Create README.md

README.md

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+---
+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
+```