Showing 4 open source projects for "audio transcript"

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  • 1
    Podcastfy.ai

    Podcastfy.ai

    Transforming Multimodal Content into Captivating Multilingual Audio

    Podcastfy is an open-source Python package that transforms multi-modal content (text, images) into engaging, multi-lingual audio conversations using GenAI. Input content includes websites, PDFs, youtube videos as well as images. Unlike UI-based tools focused primarily on note-taking or research synthesis (e.g. NotebookLM), Podcastfy focuses on the programmatic and bespoke generation of engaging, conversational transcripts and audio from a multitude of multi-modal sources enabling...
    Downloads: 0 This Week
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  • 2
    video-use

    video-use

    Edit videos with Claude Code

    Video Use is an open-source AI-powered video editing tool that allows users to transform raw footage into polished videos using natural language commands. Designed to work with Claude Code, it automates the entire editing process—from cutting clips to rendering the final output—without requiring manual timelines or complex software interfaces. The system intelligently analyzes audio transcripts and visual cues to make precise, context-aware editing decisions. It supports a wide range of...
    Downloads: 20 This Week
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  • 3
    AudioEnhancerMAX

    AudioEnhancerMAX

    Local-first AI audio processing, transcription and mastering

    AudioEnhancerMAX is an open-source, local-first audio processing suite for podcasters, creators, and audio professionals. It combines enhancement, mastering, transcription, TTS, monitoring, and trusted-LAN edge workers in one FastAPI app. The core pipeline runs on your machine: uploads, DSP processing, Whisper transcription, local Ollama/Gemma smart mode, Kokoro TTS, timing history, and reports stay under user control when local dependencies are installed. Optional Edge Neural TTS and...
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    Downloads: 18 This Week
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  • 4
    Speech Made Visible
    Speech Made Visible is an experiment in showing some of the qualities of speech in printed text. Analyze a recording for attributes like pitch, intensity (loudness), and speed; then style the words in a transcript to suggest those characteristics.
    Downloads: 1 This Week
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