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benderrodriguez/whisper-large-v3-turbo-webgpu
whisper-large-v3-turbo-webgpu is a automatic speech recognition model from benderrodriguez. Use it when you need speech turned into text. It is set up for whisper-gpu. The card lists the license as apache-2.0.
ivrit-ai/whisper-large-v3-turbo repacked for whisper-gpu, a from-scratch TypeScript + WGSL Whisper engine that runs in the browser. These files are not loadable by transformers or whisper.cpp — they are a flat tensor…
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Updated Jul 27, 2026
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From the Hugging Face model README
ivrit-ai/whisper-large-v3-turbo repacked for whisper-gpu,
a from-scratch TypeScript + WGSL Whisper engine that runs in the browser. These files are not
loadable by transformers or whisper.cpp — they are a flat tensor dump laid out for WebGPU storage
buffers.
| file | contents |
|---|---|
manifest.json | model config, audio config, shard table, and a tensor index (name → shard, offset, shape, dtype) |
tokenizer.json | vocabulary, merges, special tokens, language ids |
weights-*.bin | raw tensor bytes, packed f16 |
Weights are stored as packed f16 and read in the shaders via unpack2x16float with f32
accumulation, so no adapter needs the optional shader-f16 feature. Every kernel fits the WebGPU
default limits (256 invocations per workgroup, 16 KiB workgroup storage, 128 MiB storage binding),
which is what lets the same shaders run on iOS Safari.
import { Whisper } from "whisper-gpu";
import { FetchSource } from "whisper-gpu/loader";
const src = new FetchSource(
"https://huggingface.co/benderrodriguez/whisper-large-v3-turbo-webgpu/resolve/main",
);
const whisper = await Whisper.load(ctx, src);
for await (const seg of whisper.transcribe(audio, { language: "he" })) console.log(seg.text);
Apache 2.0, inherited from the base model.