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audio_predict.py
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audio_predict.py
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import os
import pathlib
import keras
import numpy as np
import glob
from numpy.core.arrayprint import dtype_is_implied
import gc
import tensorflow as tf
import tensorflow.python.keras as k
import tensorflow_io as tfio
from IPython.display import Audio
import audio_functions as af
import audio_config as cfg
AUDIOPATH = "./audio/"
OUTPATH = AUDIOPATH + "out/"
model = af.build_model_v2()
model = af.compile_model(model)
model.summary()
af.load_weights(model)
def ProcessFile(fname=""):
print(f"Reading audio file '{fname}'")
data = af.read_audiofile(fname)
sl = len(data) // cfg.InputSize * cfg.InputSize
data = np.stack(np.split(data[:sl], cfg.InputSize), axis=1)
data_len = len(data) // cfg.BatchSize * cfg.BatchSize
data = data[0:data_len]
# process data in chunks
Y_pred = []
i, chunksize = 0, cfg.BatchSize * 1024
for idx in range(0, len(data), chunksize):
Y_pred += list(model.predict(data[idx:(i+1)*chunksize], verbose=1, batch_size=cfg.BatchSize))
i += 1
Y_pred = np.array(Y_pred)
# collapse batch column, reduce dimension
Y_pred = Y_pred.reshape((Y_pred.shape[0] * Y_pred.shape[1], Y_pred.shape[2]), order='F')
out_fname, file_extension = os.path.splitext(fname)
out_fname = f"{out_fname}-{cfg.Mp3BitRate}.wav"
out_fname = out_fname.replace(AUDIOPATH, OUTPATH)
af.write_audiofile_scipy(Y_pred, out_fname)
files = glob.glob(AUDIOPATH + "*.wav")
for file in files:
ProcessFile(file)
print ("Ready.")