PibdZddlZddlZddlZddlZddlmZddlmZddlm Z ddl m Z ddl m Z dd lmZdd lmZdd lmZmZmZdd lmZgd Zeddde Z dddddddddddd deejdedeejdedededed eeeejfd!eejjd"ed#ed$eeeejfejffd%Z ddddd&d'd(dd)deejdedeejded*ed+eed,eed!eejjd$ejfd-Z!dejd ejd.ededed$ejf d/Z"d0Z#ej$d1d2gd3dd45d6Z%ej$d7d8gd9dd5d:Z&ej$d;dZ'd?Z(ej$d@dAgdBdd5dCZ)ej$dDdEgdFdd5dGZ*dS)Hz^ Beat and tempo ============== .. autosummary:: :toctree: generated/ beat_track plp N)core)onset)util)fourier_tempogram)tempo)ParameterError)moved)OptionalTupleUnion) _FloatLike_co) beat_trackrplpzlibrosa.beat.tempoz0.10.0z1.0) moved_fromversionversion_removedi"Vig^@dTframes) ysronset_envelope hop_length start_bpm tightnesstrimbpmpriorunitssparserrrrrrrrrrr returnc x|3|tdtj|||tj}| r#|jdkrtd|jd|sb| rdtjgtfStj |j dd t tj |tfS|t||||| }tj|} t!j| |jt%| j } t'|| t||z ||} | rjtj| } | d krnO| dkr|t+j| |fS| dkr|t+j| ||fStd| || fS)aDynamic programming beat tracker. Beats are detected in three stages, following the method of [#]_: 1. Measure onset strength 2. Estimate tempo from onset correlation 3. Pick peaks in onset strength approximately consistent with estimated tempo .. [#] Ellis, Daniel PW. "Beat tracking by dynamic programming." Journal of New Music Research 36.1 (2007): 51-60. http://labrosa.ee.columbia.edu/projects/beattrack/ Parameters ---------- y : np.ndarray [shape=(..., n)] or None audio time series sr : number > 0 [scalar] sampling rate of ``y`` onset_envelope : np.ndarray [shape=(..., m)] or None (optional) pre-computed onset strength envelope. hop_length : int > 0 [scalar] number of audio samples between successive ``onset_envelope`` values start_bpm : float > 0 [scalar] initial guess for the tempo estimator (in beats per minute) tightness : float [scalar] tightness of beat distribution around tempo trim : bool [scalar] trim leading/trailing beats with weak onsets bpm : float [scalar] or np.ndarray [shape=(...)] (optional) If provided, use ``bpm`` as the tempo instead of estimating it from ``onsets``. If multichannel, tempo estimates can be provided for all channels. Tempo estimates may also be time-varying, in which case the shape of ``bpm`` should match that of ``onset_envelope``, i.e., one estimate provided for each frame. prior : scipy.stats.rv_continuous [optional] An optional prior distribution over tempo. If provided, ``start_bpm`` will be ignored. units : {'frames', 'samples', 'time'} The units to encode detected beat events in. By default, 'frames' are used. sparse : bool If ``True`` (default), detections are returned as an array of frames, samples, or time indices (as specified by ``units=``). If ``False``, detections are encoded as a dense boolean array where ``beats[..., n]`` is true if there's a beat at frame index ``n``. .. note:: multi-channel input is only supported when ``sparse=False``. Returns ------- tempo : float [scalar, non-negative] or np.ndarray estimated global tempo (in beats per minute) If multi-channel and ``bpm`` is not provided, a separate tempo will be returned for each channel. .. note:: By default, the tempo is returned as an ndarray even for mono input. In this case, the array will have a single element