o )i&l@sdZddlZddlZddlmZddlmZddlmZddlm Z ddl m Z dd l m Z mZmZmZmZmZmZmZdd lmZdd lmZdd lmZmZmZmZmZmZm Z m!Z!gd Z"ddddddddddd dej#dee ee e fee fde$de%dee!ee!e!fee!fde&dee&dee&ded e%d!ed"eej#ej#ffd#d$Z'ddddddddddd dej#dee ee e fee fde$de%dee!ee!e!fee!fde&dee&dee&ded e%d!ed"ej#fd%d&Z(ddddddddddd dej#dee ee e fee fde$de%dee!ee!e!fee!fde&dee&dee&ded e%d!ed"ej#fd'd(Z)dej#d)e$d*e d"ej#fd+d,Z*d-d.dd/dej#d0e$d1e$d2e&d3e+d4e%d*e d"ej#fd5d6Z,dd7dej#d8eee&e&fd9e%d"ej#fd:d;Z-dde&de&d?e$d@eee$fdAed"ej#fdBdCZ/d=ej.dde&de&dAed"eej#ej#ffdEdFZ0d=ej.dde&de&dAed"ej#fdGdHZ1edIdIdIdJdej#dKe$dLeedMedd"ej#f dNdOZ2edIdIdPdej#dKe$dLeedMedd"eej#ej#ff dQdOZ2edIdIdPdej#dKe$dLeedMe%d"eej#eej#ej#fff dRdOZ2dSdddJdej#dKe$dLeedMe%d"eej#eej#ej#fff dTdOZ2edIdIdIdJdej#dKe$dLeedMedd"ej#f dUdVZ3edIdIdPdej#dKe$dLeedMedd"eej#ej#ff dWdVZ3dSdddJdej#dKe$dLeedMe%d"eej#eej#ej#fff dXdVZ3dS)Ya Effects ======= Harmonic-percussive source separation ------------------------------------- .. autosummary:: :toctree: generated/ hpss harmonic percussive Time and frequency ------------------ .. autosummary:: :toctree: generated/ time_stretch pitch_shift Miscellaneous ------------- .. autosummary:: :toctree: generated/ remix trim split preemphasis deemphasis N)core) decompose)feature)util)ParameterError)AnyCallableIterableOptionalTupleListUnionoverload)Literal) ArrayLike) _WindowSpec_PadMode _PadModeSTFT _SequenceLike_ScalarOrSequence_ComplexLike_co _IntLike_co _FloatLike_co)hpssharmonic percussive time_stretch pitch_shiftremixtrimsplit@F?iZhannTZconstant) kernel_sizepowermaskmarginn_fft hop_length win_lengthwindowcenterpad_modeyr%r&r'r(r)r*r+r,r-r.returnc  Csvtj||||| | d} tj| ||||d\} } tj| |j|||| |jdd}tj| |j|||| |jdd}||fS)aDecompose an audio time series into harmonic and percussive components. This function automates the STFT->HPSS->ISTFT pipeline, and ensures that the output waveforms have equal length to the input waveform ``y``. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. kernel_size power mask margin See `librosa.deocmpose.hpss` n_fft hop_length win_length window center pad_mode See `librosa.stft` Returns ------- y_harmonic : np.ndarray [shape=(..., n)] audio time series of the harmonic elements y_percussive : np.ndarray [shape=(..., n)] audio time series of the percussive elements See Also -------- harmonic : Extract only the harmonic component percussive : Extract only the percussive component librosa.decompose.hpss : HPSS on spectrograms Examples -------- >>> # Extract harmonic and percussive components >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_harmonic, y_percussive = librosa.effects.hpss(y) >>> # Get a more isolated percussive component by widening its margin >>> y_harmonic, y_percussive = librosa.effects.hpss(y, margin=(1.0,5.0)) r)r*r+r-r.r%r&r'r(dtyper)r*r+r-lengthrstftrristftr5shape)r/r%r&r'r(r)r*r+r,r-r.r8 stft_harm stft_percy_harmy_percr?^/var/www/html/eduruby.in/lip-sync/lip-sync-env/lib/python3.10/site-packages/librosa/effects.pyrFs<?    rc  CRtj||||| | d} tj| ||||dd} tj| |j|||| |jdd} | S)aExtract harmonic elements from an audio time-series. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. kernel_size power mask margin See `librosa.deocmpose.hpss` n_fft hop_length win_length window center pad_mode See `librosa.stft` Returns ------- y_harmonic : np.ndarray [shape=(..., n)] audio time series of just the harmonic portion See Also -------- hpss : Separate harmonic and percussive components percussive : Extract only the percussive component librosa.decompose.hpss : HPSS for spectrograms Examples -------- >>> # Extract harmonic component >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_harmonic = librosa.effects.harmonic(y) >>> # Use a margin > 1.0 for greater harmonic separation >>> y_harmonic = librosa.effects.harmonic(y, margin=3.0) r1r2rr3r4r7)r/r%r&r'r(r)r*r+r,r-r.r8r;r=r?r?r@r.:  rc  CrA)aExtract percussive elements from an audio time-series. