Created
December 15, 2015 05:50
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Split an audio file into multiple files based on detected onsets from librosa.
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| #!/usr/bin/env python | |
| import argparse | |
| import matplotlib.pyplot as plt | |
| import librosa | |
| import numpy as np | |
| import os | |
| from progressbar import ProgressBar | |
| parser = argparse.ArgumentParser( | |
| description='Split audio into multiple files and save analysis.') | |
| parser.add_argument('-i', '--input', type=str) | |
| parser.add_argument('-o', '--output', type=str, default='transients') | |
| parser.add_argument('-s', '--sr', type=int, default=44100) | |
| args = parser.parse_args() | |
| y, sr = librosa.load(args.input, sr=args.sr) | |
| o_env = librosa.onset.onset_strength(y, sr=sr, feature=librosa.cqt) | |
| onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr) | |
| def prepare(y, sr=22050): | |
| y = librosa.to_mono(y) | |
| y = librosa.util.fix_length(y, sr) # 1 second of audio | |
| y = librosa.util.normalize(y) | |
| return y | |
| def get_fingerprint(y, sr=22050): | |
| y = prepare(y, sr) | |
| cqt = librosa.cqt(y, sr=sr, hop_length=2048) | |
| return cqt.flatten('F') | |
| def normalize(x): | |
| x -= x.min(axis=0) | |
| x /= x.max(axis=0) | |
| return x | |
| def basename(file): | |
| file = os.path.basename(file) | |
| return os.path.splitext(file)[0] | |
| vectors = [] | |
| words = [] | |
| filenames = [] | |
| onset_samples = list(librosa.frames_to_samples(onset_frames)) | |
| onset_samples = np.concatenate(onset_samples, len(y)) | |
| starts = onset_samples[0:-1] | |
| stops = onset_samples[1:] | |
| analysis_folder = args.output | |
| samples_folder = os.path.join(args.output, 'samples') | |
| try: | |
| os.makedirs(samples_folder) | |
| except: | |
| pass | |
| pbar = ProgressBar() | |
| for i, (start, stop) in enumerate(pbar(zip(starts, stops))): | |
| audio = y[start:stop] | |
| filename = os.path.join(samples_folder, str(i) + '.wav') | |
| librosa.output.write_wav(filename, audio, sr) | |
| vector = get_fingerprint(audio, sr=sr) | |
| word = basename(filename) | |
| vectors.append(vector) | |
| words.append(word) | |
| filenames.append(filename) | |
| np.savetxt(os.path.join(analysis_folder, 'vectors'), vectors, fmt='%.5f', delimiter='\t') | |
| np.savetxt(os.path.join(analysis_folder, 'words'), words, fmt='%s') | |
| np.savetxt(os.path.join(analysis_folder, 'filenames.txt'), filenames, fmt='%s') |
Hello! i'm trying to split an audio into parts using librosa, but when i run your code, especially the for loop, i get this error :
ValueError: cannot convert float NaN to integer
I haven't understand where is the problem! can you help me please to solve it?
Get rid of the progress bar and the float issue will go away.
Here's how I wrote the bottom part:
for i, (start, stop) in enumerate(zip(starts, stops)):
audio = y[start:stop]
filename = str(i) + '.wav'
librosa.output.write_wav(filename, audio, sr)
It works now! thank you very much ^^
which version of librosa are you using? I'm getting the following error
librosa/onset.py", line 538, in onset_strength_multi
S = np.abs(feature(y=y, sr=sr, n_fft=n_fft, hop_length=hop_length, **kwargs))
TypeError: cqt() got an unexpected keyword argument 'n_fft'
(ppdataenv)
@cri5Castro, I am getting the same error. Guessing it is caused by updates of Librosa. Did you have any progress on this matter?
