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soniqo/Whisper-Medium-ONNX
Whisper-Medium-ONNX is a automatic speech recognition model from soniqo. Use it when you need speech turned into text. It is set up for onnxruntime. The card lists the license as apache-2.0.
Official OpenAI Whisper medium checkpoint exported as ONNX encoder/decoder graphs for speech-core and sherpa-onnx-compatible runtimes.
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From the Hugging Face model README
Official OpenAI Whisper medium checkpoint exported as ONNX encoder/decoder graphs for speech-core and sherpa-onnx-compatible runtimes.
Part of the soniqo.audio speech toolkit. This is the ONNX Runtime bundle used by
speech-corefor server, desktop, and Android-style runtimes; the graphs remain sherpa-onnx compatible. Browse ONNX bundles in the soniqo ONNX collection.
| Source | openai/whisper-medium |
| Export format | ONNX for sherpa-onnx |
| Variants | FP32, INT8, FP16 |
| Runtime | speech-core OnnxWhisperStt / ONNX Runtime; sherpa-onnx compatible |
| Total artifact size | 5277.42 MiB |
The FP16 graphs use external *.onnx.data files. Large-style bundles also use external FP32 *.weights files. Keep external-data files beside their matching .onnx files.
| File | Size | Description |
|---|---|---|
medium-decoder.fp16.onnx | 1.62 MB | Whisper decoder graph |
medium-decoder.fp16.onnx.data | 774.93 MB | External tensor data for the adjacent ONNX graph |
medium-decoder.int8.onnx | 544.56 MB | Whisper decoder graph |
medium-decoder.onnx | 1551.82 MB | Whisper decoder graph |
medium-encoder.fp16.onnx | 0.44 MB | Whisper encoder graph |
medium-encoder.fp16.onnx.data | 682.02 MB | External tensor data for the adjacent ONNX graph |
medium-encoder.int8.onnx | 356.81 MB | Whisper encoder graph |
medium-encoder.onnx | 1364.44 MB | Whisper encoder graph |
medium-tokens.txt | 0.78 MB | Tokenizer tokens for speech-core and sherpa-onnx-compatible runtimes |
Use with speech-core's native ONNX Whisper runtime:
#include <speech_core/models/onnx_whisper_stt.h>
speech_core::OnnxWhisperStt stt(
"medium-encoder.int8.onnx",
"medium-decoder.int8.onnx",
"medium-tokens.txt");
auto result = stt.transcribe(audio, length, 16000);
The same encoder/decoder/token files can also be loaded by sherpa-onnx:
import sherpa_onnx
recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
encoder="medium-encoder.fp16.onnx",
decoder="medium-decoder.fp16.onnx",
tokens="medium-tokens.txt",
language="en",
task="transcribe",
provider="cpu",
)
This repository ships the exported model artifacts. Full published WER/RTF is currently available for the turbo bundle; run the benchmark command above for local numbers on this variant.