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72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
"""
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Perform Data Augmentation (Gain, Additive Noise, Random Filtering) on Input TTS Data
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1. Read in chunks and compute clean pitch first
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2. Then add in augmentation (Noise/Level/Response)
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- Adds filtered noise from the "Demand" dataset, https://zenodo.org/record/1227121#.XRKKxYhKiUk
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- When using the Demand Dataset, consider each channel as a possible noise input, and keep the first 4 minutes of noise for training
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3. Use this "augmented" audio for feature computation, and compute pitch using CREPE on the clean input
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Notes: To ensure consistency with the discovered CREPE offset, we do the following
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- We pad the input audio to the zero-centered CREPE estimator with 80 zeros
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- We pad the input audio to our feature computation with 160 zeros to center them
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"""
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument('data', type=str, help='input raw audio data')
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parser.add_argument('output', type=str, help='output directory')
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parser.add_argument('--gpu-index', type=int, help='GPU index to use if multiple GPUs',default = 0,required = False)
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parser.add_argument('--chunk-size-frames', type=int, help='Number of frames to process at a time',default = 100000,required = False)
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args = parser.parse_args()
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_index)
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import numpy as np
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import tqdm
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import crepe
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data = np.memmap(args.data, dtype=np.int16,mode = 'r')
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# list_features = []
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list_cents = []
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list_confidences = []
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min_period = 32
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max_period = 256
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f_ref = 16000/max_period
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chunk_size_frames = args.chunk_size_frames
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chunk_size = chunk_size_frames*160
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nb_chunks = (data.shape[0]+79)//chunk_size+1
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output_data = np.zeros((0,2),dtype='float32')
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for i in tqdm.trange(nb_chunks):
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if i==0:
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chunk = np.concatenate([np.zeros(80),data[:chunk_size-80]])
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elif i==nb_chunks-1:
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chunk = data[i*chunk_size-80:]
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else:
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chunk = data[i*chunk_size-80:(i+1)*chunk_size-80]
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chunk = chunk/np.array(32767.,dtype='float32')
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# Clean Pitch/Confidence Estimate
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# Padding input to CREPE by 80 samples to ensure it aligns
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_, pitch, confidence, _ = crepe.predict(chunk, 16000, center=True, viterbi=True,verbose=0)
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pitch = pitch[:chunk_size_frames]
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confidence = confidence[:chunk_size_frames]
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# Filter out of range pitches/confidences
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confidence[pitch < 16000/max_period] = 0
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confidence[pitch > 16000/min_period] = 0
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pitch = np.reshape(pitch, (-1, 1))
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confidence = np.reshape(confidence, (-1, 1))
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out = np.concatenate([pitch, confidence], axis=-1, dtype='float32')
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output_data = np.concatenate([output_data, out], axis=0)
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output_data.tofile(args.output)
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