Script to compute the groundtruth data using CREPE

dnn/torch/neural-pitch/run_crepe.py

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