error (dimension mismatch) occurs when applying a pipeline that includes coloredNoise deformer and IRConvolution deformer.

where the original audio length is 118882 samples, impulse response length is 192000 samples.

Stochastic iterators can be more simply designed with multiple inheritance.

A special state information function can be abstracted out, and all transformers (iterable or not) can be rewritten to access parameters through the state object. This way, sampling and transformation become separate.

This might even make more sense as a context manager.

I'm trying to use the LinearPitchShift deformer on a tag_open JAMS annotation. Code looks something like this:

audiopath = '101415-3-0-2.wav'
jamspath = '101415-3-0-2.jams'
jorig = muda.load_jam_audio(jamspath, audiopath)
pitch = muda.deformers.LinearPitchShift(n_samples=3, lower=-1, upper=1)
jpitch = []
for j in pitch.transform(jorig):
    jpitch.append(j)

If I try to do the same with the DRC deformer it seems to work OK, but with the pitch deformer I get:

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-48-1c0aa6d965af> in <module>()
      1 jpitch = []
----> 2 for j in pitch.transform(jorig):
      3     jpitch.append(j)

/usr/local/lib/python2.7/site-packages/muda/base.pyc in transform(self, jam)
    141 
    142         for state in self.states(jam):
--> 143             yield self._transform(jam, state)
    144 
    145     @property

/usr/local/lib/python2.7/site-packages/muda/base.pyc in _transform(self, jam, state)
    109 
    110         if hasattr(self, 'audio'):
--> 111             self.audio(jam_w.sandbox.muda, state)
    112 
    113         if hasattr(self, 'metadata'):

/usr/local/lib/python2.7/site-packages/muda/deformers/pitch.pyc in audio(mudabox, state)
     75         mudabox._audio['y'] = pyrb.pitch_shift(mudabox._audio['y'],
     76                                                mudabox._audio['sr'],
---> 77                                                state['n_semitones'])
     78 
     79     @staticmethod

/usr/local/lib/python2.7/site-packages/pyrubberband/pyrb.pyc in pitch_shift(y, sr, n_steps, rbargs)
    163     rbargs.setdefault('--pitch', n_steps)
    164 
--> 165     return __rubberband(y, sr, **rbargs)

/usr/local/lib/python2.7/site-packages/pyrubberband/pyrb.pyc in __rubberband(y, sr, **kwargs)
     64         arguments.extend([infile, outfile])
     65 
---> 66         subprocess.check_call(arguments)
     67 
     68         # Load the processed audio.

/usr/local/Cellar/python/2.7.8_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in check_call(*popenargs, **kwargs)
    533     check_call(["ls", "-l"])
    534     """
--> 535     retcode = call(*popenargs, **kwargs)
    536     if retcode:
    537         cmd = kwargs.get("args")

/usr/local/Cellar/python/2.7.8_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in call(*popenargs, **kwargs)
    520     retcode = call(["ls", "-l"])
    521     """
--> 522     return Popen(*popenargs, **kwargs).wait()
    523 
    524 

/usr/local/Cellar/python/2.7.8_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in __init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags)
    708                                 p2cread, p2cwrite,
    709                                 c2pread, c2pwrite,
--> 710                                 errread, errwrite)
    711         except Exception:
    712             # Preserve original exception in case os.close raises.

/usr/local/Cellar/python/2.7.8_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in _execute_child(self, args, executable, preexec_fn, close_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, to_close, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite)
   1325                         raise
   1326                 child_exception = pickle.loads(data)
-> 1327                 raise child_exception
   1328 
   1329 

OSError: [Errno 2] No such file or directory

Currently, muda.save is a destructive operation: when the jam is serialized, we first pop out the audio buffer jam.sandbox.muda['y'] to avoid serializing the audio. This is obviously bad.

JObject serialization already skips fields that start with _.

A simple fix here would be the following:

• Modify jam_pack to construct muda as a JObject (or Sandbox) rather than a dict
• Push the audio content down a level, ie, muda._audio['y'] and muda._audio['sr']
• Modify all deformation logic accordingly

This way, muda._audio will be skipped during serialization, and save can be a non-destructive operation.

In order to support the pitch shifting deformation for chord annotations (see #1), we'll need a general utility to transpose chords regardless of alphabet.

Probably we will need to depend on JAMS to define the chord namespace. We may also need to depend on mir_eval to do chord symbol manipulation.

Pro:

• Makes everything trivially parallelizable
• Simplifies serialization

Con:

• Difficult to maintain

Librosa 0.7 deprecated some features that we use (IO) to be removed in 0.8. We should future-proof muda against this.

Following Ich's comments offline: for full reproducibility, we should include muda.version in the output jams muda sandbox.

Hi, Brian it's me again. I tried to use the Dynamic range compression to deform some audio signal and met some error, could you help me figure it out? Thanks!

def augment_features(parent_dir,sub_dirs,file_ext="*.wav"):
    for l, sub_dir in enumerate(sub_dirs):
        for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):
            name = fn.split('/')[2].split('.')[0]
            jam = jams.load('audio/7061-6-0-0_bgnoise0.jams')
            muda.load_jam_audio(jam, fn)
            
            #pitch shift1
            pitch1 = muda.deformers.LinearPitchShift(n_samples=4,lower=-2,upper=2)
            for i, jam_out in enumerate(pitch1.transform(jam)):
                muda.save('audio1/test/'+name+'_p1_{:02d}.wav'.format(i),'audio1/test/'+name+'_p1_{:02d}.jams'.format(i), jam_out)
            
            #pitch shift2 
            pitch2 = muda.deformers.LinearPitchShift(n_samples=4,lower=-4,upper=4)
            for i, jam_out in enumerate(pitch2.transform(jam)):
                muda.save('audio1/test/'+name+'_p2_{:02d}.wav'.format(i),'audio1/test/'+name+'_p2_{:02d}.jams'.format(i), jam_out)
            
            #time stetching
            tstretch = muda.deformers.LogspaceTimeStretch(n_samples=4,lower=-3.5,upper=3.5)
            for i, jam_out in enumerate(tstretch.transform(jam)):
                muda.save('audio1/test/'+name+'_ts_{:02d}.wav'.format(i),'audio1/test/'+name+'_ts_{:02d}.jams'.format(i), jam_out)
            
            #DRC
            drc = muda.deformers.DynamicRangeCompression(preset=['radio','film standard', 'speech', 'radio'])
            for i, jam_out in enumerate(drc.transform(jam)):
                muda.save('audio1/test/'+name+'_drc_{:02d}.wav'.format(0),'audio1/test/'+name+'_drc_{:02d}.jams'.format(0), jam_out)
           
            
            
parent_dir = "audio1"      
for k in range(1,11):
        fold_name = 'fold' + str(k)
        augment_features(parent_dir,[fold_name])

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-16-9541ffff5e33> in <module>()
     36 for k in range(1,11):
     37         fold_name = 'fold' + str(k)
---> 38         augment_features(parent_dir,[fold_name])

<ipython-input-16-9541ffff5e33> in augment_features(parent_dir, sub_dirs, file_ext)
     25             #DRC
     26             drc = muda.deformers.DynamicRangeCompression(preset=['radio','film standard', 'speech', 'radio'])
---> 27             for i, jam_out in enumerate(drc.transform(jam)):
     28                 muda.save('audio1/test/'+name+'_drc_{:02d}.wav'.format(0),'audio1/test/'+name+'_drc_{:02d}.jams'.format(0), jam_out)
     29             #for i, jam_out in enumerate(drc.transform(jam)):

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda-0.1.2-py2.7.egg/muda/base.pyc in transform(self, jam)
    142 
    143         for state in self.states(jam):
--> 144             yield self._transform(jam, state)
    145 
    146     @property

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda-0.1.2-py2.7.egg/muda/base.pyc in _transform(self, jam, state)
    110 
    111         if hasattr(self, 'audio'):
--> 112             self.audio(jam_w.sandbox.muda, state)
    113 
    114         if hasattr(self, 'metadata'):

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda-0.1.2-py2.7.egg/muda/deformers/sox.pyc in audio(mudabox, state)
    146         mudabox._audio['y'] = drc(mudabox._audio['y'],
    147                                   mudabox._audio['sr'],
--> 148                                   state['preset'])

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda-0.1.2-py2.7.egg/muda/deformers/sox.pyc in drc(y, sr, preset)
     91     '''
     92 
---> 93     return __sox(y, sr, 'compand', *PRESETS[preset])
     94 
     95 

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda-0.1.2-py2.7.egg/muda/deformers/sox.pyc in __sox(y, sr, *args)
     57         arguments.extend(args)
     58 
---> 59         subprocess.check_call(arguments)
     60 
     61         y_out, sr = psf.read(outfile)

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/subprocess.pyc in check_call(*popenargs, **kwargs)
    534     check_call(["ls", "-l"])
    535     """
--> 536     retcode = call(*popenargs, **kwargs)
    537     if retcode:
    538         cmd = kwargs.get("args")

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/subprocess.pyc in call(*popenargs, **kwargs)
    521     retcode = call(["ls", "-l"])
    522     """
--> 523     return Popen(*popenargs, **kwargs).wait()
    524 
    525 

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/subprocess.pyc in __init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags)
    709                                 p2cread, p2cwrite,
    710                                 c2pread, c2pwrite,
--> 711                                 errread, errwrite)
    712         except Exception:
    713             # Preserve original exception in case os.close raises.

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/subprocess.pyc in _execute_child(self, args, executable, preexec_fn, close_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, to_close, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite)
   1341                         raise
   1342                 child_exception = pickle.loads(data)
-> 1343                 raise child_exception
   1344 
   1345 

OSError: [Errno 2] No such file or directory

I probably missed something very basic. So I was trying to test the muda code examples from the documentation as follows.

jams_obj = muda.load_jam_audio(jam_file, song_file)
pitch = muda.deformers.LinearPitchShift(n_samples=5, lower=-2, upper=2)

for i, jam_out in pitch.transform(jams_obj):
    muda.save('output_{:02d}.wav'.format(i),
              'output_{:02d}.jams'.format(i),
               jam_out)

and I encountered the following error message:

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-11-589bfe4aecc5> in <module>()
----> 1 for i, jam_out in pitch.transform(jams_obj):
      2     muda.save('output_{:02d}.ogg'.format(i),
      3               'output_{:02d}.jams'.format(i),
      4                jam_out)

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/muda/base.pyc in transform(self, jam)
    141 
    142         for state in self.states(jam):
--> 143             yield self._transform(jam, state)
    144 
    145     @property

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/muda/base.pyc in _transform(self, jam, state)
    109 
    110         if hasattr(self, 'audio'):
--> 111             self.audio(jam_w.sandbox.muda, state)
    112 
    113         if hasattr(self, 'metadata'):

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/muda/deformers/pitch.pyc in audio(mudabox, state)
     75         mudabox._audio['y'] = pyrb.pitch_shift(mudabox._audio['y'],
     76                                                mudabox._audio['sr'],
---> 77                                                state['n_semitones'])
     78 
     79     @staticmethod

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyrubberband/pyrb.pyc in pitch_shift(y, sr, n_steps, rbargs)
    163     rbargs.setdefault('--pitch', n_steps)
    164 
--> 165     return __rubberband(y, sr, **rbargs)

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pyrubberband/pyrb.pyc in __rubberband(y, sr, **kwargs)
     64         arguments.extend([infile, outfile])
     65 
---> 66         subprocess.check_call(arguments)
     67 
     68         # Load the processed audio.

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in check_call(*popenargs, **kwargs)
    533     check_call(["ls", "-l"])
    534     """
--> 535     retcode = call(*popenargs, **kwargs)
    536     if retcode:
    537         cmd = kwargs.get("args")

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in call(*popenargs, **kwargs)
    520     retcode = call(["ls", "-l"])
    521     """
--> 522     return Popen(*popenargs, **kwargs).wait()
    523 
    524 

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in __init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags)
    708                                 p2cread, p2cwrite,
    709                                 c2pread, c2pwrite,
--> 710                                 errread, errwrite)
    711         except Exception:
    712             # Preserve original exception in case os.close raises.

