notes

Contents:

  • C++
  • Sphinx
  • git
  • docker
  • LaTeX
  • Kaldi
  • bash
  • CUDA
  • torch
  • Python
  • java
  • javascript
  • HTML
  • css
  • pybind11
  • Protocol Buffers
  • gRPC
  • lwn.net
  • Linker and Loader
  • espnet
  • cmake
  • huggingface
  • EECS E6870 Speech Recognition
  • ncnn
  • LLVM
  • Android
  • qemu
  • sox
  • MNN
  • SIMD
  • asio
  • websocketpp
  • Operating systems
  • encoding
  • ios
  • Embedded systems
  • ssh
  • onnx
  • csharp
  • Flask
  • ARM
  • VirtualBox
  • Go
  • Whisper
    • Basics
      • transforms
      • optimum
  • Windows
  • qt
  • webassembly
  • spleeter
  • django
  • React
  • tts
  • rust
  • ELF
  • ROS2
  • OpenFst
  • Colab
  • Dart
  • Flutter
  • Keyword spotting (KWS)
  • Papers
  • Pascal
  • ggml
  • Amphion
  • HarmonyOS
  • icefall
  • RKNN
  • lhotse
  • ffmpeg
  • vlc
notes
  • Whisper
  • Basics
  • Edit on GitHub

Basics

https://github.com/microsoft/onnxruntime/issues/15235#issuecomment-1487609280

https://medium.com/microsoftazure/build-and-deploy-fast-and-portable-speech-recognition-applications-with-onnx-runtime-and-whisper-5bf0969dd56b

transforms

from transformers import AutoProcessor, pipeline
model_path = "optimum/whisper-tiny.en"
processor = WhisperProcessor.from_pretrained(model_path)

https://huggingface.co/optimum/whisper-tiny.en/blob/main/preprocessor_config.json defines the preprocessor.

optimum

Previous Next

© Copyright 2022, fangjun.

Built with Sphinx using a theme provided by Read the Docs.