Update README.md

README.md

@@ -27,11 +27,11 @@ metrics:
 
 This repository provides all the necessary tools to perform audio source separation with a [SepFormer](https://arxiv.org/abs/2010.13154v2) 
 model, implemented with SpeechBrain, and pretrained on Libri3Mix dataset. For a better experience we encourage you to learn more about
-[SpeechBrain](https://speechbrain.github.io). The model performance is 19.8 dB SI-SNRi on the test set of Libri3Mix dataset.
+[SpeechBrain](https://speechbrain.github.io). The model performance is 8.88 dB SI-SNRi on the test set of Libri4Mix 48k dataset.
 
 | Release | Test-Set SI-SNRi | Test-Set SDRi |
 |:-------------:|:--------------:|:--------------:|
-| 16-09-22 | 19.0dB | 19.4dB |
+| 29-01-24 | 8.88dB | 9.44dB |
 
 
 ## Install SpeechBrain
@@ -39,7 +39,10 @@ model, implemented with SpeechBrain, and pretrained on Libri3Mix dataset. For a
 First of all, please install SpeechBrain with the following command:
 
 ```
-pip install speechbrain
+!git clone https://github.com/hahmadraza/speechbrain_48k.git
+%cd speechbrain_48k
+!pip install -r requirements.txt
+!pip install --editable .
 ```
 
 Please notice that we encourage you to read our tutorials and learn more about
@@ -51,17 +54,18 @@ Please notice that we encourage you to read our tutorials and learn more about
 from speechbrain.pretrained import SepformerSeparation as separator
 import torchaudio
 
-model = separator.from_hparams(source="speechbrain/sepformer-libri3mix", savedir='pretrained_models/sepformer-libri3mix')
+model = separator.from_hparams(source="hahmadraz/sepformer-libri4mix", savedir='pretrained_models/sepformer-libri4mix-48k/')
 
 est_sources = model.separate_file(path='speechbrain/sepformer-wsj03mix/test_mixture_3spks.wav') 
 
-torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 8000)
-torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 8000)
-torchaudio.save("source3hat.wav", est_sources[:, :, 2].detach().cpu(), 8000)
+torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 48000)
+torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 48000)
+torchaudio.save("source3hat.wav", est_sources[:, :, 2].detach().cpu(), 48000)
+torchaudio.save("source3hat.wav", est_sources[:, :, 3].detach().cpu(), 48000)
 
 ```
 
-The system expects input recordings sampled at 8kHz (single channel).
+The system expects input recordings sampled at 48kHz (single channel).
 If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface.
 
 ### Inference on GPU
@@ -86,7 +90,7 @@ pip install -e .
 cd  recipes/LibriMix/separation
 python train.py hparams/sepformer.yaml --data_folder=your_data_folder
 ```
-Note: change num_spks to 3 in the yaml file.
+Note: change num_spks to 4 in the yaml file.
 
 
 You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1DN49LtAs6cq1X0jZ8tRMlh2Pj6AecClz).