and be one-dimensional. This is to ensure consistent return types for multi-channel input. beats : np.ndarray estimated beat event locations. If `sparse=True` (default), beat locations are given in the specified units (default is frame indices). If `sparse=False` (required for multichannel input), beat events are indicated by a boolean for each frame. .. note:: If no onset strength could be detected, beat_tracker estimates 0 BPM and returns an empty list. Raises ------ ParameterError if neither ``y`` nor ``onset_envelope`` are provided, or if ``units`` is not one of 'frames', 'samples', or 'time' See Also -------- librosa.onset.onset_strength Examples -------- Track beats using time series input >>> y, sr = librosa.load(librosa.ex('choice'), duration=10) >>> tempo, beats = librosa.beat.beat_track(y=y, sr=sr) >>> tempo 135.99917763157896 Print the frames corresponding to beats >>> beats array([ 3, 21, 40, 59, 78, 96, 116, 135, 154, 173, 192, 211, 230, 249, 268, 287, 306, 325, 344, 363]) Or print them as timestamps >>> librosa.frames_to_time(beats, sr=sr) array([0.07 , 0.488, 0.929, 1.37 , 1.811, 2.229, 2.694, 3.135, 3.576, 4.017, 4.458, 4.899, 5.341, 5.782, 6.223, 6.664, 7.105, 7.546, 7.988, 8.429]) Output beat detections as a boolean array instead of frame indices >>> tempo, beats_dense = librosa.beat.beat_track(y=y, sr=sr, sparse=False) >>> beats_dense array([False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, ..., False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]) Track beats using a pre-computed onset envelope >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr, ... aggregate=np.median) >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env, ... sr=sr) >>> tempo 135.99917763157896 >>> beats array([ 3, 21, 40, 59, 78, 96, 116, 135, 154, 173, 192, 211, 230, 249, 268, 287, 306, 325, 344, 363]) Plot the beat events against the onset strength envelope >>> import matplotlib.pyplot as plt >>> hop_length = 512 >>> fig, ax = plt.subplots(nrows=2, sharex=True) >>> times = librosa.times_like(onset_env, sr=sr, hop_length=hop_length) >>> M = librosa.feature.melspectrogram(y=y, sr=sr, hop_length=hop_length) >>> librosa.display.specshow(librosa.power_to_db(M, ref=np.max), ... y_axis='mel', x_axis='time', hop_length=hop_length, ... ax=ax[0]) >>> ax[0].label_outer() >>> ax[0].set(title='Mel spectrogram') >>> ax[1].plot(times, librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[1].vlines(times[beats], 0, 1, alpha=0.5, color='r', ... linestyle='--', label='Beats') >>> ax[1].legend() Nz$y or onset_envelope must be providedrrr aggregaterz'sparse=True (default) does not support zK-dimensional inputs. Either set sparse=False or convert the signal to mono.dtypeshaper')rrrrrndimaxesrsamples)rtime)rrzInvalid unit type: )r ronset_strengthnpmedianr,anyarrayintzerosr*float zeros_likebool_tempo atleast_1dr expand_torange__beat_tracker flatnonzerorframes_to_samplesframes_to_time)rrrrrrrrrrr _bpm bpm_expandedbeatss `/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/librosa/beat.pyrr$sb 9 !GHH H-B:   K.%**J!&JJJKK K     ?  ?"C0001 1H>#7#$'5':',TY'7'7999L ><rZ9OQZ\` a aE @u%% H    i  /*MMMN N f__,UzbQQQR R !>u!>!>?? ? <ii,)rrrr win_length tempo_min tempo_maxrrHrIrJcB|"tj|||tj}||||krt d|d|t ||||}t j|||} |d|d| |kddf<|d|d| |kddf<tj | |j d } tj d tj |z} || | | z } | d d } d|| | k<|tj|dztj |d d zz}t j|d||jd} tj| dd| } tj| dS)aPredominant local pulse (PLP) estimation. [#]_ The PLP method analyzes the onset strength envelope in the frequency domain to find a locally stable tempo for each frame. These local periodicities are used to synthesize local half-waves, which are combined such that peaks coincide with rhythmically salient frames (e.g. onset events on a musical time grid). The local maxima of the pulse curve can be taken as estimated beat positions. This method may be preferred over the dynamic programming method of `beat_track` when the tempo is expected to vary significantly over time. Additionally, since `plp` does not require the entire signal to make predictions, it may be preferable when beat-tracking long recordings in a streaming setting. .. [#] Grosche, P., & Muller, M. (2011). "Extracting predominant local pulse information from music recordings." IEEE Transactions on Audio, Speech, and Language Processing, 19(6), 1688-1701. Parameters ---------- y : np.ndarray [shape=(..., n)] or None audio time series. Multi-channel is supported. sr : number > 0 [scalar] sampling rate of ``y`` onset_envelope : np.ndarray [shape=(..., n)] or None (optional) pre-computed onset strength envelope hop_length : int > 0 [scalar] number of audio samples between successive ``onset_envelope`` values win_length : int > 0 [scalar] number of frames to use for tempogram analysis. By default, 384 frames (at ``sr=22050`` and ``hop_length=512``) corresponds to about 8.9 seconds. tempo_min, tempo_max : numbers > 0 [scalar], optional Minimum and maximum permissible tempo values. ``tempo_max`` must be at least ``tempo_min``. Set either (or both) to `None` to disable this constraint. prior : scipy.stats.rv_continuous [optional] A prior distribution over tempo (in beats per minute). By default, a uniform prior over ``[tempo_min, tempo_max]`` is used. Returns ------- pulse : np.ndarray, shape=[(..., n)] The estimated pulse curve. Maxima correspond to rhythmically salient points of time. If input is multi-channel, one pulse curve per channel is computed. See Also -------- beat_track librosa.onset.onset_strength librosa.feature.fourier_tempogram Examples -------- Visualize the PLP compared to an onset strength envelope. Both are normalized here to make comparison easier. >>> y, sr = librosa.load(librosa.ex('brahms')) >>> onset_env = librosa.onset.onset_strength(y=y, sr=sr) >>> pulse = librosa.beat.plp(onset_envelope=onset_env, sr=sr) >>> # Or compute pulse with an alternate prior, like log-normal >>> import scipy.stats >>> prior = scipy.stats.lognorm(loc=np.log(120), scale=120, s=1) >>> pulse_lognorm = librosa.beat.plp(onset_envelope=onset_env, sr=sr, ... prior=prior) >>> melspec = librosa.feature.melspectrogram(y=y, sr=sr) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=3, sharex=True) >>> librosa.display.specshow(librosa.power_to_db(melspec, ... ref=np.max), ... x_axis='time', y_axis='mel', ax=ax[0]) >>> ax[0].set(title='Mel