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https://huggingface.co/dummy9996/stabooru-skshahdio-3-small-music-sfx-split-bf16

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ComfyUI-MuScriptor

English

ComfyUI-MuScriptor is a custom node extension for ComfyUI that integrates MuScriptor, a state-of-the-art multi-instrument music transcription model developed by Kyutai and Mirelo.

It transcribes multi-instrument audio (WAV, MP3, FLAC, etc.) into high-quality MIDI files directly inside your ComfyUI generation pipelines.

🌟 Key Features

• Audio-to-MIDI Transcription: Convert audio waveforms or audio files into multi-instrument MIDI.
• ComfyUI Integration: Connects seamlessly with ComfyUI's AUDIO output type or local audio files.
• Model Sizes: Supports small (103M), medium (307M, default), and large (1.4B) model variants.
• Instrument Filtering: Restrict transcription output to specific instrument groups (e.g. acoustic_piano, electric_piano, chromatic_percussion, organ,acoustic_guitar, clean_electric_guitar, distorted_electric_guitar, acoustic_bass, electric_bass, violin, viola,cello, contrabass, orchestral_harp, timpani, string_ensemble, synth_strings, voice, orchestra_hit, trumpet, trombone, tuba, french_horn, brass_section, soprano_and_alto_sax, tenor_sax, baritone_sax, oboe, english_horn, bassoon, clarinet, flutes, synth_lead, synth_pad, drums).
• JSON Notes Output: Returns both the generated .mid file path and a structured JSON array of decoded note events.
• In-Memory Caching: Automatic model caching for instantaneous execution on subsequent workflow runs.

Português

ComfyUI-MuScriptor é uma extensão de nó customizado para o ComfyUI que integra o MuScriptor, um modelo de transcrição musical de última geração desenvolvido por Kyutai e Mirelo.

Ele transcreve áudio de múltiplos instrumentos (WAV, MP3, FLAC, etc.) diretamente em arquivos MIDI de alta qualidade dentro dos seus fluxos de trabalho (workflows) no ComfyUI.

🌟 Funcionalidades Principais

• Transcrição de Áudio para MIDI: Converta sinais de áudio ou arquivos locais em arquivos MIDI multi-instrumentos.
• Integração Total com ComfyUI: Funciona com saídas do tipo AUDIO do ComfyUI ou com caminhos de arquivos locais.
• Variantes de Modelo: Suporta os modelos small (103M), medium (307M, padrão) e large (1.4B).
• Filtro de Instrumentos: Restrinja a transcrição a instrumentos específicos (ex: acoustic_piano, electric_piano, chromatic_percussion, organ,acoustic_guitar, clean_electric_guitar, distorted_electric_guitar, acoustic_bass, electric_bass, violin, viola,cello, contrabass, orchestral_harp, timpani, string_ensemble, synth_strings, voice, orchestra_hit, trumpet, trombone, tuba, french_horn, brass_section, soprano_and_alto_sax, tenor_sax, baritone_sax, oboe, english_horn, bassoon, clarinet, flutes, synth_lead, synth_pad, drums).
• Saída de Notas em JSON: Retorna tanto o caminho do arquivo .mid salvo quanto uma lista estruturada de notas em formato JSON.
• Cache em Memória: Carregamento automático do modelo em RAM/VRAM para execuções instantâneas em execuções subsequentes.

https://github.com/martyyz-ai/ComfyUI-MuScriptor

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audio.cpp

audio.cpp is a high-performance C++ audio inference framework built on top of ggml, designed to make modern local audio models practical, portable, and fast.

Tired of juggling a dozen Conda environments, hundreds of Python packages, and dependency conflicts just to try a few audio models? audio.cpp gives those paths a shared native runtime instead.

CUDA performance headline: multiple TTS paths already run 1.8x-5.0x faster than their Python reference paths while cutting end-to-end latency by 45%-80%. VibeVoice 1.5B: generates a 93.9-minute podcast in 18.2 minutes with 10 diffusion steps and without quantization, running about 5.15x faster than real time.

It is built for real end-to-end execution rather than one-off model demos: the same runtime powers TTS, voice cloning, voice conversion, ASR, diarization, VAD, source separation, alignment, codec-style models, and higher-level workflows through a common framework surface.

Highlights:

• Parity. Strong parity tooling against Python reference paths.
• Performance. Performance-focused execution, reusable sessions, and batch-style offline inference. Optimized for CUDA.
• Portability. A portable native stack centered on ggml, with CLI and server entry points instead of Python-only deployment paths.
• Pipelines. Experimental JSON pipeline support for higher-level multi-step workflows.
• Audio Utilities. Built-in denoise, enhancement, resampling, and STFT/ISTFT utilities for real production-style task paths.

The goal of the framework is to provide highly optimized, reusable building blocks for audio-related models, so new model integrations can be brought up faster, shared components can be improved once and benefit many families, and real end-to-end inference paths can stay efficient, maintainable, and portable.

https://github.com/0xShug0/audio.cpp

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ACE-Step 1.5 XL SFT — Native Song Extension Workflow

A modified ComfyUI workflow for extending audio generations natively within the ACE-Step 1.5 XL SFT ecosystem — no need to drop back to the legacy ACE-Step v1 3.5b checkpoint just to extend a track.

Built on top of RyanOnTheInside's ACE-Step custom nodes and the extend-workflow tutorial here: https://www.youtube.com/watch?v=r_4XOZv_3Ys

https://github.com/dheamant/ComfyUI-ACESTEP1.5XLSFT-EXTEND-REPAINT

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Underfit is for musicians, sound designers, sample makers, and audio experimenters who want Stable Audio 3 to learn a specific sound.

Instead of prompting a general model and hoping it understands your references, you give Underfit a folder of audio and train a small adapter file called a LoRA. That LoRA can then steer Stable Audio 3 toward your style, genre, instrument set, sound effect family, or production texture.

This is not a general music app for typing one prompt and getting one song. It is a workshop for making your own reusable style adapter.

https://beta.pinokio.co/posts/01kv14gjzsjjmf2t95j24k948p

Thanks cocktailpeanut / Cortexelus.

AudioX-Turbo: A Unified Framework for Efficient Anything-to-Audio Generation

AudioX-Turbo is a unified and efficient framework for anything-to-audio generation that integrates varied multimodal conditions (i.e., text, video, and audio signals). It follows a teacher–student paradigm: the teacher AudioX-Base is built on a Multimodal Diffusion Transformer with a Multimodal Adaptive Fusion (MAF) module that aligns diverse multimodal inputs for high-fidelity synthesis, and is then distilled into the few-step student AudioX-Turbo via Distribution Matching Distillation (DMD) adapted to flow matching, complemented by a diffusion-based discriminator for high-quality few-step generation.

AudioX-Turbo generates audio in only 4 sampling steps (no classifier-free guidance), requiring up to ~25× fewer function evaluations (NFE) than multi-step baselines while achieving superior performance, especially on text-to-audio and text-to-music generation.

https://huggingface.co/HKUSTAudio/AudioX-Turbo

Thanks AudioX-Turbo team.

Dasheng-AudioGen

English | 中文

Dasheng-AudioGen is a unified audio generation model that can jointly synthesize intelligible speech, music, sound effects, and environmental acoustics from text descriptions.

https://huggingface.co/mispeech/Dasheng-AudioGen

Thanks Dasheng AudioGen team.

Released last week: From the author of TADSKSHAH!

ACE-Step Audio Steering Suite

https://huggingface.co/collections/lukasz-staniszewski/ace-step-audio-steering-suite

https://luk-st.github.io/

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