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echogarden

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An easy-to-use speech toolset. Includes tools for synthesis, recognition, alignment, speech translation, language detection, source separation and more.

Package Exports

    This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (echogarden) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

    Readme

    Echogarden

    Echogarden is an easy-to-use speech toolset that includes a variety of speech processing tools.

    • Easy to install, run, and update
    • Runs on Windows (x64), macOS (x64, ARM64) and Linux (x64, ARM64)
    • Written in TypeScript, for the Node.js runtime
    • Doesn't require Python, Docker, or other system-level dependencies
    • Doesn't rely on essential platform-specific binaries. Engines are either ported via WebAssembly, imported using the ONNX runtime, or written in pure JavaScript

    Features

    • Text-to-speech using the VITS neural architecture, and 15 other offline and online engines, including cloud services by Google, Microsoft, Amazon, OpenAI and Elevenlabs
    • Speech-to-text using OpenAI Whisper, and several other engines, including cloud services by Google, Microsoft, Amazon and OpenAI
    • Speech-to-transcript alignment using several variants of dynamic time warping (DTW, DTW-RA), including support for multi-pass (hierarchical) processing, or via guided decoding using Whisper recognition models. Supports 100+ languages
    • Speech-to-text translation, translates speech in any of the 98 languages supported by Whisper, to English, with near word-level timing for the translated transcript
    • Speech-to-translated-transcript alignment attempts to synchronize spoken audio in one language, to a provided English-translated transcript, using the Whisper engine
    • Language detection identifies the language of a given audio or text. Provides Whisper or Silero engines for audio, and TinyLD or FastText for text
    • Voice activity detection attempts to identify segments of audio where voice is active or inactive. Includes WebRTC VAD, Silero VAD, RNNoise-based VAD and a custom Adaptive Gate
    • Speech denoising attenuates background noise from spoken audio. Includes the RNNoise engine
    • Source separation isolates voice from any music or background ambience. Supports the MDX-NET deep learning architecture
    • Word-level timestamps for all recognition, synthesis, alignment and translation outputs
    • Advanced subtitle generation, accounting for sentence and phrase boundaries
    • For the VITS and eSpeak-NG synthesis engines, includes enhancements to improve TTS pronunciation accuracy: adds text normalization (e.g. idiomatic date and currency pronunciation), heteronym disambiguation (based on a rule-based model) and user-customizable pronunciation lexicons
    • Internal package system that auto-downloads and installs voices, models and other resources, as needed

    Installation

    Ensure you have Node.js v18.0.0 or later installed.

    then:

    npm install echogarden -g

    Additional required tools:

    • ffmpeg: used for codec conversions
    • sox: used for the CLI's audio playback

    Both tools are auto-downloaded as internal packages on Windows and Linux.

    On macOS, only ffmpeg is currently auto-downloaded. It is recommended to install sox via a system package manager like Homebrew (brew install sox) to ensure it is available on the system path.

    Updating to latest version

    npm update echogarden -g

    Using the toolset

    Tools are accessible via a command-line interface, which enables powerful customization and is especially useful for long-running bulk operations.

    Development of more graphical and interactive tooling is planned. A text-to-speech browser extension is currently under development (but not released yet).

    If you are a developer, you can also import the package as a module or interface with it via a local WebSocket service (currently experimental).

    Documentation

    Credits

    This project consolidates, and builds upon the effort of many different individuals and companies, as well as contributing a number of original works.

    Developed by Rotem Dan (IPA: /ˈʁɒːtem ˈdän/).

    License

    GNU General Public License v3

    Licenses for components, models and other dependencies are detailed on this page.