Instructions to use Youngwon/whisper-large-v3-turbo_timestamped-external-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Youngwon/whisper-large-v3-turbo_timestamped-external-data with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('automatic-speech-recognition', 'Youngwon/whisper-large-v3-turbo_timestamped-external-data');
whisper-large-v3-turbo_timestamped (external data)
This repository re-packages the q4 and q4f16 ONNX weights from
onnx-community/whisper-large-v3-turbo_timestamped
(revision b3f77bf9a8c4d5ea3415827033d1ffea7955fd9a), an ONNX export of
openai/whisper-large-v3-turbo (MIT).
The only change is the file layout. Top-level weights moved from inside the .onnx file into a separate .onnx_data file (ONNX external data). The weight values are the same. With this layout, onnxruntime-web (WebGPU) can upload the weights straight to the GPU, which uses less WASM memory in the browser. Tensors inside subgraphs stay in the .onnx file.
Used by transbee.
| file | weights |
|---|---|
onnx/encoder_model_q4f16.onnx |
onnx/encoder_model_q4f16.onnx_data |
onnx/decoder_model_merged_q4f16.onnx |
onnx/decoder_model_merged_q4f16.onnx_data |
onnx/encoder_model_q4.onnx |
onnx/encoder_model_q4.onnx_data |
onnx/decoder_model_merged_q4.onnx |
onnx/decoder_model_merged_q4.onnx_data |
License
MIT, same as the original Whisper model. Copyright OpenAI.
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Model tree for Youngwon/whisper-large-v3-turbo_timestamped-external-data
Base model
openai/whisper-large-v3 Finetuned
openai/whisper-large-v3-turbo