import { pipeline } from "@huggingface/transformers";

const transcriber = await pipeline("automatic-speech-recognition", "<your-hf-username>/distil-whisper-large-v3-german_timestamped-ONNX", {
  device: "webgpu",
  dtype: "fp16", // or "fp32", "q4", "q8"
});

const output = await transcriber("audio.mp3", {
  chunk_length_s: 30,
  return_timestamps: "word",
});

console.log(output.text);
console.log(output.chunks?.slice(0, 3));

Recommended choices:

fp32: highest precision, typically for server-side or high-memory WebGPU
fp16: smaller WebGPU model with good speed/quality tradeoff
q8: smaller CPU/WASM model
q4: smallest model, best for constrained devices

Source Model

Base model: primeline/distil-whisper-large-v3-german
Export format: ONNX
Intended runtime: Transformers.js

flackzz/distil-whisper-large-v3-german_timestamped-ONNX

distil-whisper-large-v3-german_timestamped-ONNX

Usage (Transformers.js)

Source Model

flackzz/
distil-whisper-large-v3-german_timestamped-ONNX