A Google Colab notebook project designed to translate videos into multiple languages with lip-syncing. It targets content creators and researchers looking for an automated solution to dub and synchronize video content, simplifying the localization process.

How It Works

The project outlines a five-step pipeline executed within a Google Colab notebook. It begins by extracting audio from the uploaded video and transcribing it using OpenAI Whisper. The transcribed text is then translated via Google Translate. Subsequently, Coqui TTS is employed for voice cloning and synthesizing the translated text, aiming to retain the original speaker's voice characteristics. Finally, the synthesized audio is lip-synced to the original video using either OpenTalker or Wav2Lip.

Quick Start & Requirements

The project is provided as a Google Colab notebook. Users can run the notebook directly, uploading their video as the first step. Dependencies are managed within the notebook environment. No specific hardware requirements beyond standard Google Colab capabilities are mentioned. Links to the underlying repositories (Whisper, Coqui TTS, OpenTalker, Wav2Lip) are included within the notebook.

Highlighted Details

• End-to-end automated pipeline for video translation and lip-syncing.
• Leverages powerful AI models for transcription (Whisper), translation (Google Translate), voice synthesis (Coqui TTS), and lip-syncing (OpenTalker/Wav2Lip).
• Includes voice cloning functionality to synthesize translated audio in the original voice.

Maintenance & Community

The project is explicitly stated as "not maintained anymore." Users are encouraged to fork and modify the repository. No community channels or roadmaps are provided.

Licensing & Compatibility

The README does not specify a software license. Consequently, licensing terms for commercial use or integration into closed-source projects are unclear.

Limitations & Caveats

The project is no longer maintained, which may imply potential issues with outdated dependencies, unaddressed bugs, or compatibility problems with newer AI model versions. The quality of the output (translation accuracy, voice cloning fidelity, lip-sync effectiveness) is dependent on the performance of the individual AI models used and the quality of the input video and audio.