def _process_utterance(out_dir,wav_path,sp2ind_dir,text):
sp_f = open(sp2ind_dir,'r')
sp2ind = json.load(sp_f)
sp = wav_path.split('/')[-1].split('.')[0].split('_')[0]
if sp in sp2ind:
sp_ind = sp2ind[sp]
else:
sp_ind = -1
wav = audio.load_wav(wav_path)
if not 'test' in wav_path:
wav,_ = librosa.effects.trim(wav,top_db=60,frame_length=2048,hop_length=512)
if hparams.highpass_cutoff > 0.0:
wav = audio.low_cut_filter(wav, hparams.sample_rate, hparams.highpass_cutoff)
if is_mulaw_quantize(hparams.input_type):
# Trim silences in mul-aw quantized domain
silence_threshold = 0
if silence_threshold > 0:
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
start, end = audio.start_and_end_indices(out, silence_threshold)
wav = wav[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels - 1)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
constant_values = P.mulaw(0.0, hparams.quantize_channels - 1)
out_dtype = np.float32
else:
# [-1, 1]
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.logmelspectrogram(wav).astype(np.float32).T
mfcc = audio.mfcc(wav).astype(np.float32).T
if hparams.global_gain_scale > 0:
wav *= hparams.global_gain_scale
# Time domain preprocessing
if hparams.preprocess is not None and hparams.preprocess not in ["", "none"]:
f = getattr(audio, hparams.preprocess)
wav = f(wav)
# Clip
if np.abs(wav).max() > 1.0:
print("""Warning: abs max value exceeds 1.0: {}""".format(np.abs(wav).max()))
# ignore this sample
#return ("dummy", "dummy","dummy", -1,-1, "dummy")
wav = np.clip(wav, -1.0, 1.0)
# Set waveform target (out)
if is_mulaw_quantize(hparams.input_type):
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
elif is_mulaw(hparams.input_type):
out = P.mulaw(wav, hparams.quantize_channels - 1)
else:
out = wav
# zero pad
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.pad_lr(out, hparams.fft_size, audio.get_hop_size())
if l > 0 or r > 0:
out = np.pad(out, (l, r), mode="constant", constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
# Write the spectrograms to disk:
#name = splitext(basename(wav_path))[0]
#audio_filename = '%s-wave.npy' % (name)
#mel_filename = '%s-feats.npy' % (name)
audio_filename = f'{out_dir}wave.npy'
mel_filename = f'{out_dir}mel.npy'
mfcc_filename = f'{out_dir}mfcc.npy'
assert mfcc.shape[0] == N
np.save(audio_filename,
out.astype(out_dtype), allow_pickle=False)
np.save(mel_filename,
mel_spectrogram.astype(np.float32), allow_pickle=False)
np.save(mfcc_filename,
mfcc.astype(np.float32), allow_pickle=False)
# Return a tuple describing this training example:
return (out_dir, N, sp_ind,text)
def _process_utterance(out_dir, index, wav_path, text, trim_silence=False):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Trim begin/end silences
# NOTE: the threshold was chosen for clean signals
# TODO: Remove, get this out of here.
if trim_silence:
wav, _ = librosa.effects.trim(wav,
top_db=60,
frame_length=2048,
hop_length=512)
if hparams.highpass_cutoff > 0.0:
wav = audio.low_cut_filter(wav, hparams.sample_rate,
hparams.highpass_cutoff)
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# Trim silences in mul-aw quantized domain
silence_threshold = 0
if silence_threshold > 0:
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
start, end = audio.start_and_end_indices(out, silence_threshold)
wav = wav[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels - 1)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
constant_values = P.mulaw(0.0, hparams.quantize_channels - 1)
out_dtype = np.float32
else:
# [-1, 1]
constant_values = 0.0
out_dtype = np.float32
# Compute a mel-scale spectrogram from the trimmed wav:
# (N, D)
mel_spectrogram = audio.logmelspectrogram(wav).astype(np.float32).T
if hparams.global_gain_scale > 0:
wav *= hparams.global_gain_scale
# Time domain preprocessing
if hparams.preprocess is not None and hparams.preprocess not in [
"", "none"
]:
f = getattr(audio, hparams.preprocess)
wav = f(wav)
# Clip
if np.abs(wav).max() > 1.0:
print("""Warning: abs max value exceeds 1.0: {}""".format(
np.abs(wav).max()))
# ignore this sample
return ("dummy", "dummy", -1, "dummy")
wav = np.clip(wav, -1.0, 1.0)
# Set waveform target (out)
if is_mulaw_quantize(hparams.input_type):
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
elif is_mulaw(hparams.input_type):
out = P.mulaw(wav, hparams.quantize_channels - 1)
else:
out = wav
# zero pad
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.pad_lr(out, hparams.fft_size, audio.get_hop_size())
