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README.md

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+---
+license: apache-2.0
+language: [wuu]
+tags: [automatic-speech-recognition, onnx, onnx-asr, whisper, wu-chinese]
+base_model: kaiwang0574/whisper-wu
+---
+
+# whisper-wu — ONNX
+
+ONNX export of [kaiwang0574/whisper-wu](https://huggingface.co/kaiwang0574/whisper-wu)
+(a LoRA adapter over `openai/whisper-small`, fine-tuned for Wu Chinese by
+[kaiwang0574](https://huggingface.co/kaiwang0574)) 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.
+
+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.
+
+```python
+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](https://huggingface.co/datasets/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).