def main(_):
# REVIEW josephz: This paradigm was copied from inference-hack.py
# initialize_globals()
sample_dir = "sample"
# sample_names = ["new_test"]
sample_names = ["rolling_in_the_deep"]
post_processor = PostProcessor()
post_processor.load_weights("weights.h5")
# sample_names = ["perfect_features"]
# sample_names = ["rolling_in_the_one_more_time"]
for sample_name in sample_names:
console.h1("Processing %s" % sample_name)
console.time("total processing for " + sample_name)
sample_path = sample_dir + "/" + sample_name
style_path = sample_path + "/style.mp3"
content_path = sample_path + "/content.mp3"
stylized_img_path = sample_path + "/stylized.png"
stylized_img_raw_path = sample_path + "/stylized_raw.png"
stylized_audio_path = sample_path + "/stylized.mp3"
stylized_audio_raw_path = sample_path + "/stylized_raw.mp3"
# Read style audio to spectrograms.
style_audio, style_sample_rate = conversion.file_to_audio(style_path)
style_img, style_phase = conversion.audio_to_spectrogram(
style_audio, fft_window_size=1536)
# Read content audio to spectrograms.
content_audio, content_sample_rate = conversion.file_to_audio(
content_path)
content_img, content_phase = conversion.audio_to_spectrogram(
content_audio, fft_window_size=1536)
stylized_img_raw, stylized_img = stylize(content_img, style_img,
content_phase, style_phase,
content_path, style_path,
post_processor)
# Save raw stylized spectrogram and audio.
stylized_audio_raw = conversion.amplitude_to_audio(
stylized_img_raw,
fft_window_size=1536,
phase_iterations=15,
phase=content_phase)
conversion.image_to_file(stylized_img_raw, stylized_img_raw_path)
conversion.audio_to_file(stylized_audio_raw, stylized_audio_raw_path)
# Save post-processed stylized spectrogram and audio.
stylized_audio = conversion.amplitude_to_audio(stylized_img,
fft_window_size=1536,
phase_iterations=15,
phase=content_phase)
# np.save("stylized_img.npy", stylized_img)
# np.save("content_phase.npy", content_phase)
conversion.image_to_file(stylized_img, stylized_img_path)
conversion.audio_to_file(stylized_audio, stylized_audio_path)
console.timeEnd("total processing for " + sample_name)
console.info("Finished processing %s; saved to %s" %
(sample_name, stylized_audio_path))
#!/usr/bin/env python
import conversion
import console
import numpy as np
from post_processor import PostProcessor
post_processor = PostProcessor()
post_processor.load_weights("weights.h5")
stylized = conversion.file_to_image("sample/rolling_in_the_deep/stylized.png")
content_harmonics = conversion.file_to_image("sample/rolling_in_the_deep/content.mp3.harmonics.png")
content_sibilants = conversion.file_to_image("sample/rolling_in_the_deep/content.mp3.harmonics.png")
stylized = post_processor.predict_unstacked(amplitude=stylized, harmonics=content_harmonics, sibilants=content_sibilants)
conversion.image_to_file(stylized, "/Users/ollin/Desktop/boop.png")
