Áudio
audio.cpp
A pure C++ ggml inference engine for audio models — TTS, STT, VAD, voice conversion and music.
★ 3,4 mil estrelasNOASSERTION
Ver o guia →GPA é um projeto de áudio de código aberto de AutoArk (AutoArk/GPA no GitHub). Tem 3.109 estrelas no GitHub e 206 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 25 de maio de 2026.
GPA — one tiny model that does ASR, TTS and voice conversion (General Purpose Audio).
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
ASR, TTS and voice conversion in a single tiny model instead of three separate systems.
Native inference and training pipelines with published TTS/ASR evaluation tables.
GPA-TTS ships INT8/FP16/FP32 and INT4 ONNX variants for constrained hardware.
CLI, API and UI around the v1.5 ONNX runtime for easy integration.
GPA-0.3B-preview and GPA-v1.5 checkpoints are released, with a public roadmap (deployment recipes and a native VC path are listed as next steps).
An LLM-based design for both audio generation and understanding.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/AutoArk/GPA.git
cd GPAClone the repo and follow the GPA-v1.5 (GPA_1.5/) or GPA-TTS (GPA_TTS/) folder's own README for environment setup, checkpoints and inference commands.
git clone https://github.com/AutoArk/GPA.git
cd GPA # see GPA_1.5/README.md or GPA_TTS/README.md for the exact commandsUse the v1.5 ONNX Runtime CLI/API/UI or the GPA-TTS runtime as documented in their sub-READMEs.
# see README for the exact command in GPA_1.5/ or GPA_TTS/It is maintained by AutoArk on GitHub (AutoArk/GPA), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
No — GPA runs locally from released checkpoints; there is no hosted API key involved.
Compared with separate specialist models for ASR, TTS and VC, GPA's distinguishing focus is: gPA — one tiny model that does ASR, TTS and voice conversion (General Purpose Audio). Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/AutoArk/GPA (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
A pure C++ ggml inference engine for audio models — TTS, STT, VAD, voice conversion and music.
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A mental-model-first guide from waveforms to voice systems.
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Easily train a good VC model with voice data <= 10 mins!
Os outros projetos de Áudio com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se GPA não for o ideal para você.
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Projetos de Áudio próximos no catálogo, os mais próximos em estrelas de GPA: continue explorando dos dois lados dele.
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A pure C++ ggml inference engine for audio models — TTS, STT, VAD, voice conversion and music.
MARS5 speech model from CAMB.AI — clone a voice from 5 seconds of audio with striking prosody
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Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.