Audio
Retrieval-based-Voice-Conversion-WebUI
Easily train a good VC model with voice data <= 10 mins!
★ 38,6 Tsd. SterneMIT
Anleitung ansehen →dograh ist ein Open-Source-Projekt aus der Kategorie Audio von dograh-hq (dograh-hq/dograh auf GitHub). Es hat 5.800 Sterne auf GitHub und 1.471 Forks, wird unter der Lizenz BSD-2-Clause veröffentlicht und wurde zuletzt am 2. Oktober 2026 auf GitHub aktualisiert.
Open source voice AI platform. Self-hosted alternative to Vapi and Retell. On Prem, BYOK across Speech to Speech or LLM/STT/TTS, with a visual workflow build…
Transcription, synthesis, voice changing, or editing — each tool here majors in one.
Binaries, pip packages, or Docker; models download on first run for AI tools.
Drop in a recording or type what you want spoken.
WAV/MP3/OGG outputs drop straight into editors or apps.
Built on Python, so it plugs into the wider data and ML tooling ecosystem.
Tell speakers apart in meetings and interviews automatically.
Clone a voice from a short sample for narration and dubbing.
Whisper-grade transcription and natural neural voices, free.
Many run fully on-device — nothing you say leaves the machine.
Open licenses let you ship voice features without per-minute API bills.
Download the source and move into the project folder.
git clone https://github.com/dograh-hq/dograh.git
cd dograhEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at dograh-hq/dograh with 1,471 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Licensed under BSD-2-Clause — check the repo for commercial-use terms.
Most of these run fully offline after the model download.
Check each license — many allow it, but voice-cloning has ethical and legal boundaries.
Whisper-class models rival paid services on clear audio; noisy recordings need cleanup first.
Easily train a good VC model with voice data <= 10 mins!
🧠 Leon is your open-source personal assistant.
A robust, efficient, low-latency speech-to-text library with advanced voice activity detection, wake word activation and instant transcription.
Fish Speech — Fish Audio's expressive open text-to-speech and voice cloning: SOTA-quality TTS you can self-host, under the Fish Audio Community License (check terms)
Private cross-platform speech-to-text with LLM cleanup built in.
Benchmark voice-cloning robustness across 32 TTS/VC models.
Die anderen Projekte der Kategorie Audio mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls dograh nicht das Richtige ist.
利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
Robust Speech Recognition via Large-Scale Weak Supervision
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
Port of OpenAI's Whisper model in C/C++
Verwandte Projekte der Kategorie Audio im Katalog, die dograh bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
Silero Models: pre-trained text-to-speech models made embarrassingly simple
Fast and local neural text-to-speech engine
Human-level text-to-speech through style diffusion and adversarial training with speech language models.
Automatic Speech Recognition with Speaker Diarization based on OpenAI Whisper
On-device Speech AI for Apple Silicon
Open-source AI video localization and dubbing for YouTube/Bilibili: speech recognition, subtitle translation, voice cloning, audio mixing and rendering. 开源 A…
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.