whisper-amharic-small-v2 β ONNX
ONNX export of chappM/whisper-amharic-small-v2
(Whisper small fine-tuned for Amharic by chappM) for
onnx-asr (standard whisper model type β works with
stock onnx-asr, no patches needed). fp32 and int8 variants included.
License: openrail, inherited from the source model.
First specialized ONNX ASR model for Amharic in this collection.
Usage
import onnx_asr
model = onnx_asr.load_model("whisper", "path/to/this/repo") # or quantization="int8"
print(model.recognize("audio_16khz.wav", language="am"))
Verified on a FLEURS Amharic (am_et) test clip (a sentence with two number sequences,
"117" and "45"):
- Reference: "α α΄αΆα½ α‘α΅α αα΅α₯ α α«α΅ α¨α α¨αΆ α°ααΈα«α³αΎα½ α©α«αΈαα αα¨α¨α΅ α αα»αα α ααα α΅ααα α«αα΅ α¨117α± α¨α α¨αΆ α°ααΈα«α³αΎα½ 45α± α αα΅α΅α© α°α¨α αααα΅ α αα»αα"
- fp32 (RTF 0.94): correct through the first clause, then degrades into a repetition loop around the digit-heavy portion of the sentence.
- int8 (RTF 0.30): also correct through the first clause, degrades less severely on the same digit-heavy portion, and terminates instead of looping.
Known limitation, not a quantization artifact: this checkpoint struggles with digit sequences spoken as words ("one hundred seventeen", "forty-five") in this test clip, in both precisions β likely a training-data gap in the base fine-tune rather than something introduced by this ONNX conversion. The first ~half of the sentence transcribes correctly and fluently in both variants; treat this export as verified-with-caveats rather than fully clean. RTF measured on an AMD Ryzen 5 7600 (CPU, 4 OMP threads, shared/loaded box β 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).