Update README.md

README.md

@@ -60,7 +60,7 @@ Please notice that we encourage you to read our tutorials and learn more about
 import torchaudio
 from speechbrain.pretrained import EncoderClassifier
 classifier = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
-signal, fs =torchaudio.load('speechbrain/spkrec-ecapa-voxceleb/example1.wav')
+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).
@@ -71,7 +71,8 @@ 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")
-score, prediction = verification.verify_files("speechbrain/spkrec-ecapa-voxceleb/example1.wav", "speechbrain/spkrec-ecapa-voxceleb/example2.flac")
+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
 ```
  The prediction is 1 if the two signals in input are from the same speaker and 0 otherwise.