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mravanelli / SincNet

SincNet is a neural architecture for efficiently processing raw audio samples.
MIT License
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how to make TIMIT_labels.npy file ? #12

Closed eeric closed 5 years ago

eeric commented 5 years ago

how to make TIMIT_labels.npy file ?

mravanelli commented 5 years ago

Hi! The TIMIT_labels.npy is a simple python dictionary that has sentence_id as key and an integer as value. The integers (that range between 0 and N_speakers-1) are the labels of our speaker-identification system and tell us which is the speaker that is speaking in each utterance. The construction of this dictionary depends on how the dataset is organized and where the speaker-id information is stored. For TIMIT is very easy. If you consider a certain file (e.g., TIMIT/train/dr3/fcmg0/sx72.wav, the folder "fcmg0" is the speaker. You only have to associate "fcmg0" to a unique integer (from 0 to N_speakers-1), which is different from that used for the other speakers.

Mirco

On Fri, 25 Jan 2019 at 03:46, eeric notifications@github.com wrote:

how to make TIMIT_labels.npy file ?

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eeric commented 5 years ago

yeh, why did you use .npy fomat?

mravanelli commented 5 years ago

Pickle is probably the best option, but it is possible to save/load a dictionary also using Numpy. It has very simple save/loading function (using np.save, np.load).

But yes, you can use any format here. What is really important is to end up with a dictionary with the information reported above.

On Fri, 25 Jan 2019 at 20:07, eeric notifications@github.com wrote:

yeh, why did you use .npy fomat?

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eeric commented 5 years ago

ok, thank you !