Update README.md

README.md

@@ -64,7 +64,9 @@ nt_extractor.eval()
 ```
 
 You may have noticed that the model produces normalized vectors as embeddings.
-Next, we define a function that extracts the non-timbral embedding from an audio signal. In this tutorial version, the audio file is expected to be sampled at 16kHz.
+
+Next, we define a function that extracts the non-timbral embedding from an audio signal. In this tutorial version, the audio file is expected to be sampled at 16kHz. 
+Depending on the available memory (cpu or gpu), you may change the value of MAX_SIZE, which is used to truncate the long audio signals.
 
 ```
 import torchaudio
@@ -99,6 +101,8 @@ the [VoxCeleb1-clean test set](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/me
 (with a decision threshold of **0.467**). This value can be interpreted as the ability to identify speakers only with non-timbral cues. A discussion about this interpretation can be
 found in the paper mentioned hereabove, as well as other experiments showing correlations between these embeddings and non-timbral voice attributes.
 
+Please note that the EER value can vary a little depending on the MAX_SIZE defined to reduce long audios (max 30 seconds in our case).
+
 # Limitations
 The fine tuning data used to produce this model (VoxCeleb, VCTK) are mostly in english, which may affect the performance on other languages.