OpenVoiceOS/whisper-wu-onnx

whisper-wu — ONNX

ONNX export of kaiwang0574/whisper-wu (a LoRA adapter over openai/whisper-small, fine-tuned for Wu Chinese by kaiwang0574) for onnx-asr (standard whisper model type — works with stock onnx-asr, no patches needed). fp32 and int8 variants included.

The source repo only publishes a PEFT/LoRA adapter (adapter_config.json + adapter_model.safetensors, base_model_name_or_path: openai/whisper-small), not a merged checkpoint. This export merges the adapter into the base model (peft.merge_and_unload()) before running the standard ONNX export pipeline.

License: apache-2.0, inherited from the source model.

First specialized ONNX ASR model for Wu Chinese in this collection.

Usage

Whisper has no dedicated wuu language token; use language="zh" — the Wu-dialect behavior comes from the fine-tune itself.

import onnx_asr
model = onnx_asr.load_model("whisper", "path/to/this/repo")  # or quantization="int8"
print(model.recognize("audio_16khz.wav", language="zh"))

Verified on a clip from MagicHub/magicdata-dialect-wu-chinese-tts-lite (Jiangsu dialect corpus; FLEURS does not cover Wu Chinese):

• Reference: 倷今朝阿去园林里白相？里向个荷花开的交关好看。
• fp32 (RTF 0.21): 內徑在安赤與林立巴線裡,只剩個好伙計的接歸喊口。
• int8 (RTF 0.17): 內境在安赤與林立巴線,只像個好火開的接歸駭口。

Verified with caveats, honestly: the ONNX fp32 output was cross-checked against the merged model run natively through transformers on the same clip, and the two match closely ("內徑在安赤與林立巴線裡,只剩下個好火開的接歸客戶。") — confirming the ONNX conversion is faithful. The transcript itself, however, diverges substantially from the reference text. This is a real accuracy limitation of the small LoRA adapter (rank 16, q/k/v/o_proj only) on this low-resource dialect, not an artifact of this export. Treat this as a correctly converted mirror of the source model, not a claim of strong Wu Chinese accuracy. 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).