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ba17_stdm_1's Introduction

Machine Learning for Speaker Clustering

Bachelor's Thesis - by Patrick Gerber and Sebastian Glinski-Haefeli

Processing

Pre-Processing

  • src/core/spectrogram_generation/main_data_extractor.py: Extract mel-spectrograms from speaker audio files and save them to data/training/TIMIT_extracted in .pickle file format

Processing (Training)

  • src/network_runner.py: Wrapper functions to start training a model with appropriate parameters. In this case it's the network src/nets/bilstm_2layer_dropout_plus_2dense.py regarding the results from the bachelor's thesis ba17_stdm_1.

Post-Processing

  • src/keras_cluster_output_generator.py: Execute clustering procedure on appropriate extracted speaker data with the previously trained network (-model)
  • src/clustering/cluster_tester.py: Calculate missclassification rate and optionally show a scatter plot for processed clusterings

Required folder structure

  • data/experiments/logs/
  • data/experiments/nets/
  • data/experiments/plots/
  • data/speaker_lists/
  • data/training/TIMIT/
  • data/training/TIMIT_extracted/

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