A newer version of this model is available:
HiDolen/Mini-BS-RoFormer-18M
Model Card for Model ID
Model for the Music source separation task. Its implementation is referenced to the existing BS-RoFormer code.
针对音乐音频分离任务的模型。改编自 现有的 BS-RoFormer 模型代码。
Model Details
模型参数:
- depth = 3
- hidden_size = 256
- intermediate_size = 256 * 2
总参数量只有 8.8M,在 MUSDB18HQ 数据的 val 集上达到平均 SDR 6.5 的性能。分轨具体 SDR:
- bass,5.66
- drums,6.77
- other,6.06
- vocal,7.44
Uses
使用的 transformers 库版本为 4.55.4。为了正常运行模型还需要安装库 soudfile 和 einops。
CPU 推理:
from transformers import AutoModel
import soundfile
import torch
model_name = "HiDolen/Mini-BS-RoFormer"
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True,
)
# 加载音频
file = "./Bruno Mars - Runaway Baby.mp3"
waveform, sr = soundfile.read(file)
assert sr == 44100 # 采样率必须为 44100Hz
waveform = torch.tensor(waveform).T.float()
# 进行推理
result = model.separate(
waveform,
chunk_size=44100 * 6,
overlap_size=44100 * 3,
gap_size=0,
batch_size=16,
verbose=True,
)
# 保存处理结果
for i in range(result.shape[0]):
soundfile.write(f"separated_stem_{i}.wav", result[i].cpu().numpy().T, 44100)
GPU 推理:
from transformers import AutoModel
import soundfile
import torch
model_name = "HiDolen/Mini-BS-RoFormer"
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True,
)
model.to("cuda")
# 加载音频
file = "./Bruno Mars - Runaway Baby.mp3"
waveform, sr = soundfile.read(file)
assert sr == 44100 # 采样率必须为 44100Hz
waveform = torch.tensor(waveform).T.float()
waveform = waveform.to("cuda")
# 进行推理
result = model.separate(
waveform,
chunk_size=44100 * 6,
overlap_size=44100 * 3,
gap_size=0,
batch_size=16,
verbose=True,
)
# 保存处理结果
for i in range(result.shape[0]):
soundfile.write(f"separated_stem_{i}.wav", result[i].cpu().numpy().T, 44100)
Training Details
使用 MUSDB18HQ 数据进行训练。
不使用原论文中提到的 Multi-STFT 损失项以提高训练速度。
学习率 5e-4,以 batch_size=6 训练 60k 步。
Acknowledgments
- https://github.com/lucidrains/BS-RoFormer
- https://arxiv.org/abs/2309.02612 (Music Source Separation with Band-Split RoPE Transformer)
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