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Update on data_proc.py - troubleshooting data_proc #71

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Update on data_proc.py - troubleshooting data_proc #71

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44 changes: 15 additions & 29 deletions ml/data_proc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,44 +2,30 @@
Testing file for data_pipeline.py.
Run this file to generate all output data folders on disk and load them into dataloaders in memory.
"""
# DON"T PUSH THIS PART TO GITHUB!! The original code works for everyone else
import sys
import os

print(sys.path) # Debugging: Check where Python is looking for modules

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
#####################################

from data_processing.audio_processor import AudioProcessor
from data_processing.spectrogram_processor import SpectrogramProcessor
from data_processing.extracted_features_processor import ExtractedFeaturesProcessor
from data_processing.audio_augment import DataAugmentProcessor
#from data_processing.data_pipeline import DataPipeline
from data_processing.data_pipeline import DataPipeline


# GENERATE OUTPUT DATA FOLDERS
# Generate output folder of all processed audio
audio_proc = AudioProcessor()
#audio_proc.process_all_audio()
audio_proc.process_all_audio()

spectroproc = SpectrogramProcessor(stft=False)
#spectroproc.process_all_images()
# spectroproc = SpectrogramProcessor(stft=True)
# spectroproc.process_all_images()

extractproc = ExtractedFeaturesProcessor(feature_type="fbank")
# extractproc = ExtractedFeaturesProcessor(feature_type="fbank")
#extractproc.process_all_images()

augment_proc = DataAugmentProcessor(audio_path=None)

# Parameters
percent = 0.5
vol_shift = 0
time_shift = 0.4 # between 0.1-0.5 seconds since clip is only 5 secs long
pitch_shift = 3 # between 1 to 4 semitones
freq_mask = 10 # between 10-50
time_mask = 30 # between 10-50

augment_proc.augment_all_audio(
["TS", "PS", "FM", "TM"], # Augmentations to apply, apply only 3 or less at once
["positive"], # Labels to augment, can be ["positive"], ["negative"], or ["positive", "negative"]
percent,
vol_shift,
time_shift,
pitch_shift,
freq_mask,
time_mask,
)

#datapipe = DataPipeline(test_size=0.2, val_size=0.3, audio_processor=audio_proc, image_processor=spectroproc)
#datapipe.process_all()

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