vad-non-streaming-asr-from-file

This example demonstrates how to use Voice Activity Detection (VAD) with non-streaming speech recognition from an audio file in Flutter with sherpa-onnx.

It uses:

Silero VAD v4 for voice activity detection
Zipformer CTC for speech recognition (default model)

Other supported ASR models: SenseVoice, Whisper. Change selectedModelIndex in lib/model_config.dart to switch models.

It works on the following platforms:

Android
iOS
Linux
macOS (both arm64 and x86_64 are supported)
Windows
Web

Features

Configurable VAD parameters (threshold, min silence, min speech, max speech)
Progress bar showing processing status
Displays elapsed time, audio duration, and RTF after processing
Displays ASR transcription for each detected segment
Click a segment to play it back

How to build

1. Download the models

Download the VAD model and an ASR model.

Default: Zipformer CTC (Chinese)

bash

cd flutter-examples/vad-non-streaming-asr-from-file/assets
rm -f .gitkeep

# VAD model
curl -SL -o silero_vad.onnx https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx

# ASR model (Zipformer CTC)
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2

# Remove files not required by the app (test wavs, vocab, etc.)
cd sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03
rm -f *.wav README.md bbpe.model bbpe.vocab
rm -rf test_wavs
cd ..

cd ..
./generate-asset-list.py

After downloading, the assets/ directory should contain only:

assets/
├── silero_vad.onnx
└── sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/
    ├── model.int8.onnx
    └── tokens.txt

Alternative: Whisper tiny.en (English)

To use Whisper instead of Zipformer CTC, download the Whisper model and change selectedModelIndex to 2 in lib/model_config.dart:

bash

cd flutter-examples/vad-non-streaming-asr-from-file/assets
rm -f .gitkeep

# VAD model
curl -SL -o silero_vad.onnx https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx

# ASR model (Whisper tiny.en)
curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2
tar xvf sherpa-onnx-whisper-tiny.en.tar.bz2
rm sherpa-onnx-whisper-tiny.en.tar.bz2

cd ..
./generate-asset-list.py

Then edit lib/model_config.dart and set const int selectedModelIndex = 2;.

Note: generate-asset-list.py is a symlink to ../tts/generate-asset-list.py.

2. Build the APP

For Linux

Install libmpv-dev first (required by the media_kit audio/video player):

bash

sudo apt-get install -y libmpv-dev
flutter build linux

For macOS

bash

flutter build macos

For Windows

bash

flutter build windows

For Android

bash

flutter build apk --split-per-abi --target-platform android-arm64

For web

bash

flutter run -d chrome

For iOS

Connect your iPhone, then:

bash

flutter devices
flutter run -d <device-id> --release