spectrogram') >>> ax[0].label_outer() >>> ax[1].plot(librosa.times_like(onset_env), ... librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[1].plot(librosa.times_like(pulse), ... librosa.util.normalize(pulse), ... label='Predominant local pulse (PLP)') >>> ax[1].set(title='Uniform tempo prior [30, 300]') >>> ax[1].label_outer() >>> ax[2].plot(librosa.times_like(onset_env), ... librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[2].plot(librosa.times_like(pulse_lognorm), ... librosa.util.normalize(pulse_lognorm), ... label='Predominant local pulse (PLP)') >>> ax[2].set(title='Log-normal tempo prior, mean=120', xlim=[5, 20]) >>> ax[2].legend() PLP local maxima can be used as estimates of beat positions. >>> tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env) >>> beats_plp = np.flatnonzero(librosa.util.localmax(pulse)) >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True) >>> times = librosa.times_like(onset_env, sr=sr) >>> ax[0].plot(times, librosa.util.normalize(onset_env), ... label='Onset strength') >>> ax[0].vlines(times[beats], 0, 1, alpha=0.5, color='r', ... linestyle='--', label='Beats') >>> ax[0].legend() >>> ax[0].set(title='librosa.beat.beat_track') >>> ax[0].label_outer() >>> # Limit the plot to a 15-second window >>> times = librosa.times_like(pulse, sr=sr) >>> ax[1].plot(times, librosa.util.normalize(pulse), ... label='PLP') >>> ax[1].vlines(times[beats_plp], 0, 1, alpha=0.5, color='r', ... linestyle='--', label='PLP Beats') >>> ax[1].legend() >>> ax[1].set(title='librosa.beat.plp', xlim=[5, 20]) >>> ax[1].xaxis.set_major_formatter(librosa.display.TimeFormatter()) Nr#z tempo_max=z must be larger than tempo_min=)rrrrH)rrrHr.r+g.AT)axiskeepdims?rr()rn_fftlengthrM)rr0r1r2r rrfourier_tempo_frequenciesrr<r,log1pabslogpdfmaxtinyistftr*clip normalize) rrrrrHrIrJrftgramtempo_frequenciesftmag peak_valuespulses rErr sN-B:   !69 ;Q;Q N N N9 N N   %  F6 *89s% 11114589s% 111145'8v{QSTTT HS26&>>) * *E  /000))d)33K"#F5;  di3& T 0R0R)S)SSSF J1J~7KB7O   E GE1dE * *E >%b ) ) ))rF frame_ratec*tj|dkrtd|d|dkrtd|jdd|jdfvrtd|jd|jtj|d z|z }t t ||}t|||\}}t|} tj |t } t|| | t|| |} | S) a[Tracks beats in an onset strength envelope. Parameters ---------- onset_envelope : np.ndarray [shape=(..., n,)] onset strength envelope bpm : float [scalar] or np.ndarray [shape=(...)] tempo estimate frame_rate : float [scalar] frame rate of the spectrogram (sr / hop_length, frames per second) tightness : float [scalar, positive] how closely do we adhere to bpm? trim : bool [scalar] trim leading/trailing beats with weak onsets? Returns ------- beats : np.ndarray [shape=(n,)] frame numbers of beat events rzbpm=z must be strictly positivez#tightness must be strictly positiver(rzInvalid bpm shape=z% does not match onset envelope shape=gN@r&) r1r3r r*round__beat_local_score__normalize_onsets__beat_track_dp __last_beatr8r9__dp_backtrack __trim_beats) rrrarrframes_per_beat localscorebacklinkcumscoretailrDs rEr>r>s,. vcQhECCCCCDDDA~~BCCC y}Q 4R 8999x#)xxbpbvxxyyyhzD03677O$$6~$F$FXXJ)_iPPHh x D M. 5 5 