flux1-schnell-q8
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Explore AI models 12
muscriptor-large
cocktailpeanut**MuScriptor** is an open-weight model for **general-purpose, multi-instrument automatic music transcription (AMT)**: it converts a music recording (any genre, multiple simultaneous instruments) into a stream of notes played. This repository hosts the **large** variant (≈1.3B parameters) — the **flagship, best-quality checkpoint**. For a smaller footprint use `muscriptor-medium` (≈300M, good trade-off) or `muscriptor-small` (≈100M, fastest). `load_model` accepts a size keyword (`"small"`/`"medium"`/`"large"`), a local `.safetensors` path, or an `hf://` / `https://` URL. Weights loaded by size keyword (or any `hf://` URL) are cached in the standard Hugging Face cache (`~/.cache/huggingface/hub`, configurable via `HF_HOME`); weights fetched from a plain `http(s)://` URL are cached under `~/.cache/muscriptor/`. Input audio can be WAV or any format `libsndfile` reads (mp3, flac, ogg, m4a, …); it is resampled to 16 kHz mono internally.
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View details ↗muscriptor-medium
cocktailpeanut**MuScriptor** is an open-weight model for **general-purpose, multi-instrument automatic music transcription (AMT)**: it converts a music recording (any genre, multiple simultaneous instruments) into a stream of notes played. This repository hosts the **medium** variant (≈300M parameters), the default checkpoint downloaded by the `muscriptor` library. `muscriptor-medium` balances quality and footprint. For the best transcription quality use `muscriptor-large` (≈1.3B); for the smallest/fastest option use `muscriptor-small` (≈100M). `load_model` accepts a size keyword (`"small"`/`"medium"`/`"large"`), a local `.safetensors` path, or an `hf://` / `https://` URL. Weights loaded by size keyword (or any `hf://` URL) are cached in the standard Hugging Face cache (`~/.cache/huggingface/hub`, configurable via `HF_HOME`); weights fetched from a plain `http(s)://` URL are cached under `~/.cache/muscriptor/`. Input audio can be WAV or any format `libsndfile` reads (mp3, flac, ogg, m4a, …); it is resampled to 16 kHz mono internally.
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View details ↗muscriptor-small
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View details ↗oa
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View details ↗optimus
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View details ↗reina
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View details ↗rm
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View details ↗sf3d
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View details ↗stable-audio-3-medium
cocktailpeanut`Stable Audio 3` is a family of fast latent diffusion models (small, medium, large) for variable length audio generation and editing. Since our models can generate several minutes of audio, variable-length generations are key to avoid the cost of producing full-length generations for short sounds. We also support inpainting, enabling targeted audio editing and the continuation of short recordings. Our latent diffusion models operate on top of a novel semantic-acoustic autoencoder that projects audio into a compact latent space, enabling efficient diffusion-based generation while preserving audio fidelity and encouraging semantic structure in the latent. Finally, we run adversarial post-training to both accelerate inference and improve generation quality, reducing the number of inference steps while improving fidelity and prompt adherence. Stable Audio 3 models are trained on licensed and Creative Commons data to generate music and sounds in less than a 2s on an H200 GPU and less than a few seconds on a MacBook Pro M4. We release the weights of small and medium, that can run on consumer-grade hardware, together with their training and inference pipeline. This model can be used with: 1. the `stable-audio-3` inference and fine-tuning library 2. the `stable-audio-tools` research library We use a publicly available pre-trained T5Gemma model (t5gemma-b-b-ul2) for text conditioning. T5Gemma is redistributed under the Gemma Terms of Use.
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View details ↗stable-audio-3-small-music
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View details ↗stable-audio-3-small-sfx
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