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README.md

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+---
+license: cc-by-nc-4.0
+language: [en, de]
+tags: [automatic-speech-recognition, onnx, onnx-asr, whisper, verbatim-asr]
+base_model: nyrahealth/CrisperWhisper
+---
+
+# CrisperWhisper — ONNX
+
+ONNX export of [nyrahealth/CrisperWhisper](https://huggingface.co/nyrahealth/CrisperWhisper)
+(Whisper large-v3 fine-tuned for verbatim transcription — includes filler words,
+disfluencies, pauses) for [onnx-asr](https://github.com/istupakov/onnx-asr) (standard
+`whisper` model type — works with stock onnx-asr, no patches needed). Addresses
+[onnx-asr#140](https://github.com/istupakov/onnx-asr/issues/140).
+
+fp32 and int8 variants included; int8 decoder produced by quantizing the pre-merge
+decoders and re-merging (the merged graph's If-subgraphs defeat direct quantization).
+
+**License**: original model is CC-BY-NC-4.0 (nyra health / nyralabs). This ONNX export
+is a derivative, non-commercial use only, and inherits the same license and
+attribution requirement. Credit: [nyrahealth/CrisperWhisper](https://huggingface.co/nyrahealth/CrisperWhisper).
+
+**Export gotcha (fixed here)**: `optimum`'s ONNX export re-saves the tokenizer and
+silently truncates CrisperWhisper's `vocab.json` (698KB vs the source repo's 878KB),
+shifting every special-token ID (e.g. `<|startoftranscript|>` came out as 45073
+instead of the correct 50258) and producing near-blank/looping decode output. This
+export replaces `vocab.json`/`added_tokens.json` with the originals copied verbatim
+from the source repo.
+
+## 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="en"))  # or "de"
+```
+
+Verified on FLEURS English and German clips: fp32 and int8 outputs match each other
+and the native `transformers` pipeline output exactly, for both languages. RTF on an
+AMD Ryzen 5 7600 (6C/12T CPU, shared/loaded host): ~1.7-3.0 fp32, ~0.8-1.5 int8.