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In interfaces.py:
# Storing waveform in the specified device
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
Why do we need to save the audio? It takes so many resources! I commented on all the lines but it still stores the speech file. How can we don't save the audio file?
The text was updated successfully, but these errors were encountered:
This does not save the audio in any new hard drive or anything like that. Here basically the .to() methods just move data from CPU-side RAM to GPU-side VRAM. This is of course absolutely necessary - the data needs to be where the computation happens.
Thank you for your response. I used the simple demo below
classifier = foreign_class(source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP", pymodule_file="custom_interface.py", classname="CustomEncoderWav2vec2Classifier")
but every time it copied the original speech file to the local directory. Anyone experienced this?
Ah. I replied a little hastily last time. The code you quoted indeed does what I say, but I understand what you were really after. The fetch functionality does this type of copying/symlinking, and that has turned out to be a very annoying feature for many users. However we're going to implement changes soon. See discussion e.g. in PR #1268
In interfaces.py:
# Storing waveform in the specified device
wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
Why do we need to save the audio? It takes so many resources! I commented on all the lines but it still stores the speech file. How can we don't save the audio file?
The text was updated successfully, but these errors were encountered: