feat: cover song identification via stream
I found a tuto with the streaming way to performe cover song identification. I also add a way to download some sound with youtube dl. to download musique now just type: youtube-dl --config-location config_youtube-dl
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Lapin
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.gitignore
vendored
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.gitignore
vendored
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*.sw*
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*.mp3
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config_youtube-dl
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config_youtube-dl
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--batch-file song_to_test
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--no-overwrites
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--continue
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--extract-audio
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--audio-format mp3
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-o ./audio/%(title)s.%(ext)s
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## tutorial from: https://essentia.upf.edu/essentia_python_examples.html
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##In this tutorial, we use HPCP, ChromaCrossSimilarity and CoverSongSimilarity algorithms from essentia.
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import essentia.standard as estd
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from essentia.pytools.spectral import hpcpgram
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yesterday_original = 'audio/Yesterday (Remastered 2009).mp3'
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yesterday_cover_01 = 'audio/Yesterday - The Beatles - Connie Talbot (Cover).mp3'
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yesterday_cover_02 = 'audio/The Beatles - Yesterday Saxophone Cover Alexandra Ilieva Thomann.mp3'
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different_song = 'audio/Bella Poarch - Build a Btch (Official Music Video).mp3'
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# query cover song
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original_song = estd.MonoLoader(filename=yesterday_original, sampleRate=32000)()
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true_cover_01 = estd.MonoLoader(filename=yesterday_cover_01, sampleRate=32000)()
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true_cover_02 = estd.MonoLoader(filename=yesterday_cover_02, sampleRate=32000)()
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# wrong match
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false_cover_1 = estd.MonoLoader(filename=different_song, sampleRate=32000)()
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## Now let’s compute Harmonic Pitch Class Profile (HPCP) chroma features of these audio signals.
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query_hpcp = hpcpgram(original_song, sampleRate=32000)
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true_cover_hpcp_1 = hpcpgram(true_cover_01, sampleRate=32000)
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true_cover_hpcp_2 = hpcpgram(true_cover_02, sampleRate=32000)
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false_cover_hpcp = hpcpgram(false_cover_1, sampleRate=32000)
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## Next steps are done using the essentia ChromaCrossSimilarity function,
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##
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## Stacking input features
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##
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## Key invariance using Optimal Transposition Index (OTI) [3].
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##
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## Compute binary chroma cross similarity using cross recurrent plot as described in [1] or using OTI-based chroma binary method as detailed in [3]
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crp = estd.ChromaCrossSimilarity(frameStackSize=9,
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frameStackStride=1,
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binarizePercentile=0.095,
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oti=True)
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true_pair_crp_1 = crp(query_hpcp, true_cover_hpcp_1)
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true_pair_crp_2 = crp(query_hpcp, true_cover_hpcp_2)
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## Compute binary chroma cross similarity using cross recurrent plot of the non-cover pairs
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false_pair_crp = crp(query_hpcp, false_cover_hpcp)
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## Alternatively, you can also use the OTI-based binary similarity method as explained in [2] to compute the cross similarity of two given chroma features.
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csm = estd.ChromaCrossSimilarity(frameStackSize=9,
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frameStackStride=1,
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binarizePercentile=0.095,
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oti=True,
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otiBinary=True)
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oti_csm = csm(query_hpcp, false_cover_hpcp)
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## Finally, we compute an asymmetric cover song similarity measure from the pre-computed binary cross simialrity matrix of cover/non-cover pairs using various contraints of smith-waterman sequence alignment algorithm (eg. serra09 or chen17).
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##
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## Computing cover song similarity distance between ‘Yesterday - accapella cover’ and ‘Yesterday - The Beatles’
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score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
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disExtension=0.5,
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alignmentType='serra09',
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distanceType='asymmetric')(true_pair_crp_1)
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print('Cover song similarity distance: %s' % distance)
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## other similar
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score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
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disExtension=0.5,
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alignmentType='serra09',
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distanceType='asymmetric')(true_pair_crp_2)
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print('Cover song similarity distance: %s' % distance)
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## Computing cover song similarity distance between Yesterday - accapella cover and Come Together cover - The Aerosmith.
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score_matrix, distance = estd.CoverSongSimilarity(disOnset=0.5,
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disExtension=0.5,
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alignmentType='serra09',
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distanceType='asymmetric')(false_pair_crp)
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print('Cover song similarity distance: %s' % distance)
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101
cover_song_stream.py
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cover_song_stream.py
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## tutorial from: https://mtg.github.io/essentia-labs/news/2019/09/05/cover-song-similarity/
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#################
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# standard part #
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#################
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import essentia.standard as estd
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from essentia.pytools.spectral import hpcpgram
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yesterday_original = 'audio/Yesterday (Remastered 2009).mp3'
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yesterday_cover_01 = 'audio/Yesterday - The Beatles - Connie Talbot (Cover).mp3'
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wrong_song = 'audio/Bella Poarch - Build a Btch (Official Music Video).mp3'
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song_reference = yesterday_original
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# query cover song
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original_song = estd.MonoLoader(filename=song_reference, sampleRate=32000)()
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## Now let’s compute Harmonic Pitch Class Profile (HPCP) chroma features of these audio signals.
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true_cover_hpcp = hpcpgram(original_song, sampleRate=32000)
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#################
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# Straming part #
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#################
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import essentia.streaming as estr
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from essentia import array, run, Pool
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query_filename = wrong_song
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# Let's instantiate all the required essentia streaming algorithms
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audio = estr.MonoLoader(filename=query_filename, sampleRate=32000)
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frame_cutter = estr.FrameCutter(frameSize=4096, hopSize=2048)
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windowing = estr.Windowing(type="blackmanharris62")
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spectrum = estr.Spectrum();
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peak = estr.SpectralPeaks(sampleRate=32000)
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whitening = estr.SpectralWhitening(maxFrequency=3500,
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sampleRate=32000);
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hpcp = estr.HPCP(sampleRate=32000,
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minFrequency=100,
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maxFrequency=3500,
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size=12);
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# Create an instance of streaming ChromaCrossSimilarity algorithm
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# With parameter `referenceFeature`,
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# we can pass the pre-computed reference song chroma features.
