def load(self, saveDataAsH5=True):
h5Path = os.path.join(self.inPath, "data.h5")
print(h5Path)
# h5Path = '/Users/Yana/Documents/myself_doc/008_Python_Project/acapellabot/train_data/data.h5'
if os.path.isfile(h5Path):
h5f = h5py.File(h5Path, "r")
self.x = h5f["x"][:]
self.y = h5f["y"][:]
else:
# Hash bins for each camelot key so we can merge
# in the future, this should be a generator w/ yields in order to eat less memory
count = 0
for dirPath, dirNames, fileNames in os.walk(os.path.join(self.inPath, 'noisy')):
for fileName in filter(lambda f: (f.endswith(".mp3") or f.endswith(".wav")) and not f.startswith("."),
fileNames):
audio, sampleRate = conversion.loadAudioFile(os.path.join(self.inPath, 'noisy', fileName))
noisy_spectrogram, _ = conversion.audioFileToSpectrogram(audio, self.fftWindowSize)
audio, sampleRate = conversion.loadAudioFile(os.path.join(self.inPath, 'clean', fileName))
clean_spectrogram, _ = conversion.audioFileToSpectrogram(audio, self.fftWindowSize)
if noisy_spectrogram.shape[1] < clean_spectrogram.shape[1]:
newInstrumental = np.zeros(clean_spectrogram.shape)
newInstrumental[:noisy_spectrogram.shape[0], :noisy_spectrogram.shape[1]] = noisy_spectrogram
noisy_spectrogram = newInstrumental
elif clean_spectrogram.shape[1] < noisy_spectrogram.shape[1]:
newAcapella = np.zeros(noisy_spectrogram.shape)
newAcapella[:clean_spectrogram.shape[0], :clean_spectrogram.shape[1]] = clean_spectrogram
clean_spectrogram = newAcapella
# simulate a limiter/low mixing (loses info, but that's the point)
# I've tested this against making the same mashups in Logic and it's pretty close
# mashup = np.maximum(clean_spectrogram, noisy_spectrogram)
# AttributeError: 'list' object has no attribute 'shape'chop into slices so everything's the same size in a batch
dim = SLICE_SIZE
mashupSlices = chop(noisy_spectrogram, dim)
acapellaSlices = chop(clean_spectrogram, dim)
count += 1
if acapellaSlices.__len__()>0:
self.x.extend(mashupSlices)
self.y.extend(acapellaSlices)
console.info(count, "Created spectrogram for", fileName, "with length", acapellaSlices.__len__())
# Add a "channels" channel to please the network
self.x = np.array(self.x)[:, :, :, np.newaxis]
self.y = np.array(self.y)[:, :, :, np.newaxis]
console.info('Train data shape: x: ', self.x.shape, ' y: ', self.y.shape)
# Save to file if asked
if saveDataAsH5:
h5f = h5py.File(h5Path, "w")
h5f.create_dataset("x", data=self.x)
h5f.create_dataset("y", data=self.y)
h5f.close()
def isolateVocals(self, path, fftWindowSize, phaseIterations=10):
console.log("Attempting to isolate vocals from", path)
audio, sampleRate = conversion.loadAudioFile(path)
spectrogram, phase = conversion.audioFileToSpectrogram(audio, fftWindowSize=fftWindowSize)
console.log("Retrieved spectrogram; processing...")
expandedSpectrogram = conversion.expandToGrid(spectrogram, self.peakDownscaleFactor)
expandedSpectrogramWithBatchAndChannels = expandedSpectrogram[np.newaxis, :, :, np.newaxis]
print(expandedSpectrogramWithBatchAndChannels.shape)
# 预测
predictedSpectrogramWithBatchAndChannels = self.model.predict(expandedSpectrogramWithBatchAndChannels)
predictedSpectrogram = predictedSpectrogramWithBatchAndChannels[0, :, :, 0] # o /// o
newSpectrogram = predictedSpectrogram[:spectrogram.shape[0], :spectrogram.shape[1]]
console.log("Processed spectrogram; reconverting to audio")
newAudio = conversion.spectrogramToAudioFile(newSpectrogram, sampleRate, fftWindowSize=fftWindowSize, phaseIterations=phaseIterations)
pathParts = os.path.split(path)
fileNameParts = os.path.splitext(pathParts[1])
outputFileNameBase = os.path.join(pathParts[0], fileNameParts[0] + "_unet")
console.log("Converted to audio; writing to", outputFileNameBase)
conversion.saveAudioFile(newAudio, outputFileNameBase + ".wav", sampleRate)
conversion.saveSpectrogram(newSpectrogram, outputFileNameBase + ".png")
conversion.saveSpectrogram(spectrogram, os.path.join(pathParts[0], fileNameParts[0]) + ".png")
console.log("Vocal isolation complete")