Learning speaker identification

README.md

@@ -48,7 +48,7 @@ This system is composed of an ECAPA-TDNN model. It is a combination of convoluti
 First of all, please install SpeechBrain with the following command:
 
 ```
-pip install git+https://github.com/speechbrain/speechbrain.git@develop
+pip install speechbrain
 ```
 
 Please notice that we encourage you to read our tutorials and learn more about
@@ -58,7 +58,7 @@ Please notice that we encourage you to read our tutorials and learn more about
 
 ```python
 import torchaudio
-from speechbrain.inference.speaker import EncoderClassifier
+from speechbrain.pretrained import EncoderClassifier
 classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
 signal, fs =torchaudio.load('tests/samples/ASR/spk1_snt1.wav')
 embeddings = classifier.encode_batch(signal)
@@ -69,7 +69,7 @@ The code will automatically normalize your audio (i.e., resampling + mono channe
 ### Perform Speaker Verification
 
 ```python
-from speechbrain.inference.speaker import SpeakerRecognition
+from speechbrain.pretrained import SpeakerRecognition
 verification = SpeakerRecognition.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="pretrained_models/spkrec-ecapa-voxceleb")
 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