This is a Speaker Recognition system with GUI.
For more details of this project, please see:
- (Optional)Python bindings for bob:
- install blitz, openblas, boost
pip install --user bob.extension bob.blitz bob.core bob.sp bob.ap
Note: We have a MFCC implementation on our own which will be used as a fallback when bob is unavailable. But it's not so efficient as the C implementation in bob.
Voice Activity Detection(VAD):
- Gaussian Mixture Model (GMM)
- Universal Background Model (UBM)
- Continuous Restricted Boltzman Machine (CRBM)
- Joint Factor Analysis (JFA)
Our GUI has basic functionality for recording, enrollment, training and testing, plus a visualization of real-time speaker recognition:
You can See our demo video (in Chinese). Note that real-time speaker recognition is extremely hard, because we only use corpus of about 1 second length to identify the speaker. Therefore the system doesn't work very perfect.
The GUI part is quite hacky for demo purpose and is not maintained anymore today. Take it as a reference, but don't expect it to work out of the box. Use command line tools to try the algorithms.
Command Line Tools
usage: speaker-recognition.py [-h] -t TASK -i INPUT -m MODEL Speaker Recognition Command Line Tool optional arguments: -h, --help show this help message and exit -t TASK, --task TASK Task to do. Either "enroll" or "predict" -i INPUT, --input INPUT Input Files(to predict) or Directories(to enroll) -m MODEL, --model MODEL Model file to save(in enroll) or use(in predict) Wav files in each input directory will be labeled as the basename of the directory. Note that wildcard inputs should be *quoted*, and they will be sent to glob module. Examples: Train: ./speaker-recognition.py -t enroll -i "./bob/ ./mary/ ./person*" -m model.out Predict: ./speaker-recognition.py -t predict -i "./*.wav" -m model.out