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/subprocess.pyc in _execute_child(self, args, executable, preexec_fn, close_fds, cwd, env, universal_newlines, startupinfo, creationflags, shell, to_close, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite)
   1333                         raise
   1334                 child_exception = pickle.loads(data)
-> 1335                 raise child_exception
   1336 
   1337 

OSError: [Errno 2] No such file or directory

Please give me any pointers on what I have been missing... I checked the content of jams_obj and the audio is loaded.

Brian Just started reading through your paper and decided to take a look at the code (rather than just read a paper that describes the code.r)

Your documentation (README) is a bit lacking on how to use your library. While an experienced developer can grok the code and make heads or tails of it; your best bet would be to include code samples of usage and explanation of your design patterns.

This is a great example of the level of detail (while somewhat overkill) for a good project:

http://kenwheeler.github.io/slick/

https://docs.angularjs.org/api

EDIT: I had a second look. I noticed you commented the code (which I think is fantastic) however, most practitioners would dive into the read me and clearly (markdown) readable usage.

It would be helpful to be able to pass jams kwargs to muda's load_jam_audio() and save() methods. Example use case: I want to skip json validation upon loading (via muda.load_jam_audio()) or saving (via muda.save()).

• chord
  • just as chord_harte
• pitch_class
  • quantize and modify the tonic
• melody_midi
  • simple addition

• travis
• coveralls
• nose
• full coverage

Simple(ish) deformers (many from audio degradation toolbox):

• Low-, band-, high-pass filter
• bit-crushing
• companding+quantization noise
  • should wait until librosa/librosa#1022 is finished and merged
• mp3 compression
• Clipping
• Time stretch
• Pitch shift
  • Tricky thing here: may require tuning estimation and quantization to determine whether or not to change a note's pitch class
• Event blur
  • Cf: Ulrich et al, 2014
  • Find all events with duration == 0 and duplicate them at some random offset and degradation in confidence
• Chord simplifier
  • Eg, drop the 7th to simulate lazy annotators
  • Or 'A:min(*3) -> A:maj'
• Time clip
• Dynamic range compression
• Aliasing (resampling without LPF)
• Resampling
• Colored noise
• reversal

Advanced deformers:

• Impulse response: convolve with a given filter
  • http://www.isophonics.net/content/room-impulse-response-data-set
• Mixing (eg, crowd noise)
  • including mixing annotated examples
• Bypass (make filters in a chain skippable)

Analogous to pipeline, but allows fan-out of a deformation over a container of deformations.

While adding tests for #40, I noticed what seem to be a couple of errors in the test_deformers.py.

In test_linear_pitchshift,
shouldn't assert lower <= d_tones <= 2.0**upper be assert lower <= d_tones <= upper?

also, in __test_effect,
I think you intend for there to be an assert there, correct? i.e. ann_orig == ann_new -> assert ann_orig == ann_new

Currently, JAMS objects are being used via the top-level sandbox to ferry data through deformation pipelines. This is a little clunky for a few reasons, some more obvious than others. For my part, a big one is transforming JAMS without audio / transforming audio without JAMS.

The important thing to note though is that the JAMS object is pretty powerful, which makes it super easy to do things with and to it. We can't say the same for the audio signal, and the JAMS object doesn't (and shouldn't) offer similar functionality for wrangling muda history, for example.

I'd be keen to encapsulate audio and annotation data as separate attributes of a Payload object (or what have you) that can pass through the deformer pipeline agnostically. Putting some smarts into the different containers will also make it easier to introduce other audio deformations later, like stereo / spatialization, and keep good records on applied deformations.

And, as another win (in my book at least), it could allow us to leverage different audio reading/writing backends, which can be justifiable in different scenarios.

thoughts?

Probably we want to depend on librosa 0.4(-dev) and enable caching on a per-object basis.

• The deformation script should reset cache after each track is processed

The pipeline object allows for cascading of multiple deformers into a single chain.

Sometimes, we just want to apply a set of deformations independently, but there's no direct way to do that currently.

It would be easy to implement something analogous to a sklearn feature union that does round-robin sampling from each deformer.

This should be relatively easy to do.

#1 lists several deformations left to implement here, and we currently rely on some ad-hoc constellation of backends to implement what we have now.

