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---
license: cc-by-nc-sa-4.0
---
### Description
This model is used to separate reverb and delay effects in vocals. In addition, it can also separate partial harmony, but it cannot completely separate them. I added random high cut after the reverberation and delay effects in the dataset, so the model's handling of high frequencies is not particularly aggressive.<br>
You can try listening to the performance of this model [here](./examples)!
### How to use the model?
Try it with [ZFTurbo's Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training)
### Model
Configs: [config_dereverb-echo_mel_band_roformer.yaml](./config_dereverb-echo_mel_band_roformer.yaml)<br>
Model: [dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt](./dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt)<br>
Instruments: [dry, other]<br>
Finetuned from: `model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt`<br>
Datasets:
- Training datasets: 270 songs from [opencpop](https://github.com/wenet-e2e/opencpop) and [GTSinger](https://github.com/GTSinger/GTSinger)
- Validation datasets: 30 songs from my own collection
- All random reverbs and delay effects are generated by [this python script](./scripts/create_reverb_delay.py) and sorted into the mustb18 dataset format.
Metrics: Based on the sdr value of 30 songs for validation.
```
Instr dry sdr: 13.1507 (Std: 4.1088)
Instr dry l1_freq: 53.7715 (Std: 13.3363)
Instr dry si_sdr: 12.7707 (Std: 4.6134)
Instr other sdr: 6.8830 (Std: 2.5547)
Instr other l1_freq: 52.7358 (Std: 11.8587)
Instr other si_sdr: 5.9448 (Std: 2.8721)
Metric avg sdr : 10.0169
Metric avg l1_freq : 53.2536
Metric avg si_sdr : 9.3577
```
### Training log
Training logs: [train.log](./train.log)<br>
The following image is the TensorBoard visualization training log generated by [this script](./scripts/start_tensorboard.py).
### Thanks
- Mel-Band-Roformer [[Paper](https://arxiv.org/abs/2310.01809), [Repository](https://github.com/lucidrains/BS-RoFormer)]
- [ZFTurbo](https://github.com/ZFTurbo)'s training code [[Music-Source-Separation-Training](https://github.com/ZFTurbo/Music-Source-Separation-Training)]
- [CN17161](https://github.com/CN17161) provided GPUs.
- [Glucy-2](https://github.com/Glucy-2) provided technical assistance. |