Skip to content

Commit bc74b5a

Browse files
committed
Update Checkpoints
1 parent 7ca5687 commit bc74b5a

File tree

3 files changed

+86
-2
lines changed

3 files changed

+86
-2
lines changed

.gitignore

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -140,7 +140,7 @@ outputs
140140
.vscode
141141
*.mp3
142142

143-
enh-checkpoints/*
144-
sep-checkpoints/*
143+
enh-checkpoints/*.pth
144+
sep-checkpoints/*.pth
145145

146146
*.DS_Store

enh-checkpoints/README.md

Lines changed: 42 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,42 @@
1+
# 🤗 Speech Enhancement Checkpoints
2+
3+
Welcome to the Checkpoints folder! Here you will find instructions on how to download and set up pre-trained speech enhancement models for testing.
4+
5+
## Instructions
6+
7+
1. **Download Pre-trained Models**:
8+
- Visit the [GitHub Release page](https://github.com/JusperLee/LibriSpace/releases) of the LibriSpace repository.
9+
- Download the desired pre-trained model files. Each model is available as a zip file named according to the following list:
10+
11+
2. **Extract the Models**:
12+
- Extract the downloaded zip files into this `LibriSpace/enh-checkpoints` folder. Each zip file contains the pre-trained model weights and necessary files for model testing.
13+
14+
3. **Test the Models**:
15+
- Once extracted, you can use the models for testing purposes with your speech enhancement tasks. Ensure that your testing scripts point to the correct model paths.
16+
17+
## Model List
18+
19+
| File Name | Link |
20+
|-------------------------------|---------------------------------------------------------------------------|
21+
| DCCRN-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/DCCRN-Music.zip) |
22+
| DCCRN-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/DCCRN-Noise.zip) |
23+
| FastFullband-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/FastFullband-Music.zip) |
24+
| FastFullband-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/FastFullband-Noise.zip) |
25+
| Fullband-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/Fullband-Music.zip) |
26+
| Fullband-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/Fullband-Noise.zip) |
27+
| FullSubNet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/FullSubNet-Music.zip) |
28+
| FullSubNet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/FullSubNet-Noise.zip) |
29+
| FullSubNet-Plus-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/FullSubNet-Plus-Music.zip) |
30+
| FullSubNet-Plus-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/FullSubNet-Plus-Noise.zip) |
31+
| G2Net-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/G2Net-Music.zip) |
32+
| G2Net-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/G2Net-Noise.zip) |
33+
| GaGNet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/GaGNet-Music.zip) |
34+
| GaGNet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/GaGNet-Noise.zip) |
35+
| InterSubNet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/InterSubNet-Music.zip) |
36+
| InterSubNet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/InterSubNet-Noise.zip) |
37+
| SUDORMRF-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/SUDORMRF-Music.zip) |
38+
| SUDORMRF-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/SUDORMRF-Noise.zip) |
39+
| TaylorSENet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/TaylorSENet-Music.zip) |
40+
| TaylorSENet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Enh/TaylorSENet-Noise.zip) |
41+
42+
Download these model zip files from [LibriSpace GitHub Releases](https://github.com/JusperLee/LibriSpace/releases) and extract them here to start using the pre-trained models for your speech enhancement applications.

sep-checkpoints/README.md

Lines changed: 42 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,42 @@
1+
# 🤗 Speech Separation Checkpoints
2+
3+
Welcome to the Checkpoints folder! Here you will find instructions on how to download and set up pre-trained speech separation models for testing.
4+
5+
## Instructions
6+
7+
1. **Download Pre-trained Models**:
8+
- Visit the [GitHub Release page](https://github.com/JusperLee/LibriSpace/releases) of the LibriSpace repository.
9+
- Download the desired pre-trained model files. Each model is available as a zip file named according to the following list:
10+
11+
2. **Extract the Models**:
12+
- Extract the downloaded zip files into this `LibriSpace/sep-checkpoints` folder. Each zip file contains the pre-trained model weights and necessary files for model testing.
13+
14+
3. **Test the Models**:
15+
- Once extracted, you can use the models for testing purposes with your speech separation tasks. Ensure that your testing scripts point to the correct model paths.
16+
17+
## Model List
18+
19+
| File Name | Link |
20+
|-------------------------------|---------------------------------------------------------------------------|
21+
| AFRCNN-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/AFRCNN-Music.zip) |
22+
| AFRCNN-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/AFRCNN-Noise.zip) |
23+
| BSRNN-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/BSRNN-Music.zip) |
24+
| BSRNN-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/BSRNN-Noise.zip) |
25+
| DPRNN-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/DPRNN-Music.zip) |
26+
| DPRNN-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/DPRNN-Noise.zip) |
27+
| DPTNet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/DPTNet-Music.zip) |
28+
| DPTNet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/DPTNet-Noise.zip) |
29+
| Mossformer2-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/Mossformer2-Music.zip) |
30+
| Mossformer2-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/Mossformer2-Noise.zip) |
31+
| Mossformer-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/Mossformer-Music.zip) |
32+
| Mossformer-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/Mossformer-Noise.zip) |
33+
| Skim-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/Skim-Music.zip) |
34+
| Skim-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/Skim-Noise.zip) |
35+
| SuDORMRF-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/SuDORMRF-Music.zip) |
36+
| SUDORMRF-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/SUDORMRF-Noise.zip) |
37+
| TDANet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/TDANet-Music.zip) |
38+
| TDANet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/TDANet-Noise.zip) |
39+
| TFGNet-Music | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/TFGNet-Music.zip) |
40+
| TFGNet-Noise | [checkpoint](https://github.com/JusperLee/LibriSpace/releases/download/Sep/TFGNet-Noise.zip) |
41+
42+
Download these model zip files from [LibriSpace GitHub Releases](https://github.com/JusperLee/LibriSpace/releases) and extract them here to start using the pre-trained models for your speech separation applications.

0 commit comments

Comments
 (0)