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探索 AI 模型 21
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗MIO-7B-Base
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查看详情 ↗MIO-7B-Instruct
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查看详情 ↗MuPT-v0-4096-1.97B
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查看详情 ↗music2vec-v1
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查看详情 ↗OProver-8B
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查看详情 ↗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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查看详情 ↗YuE-s1-0.5B
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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查看详情 ↗YuE-s2-1B-general
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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗YuE2-Vae-legacy
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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。