Model for the music source separation task, decomposes audio into four stems: bass, drums, other, and vocals. 针对音乐音频分离的 BS-RoFormer 模型，将音频分离为 bass、drums、other、vocals。 在 MUSDB18HQ 数据的 val 集上的性能（单位 SDR，越高越好）： 使用的 transformers 库版本为 5.18.0，PyTorch 版本为 2.14.1。为了正常运行模型还需要安装库 soundfile、einops 和 librosa。 GPU 推理： 以上代码会分离出 bass、drums、other 和 vocal 四个轨道。 Mini-BS-RoFormer-V2.5 相比于 V2 的主要改进： 2. 利用注意力池化达成各 stem 共享 mask 解码器 3. 去掉 MUSDB18HQ 数据集中的坏数据进行训练 使用 MUSDB18HQ 数据集的 train 和 test 集进行训练。 学习率 3e-4，以 batchsize=8 训练 246k 步。 对 transformer 层使用 Muon 优化器，其他网络层使用 AdamW 优化器。 - https://github.com/lucidrains/BS-RoFormer - https://arxiv.org/abs/2309.02612 (Music Source Separation with Band-Split RoPE Transformer)
Independent publisher
HiDolen
HiDolen
Audio and LLM
Models in Library1
Datasets in Library0
Models on Hugging Face5
Followers6
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Published tasks: Audio to audio.
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