@cri5Castro - Update: I did some investigation, and the syntax is indeed a bit updated. If you want to use the Constant-Q spectrogram, just replace the code for the variable o_env with:
C = np.abs(librosa.cqt(y=y, sr=sr))
o_env = librosa.onset.onset_strength(sr=sr, S=librosa.amplitude_to_db(C, ref=np.max))
Reference to documentation: https://librosa.github.io/librosa/generated/librosa.onset.onset_strength.html
I ran this and got
Traceback (most recent call last):
File "/Users/sam/Documents/Dev/mp3-sound-detection/src/./split_transients.py", line 57, in <module>
for i, (start, stop) in enumerate(pbar(zip(starts, stops))):
File "/usr/local/lib/python3.9/site-packages/progressbar/progressbar.py", line 152, in __next__
self.start()
File "/usr/local/lib/python3.9/site-packages/progressbar/progressbar.py", line 291, in start
self.update(0)
File "/usr/local/lib/python3.9/site-packages/progressbar/progressbar.py", line 262, in update
self.fd.write(self._format_line() + '\r')
File "/usr/local/lib/python3.9/site-packages/progressbar/progressbar.py", line 222, in _format_line
widgets = ''.join(self._format_widgets())
File "/usr/local/lib/python3.9/site-packages/progressbar/progressbar.py", line 202, in _format_widgets
widget = widgets.format_updatable(widget, self)
File "/usr/local/lib/python3.9/site-packages/progressbar/widgets.py", line 39, in format_updatable
if hasattr(updatable, 'update'): return updatable.update(pbar)
File "/usr/local/lib/python3.9/site-packages/progressbar/widgets.py", line 230, in update
return '%3d%%' % pbar.percentage()
ValueError: cannot convert float NaN to integer
The following code works, I had to ditch the progress bar though
#!/usr/bin/env python
import argparse
import matplotlib.pyplot as plt
import librosa
import numpy as np
import os
import soundfile as sf
parser = argparse.ArgumentParser(
description='Split audio into multiple files and save analysis.')
parser.add_argument('-i', '--input', type=str)
parser.add_argument('-o', '--output', type=str, default='transients')
parser.add_argument('-s', '--sr', type=int, default=44100)
args = parser.parse_args()
def prepare(y, sr=22050):
y = librosa.to_mono(y)
y = librosa.util.fix_length(y, sr) # 1 second of audio
y = librosa.util.normalize(y)
return y
def get_fingerprint(y, sr=22050):
y = prepare(y, sr)
cqt = librosa.cqt(y, sr=sr, hop_length=2048)
return cqt.flatten('F')
def normalize(x):
x -= x.min(axis=0)
x /= x.max(axis=0)
return x
def basename(file):
file = os.path.basename(file)
return os.path.splitext(file)[0]
vectors = []
words = []
filenames = []
y, sr = librosa.load(args.input, sr=args.sr)
C = np.abs(librosa.cqt(y=y, sr=sr))
o_env = librosa.onset.onset_strength(sr=sr, S=librosa.amplitude_to_db(C, ref=np.max))
onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr)
onset_samples = list(librosa.frames_to_samples(onset_frames))
onset_samples = np.concatenate(onset_samples, len(y))
starts = onset_samples[0:-1]
stops = onset_samples[1:]
analysis_folder = args.output
samples_folder = os.path.join(args.output, 'samples')
try:
os.makedirs(samples_folder)
except:
pass
enumeration = enumerate(zip(starts, stops))
#print(list(enumeration))
for i, (start, stop) in enumeration:
audio = y[start:stop]
filename = os.path.join(samples_folder, str(i) + '.wav')
sf.write(filename, audio, sr)
vector = get_fingerprint(audio, sr=sr)
word = basename(filename)
vectors.append(vector)
words.append(word)
filenames.append(filename)
np.savetxt(os.path.join(analysis_folder, 'vectors'), vectors, fmt='%.5f', delimiter='\t')
np.savetxt(os.path.join(analysis_folder, 'words'), words, fmt='%s')
np.savetxt(os.path.join(analysis_folder, 'filenames.txt'), filenames, fmt='%s')
Here is a working version with ProgressBar and librosa 0.8,1
#!/usr/bin/env python
import argparse
import soundfile
import librosa
import numpy as np
import os
from progressbar import ProgressBar, Percentage, Bar
parser = argparse.ArgumentParser(
description='Split audio into multiple files and save analysis.')
