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opus/dnn/torch/neural-pitch
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Jean-Marc Valin f0ec990dba
Switching to neural pitch estimator 
Remove old pitch estimator and retrain all models
2023-10-06 03:14:56 -04:00
..
data_augmentation.py Python code for neural pitch 2023-09-26 12:12:47 -04:00
download_demand.sh Python code for neural pitch 2023-09-26 12:12:47 -04:00
evaluation.py refactoring and cleanup 2023-09-29 15:31:45 +02:00
experiments.py Python code for neural pitch 2023-09-26 12:12:47 -04:00
export_neuralpitch_weights.py First version of pitch DNN C code 2023-10-01 03:59:17 -04:00
models.py Remove unneeded (I think) tanh at the end 2023-10-01 21:34:58 -04:00
neural_pitch_update.py refactoring and cleanup 2023-09-29 15:31:45 +02:00
ptdb_process.sh Python code for neural pitch 2023-09-26 12:12:47 -04:00
README.md Python code for neural pitch 2023-09-26 12:12:47 -04:00
run_crepe.py Script to compute the groundtruth data using CREPE 2023-09-27 13:00:12 -04:00
training.py Switching to neural pitch estimator 2023-10-06 03:14:56 -04:00
utils.py Python code for neural pitch 2023-09-26 12:12:47 -04:00

README.md

Neural Pitch Estimation

  • Dataset Installation

    1. Download and unzip PTDB Dataset: wget https://www2.spsc.tugraz.at/databases/PTDB-TUG/SPEECH_DATA_ZIPPED.zip unzip SPEECH_DATA_ZIPPED.zip

    2. Inside "SPEECH DATA" above, run ptdb_process.sh to combine male/female

    3. To Download and combine demand, simply run download_demand.sh

  • LPCNet preparation

    1. To extract xcorr, add lpcnet_extractor.c and add relevant functions to lpcnet_enc.c, add source for headers/c files and Makefile.am, and compile to generate ./lpcnet_xcorr_extractor object

  • Dataset Augmentation and training (check out arguments to each of the following)

    1. Run data_augmentation.py
    2. Run training.py using augmented data
    3. Run experiments.py