5E8T5)))%Z==E LrFcf|ddd}||tj|zz S)z2Normalize onset strength by its standard deviationrr(T)ddofrMrN)stdrrX)onsetsnorms rEreres3 ::12: 5 5D TDIf--- ..rFz(void(float32[:], float32[:], float32[:])z(void(float64[:], float64[:], float64[:])z (t),(n)->(t)F)nopythoncachec >t|}t|dkrtjdtj|d |ddzdz|dz dzz}t|}t t|D]p}d||<t t d||dzz|z dzt ||dzz|D]*}||xx|||||dzz|z zz cc<+qdSt|t|krt t|D]}tjdtj|| ||dzdz||z dzz}dt||zdz}d||<t t d||dzz|z dzt ||dzz|D]*}||xx|||||dzz|z zz cc<+dSdS)Nrgrg@@r%)lenr1exparanger=rWminr5)rrjrkNwindowKiks rErdrdsy NA ?q   ?1+=*=q?QTU?U V VY] ]`opq`r rwxxxyy KKs>**++ I IAJqM3q!a1f*q.1"455s1qAv:q7I7IJJ I I1 ^A1HaK-H!HH  I  I I _  ^!4!4 4 4s>**++ L LAVDBIq/A.A?STCUXYCY$Z$Z]a$adstudv$v{|#||}}FC*+++a/AJqM3q!a1f*q.1"455s1qAv:q7I7IJJ L L1 ^AQJN-K!KK  L 5 4 L LrFz;void(float32[:], float32[:], float32, int32[:], float32[:])z;void(float64[:], float64[:], float32, int32[:], float64[:])z(t),(n),()->(t),(t)c ld|z}d}d|d<|d|d<tt|dk}t|D]\}} tj } d} t |t j|||zdz z |d|||zzz dz dD]X} | dkrnO|| |t j|| z t j|||zz dzzz } | | kr| } | } Y| dkr | | z||<n| ||<|r | |krd||<| ||<d}dS) z&Core dynamic program for beat trackingg{Gz?Tr(rrrwFN) rWr5rx enumerater1infr=rclog)rkrjrrlrm score_thresh first_beattvrscore_i best_score beat_locationlocscores rErfrf.s*..***LJHQKQ-HQK S ! !A % & &B ++ 7vX  RXob1f&=&ABBBAO\^ab\bLcHcDcfgDgilmm $ $CQwwSMIC26/Z\_`Z`JaCbCb1bef0f$ffEz!!" #  A  !J.HQKK"HQK  'L00HQKK'HQKJJ7rFz+void(float32[:], bool_[:], bool_, bool_[:])z+void(float64[:], bool_[:], bool_, bool_[:])z(t),(t),()->(t)c||dd<tjd}tj|||t|dzt|t|dzz}|rd|dzdzz}nd}d}|||krd||<|dz }|||kt|dz }|||krd||<|dz}|||kdS) zCRemove spurious leading and trailing beats from the detection arrayNrwrOr%rFr)r1hanningconvolverxmean)rkrDr beats_trimmedw smooth_boe thresholdns rEririasM!!! 1 AZ.223q6619S__SQRVVUVY=V3VWJ JM//11367   A Q-9 $ $ a Q Q-9 $ $ J!A Q-9 $ $ a Q Q-9 $ $ DrFcptj|d}tj||}tj|d}dtj|z}tj|jddt}t|||||S)z/Identify the position of the last detected beatr(rR)datamaskrONr)) rlocalmaxr1ma masked_arrayr2getdataemptyr*r5__last_beat_selector)rmr masked_scoresmedians thresholdsrns rErgrgs M( , , , ,DE&&H4&@@Mell=rl22Gru}}W---J 8(."-S 9 9 9D4T::: KrFz-void(float32[:], bool_[:], float32, int64[:])z-void(float64[:], bool_[:], float64, int64[:])z(t),(t),()->()ct|dz }||d<|dkr(||s|||kr||d<dS|dz}|dk&dSdS)zmVectorized helper to identify the last valid beat position: cumscore[n] > threshold and not mask[n] rrN)rx)rmrroutrs rErrsj H A CF q&&Aw 8A;)33CF E FA q&&&&&&rFzvoid(int32[:], int32, bool_[:])zvoid(int64[:], int64, bool_[:])z (t),()->(t)c@|}|dkrd||<||}|dkdSdS)z>Populate the beat indicator array from a sequence of backlinksrTN) backlinksrnrDrs rErhrhs7 A q&&a aL q&&&&&&rF)+__doc__numpyr1scipy scipy.statsnumbarrrfeaturerrr:util.exceptionsr util.decoratorsr typingr r r _typingr__all__ndarrayr7r5r9stats rv_continuousstrrrr>re guvectorizerdrfrirgrrhrrFrErs  &&&&&&$$$$$$++++++""""""))))))))))"""""" ( ( ( X-xQVWWW   #+/6:15ddd d dRZ( d  d  dd d % rz12 3d EK- .d d d 5 * +RZ 78ddddR#+/!#!$15z*z*z* z* z*RZ( z*  z*  z*z*z* EK- .z*Zz*z*z*z*z3J3%'Z3=B3OT3\`3Z3333n///  6 6  U $$$LL$$LD I I  T ###))##)X55      <    ; ;  T           - -  T      rF