It looks like pedalboard is on track to cover a large swath of the functionality we need from the audio side. Switching our backend processing over will simplify things, make it easier to cover more transformations, and make the whole project a bit more maintainable.

In the short term, it ought to be an easy switch. The only thing to be careful of is the parallel implementation of annotation transformations to match the audio.

In the longer term, it might be worth doing some kind of deferred processing of audio instead of making intermediate copies of the signal. The present implementation doesn't support this, but it could be more efficient if we provide some kind of lazy evaluation / pedalboard constructor that only generates audio when necessary.

Title says it all, but here's how:

1. Store audio + sample rate in the top level sandbox:

    >>> J.sandbox['audio'] = {'y': y, 'sr': sr, 'abce_history': [] }

1. Store deformation history in the sandbox for each part touched by the deformer. For example, a time stretcher will touch box the audio object and all annotation objects, so each of these should have their history updated accordingly with the deformation parameters.
2. At the end of the pipe, eject the audio buffer + sample rate, and split off the resulting annotation object for output.

I just pip-installed muda into my virtualenv, and drc_presets.json was missing. I had a quick glimpse into setup.py, and it seems to be omitted. One option is to specify the package_data option, see http://stackoverflow.com/questions/1612733/including-non-python-files-with-setup-py

JAMS is likely to drop the pandas dataframe backing in the near future. Even in the short-term, pandas 0.20 breaks a variety of things involving in-place manipulation, so we should really just get out ahead of it and do things properly.

In order to fully specify the BackgroundNoise deformation in the stored state, we need to know the start and stop sample indices, not just the weight and the file.

Hello!

I am reproducing a paper by Salomon & Bello, and need to make transformations according to the jams files in UrbanSound8K-JAMS, for which I am using muda.replay().

It seems that they used a previous version of Background noise which didn't use start and stop parameters, probably prior to this commit. I am thinking of just setting a default value for the case where there is no start and stop in the status, something like this:

try:
     start = state['start']
     stop = state['stop']
except  KeyError:
     start = 0
     stop = len(mudabox._audio['y'])

I will post if this works fine latter on. Would appreciate some feedback on this.

I have a dataset of audio clips of the same length. Half of these clips are positive (contain a bird flight call), while the other half is negative (contain only background noise).
I want to augment the dataset by mixing clips together, without changing the label.

But I ran into an error in 'muda.deformers.background.sample_clip_indices'. If I understand the stack trace correctly (see below my signature), the error happens when executing
start = np.random.randint(0, len(soundf) - n_target)
with len(soundf) - n_target equal to zero.

I made a Gist to reproduce the bug:
https://gist.github.com/lostanlen/15fe9c879fdd24fe9023fa430314cd51
It disappears when the difference in lengths is strictly larger than zero.

Is this expected behavior? It seems to me that my issue could be fixed with

if len(soundf) > n_target:
    start = np.random.randint(0, len(soundf) - n_target)
else:
    start = 0

Best,
Vincent.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-43-9ecbf36219b3> in <module>()
     39 # Create short deformer
     40 short_deformer = muda.deformers.BackgroundNoise(files=[short_noise_path])
---> 41 short_jam_transformer = next(short_deformer.transform(jam_original)) # error

/Users/vl238/miniconda3/lib/python3.5/site-packages/muda/base.py in transform(self, jam)
    142         '''
    143 
--> 144         for state in self.states(jam):
    145             yield self._transform(jam, state)
    146 

/Users/vl238/miniconda3/lib/python3.5/site-packages/muda/deformers/background.py in states(self, jam)
    154         for fname in self.files:
    155             for _ in range(self.n_samples):
--> 156                 start, stop = sample_clip_indices(fname, len(mudabox._audio['y']), mudabox._audio['sr'])
    157                 yield dict(filename=fname,
    158                            weight=np.random.uniform(low=self.weight_min,

/Users/vl238/miniconda3/lib/python3.5/site-packages/muda/deformers/background.py in sample_clip_indices(filename, n_samples, sr)
     40 
     41         # Draw a random clip
---> 42         start = np.random.randint(0, len(soundf) - n_target)
     43         stop = start + n_target
     44 

mtrand.pyx in mtrand.RandomState.randint (numpy/random/mtrand/mtrand.c:16117)()