parser.add_argument('-i', '--input', type=str)
parser.add_argument('-o', '--output', type=str, default='transients')
parser.add_argument('-s', '--sr', type=int, default=44100)
args = parser.parse_args()
print(f'Loading {args.input}')
y, sr = librosa.load(args.input, sr=args.sr)
print('Calculating CQT')
C = np.abs(librosa.cqt(y=y, sr=sr))
print('Extracting onsets')
o_env = librosa.onset.onset_strength(y, sr=sr, S=librosa.amplitude_to_db(C, ref=np.max))
onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr)
def prepare(y, sr=22050):
y = librosa.to_mono(y)
y = librosa.util.fix_length(y, sr) # 1 second of audio
y = librosa.util.normalize(y)
return y
def get_fingerprint(y, sr=22050):
y = prepare(y, sr)
cqt = librosa.cqt(y, sr=sr, hop_length=2048)
return cqt.flatten('F')
def normalize(x):
x -= x.min(axis=0)
x /= x.max(axis=0)
return x
def basename(file):
file = os.path.basename(file)
return os.path.splitext(file)[0]
vectors = []
words = []
filenames = []
onset_samples = list(librosa.frames_to_samples(onset_frames))
onset_samples = np.concatenate(onset_samples, len(y))
starts = onset_samples[0:-1]
stops = onset_samples[1:]
analysis_folder = args.output
samples_folder = os.path.join(args.output, 'samples')
num_segments = len(onset_samples)
print(f'Writing {num_segments} segments to {samples_folder}')
try:
os.makedirs(samples_folder)
except:
pass
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=num_segments).start()
for i, (start, stop) in enumerate(zip(starts, stops)):
audio = y[start:stop]
filename = os.path.join(samples_folder, str(i) + '.wav')
soundfile.write(filename, audio, sr)
vector = get_fingerprint(audio, sr=sr)
word = basename(filename)
vectors.append(vector)
words.append(word)
filenames.append(filename)
pbar.update(i+1)
pbar.finish()
np.savetxt(os.path.join(analysis_folder, 'vectors'), vectors, fmt='%.5f', delimiter='\t')
np.savetxt(os.path.join(analysis_folder, 'words'), words, fmt='%s')
np.savetxt(os.path.join(analysis_folder, 'filenames.txt'), filenames, fmt='%s')
thank you @elgiano !
This works with librosa==0.10.1 and numpy==1.26.4:
#!/usr/bin/env python
import argparse
import soundfile
import librosa
import numpy as np
import os
from progressbar import ProgressBar, Percentage, Bar
parser = argparse.ArgumentParser(
description='Split audio into multiple files and save analysis.')
parser.add_argument('-i', '--input', type=str)
parser.add_argument('-o', '--output', type=str, default='transients')
parser.add_argument('-s', '--sr', type=int, default=44100)
args = parser.parse_args()
print(f'Loading {args.input}')
y, sr = librosa.load(args.input, sr=args.sr)
print('Calculating CQT')
C = np.abs(librosa.cqt(y=y, sr=sr))
print('Extracting onsets')
o_env = librosa.onset.onset_strength(y=y, sr=sr, S=librosa.amplitude_to_db(C, ref=np.max))
onset_frames = librosa.onset.onset_detect(onset_envelope=o_env, sr=sr)
def prepare(y, sr=22050):
y = librosa.to_mono(y)
y = librosa.util.fix_length(y, size=sr) # 1 second of audio
y = librosa.util.normalize(y)
return y
def get_fingerprint(y, sr=22050):
y = prepare(y, sr)
cqt = librosa.cqt(y, sr=sr, hop_length=2048)
return cqt.flatten('F')
def normalize(x):
x -= x.min(axis=0)
x /= x.max(axis=0)
return x
def basename(file):
file = os.path.basename(file)
return os.path.splitext(file)[0]
vectors = []
words = []
filenames = []
onset_samples = list(librosa.frames_to_samples(onset_frames))
onset_samples = np.concatenate(onset_samples, len(y))
starts = onset_samples[0:-1]
stops = onset_samples[1:]
analysis_folder = args.output
samples_folder = os.path.join(args.output, 'samples')
num_segments = len(onset_samples)
print(f'Writing {num_segments} segments to {samples_folder}')
try:
os.makedirs(samples_folder)
except:
pass
pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=num_segments).start()
for i, (start, stop) in enumerate(zip(starts, stops)):
audio = y[start:stop]
filename = os.path.join(samples_folder, str(i) + '.wav')
soundfile.write(filename, audio, sr)
vector = get_fingerprint(audio, sr=sr)
word = basename(filename)
vectors.append(vector)
words.append(word)
filenames.append(filename)
pbar.update(i+1)
pbar.finish()
np.savetxt(os.path.join(analysis_folder, 'vectors'), vectors, fmt='%.5f', delimiter='\t')
np.savetxt(os.path.join(analysis_folder, 'words'), words, fmt='%s')
np.savetxt(os.path.join(analysis_folder, 'filenames.txt'), filenames, fmt='%s')Usage:
python Scripts/split2.py -i "Voices/Alexine Dreams 24-02-17.mp3"
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Hello! i'm trying to split an audio into parts using librosa, but when i run your code, especially the for loop, i get this error :
ValueError: cannot convert float NaN to integer
I haven't understand where is the problem! can you help me please to solve it?