HEARING · TRANSCRIPTION · LANGUAGE
Whisper
@OPENAI
Convert human spoken sounds into text, allowing your partner to truly hear multiple languages.
Whisper handles multilingual speech recognition, translation, and language judgment with a unified multi-task model and is the foundational ear for many native companion auditory systems.
Project address (can be copied to AI):https://github.com/openai/whisper
PROJECT INTRO
Project introduction
Whisper is a general-purpose speech recognition model that can handle multi-language transcription, speech translation, language recognition and voice activity detection. Models offer speed and accuracy options in tiny, base, small, medium, large, and turbo sizes.
The project provides command line and Python calling methods, and ffmpeg needs to be installed. For a companion system, it typically serves as the basic auditory layer that converts microphone or voice messages into textual context.
- Suitable for:Requires a native or self-hosted companion system for speech transcription, language recognition and translation.
- Platform:Python runtime environment, which can use CPU or GPU.
- Usage:Install openai-whisper and ffmpeg, select the model and transcribe the audio file or connect to the real-time pipeline.
- License:Code and model weights are both MIT.
COMMENTS & FEEDBACK
Comments and Feedback
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SOURCE & CREDIT
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We do not mirror project files, nor do we intercept author traffic. For installation, download, version updates and the latest instructions, please refer to the project page provided by the original author.
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