ValueError: low >= high

At least two ideas jump out at me re: reproducibility:

1. RandomDoAThing deformers could optionally take seed params, but always use one internally (and serialize accordingly).
2. It'd be great if we could reconstruct a deformation pipeline exactly from the "history" object ... which really means either (a) the serialization object should encompass state, which isn't the case for RandomDoAThing deformers, or (b) there's a higher-level object that combines state and pipeline as different objects. The difference here is small (and maybe semantic), but it's a difference between a class and an instance (the pipeline is the class, the state is the instance). This might have interesting repercussions for the design of the Pipeline, which is perhaps more aptly called a PipelineFactory.

please yell if any of this is unclear, I'm kind of stream-of-consciousness working through the idea.

Hello,
I installed muda 0.2.0 successfully, but when I import it I get the following error:

import muda
Traceback (most recent call last):
File "", line 1, in
File "/usr/local/lib/python2.7/dist-packages/muda/init.py", line 5, in
from . import deformers
File "/usr/local/lib/python2.7/dist-packages/muda/deformers/init.py", line 7, in
from .pitch import *
File "/usr/local/lib/python2.7/dist-packages/muda/deformers/pitch.py", line 6, in
import librosa
File "/usr/local/lib/python2.7/dist-packages/librosa/init.py", line 12, in
from . import core
File "/usr/local/lib/python2.7/dist-packages/librosa/core/init.py", line 104, in
from .time_frequency import * # pylint: disable=wildcard-import
File "/usr/local/lib/python2.7/dist-packages/librosa/core/time_frequency.py", line 10, in
from ..util.exceptions import ParameterError
File "/usr/local/lib/python2.7/dist-packages/librosa/util/init.py", line 67, in
from .utils import * # pylint: disable=wildcard-import
File "/usr/local/lib/python2.7/dist-packages/librosa/util/utils.py", line 111, in
def valid_audio(y, mono=True):
File "/usr/local/lib/python2.7/dist-packages/librosa/cache.py", line 49, in wrapper
if self.cachedir is not None and self.level >= level:
File "/usr/local/lib/python2.7/dist-packages/joblib/memory.py", line 847, in cachedir
DeprecationWarning, stacklevel=2)
TypeError: expected string or buffer

How can I fix it?

Hi:

I would like to do data-augmentation for my bunch of wav files.
Can I use MUDA without JAMS file?
Thanks!

A simple deformation to take an input object (mudabox) and carve it into a sequence of clips, of size as specified by the user.

I tried to run the muda code example from documentaion:

import muda 
import librosa 
clip=muda.load_jam_audio('audio/7061-6-0-0_bgnoise0.jams','audio/6902-2-0-7.wav')
pitch = muda.deformers.LinearPitchShift(n_samples=5,lower=-1,upper=1)
for i, jam_out in pitch.transform(clip):
          muda.save('output_{:02d}.wav'.format(i),'output_{:02d}.jams'.format(i),jam_out) `

but this error occurs:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-7-e235d022c3b3> in <module>()
      1 pitch = muda.deformers.LinearPitchShift(n_samples=5,lower=-1,upper=1)
----> 2 for i, jam_out in pitch.transform(clip):
      3     muda.save('output_{:02d}.wav'.format(i),'output_{:02d}.jams'.format(i),jam_out)

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda/base.pyc in transform(self, jam)
    140         '''
    141 
--> 142         for state in self.states(jam):
    143             yield self._transform(jam, state)
    144 

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda/deformers/pitch.pyc in states(self, jam)
    251                              endpoint=True)
    252 
--> 253         for state in AbstractPitchShift.states(self, jam):
    254             for n_semitones in shifts:
    255                 state['n_semitones'] = n_semitones

/home/uri7910/anaconda2/envs/tensorflow011/lib/python2.7/site-packages/muda/deformers/pitch.pyc in states(self, jam)
     67     def states(self, jam):
     68         mudabox = jam.sandbox.muda
---> 69         state = dict(tuning=librosa.estimate_tuning(y=mudabox._audio['y'],
     70                                                     sr=mudabox._audio['sr']))
     71         yield state

AttributeError: 'dict' object has no attribute '_audio' 

/home/js7561/dev/librosa/librosa/core/pitch.py:157: DeprecationWarning: object of type <class 'numpy.float64'> cannot be safely interpreted as an integer.
  bins = np.linspace(-0.5, 0.5, np.ceil(1./resolution), endpoint=False)

Explicitly casting the output of np.ceil to int should fix this, yeah?

