Tools, frameworks, and models for local AI
9 resources found for "Audio"
La taglia 12B della famiglia Gemma 4 di Google: 11,95B dense, contesto 256K e multimodalità integrata senza encoder separati (testo+immagini+audio). Licenza Apache 2.0. In Q4_K_M occupa circa 7,4GB: il punto di equilibrio tra qualità e requisiti hardware per l'uso locale su 8-16GB.
Compact model from Google's Gemma 4 family: 8B total parameters, of which 4.5B effective thanks to per-layer embeddings (PLE). 131K-token context, 140+ languages, text+image and audio input. The entry size of the Gemma 4 family for local use on 8-16GB.
Kandinsky Lab's foundation models for synchronized video and audio generation: from a prompt they generate video clips with aligned audio, released as open weights. Top paper of the day on Hugging Face (2026-10-06), with checkpoints and Spaces already available.
82-million-parameter speech synthesis model on a VITS architecture, Apache-2.0 for the weights, with voices in dozens of languages and surprisingly high quality for its size. Runs efficiently on CPU alone.
Generates up to 30 seconds of music or sound effects from a text description. The open reference for music models: light and versatile.
Modello di sintesi vocale multilingue di NVIDIA: 357M parametri, 12 lingue (italiano e francese inclusi), architettura encoder-decoder transformer. Compatto e pensato per esecuzione in locale a bassa latenza; pesi disponibili su Hugging Face.
11B end-to-end full-duplex voice model: audio understanding and generation in a single architecture, without the cascaded ASR→LLM→TTS stack. First open full-duplex model with tool calling during conversation, ~450ms responses, interruptions, and natural turns.
Up to 47 seconds of text-to-audio: high-quality music and sound effects. Excellent for local audio production and sound design.
Il modello di trascrizione di riferimento per l'IA locale: 809M di parametri, 99 lingue, licenza MIT. Versione distillata di Large-v3 con decoder ridotto da 32 a 4 layer: trascrive circa 8 volte più veloce con perdita di accuratezza trascurabile. Gira in tempo reale su una GPU consumer e anche solo su CPU.