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# In this case, we use the pre-computed HPCP feature
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# of the 'true_cover_song'.
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# With parameter `oti`, we can tranpose the pitch
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# of the reference song HPCP feature
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# to an given OTI [5] (if it's known before hand).
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# By default we set `oti=0`
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sim_matrix = estr.ChromaCrossSimilarity(
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referenceFeature=true_cover_hpcp,
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oti=0)
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# Create an instance of the cover song similarity alignment algorithm
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# 'pipeDistance=True' stdout distance values for each input stream
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alignment = estr.CoverSongSimilarity(pipeDistance=True)
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# essentia Pool instance (python dict like object) to aggregrate the outputs
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pool = Pool()
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# Connect all the required algorithms in a essentia streaming network
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# ie., connecting inputs and outputs of the algorithms
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# in the required workflow and order
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audio.audio >> frame_cutter.signal
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frame_cutter.frame >> windowing.frame
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windowing.frame >> spectrum.frame
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spectrum.spectrum >> peak.spectrum
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spectrum.spectrum >> whitening.spectrum
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peak.magnitudes >> whitening.magnitudes
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peak.frequencies >> whitening.frequencies
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peak.frequencies >> hpcp.frequencies
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whitening.magnitudes >> hpcp.magnitudes
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hpcp.hpcp >> sim_matrix.queryFeature
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sim_matrix.csm >> alignment.inputArray
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alignment.scoreMatrix >> (pool, 'scoreMatrix')
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alignment.distance >> (pool, 'distance')
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# Run the algorithm network
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run(audio)
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# This process will stdout the cover song similarity distance
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# for every input stream in realtime.
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# It also aggregrates the Smith-Waterman alignment score matrix
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# and cover song similarity distance for every accumulating
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# input audio stream in an essentia pool instance (similar to a python dict)
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# which can be accessed after the end of the stream.
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# Now, let's check the final cover song similarity distance value
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# computed at the last input stream.
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print(pool['distance'][-1])
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14
song_to_test
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song_to_test
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https://www.youtube.com/watch?v=TQemQRL_YVQ # yesterday original
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https://www.youtube.com/watch?v=sGSZA6mYo4c # yesterday cover 1
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https://www.youtube.com/watch?v=Dyjrnxj70dU # yesterday cover 2
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https://youtu.be/EzRtlhjyNZM # gangsta rap
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https://youtu.be/mm_PH5BadTk # gangsta rap
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https://youtu.be/26Nuj6dhte8 # Georges Brassens - La Mauvaise Réputation
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https://youtu.be/i2wmKcBm4Ik # Jacques Brel - Ne Me Quitte Pas
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https://youtu.be/nUE80DTNxK4 # Barbara - Dis, quand reviendras-tu
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https://youtu.be/UGtKGX8B9hU # le cafe - oldelaf _ future shorts
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78
todo
78
todo
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* comprendre un peu la logique du streaming avec essentia
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* refaire l'exemple em mode streaming
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?? Est-ce que ca va etre rapide a s'executer ??
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?? Est-ce que ca va etre rapide a s'execute ??
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#################################################
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Bon en fait j'ai trouver le code d'exemple don j'ai besoin.
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Ca commence part recuperer en mode standar la description d'un fichier sonore.
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Puis avec un input en mode stream ca compart la distance avec la chansson.
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Ce qu'il reste a faire:
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* avoir un script pour telecharger les musique a tester.
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* avoir un input type micro
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* avoir une entree avec jack (jackd)
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* Faire tourner plusieur processus pour pouvoir annalyser plusieurs track en meme temps.
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*
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1) un scritp qui telecharge les son:
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J'ai besoin d'un fichier de config qui telecharge les musique en extrayan le son
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et les place dans le bon dossier. Et dans un format que je peut lire avec essentia.
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* avoir un
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*
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*
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*
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Les ellement pour la config:
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--config-location PATH Location of the configuration file;
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either the path to the config or its
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containing directory.
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-a, --batch-file FILE File containing URLs to download ('-'
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for stdin), one URL per line. Lines
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starting with '#', ';' or ']' are
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considered as comments and ignored.
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-c, --continue Force resume of partially downloaded
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files. By default, youtube-dl will
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--encoding ENCODING Force the specified encoding
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(experimental) resume downloads if possible.
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-x, --extract-audio Convert video files to audio-only files
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(requires ffmpeg/avconv and
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ffprobe/avprobe)
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--audio-format FORMAT Specify audio format: "best", "aac",
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"flac", "mp3", "m4a", "opus", "vorbis",
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or "wav"; "best" by default; No effect
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without -x
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-w, --no-overwrites Do not overwrite files
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vv -E-X-M-P-L-E- -C-O-N-F-I-G- -F-I-L-E- vv
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# Lines starting with # are comments
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# Always extract audio
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-x
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# Do not copy the mtime
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--no-mtime
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# Use this proxy
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--proxy 127.0.0.1:3128
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# Save all videos under Movies directory in your home directory
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-o ~/Movies/%(title)s.%(ext)s
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^^ - - - - - - - - - - - - - - - - - - - ^^
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to run download:
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$> youtube-dl --config-location config_youtube-dl
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Dependance:
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youtube-dl: sudo apt-get install -y ffmpeg
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