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sengtha/whisper-base-khmer

sourceHugging Facecc-by-sa-4.0updated 2mo agoView on Hugging Face

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whisper-base-khmer

A Khmer speech-to-text (STT) model — `openai/whisper-base` fine-tuned on Khmer speech. Built for [iAny](https://iany.app), the offline, on-device Khmer AI platform, and released open source so anyone can build Khmer voice tools.

Runs fully on-device / offline in the iAny app and mobile app. Part of iAny's mission: the best open Khmer AI, with the community and for the community.

What it does

Transcribes spoken Khmer audio (16 kHz mono) into Khmer text. Multilingual Whisper already knows Khmer; this fine-tune sharpens it on real Khmer corpora + community phone/room audio.

Formats in this repo

Format	Files	For
PyTorch / Transformers	`model.safetensors`, `config.json`, tokenizer	training, server, research
ONNX	`onnx/encoder_model.onnx`, `onnx/decoder_model_merged.onnx`	browser / PWA (Transformers.js)
GGML	`ggml-base-khmer-q5_1.bin`	mobile / edge (whisper.cpp / whisper.rn)

(CTranslate2 for faster-whisper can be produced with ct2-transformers-converter.)

Usage

🤗 Transformers

python

from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="sengtha/whisper-base-khmer",
               chunk_length_s=30, generate_kwargs={"language": "km", "task": "transcribe"})
print(asr("audio.wav")["text"])

Transformers.js (in the browser)

import { pipeline } from '@huggingface/transformers'
const asr = await pipeline('automatic-speech-recognition', 'sengtha/whisper-base-khmer', { dtype: 'fp32' })
const { text } = await asr(audioFloat32Array, { language: 'km', task: 'transcribe' })

whisper.cpp (GGML, on mobile/edge)

bash

./whisper-cli -m ggml-base-khmer-q5_1.bin -l km -f audio.wav

Training data

—[DDD-Cambodia/khmer-speech-dataset](https://huggingface.co/datasets/DDD-Cambodia/khmer-speech-dataset) — large multi-speaker Khmer read speech (CC-BY-SA-4.0).
—[sengtha/iany-khmer-voice](https://huggingface.co/datasets/sengtha/iany-khmer-voice) — real phone/room audio contributed at iany.app/voice (CC-BY-SA-4.0), oversampled so the model weights real-world conditions. This set grows with community contributions, and the model is periodically retrained.

Evaluation

Character Error Rate (CER) is the meaningful metric for Khmer; report space-normalized CER since Khmer has no word spaces and Whisper inserts them. WER is not meaningful for Khmer (ambiguous word segmentation).

Test set	CER (space-normalized)	CER (raw)
FLEURS `km_kh` test (standard benchmark, out-of-domain read speech)	~22.8	~23.9
In-domain dev (training corpora)	~19	—

FLEURS is clean, formal read speech with foreign proper nouns, so it's a harder, out-of-domain stress test — treat it as a comparability anchor, not a ceiling.

Limitations

—Best on clear, careful speech; spontaneous/noisy phone audio in a room is harder for a base-size model. More community /voice data + retraining is the path to better real-world accuracy.
—Weak on numbers and embedded English/proper nouns.
—On low-confidence audio it can emit byte-fallback characters (rare); downstream apps may strip U+FFFD.

Intended use & responsible use

Voice input, dictation aids, transcription, and Khmer accessibility tools. It is an aid, not a certified transcription service — review output where accuracy matters.

License & attribution

Released under CC-BY-SA-4.0, inherited from the DDD-Cambodia training data. You must credit DDD-Cambodia and share derivatives alike. The base model openai/whisper-base is MIT.

Citation / credit

Trained and released by [iAny](https://iany.app) (E-KHMER Technology). Thanks to DDD-Cambodia and every contributor at iany.app/voice. Training recipe: github.com/sengtha/iAny · docs/RUNPOD-KHMER-STT.md.