Whisper Large v3 Turbo - ONNX
Official OpenAI Whisper large-v3-turbo 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.
Model
| Source | openai/whisper-large-v3-turbo |
| Export format | ONNX for sherpa-onnx |
| Variants | FP32, INT8, FP16 |
| Runtime | speech-core OnnxWhisperStt / ONNX Runtime; sherpa-onnx compatible |
| Total artifact size | 5619.69 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.
Files
| File | Size | Description |
|---|---|---|
turbo-decoder.fp16.onnx |
0.59 MB | Whisper decoder graph |
turbo-decoder.fp16.onnx.data |
302.88 MB | External tensor data for the adjacent ONNX graph |
turbo-decoder.int8.onnx |
344.34 MB | Whisper decoder graph |
turbo-decoder.onnx |
0.97 MB | Whisper decoder graph |
turbo-decoder.weights |
605.76 MB | External tensor data for the adjacent ONNX graph |
turbo-encoder.fp16.onnx |
0.61 MB | Whisper encoder graph |
turbo-encoder.fp16.onnx.data |
1239.93 MB | External tensor data for the adjacent ONNX graph |
turbo-encoder.int8.onnx |
643.37 MB | Whisper encoder graph |
turbo-encoder.onnx |
0.6 MB | Whisper encoder graph |
turbo-encoder.weights |
2479.86 MB | External tensor data for the adjacent ONNX graph |
turbo-tokens.txt |
0.78 MB | Tokenizer tokens for speech-core and sherpa-onnx-compatible runtimes |
Usage
Use with speech-core's native ONNX Whisper runtime:
#include <speech_core/models/onnx_whisper_stt.h>
speech_core::OnnxWhisperStt stt(
"turbo-encoder.int8.onnx",
"turbo-decoder.int8.onnx",
"turbo-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="turbo-encoder.fp16.onnx",
decoder="turbo-decoder.fp16.onnx",
tokens="turbo-tokens.txt",
language="en",
task="transcribe",
provider="cpu",
)
Full FLEURS WER / RTF
M5 Pro, local FLEURS test split, sherpa-onnx CPU provider, language hint supplied, temperature fallback disabled.
| Dataset | INT8 WER / RTF | FP16 WER / RTF |
|---|---|---|
fleurs-en_us |
5.32% / 0.239 | 5.27% / 0.189 |
fleurs-fr_fr |
6.84% / 0.188 | 6.57% / 0.215 |
fleurs-ar_eg |
14.53% / 0.201 | 14.30% / 0.215 |
Note: the French split contains one 33.54 s clip; sherpa-onnx Whisper processes the first 30 s.
Links
- Website: soniqo.audio
- Speech Core: github.com/soniqo/speech-core
- Docs: soniqo.audio/speech-core
- Blog: soniqo.audio/blog
- Downloads last month
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Model tree for soniqo/Whisper-Large-v3-Turbo-ONNX
Base model
openai/whisper-large-v3