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View details ↗DISCOVER & COMPARE
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View details ↗MERT-v1-330M
m-a-pThe development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
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View details ↗MERT-v1-95M
m-a-pThe development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
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View details ↗MERT-v2-30s
m-a-p基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MERT-v2-FullSong
m-a-p🤗 MERT-v2-FullSong Music representations with full-song context 24 kHz mono · 632M parameters · 24 layers · 1,024 dimensions · 25 Hz 🎵 YuE2 project · 🚀 Quick start · 📊 MARBLE · 🎛️ Layer guide · 📄 arXiv · PDF · 📚 Citation **MERT-v2-FullSong is a bidirectional music encoder adapted to complete songs lasting 30–360 seconds.** Extract general-purpose music representations at the frame or recording level. Load with standard Hugging Face Transformers, using the familiar MERT workflow.
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View details ↗MIO-7B-Base
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View details ↗MIO-7B-Instruct
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View details ↗MuPT-v0-4096-1.97B
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View details ↗music2vec-v1
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View details ↗OProver-8B
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View details ↗SheetSage2
m-a-p🤗 SheetSage2 Music audio to editable scores Melody · Chords · Beats · Key · Structure 🎵 YuE2 project · 💻 Code · 🚀 Quick start · 📊 Benchmarks · 📄 SheetSage2 arXiv · 📄 YuE2 arXiv · YuE2 PDF · 📚 Citation **SheetSage2 turns music recordings into lead sheets and timed musical annotations.** Transcribe a complete song, edit its ABC or MIDI, render a piano preview, or extract embeddings and token predictions for your own tools.
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View details ↗YuE-s1-0.5B
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View details ↗YuE-s2-1B-general
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View details ↗YuE2-3B
m-a-p🎧 Demo · 🗳️ Music Arena · 🚀 Quick start · 🎙️ Cover · 🤖 Edit · ⚡ Speed · 📊 Benchmarks · 📄 arXiv · PDF · 📄 SheetSage2 arXiv · 📚 Citation **YuE2 is an open music generation model with frontier song quality competitive with Suno v5/v6.** Turn lyrics and a style prompt into a complete song with vocals and accompaniment, then shape its melody and chords through an editable score. > **🎧 YuE2 needs your ears** > > Help evaluate YuE2 alongside leading proprietary music models in our blind listening arena. Listen to anonymous music clips, choose your preference, and contribute to our listener preference study. > > **Listen & vote →** · No account needed.
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View details ↗YuE2-Vae
m-a-p🎧 Demo · 🚀 Quick start · ⚡ Speed · 📊 Benchmarks · 📄 arXiv · PDF · 📚 Citation **YuE2-Vae turns acoustic latents into 48 kHz stereo music.** It is the default decoder for song generation and listening, with full-song decoding, an audio encoder, and Hugging Face loading. Use it with **🤗 YuE2-3B** for full-song generation, or load it on its own to encode and decode audio. 🤗 Alternative decoder: YuE2-Vae-legacy.
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View details ↗YuE2-Vae-legacy
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