Update model card README

README.md

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+---
+license: mit
+metrics:
+- wer
+- cer
+pipeline_tag: automatic-speech-recognition
+---
+# Whisper Small Khmer ASR
+
+Fine-tuned variant of [`openai/whisper-small`](https://huggingface.co/openai/whisper-small) for Khmer automatic speech recognition. The model was trained with the utilities in `whisper` and is intended for transcription workloads that prioritize Khmer text normalization, including numerals, currency, and date expressions.
+
+## Model Card
+
+| Attribute | Value |
+| --- | --- |
+| **Base model** | `openai/whisper-small` |
+| **Language** | Khmer (`km-KH`) |
+| **Task** | Automatic Speech Recognition (speech-to-text) |
+| **Sample rate** | 16 kHz audio, automatically resampled |
+| **Input length** | Up to 30 s clips (truncated during batching) |
+| **Finetuning data** | `asr_mixed_dataset.txt` (internal manifests, normalized through `dataset_builder.segment_text`) |
+| **Epochs** | 10 |
+| **Batch size** | 2 (gradient accumulation 1) |
+| **Optimizer** | AdamW (managed by `Seq2SeqTrainer`) |
+| **Learning rate** | 1e-6 with cosine scheduler & 1k warmup steps |
+| **Normalization** | Khmer-specific regex and rule-based normalization (`khmerspeech`, `khmercut`) |
+| **Dataset** | Training with Mixed Khmer & English audio with 199K samples (225 hours), train all khmer public dataset + humaned label dataset
+| **Training Time** | Training with Mixed precision with RTX-5090 VRAM 32GB for 1 days
+
+> Limitations: performance has been validated only on internal validation/test splits. Long-form audio, accents outside the training distribution, or noisy backgrounds may degrade accuracy.
+
+
+## Inference Examples
+
+```python
+import torch
+import torchaudio
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+
+AUDIO_PATH = "audio_path.wav" 
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model_id = "metythorn/whisper-small"
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id,
+    torch_dtype=torch_dtype,
+    low_cpu_mem_usage=True,
+    use_safetensors=True,
+)
+model.to(device)
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+speech_waveform, sr = torchaudio.load(AUDIO_PATH)
+
+# Whisper expects 16kHz mono
+if sr != 16000:
+    speech_waveform = torchaudio.functional.resample(
+        speech_waveform, 
+        orig_freq=sr, 
+        new_freq=16000
+    )
+speech_waveform = speech_waveform.squeeze().numpy()
+result = pipe(speech_waveform)
+
+print("Transcription:", result["text"])
+
+```