| --- |
| license: apache-2.0 |
| language: [id, ind] |
| tags: [automatic-speech-recognition, onnx, onnx-asr, whisper, indonesian] |
| base_model: cahya/whisper-medium-id |
| --- |
| |
| # whisper-medium-id — ONNX |
|
|
| ONNX export of [cahya/whisper-medium-id](https://huggingface.co/cahya/whisper-medium-id) |
| (Whisper medium fine-tuned for Indonesian by [cahya](https://huggingface.co/cahya)) for |
| [onnx-asr](https://github.com/istupakov/onnx-asr) (standard `whisper` model type — works with |
| stock onnx-asr, no patches needed). fp32 and int8 variants included. |
|
|
| License: apache-2.0, inherited from the source model. |
|
|
| ## Usage |
|
|
| ```python |
| import onnx_asr |
| model = onnx_asr.load_model("whisper", "path/to/this/repo") # or quantization="int8" |
| print(model.recognize("audio_16khz.wav", language="id")) |
| ``` |
|
|
| Verified on a FLEURS Indonesian (`id_id`) test clip: |
|
|
| - Reference: "tim-tim virtual memiliki standar keunggulan yang sama dengan tim konvensional tetapi ada sedikit perbedaan" |
| - fp32: "Tim-tim virtual memiliki standar keunggulan yang sama dengan tim konvensional, tetapi ada sedikit perbedaan." (RTF 0.87) |
| - int8: identical transcript (RTF 0.34) |
|
|
| Both exact matches to the reference (modulo punctuation/casing). RTF measured on an AMD |
| Ryzen 5 7600 (CPU, 4 OMP threads, shared/loaded box — not a clean benchmark number). |
|
|
| Int8 decoder was produced by quantizing the pre-merge decoders separately and re-merging |
| (`merge_decoders(..., strict=False)`); direct quantization of the merged decoder graph does |
| not shrink it (its `If` subgraphs are skipped by onnxruntime's dynamic quantizer). |
|
|
