Create a python re-write of matchering, because matlab is not that easy to acquire for normal user.

Differences

• simplified error messages and display messages
• no explicit memory cleaning operation, but done by in-place operation and python own garbage collection
• vectorized rms calculation
• display rms coefficient in db scale
• replace smoothing operation with average filter when filtering match fft . May change to 'loess' method in the future
• no limiter. I haven't add the limiter function yet, but this can be easily done in the future

Have run this script on my laptop very smoothly, but I can't compare the differences cuz I currently don't have matlab installed device (the last time I use matlab was in college lol).

Request Method: | GET -- | -- http://127.0.0.1:8000/dashboard/track/8/master 3.0.2 RuntimeError Error opening './media/goody/mastered/my_song_master_16bit.wav': System error. /home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6/site-packages/soundfile.py in _error_check, line 1357 /home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/bin/python 3.6.9 ['/home/goodness/Documents/django_dev/MeshakProj', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python36.zip', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6/lib-dynload', '/usr/lib/python3.6', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6/site-packages'] Thu, 30 Jan 2020 10:52:20 +0100

Thanks for your hard working on containerize the whole project ! I want to say congrats to everyone who have participated in it, thank you :clap: I would also like to ask is there any plan on create a demo site so everyone can access the serverce ?

BTW I have run it on a private docker server and it works well, but the loading time is a little bit long though (~= 10 seconds).

Hi, I would like to automate some tasks to process our rehearsel recordings (ideation for new songs). I currently use the docker image and I wondered whether it would be possible to make a GET request to the site supplying reference and target files/filepaths and receive the resulting file (or automatically put it into a shared folder). E.g. just call http://127.0.0.1:8360?ref=reference.wav&target=target.wav&output=target_by_reference.wav

I know I could use the python version and the CLI but I would like to keep the docker version for simplicity.

More background: I mainly use the same 3 reference tracks from our own album and this way I could create a script to copy the recordings from the SD card, show a simple GUI to select the reference track for each recording and provide the names, call matchering, convert the resulting audio file to mp3 and copy it to our band sharing file space.

Request Method: | GET -- | -- http://127.0.0.1:8000/dashboard/track/6/master 3.0.2 ModuleError 4001: Audio stream error in the TARGET file /home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6/site-packages/matchering/loader.py in load, line 42 /home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/bin/python 3.6.9 ['/home/goodness/Documents/django_dev/MeshakProj', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python36.zip', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6/lib-dynload', '/usr/lib/python3.6', '/home/goodness/.local/share/virtualenvs/MeshakProj--GI6wqXg/lib/python3.6/site-packages'] Thu, 30 Jan 2020 09:19:52 +0100

Hi,

Great repo! Just had one question: when both target and reference are stereo I think everything makes sense. But if both of them are mono, current code converts the target to stereo on loading by duplicating its channel. After all the matching process, the output is a stereo file containing two identical channels, which is essentially mono. Would there be any issue if we don't convert the input/target to stereo in the beginning?

Thanks, Zhenyu

Hello guys! matchering is an amazing lib, thank you very much!

I wanna try to play around with some mp3 files in matchering but can't find any documentation about it

I want to upload a target and reference (both mp3) and extract the mastered target as mp3 as well. Is it possible? where can I find information about the python syntax to make it possible?

Thank you!

Hello Sergree,

First, thanks for this great tool! i really see a great future for matcherng in the future for fast mixing of radio mixes. I really want to support you and buy a coffe! but before that..

I want to know why there is a file limit and if it is possible to disable that. ''The file format is not supported or the maximum file size (256 MB) has been exceeded.''

Why i ask this? Because i think this tool van be great for fast mastering of radio mixes that are long and over 300 mb of size.

thanks!! hope to hear from you and sorry for bad english, ask me if you want to know more.

I think it would be great to have this link more visible so people can try Matchering out easier!

BTW this tool is amazing :sparkles: Thank you so much! I don't know what it does to me and my brother's music, but I've no music mastering training or knowlege other than what I've tried to learn myself by messing with Open Source audio tools and I have been trying to figure out how to make our music sound like that forever!

This is just great. :smile:

Hi,

I'm trying to use matchering to master a song but ran into an error.

Loading and analysis
Loading the TARGET file: 'target.wav'...
The TARGET file is loaded
TARGET audio length: 99615480 samples (0:37:38)
Traceback (most recent call last):
  File "master.py", line 9, in <module>
    mg.pcm24('target24bit.wav'),
  File "/Users/highcenoid/Documents/TCSP/mastered/env/lib/python3.7/site-packages/matchering/core.py", line 53, in process
    target, target_sample_rate = check(target, target_sample_rate, config, 'target')
  File "/Users/highcenoid/Documents/TCSP/mastered/env/lib/python3.7/site-packages/matchering/checker.py", line 102, in check
    else Code.ERROR_REFERENCE_LENGTH_LENGTH_TOO_SMALL
  File "/Users/highcenoid/Documents/TCSP/mastered/env/lib/python3.7/site-packages/matchering/checker.py", line 57, in __check_length
    raise ModuleError(error_code_max)
matchering.log.exceptions.ModuleError: 4002: Track length is exceeded in the TARGET file

At first I just loaded the target and reference files, then I cut the reference file to be exactly as long as the target but was still getting the error.

import matchering as mg

# Sending all log messages to the default print function
# Just delete the following line to work silently
mg.log(print)

mg.process(
    # The track you want to master
    target='../music_file/torch.wav',

