Update README.md

README.md

@@ -30,6 +30,9 @@ The model uses the ECAPA-TDNN architecture that has previously been used for spe
 more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. 
 We observed that this improved the performance of extracted utterance embeddings for downstream tasks.
 
+The system is trained with recordings sampled at 16kHz (single channel).
+The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed.
+
 The model can classify a speech utterance according to the language spoken.
 It covers 107 different languages (
 Abkhazian, 
@@ -199,6 +202,8 @@ print(emb.shape)
 ```
 To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
 
+The system is trained with recordings sampled at 16kHz (single channel).
+The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*.
 
 #### Limitations and bias