OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Audio & Voice AI

Whisper Android

Whisper Android is an Android speech-recognition project with Java and native TensorFlow Lite app variants for offline Whisper transcription.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Whisper Android — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when building or exploring an Android Whisper transcription flow that runs with TensorFlow Lite, or when you need its model-conversion resources.

Who it is for

  • Android developers adding Whisper speech-to-text to an app.
  • Java developers seeking a TensorFlow Lite Java API example.
  • Developers who prefer the TensorFlow Lite Native API.
  • Users looking for the project's prebuilt Android APKs.

Documented uses

  • Transcribe a supported WAV audio file into text.
  • Record audio through the provided Recorder class for speech recognition.
  • Convert Whisper models into TensorFlow Lite format using the included Python script.
Before you choose

Requirements & limits

The public GitHub repository is MIT-licensed and lists prebuilt APKs in its demo_and_apk folder.

  • The Java and native variants use different TensorFlow Lite APIs.
  • The integration guide specifies 16 kHz, mono, 16-bit WAV for file transcription and recording.
  • The repository includes both pre-generated TFLite models and a Python model-conversion script.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Transcribe a WAV file with the Whisper Java app

Build and run the Java variant, then configure its documented transcription flow for a WAV file.

Before you start

  • Android Studio
  • An Android device or emulator
  • A Whisper TFLite model file and vocabulary file
  • A 16 kHz, mono, 16-bit WAV audio file

Steps

  1. Open the whisper_java project folder in Android Studio.

  2. Build and run the app on an Android device or emulator.

  3. In the documented integration flow, load the model and vocabulary, set the WAV file path, select the transcription action, and start Whisper.

Expected outcome

The listener can receive status updates and the transcribed result.

Guide limitations

  • The audio format is specified as 16 kHz, mono, 16-bit WAV.
  • The guide calls for correct file paths and exception handling.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
vilassn/whisper_android · Official homepage title · retained in its source language
How we review sourcesSuggest a correction