    # Some "wet" reference track
    reference='../music_file/juanzhulian.wav',

    # Where and how to save your results
    results=[
        mg.pcm16('my_song_master_16bit.wav'),
        mg.pcm24('my_song_master_24bit.wav'),
    ]
)

Please give us a star to help the project: https://github.com/sergree/matchering
----------------------------------------
Loading and analysis
Loading the TARGET file: '../music_file/torch.wav'...
The TARGET file is loaded
TARGET audio length: 12900720 samples (0:04:52)
Audio clipping is detected in the TARGET file. It is highly recommended to use the non-clipping version
Loading the REFERENCE file: '../music_file/juanzhulian.wav'...
The REFERENCE file is loaded
REFERENCE audio length: 14971904 samples (0:05:11)
Resampling REFERENCE audio from 48000 Hz to 44100 Hz...
The REFERENCE audio was resampled
----------------------------------------
Matching levels
The maximum size of the analyzed piece: 661500 samples or 15.00 seconds
Normalizing the REFERENCE...
The REFERENCE was normalized. Final amplitude coefficient for the TARGET audio is: -0.0495 dB
Calculating mid and side channels of the TARGET...
The TARGET will be didived into 20 pieces
One piece of the TARGET has a length of 645036 samples or 14.63 seconds
Calculating RMSes of the TARGET pieces...
Extracting the loudest pieces of the TARGET audio with the RMS value more than average -11.3945 dB...
The current average RMS value in the loudest pieces is -9.3542 dB
Calculating mid and side channels of the REFERENCE...



---------------------------------------------------------------------------

MemoryError                               Traceback (most recent call last)

<ipython-input-8-b42637885823> in <module>
      9     results=[
     10         mg.pcm16('my_song_master_16bit.wav'),
---> 11         mg.pcm24('my_song_master_24bit.wav'),
     12     ]
     13 )


~/miniconda3/lib/python3.7/site-packages/matchering/core.py in process(target, reference, results, config, preview_target, preview_result)
     75         need_default=any(rr.use_limiter for rr in results),
     76         need_no_limiter=any(not rr.use_limiter and not rr.normalize for rr in results),
---> 77         need_no_limiter_normalized=any(not rr.use_limiter and rr.normalize for rr in results),
     78     )
     79 


~/miniconda3/lib/python3.7/site-packages/matchering/stages.py in main(target, reference, config, need_default, need_no_limiter, need_no_limiter_normalized)
    177         reference_mid_loudest_pieces, reference_side_loudest_pieces,\
    178         target_divisions, target_piece_size, reference_match_rms\
--> 179         = __match_levels(target, reference, config)
    180 
    181     del target, reference


~/miniconda3/lib/python3.7/site-packages/matchering/stages.py in __match_levels(target, reference, config)
     50         reference_mid_loudest_pieces, reference_side_loudest_pieces,\
     51         reference_match_rms, *_\
---> 52         = analyze_levels(reference, 'reference', config)
     53 
     54     rms_coefficient, target_mid, target_side = get_rms_c_and_amplify_pair(


~/miniconda3/lib/python3.7/site-packages/matchering/stage_helpers/match_levels.py in analyze_levels(array, name, config)
    146     name = name.upper()
    147     debug(f'Calculating mid and side channels of the {name}...')
--> 148     mid, side = lr_to_ms(array)
    149     del array
    150 


~/miniconda3/lib/python3.7/site-packages/matchering/dsp.py in lr_to_ms(array)
     64     mid = np.copy(array[:, 0])
     65     array[:, 0] -= array[:, 1]
---> 66     side = np.copy(array[:, 0])
     67     return mid, side
     68 


<__array_function__ internals> in copy(*args, **kwargs)


~/miniconda3/lib/python3.7/site-packages/numpy/lib/function_base.py in copy(a, order)
    788 
    789     """
--> 790     return array(a, order=order, copy=True)
    791 
    792 # Basic operations


MemoryError: Unable to allocate array with shape (13755436,) and data type float64

Is my wav file is too long? I am new for this package.

du -sh torch.wav
50M     torch.wav
du -sh juanzhulian.wav
58M     juanzhulian.wav

I run it on an Ubuntu system.

M1 Mac Mini running OSX 11.5.2, latest version of Matchering, and Docker 4.15.0. Getting an "Unknown Error" after Matchering performs its analysis steps. This was working at one point, but for some reason now I can't get any set of files to complete. Works perfectly on my other Mac, which is a Macbook Pro Retina 2016, running OSX 10.14 (same version of Matchering and Docker). Are there any troubleshooting steps I should attempt? Thank you!

It seems matchering.com redirects to eMastered. Also i found other domains like matchering.org, matchering.net, matchering.info which look malicious.

¯_(°ペ)_/¯

Would love to see it possibly put out a text file or some sort that includes what exactly is different about the new master that the program makes (such as EQ changes, how much stereo spread was applied, how much louder it is compared to the original file, etc.)

This program is way more accurate than something like izotope but in izotope you can see what changes will be made in the new master and apply that difference to other tracks if you so choose, rather than making them all have the exact same mastering as a specific song. Love this program and keep up the good work <3 hope this all made sense!

Getting a similar tonal balance on the most high energy (and low energy) sections of a track is crucial for matching a sound.. as well as the other items that this library aims for. This could possibly be achieved with a dynamic EQ with N bands. Applying the matching of the values already compared within these bands may improve the result.

If it would be possible to identify intensity levels of a song and apply these parameters to corresponding sections of the input song, the result may also be substantially better.

Being able to tweak the percentage application of these values would be very useful as well. (wetness)

I'd like to help work on these features if you can point me in the right direction.