Tools, frameworks, and models for local AI
17 resources found for "Audio"
The node-based visual workflow for diffusion models: the standard for SDXL, FLUX, video and audio. Maximum flexibility, from simple to professional pipelines.
Reimplementation of Whisper based on CTranslate2: up to 4 times faster than openai/whisper with the same accuracy and less memory. Supports 8-bit quantization on both CPU and GPU.
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.
Hugging Face's Python library for running the pipelines of generative models: image, video, and audio. The standard way to run models like Qwen-Image locally, with CUDA support and CPU offload for lower-memory GPUs.
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.
Sintesi vocale in locale a bassa latenza basata su ONNX: genera parlato naturale da testo in tempo quasi reale, anche su CPU. Multilingue (italiano incluso) e facile da integrare nei pipeline: download del modello, una riga di comando. MIT.
A Swift/MLX runtime for Kyutai's streaming speech recognition on Apple Silicon. The reference checkpoint is the STT-1B (English and French) in INT8/FP16, with a causal Mimi encoder (32 audio codes every 80ms). Supports INT4/INT8 exports. CC-BY-4.0 weights license.
Stability AI (London, UK): the Stable Diffusion models for image, video and audio generation, open-weight with permissive licenses. The Stable models in the catalog are the child resources.
Up to 47 seconds of text-to-audio: high-quality music and sound effects. Excellent for local audio production and sound design.
100% local macOS dictation app: on-device transcription with Whisper and a rewrite pass with a local LLM (Qwen) on the M-series GPU. Text lands in the target app; no data touches the cloud.
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.
Il port C/C++ ad alte prestazioni di Whisper: trascrizione vocale in locale ottimizzata per CPU e Apple Silicon, senza dipendenze da Python. Quantizzazione GGML (Q4_0–Q8_0) con riduzione fino al 65% a disco, streaming via whisper-stream, accelerazione Metal/Vulkan. MIT.
