co/datasets/openslr/openslr) `SLR42`
 
 __NOTE__ MLX format is usable for M-chip series of Apple.
 
@@ -68,4 +68,4 @@ Then execute this script `example.py` to see the result.
 You can also use command line in terminal
 ```bash
 mlx_whisper --model Kimang18/whisper-tiny-khmer-mlx-fp32 --task transcribe SPEECH_FILE_NAME --fp16 False
-```
+```