Update README.md

README.md

@@ -24,12 +24,12 @@ metrics:
 <br/><br/>
 
 # SepFormer trained on WHAMR! for speech enhancement (8k sampling frequency)
-This repository provides all the necessary tools to perform speech enhancement (denoising + dereverberation) with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) dataset with 8k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 8k. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset.
+This repository provides all the necessary tools to perform speech enhancement (denoising + dereverberation) with a [SepFormer](https://arxiv.org/abs/2010.13154v2) model, implemented with SpeechBrain, and pretrained on [WHAMR!](http://wham.whisper.ai/) dataset with 8k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 8k. For a better experience we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The given model performance is  dB SI-SNRi on the test set of WHAMR! dataset.
 
 
 | Release | Test-Set SI-SNR | Test-Set PESQ |
 |:-------------:|:--------------:|:--------------:|
-| 01-12-21 |  | |
+| 01-12-21 | 10.59 | 2.84 |
 
 
 ## Install SpeechBrain
@@ -48,17 +48,14 @@ 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-whamr16k", savedir='pretrained_models/sepformer-whamr16k')
+model = separator.from_hparams(source="speechbrain/sepformer-whamr-enhancement", savedir='pretrained_models/sepformer-whamr-enhancement')
 
 # for custom file, change path
-est_sources = model.separate_file(path='speechbrain/sepformer-whamr16k/test_mixture16k.wav') 
+est_sources = model.separate_file(path='speechbrain/sepformer-whamr-enhancement/example_whamr.wav') 
 
-torchaudio.save("source1hat.wav", est_sources[:, :, 0].detach().cpu(), 16000)
-torchaudio.save("source2hat.wav", est_sources[:, :, 1].detach().cpu(), 16000)
-```
+torchaudio.save("enhanced_whamr.wav", est_sources[:, :, 0].detach().cpu(), 16000)
 
-The system expects input recordings sampled at 16kHz (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
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.