1morecupofhottea/whisper-turbo-khmer-v9

Whisper Khmer v9 (aug02)

Fine-tuned openai/whisper-large-v3-turbo for Khmer (Cambodian) speech recognition, using a custom merged BPE tokenizer (whisper-khmer-tokenizer-v3, content-first vocab layout). Trained with online audio augmentation.

• —Base model: openai/whisper-large-v3-turbo
• —Tokenizer: custom merged Khmer BPE (57,908 tokens)
• —Training: online audio augmentation (11 techniques), augment_prob=0.3, noisy eval
• —Clean eval WER: ~22% at step 1200
• —Vocab layout: content-first — custom BPE tokens precede special tokens, so the standard transformers ASR pipeline decodes them correctly.

Usage

from transformers import pipeline

pipe = pipeline("automatic-speech-recognition",
                model="1morecupofhottea/whisper-turbo-khmer-v9",
                chunk_length_s=30, device=0)

result = pipe("path/to/audio.wav")
print(result["text"])  # khmercut-segmented output with ';' word-group separators

The model outputs khmercut-segmented text (words separated by spaces, word groups by ;), which matches the training data format.