This is minor, but the default values for the LogspaceTimeStretch deformer are 0.8 and 1.2. If one doesn't read the docs carefully one might assume this means stretching time by a factor of 0.8 (make audio shorter) to 1.2 (make audio longer), though in fact these values correspond to time-stretch factors between 1.74 and 2.3 (2^lower -- 2^upper), which seems somewhat arbitrary?

Setting the default values to -0.3 and +0.3 (for example) would give stretch factors in the range [0.81, 1.23] which perhaps makes more sense as default values?

Make a script to index all generated jams according to their deformation parameters and source id

The time stretcher should also apply to the time and duration Annotation object fields introduced in jams 0.2.1.

In my current project, my jams files completely specify my audio, and at training time the audio can be synthesized from the jams file. I'd ideally augment these jams files with muda without having to synthesize them first, and then I'd simply save the muda-augmented jams files. At training time, I'd synthesize the audio and process the muda deformations.

Is it possible to use muda without passing the audio initial audio file? It seems right now, if I don't use muda.load_jam_audio() to process my jams file (it just adds the empty history and library versions to the jam?), it errors when I call the transform method of my pipeline.

Is there a reason muda needs the audio file before actually processing the audio?

This way, each transformer can have its own specified order of operations.

Our dependencies and tests are a bit out of date at this point, and we should do the following:

1. bump librosa up to 0.7 (at least)
2. upgrade pytest dependencies
3. drop support for python <3.6

MUDA relies heavily on external command line libraries such as rubberband and sox (lightly wrapped in pyrubberband and pysox) for core deformations such as time-stretch, pitch-shift and drc. These system library wrappers work by writing the transformed signal to disk and then reading it back from disk into memory (presumably to feed an ML algorithm).

The external system call and particularly the additional read-write step introduce a large overhead in highly distributed/multithreaded out-of-core data pipelines. Would it not make sense to either a) allow an option to do an analagous deformation using in-memory python library (for example librosa) or b) replace the external system call altogether with an in-memory transformation?

for specifying deformation pipelines and parameters

The PitchShift deformer only takes a single semitone parameter (n_semitones), which makes it a little awkward to work with when you want to perform multiple pitch shifts. LinearPitchShift can perform multiple shifts, but they have to be linearly spaced, which might not be the desired functionality (e.g. I may want n_semitones = [ -2, -1, 1, 2]).

It would be nice if PitchShift would accept (in addition to a single value) a list for the n_semitones parameter, in which case it would generate an output audio/jams for every pitch shift value in the list (times n_samples). This would make the behavior more consistent with other deformers (e.g. DynamicRangeCompression), and would allow (what I'm really after) writing more generic code that can apply a deformer agnostically of which deformer it actually is because the deformer is fully defined during initialization.

I've got the following error when I run this code on Spyder in Windows 10.
Did I miss anything from installation?
Thanks

drc = muda.deformers.DynamicRangeCompression(preset=preset)
for i, jam_out in enumerate(drc.transform(j_orig)):
   ....

 File "<ipython-input-36-e0cb52928d80>", line 1, in <module>
    runfile('Z:/tingyao.chen/2018_0503_ASC/muda/muda_test.py', wdir='Z:/tingyao.chen/2018_0503_ASC/muda')

  File "C:\Users\yao\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 705, in runfile
    execfile(filename, namespace)

  File "C:\Users\yao\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "Z:/tingyao.chen/2018_0503_ASC/muda/muda_test.py", line 23, in <module>
    for i, jam_out in enumerate(drc.transform(j_orig)):

  File "C:\Users\yao\Anaconda3\lib\site-packages\muda\base.py", line 145, in transform
    yield self._transform(jam, state)

  File "C:\Users\yao\Anaconda3\lib\site-packages\muda\base.py", line 113, in _transform
    self.audio(jam_w.sandbox.muda, state)

