Practical overview
When it fits
Use Wenet when you are implementing or prototyping speech-to-text with an end-to-end recognition toolkit, especially if you need both a simple transcription entry point and a documented path toward training or deployment.
Who it is for
- Developers adding speech-to-text transcription to an application.
- Teams prototyping or building end-to-end automatic speech recognition systems.
- Practitioners who need documented paths from package-based transcription to training and runtime deployment.
Documented uses
- Transcribe an audio file from the command line or Python.
- Prototype an automatic speech recognition feature using an example model.
- Set up the toolkit and dependencies for training or deployment work.
Requirements & limits
The repository is public and identifies the project as open source under the Apache-2.0 license.
- The package-level transcription example is distinct from the fuller environment setup described for training and deployment.
- CPU is the default device; GPU use must be requested explicitly.
- The README cautions that a PyTorch build installed by pip may not suit the installed GPU driver.
- Building the optional x86 runtime or language-model support requires CMake 3.14 or above.
- The README lists model options for Chinese and English command-line use.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Transcribe an audio file with the Python package
Install Wenet and use its documented Python example to request a transcript.
Before you start
- Python environment with pip
- An audio file such as audio.wav
- PyTorch, installed as a package dependency
Steps
Install Wenet from its GitHub repository with pip.
In Python, load the paraformer model with wenet.load_model('paraformer').
Call model.transcribe('audio.wav') and read the result's text field.
Expected outcome
The example returns a transcription result whose text can be printed.
Guide limitations
- The example does not specify audio-format constraints or transcription accuracy.
- GPU use requires selecting a CUDA device explicitly; CPU is the default, and GPU operation depends on a compatible PyTorch build and driver.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- wenet-e2e/wenet · Official homepage title · retained in its source language