Comments (4)
This repo was one of our projects back in college. It uses simple supervised learning and was trained on only handful of speakers. I don't believe this repo as is can scale for your requirements where i assume you will have many more users to authenticate. However, if you job is just to differentiate valid and invalid users, you could change the output classes to 0/1, instead of one class for each user as in this repo, and retrain+fine-tune the model according to your needs.
from speaker-recognition.
Thanks for reply for your repo you used this line : from features import mfcc
i installed features library but there is no file of mfcc present inside it
please tell me what to do
from speaker-recognition.
I replace features from python_speech_features and issue is resolved to extract mfcc but will you please please send me a sample command to run python main.py
i am using this command
python main.py -train /home/user/invalid/Speaker-Recognition/train_data/ -test /home/user/invalid/Speaker-Recognition/test_data/ -recog /home/user/invalid/Speaker-Recognition/recog_speaker/
and getting this issue
ValueError: invalid literal for int() with base 10: '-test'
from speaker-recognition.
I also getting same problem but then I try this
python main.py -train /home/user/18_10/spkerRecog3/Speaker-Recognition/train_data/
it works
from speaker-recognition.
Related Issues (2)
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from speaker-recognition.