Update README.md

README.md

@@ -24,14 +24,13 @@ pipeline_tag: audio-classification
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-# Speaker Verification with ECAPA-TDNN embeddings on CNCeleb
+# Speaker Verification with ECAPA-TDNN on CNCeleb
 
-This repository provides all the necessary tools to perform speaker verification with a pretrained ECAPA-TDNN model using SpeechBrain. 
+This repository a pretrained ECAPA-TDNN model using SpeechBrain. 
 The system can be used to extract speaker embeddings as well. 
 It is trained on CNCeleb1 + CNCeleb2 training data. 
 
-For a better experience, we encourage you to learn more about
-[SpeechBrain](https://speechbrain.github.io). The model performance on CNCeleb1-test set(Cleaned) is:
+The model performance on CNCeleb1-test set(Cleaned) is:
 
 | Release | EER(%) | MinDCF(p=0.01) |
 |:-------------:|:--------------:|:--------------:|
@@ -41,15 +40,16 @@ For a better experience, we encourage you to learn more about
 ## Pipeline description
 
 This system is composed of an ECAPA-TDNN model. It is a combination of convolutional and residual blocks. The embeddings are extracted using attentive statistical pooling. The system is trained with Additive Margin Softmax Loss.  Speaker Verification is performed using cosine distance between speaker embeddings.
-You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1-ahC1xeyPinAHp2oAohL-02smNWO41Cc?usp=sharing).
+You can find our training results (models, logs, etc) [here]().
 
 ### Compute your speaker embeddings
 
 ```python
 import torchaudio
 from speechbrain.pretrained import EncoderClassifier
-classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
-signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
+classifier = EncoderClassifier.from_hparams(source="blackstone/spkrec-ecapa-cnceleb")
+
+signal, fs = torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
 embeddings = classifier.encode_batch(signal)
 ```
 The system is trained with recordings sampled at 16kHz (single channel).
@@ -59,7 +59,7 @@ The code will automatically normalize your audio (i.e., resampling + mono channe
 
 ```python
 from speechbrain.pretrained import SpeakerRecognition
-verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb")
+verification = SpeakerRecognition.from_hparams(source="blackstone/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-cnceleb")
 score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk2_snt1.wav") # Different Speakers
 score, prediction = verification.verify_files("tests/samples/ASR/spk1_snt1.wav", "tests/samples/ASR/spk1_snt2.wav") # Same Speaker
 ```