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Interactive Spoken Content Retrieval

Installation

  1. Lasagne, Theano
  2. Progressbar, tqdm
  3. tsne, cprint

Work flow

  1. Built language models from PTV transcripts * Transcript directory should be a directory with T0001,T0002,...T5047 transcription files * Specify transcript directory in src/transcript2docmodel.py * Takes approximately 6 hours mainly due to 100k (keyterm) * cmd: python src/transcript2docmodel.py
  2. Train agent * run.py: Specify data, fold, feature, experiment_prefix(directory to save results), result_directory with argparser * Other argument can be adjusted/added/altered, see for yourself * cmd: python src/run.py
  3. View Results * Use merge_csvs.py to merge result/*.log * cmd: python result/parse_log_to_csv.py $dir

Change feature

  1. Change feature type: src/IR/statemachine.py, run_training.py - if/else condition in constructor, featureExtraction & argparser

Change cost

  1. Change cost table: src/IR/actionmanager.py, possibly add another option in run_training.py, argparse

Visualize

  1. specify network pickle ,feature file, number of features, save_path with src/run_visualize.py
  2. use jupyter notebook to open result/plot_feature_action.ipynb & previous save h5 file

Other Notes

  • Don't ask me about the code and the data storage format, it's just as it is
  • I believe there are bugs in Wen's data, naming a few
    • Some keyterms/requests do not exist, can reproduce if I can access Wen's recognition transcripts
  • Other cutting methods: snownlp

About

Interactive Spoken Content Retrieval by Deep Reinforcement Learning

arxiv.org/abs/1609.05234

Topics

python lasagne speech

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