if l > 0 or r > 0:
out = np.pad(out, (l, r),
mode="constant",
constant_values=constant_values)
N = mel_spectrogram.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
assert_ready_for_upsampling(out, mel_spectrogram, cin_pad=0, debug=True)
# Write the spectrograms to disk:
name = splitext(basename(wav_path))[0]
audio_filename = "%s-wave.npy" % (name)
mel_filename = "%s-feats.npy" % (name)
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(
os.path.join(out_dir, mel_filename),
mel_spectrogram.astype(np.float32),
allow_pickle=False,
)
# Return a tuple describing this training example:
return (audio_filename, mel_filename, N, text)
def _process_song(out_dir, index, wav_path, text):
# Load the audio to a numpy array:
wav = audio.load_wav(wav_path)
# Trim begin/end silences
# NOTE: the threshold was chosen for clean signals
wav, _ = librosa.effects.trim(wav,
top_db=60,
frame_length=2048,
hop_length=512)
if hparams.highpass_cutoff > 0.0:
wav = audio.low_cut_filter(wav, hparams.sample_rate,
hparams.highpass_cutoff)
# Mu-law quantize
if is_mulaw_quantize(hparams.input_type):
# Trim silences in mul-aw quantized domain
silence_threshold = 0
if silence_threshold > 0:
# [0, quantize_channels)
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
start, end = audio.start_and_end_indices(out, silence_threshold)
wav = wav[start:end]
constant_values = P.mulaw_quantize(0, hparams.quantize_channels - 1)
out_dtype = np.int16
elif is_mulaw(hparams.input_type):
# [-1, 1]
constant_values = P.mulaw(0.0, hparams.quantize_channels - 1)
out_dtype = np.float32
else:
# [-1, 1]
constant_values = 0.0
out_dtype = np.float32
#### CLAIRE Work here
wav_name = os.path.splitext(os.path.basename(wav_path))[0]
os.makedirs('./pwavs', exist_ok=True)
pwav_path = './pwavs/{0}.wav'.format(wav_name)
scipy.io.wavfile.write(pwav_path, 16000, wav)
# make the chord directory if it does not exist
chord_dir = "chord_dir"
os.makedirs(chord_dir, exist_ok=True)
# create xml file with notes and timestamps
#subprocess.check_call(['./extract_chord_notes.sh', wav_path, chord_dir], shell=True)
#os.system('./extract_chord_notes.sh {0} {1}'.format(pwav_path, chord_dir))
os.system('./extract_chromagram.sh {0} {1} > /dev/null 2>&1'.format(
pwav_path, chord_dir))
note_filename = '{0}/{1}.csv'.format(chord_dir, wav_name)
#### Instead of computing the Mel Spectrogram, here return a time series of one hot encoded chords.
# vector with 1 in row for each note played
# 1000 samples per second
note_samples = int(len(wav) / 2048)
# 12 notes per octave
chords_time_series = np.zeros((24, note_samples))
#print(np.shape(chords_time_series))
with open(note_filename, newline='\n') as csvfile:
#chordreader = csv.reader(csvfile, delimeter=',')
chordreader = csvfile.readlines()
#print(chordreader)
for idx, row in enumerate(chordreader):
row = row.split(",")
chromogram_samples = np.array(row).astype(np.float)[1:]
chords_time_series[:, idx] = chromogram_samples
chords_time_series = chords_time_series.T
# if hparams.global_gain_scale > 0:
# wav *= hparams.global_gain_scale
# Time domain preprocessing
if hparams.preprocess is not None and hparams.preprocess not in [
"", "none"
]:
f = getattr(audio, hparams.preprocess)
wav = f(wav)
# wav = np.clip(wav, -1.0, 1.0)
# Set waveform target (out)
if is_mulaw_quantize(hparams.input_type):
out = P.mulaw_quantize(wav, hparams.quantize_channels - 1)
elif is_mulaw(hparams.input_type):
out = P.mulaw(wav, hparams.quantize_channels - 1)
else:
out = wav
# zero pad
# this is needed to adjust time resolution between audio and mel-spectrogram
l, r = audio.pad_lr(out, hparams.fft_size, audio.get_hop_size())
if l > 0 or r > 0:
out = np.pad(out, (l, r),
mode="constant",
constant_values=constant_values)
N = chords_time_series.shape[0]
assert len(out) >= N * audio.get_hop_size()
# time resolution adjustment
# ensure length of raw audio is multiple of hop_size so that we can use
# transposed convolution to upsample
out = out[:N * audio.get_hop_size()]
assert len(out) % audio.get_hop_size() == 0
# Write the spectrograms to disk:
name = splitext(basename(wav_path))[0]
audio_filename = '%s-wave.npy' % (name)
chords_filename = '%s-feats.npy' % (name)
np.save(os.path.join(out_dir, audio_filename),
out.astype(out_dtype),
allow_pickle=False)
np.save(os.path.join(out_dir, chords_filename),
chords_time_series.astype(out_dtype),
allow_pickle=False)
# Return a tuple describing this training example:
return (audio_filename, chords_filename, N, text)