  File "C:\Users\yao\Anaconda3\lib\site-packages\muda\deformers\sox.py", line 148, in audio
    state['preset'])

  File "C:\Users\yao\Anaconda3\lib\site-packages\muda\deformers\sox.py", line 93, in drc
    return __sox(y, sr, 'compand', *PRESETS[preset])

  File "C:\Users\yao\Anaconda3\lib\site-packages\muda\deformers\sox.py", line 59, in __sox
    subprocess.check_call(arguments)

  File "C:\Users\yao\Anaconda3\lib\subprocess.py", line 286, in check_call
    retcode = call(*popenargs, **kwargs)

  File "C:\Users\yao\Anaconda3\lib\subprocess.py", line 267, in call
    with Popen(*popenargs, **kwargs) as p:

  File "C:\Users\yao\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 210, in __init__
    super(SubprocessPopen, self).__init__(*args, **kwargs)

  File "C:\Users\yao\Anaconda3\lib\subprocess.py", line 709, in __init__
    restore_signals, start_new_session)

  File "C:\Users\yao\Anaconda3\lib\subprocess.py", line 997, in _execute_child
    startupinfo)

FileNotFoundError: [WinError 2] 系統找不到指定的檔案。                                              

This is a regression introduced by #70 -- randomized deformers now have a random state field (deformer.rng) which is of type RandomState. This type is not JSON-serializable. When we go to save the output of a deformation (via JAMS), the encoder fails.

The easiest fix here is probably something like the following:

• Move all rng fields to _rng so that JAMS skips them during serialization
• Persist the random seeds in deformers as a separate field rng that is serialized. Raise a warning about serialization if the input is not serializable.

Since this breaks the object model a little bit, we'll need to bump the version number.

The attempt to produce a time stretch transformation to an audio file raises a type error exception due to a division by a None type.

import jams
import muda

audiofile = "D:/temp/test.mp3"

jam = jams.JAMS()
jam = muda.jam_pack(jam)

jam = muda.core.load_jam_audio(jam, audiofile)

# this works
P = muda.deformers.PitchShift(n_semitones=5)
out_jams = list(P.transform(jam))

# this not
T = muda.deformers.TimeStretch(rate=2.0)
out_jams = list(T.transform(jam))

produces the following error

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-30-603bfe0e77bc> in <module>()
     13 
     14 T = muda.deformers.TimeStretch(rate=2.0)
---> 15 out_jams = list(T.transform(jam))

C:\Anaconda\lib\site-packages\muda-0.1.1-py2.7.egg\muda\base.pyc in transform(self, jam)
    141 
    142         for state in self.states(jam):
--> 143             yield self._transform(jam, state)
    144 
    145     @property

C:\Anaconda\lib\site-packages\muda-0.1.1-py2.7.egg\muda\base.pyc in _transform(self, jam, state)
    112 
    113         if hasattr(self, 'metadata'):
--> 114             self.metadata(jam_w.file_metadata, state)
    115 
    116         # Walk over the list of deformers

C:\Anaconda\lib\site-packages\muda-0.1.1-py2.7.egg\muda\deformers\time.pyc in metadata(metadata, state)
     40     def metadata(metadata, state):
     41         # Deform the metadata
---> 42         metadata.duration /= state['rate']
     43 
     44     @staticmethod

TypeError: unsupported operand type(s) for /=: 'NoneType' and 'float'

I am trying to use these (https://github.com/justinsalamon/UrbanSound8K-JAMS) jams files to deform sound files.

I am trying to use BaseTransformer after creating the jams object from the jams file and the corresponding sound file. Like this,

j_orig = muda.load_jam_audio('orig.jams', 'orig.ogg')
deformer = muda.base.BaseTransformer()
for jam_out in deformer.transform(jam_in):
     process(jam_out)

But when I do this I am getting NotImplementedError.

If I had to create deformations by creating objects from muda.deformers.* classes. Then what is the point of loading annotated jams files? Please help me understand the process.

At this point, we only use it for a printing helper function used by __repr__ functions. Since that function itself depends only on numpy and six, we may as well copy it over and drop the import.