https://github.com/idiap/cnn_qbe_std
Implementation of the work presented in "CNN based Query by Example Spoken Term Detection"
https://github.com/idiap/cnn_qbe_std
Last synced: 10 months ago
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Implementation of the work presented in "CNN based Query by Example Spoken Term Detection"
- Host: GitHub
- URL: https://github.com/idiap/cnn_qbe_std
- Owner: idiap
- License: gpl-3.0
- Created: 2018-09-03T07:05:38.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-09-03T07:05:52.000Z (almost 8 years ago)
- Last Synced: 2025-04-07T21:41:14.920Z (about 1 year ago)
- Language: Python
- Size: 1.06 MB
- Stars: 32
- Watchers: 6
- Forks: 8
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: COPYING
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README
## Description
Implementation of the work presented in **"CNN based Query by Example Spoken Term Detection"**.
We have included some example groundtruth files for training as well as development set.
The posteriors features are extracted using the setup presented in the following paper:
**"High-performance query-by-example spoken term detection on the SWS 2013 evaluation"**.
The input feature files for training/evaluation are in pytorch readable format which are
saved as python dictionaries. The keys are the names of the files in *'groundtruth files'*
and values are the features in matrix format.
## Training
```
python query_detection_dtw_cnn.py -optim adam -learning_rate 0.0001 -input_size 152 -batch_size 50 -layers 9 -depth 30 -dropout 0.2 -loss_threshold 0.1 -n_valid 50 -max_batch_dev 250 -max_batch_train 1000
```
## Evaluation
```
python query_detection_dtw_cnn_evaluation.py -input_size 152 -depth 30 -load_model -modelpath cnn_qbe_std_model.pt -outdir outpath -query_list dev_queries_sample_list.txt -search_list search_utterances_sample_list.txt
```
## Reference
```
@inproceedings{ram2018cnn,
title={CNN based Query by Example Spoken Term Detection},
author={Ram, Dhananjay and Miculicich, Lesly and Bourlard, Herv{\'e}},
booktitle={Nineteenth Annual Conference of the International Speech Communication Association (INTERSPEECH)},
year={2018}
}
```
## Contact:
dhananjay.ram@idiap.ch