Whisper Small Khmer ASR

Fine-tuned variant of 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

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

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"])