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. kernel_size power mask margin See `librosa.deocmpose.hpss` n_fft hop_length win_length window center pad_mode See `librosa.stft` Returns ------- y_percussive : np.ndarray [shape=(..., n)] audio time series of just the percussive portion See Also -------- hpss : Separate harmonic and percussive components harmonic : Extract only the harmonic component librosa.decompose.hpss : HPSS for spectrograms Examples -------- >>> # Extract percussive component >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_percussive = librosa.effects.percussive(y) >>> # Use a margin > 1.0 for greater percussive separation >>> y_percussive = librosa.effects.percussive(y, margin=3.0) r1r2rr3r4r7)r/r%r&r'r(r)r*r+r,r-r.r8r<r>r?r?r@rrBrratekwargscKsx|dkrtdtj|fi|}tj|||dd|ddd}tt|jd|}tj|f|j |d|}|S) aTime-stretch an audio series by a fixed rate. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. rate : float > 0 [scalar] Stretch factor. If ``rate > 1``, then the signal is sped up. If ``rate < 1``, then the signal is slowed down. **kwargs : additional keyword arguments. See `librosa.decompose.stft` for details. Returns ------- y_stretch : np.ndarray [shape=(..., round(n/rate))] audio time series stretched by the specified rate See Also -------- pitch_shift : pitch shifting librosa.phase_vocoder : spectrogram phase vocoder pyrubberband.pyrb.time_stretch : high-quality time stretching using RubberBand Examples -------- Compress to be twice as fast >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_fast = librosa.effects.time_stretch(y, rate=2.0) Or half the original speed >>> y_slow = librosa.effects.time_stretch(y, rate=0.5) rzrate must be a positive numberr*Nr))rCr*r)r3)r5r6) rrr8Z phase_vocodergetintroundr:r9r5)r/rCrDr8Z stft_stretchZ len_stretchZ y_stretchr?r?r@rVs&  r Zsoxr_hq)bins_per_octaveres_typescalesrn_stepsrIrJrKc Kslt|s td|ddt| |}tjt|fd|i|t|||||d}tj||jddS)aShift the pitch of a waveform by ``n_steps`` steps. A step is equal to a semitone if ``bins_per_octave`` is set to 12. Parameters ---------- y : np.ndarray [shape=(..., n)] audio time series. Multi-channel is supported. sr : number > 0 [scalar] audio sampling rate of ``y`` n_steps : float [scalar] how many (fractional) steps to shift ``y`` bins_per_octave : int > 0 [scalar] how many steps per octave res_type : string Resample type. By default, 'soxr_hq' is used. See `librosa.resample` for more information. scale : bool Scale the resampled signal so that ``y`` and ``y_hat`` have approximately equal total energy. **kwargs : additional keyword arguments. See `librosa.decompose.stft` for details. Returns ------- y_shift : np.ndarray [shape=(..., n)] The pitch-shifted audio time-series See Also -------- time_stretch : time stretching librosa.phase_vocoder : spectrogram phase vocoder pyrubberband.pyrb.pitch_shift : high-quality pitch shifting using RubberBand Examples -------- Shift up by a major third (four steps if ``bins_per_octave`` is 12) >>> y, sr = librosa.load(librosa.ex('choice')) >>> y_third = librosa.effects.pitch_shift(y, sr=sr, n_steps=4) Shift down by a tritone (six steps if ``bins_per_octave`` is 12) >>> y_tritone = librosa.effects.pitch_shift(y, sr=sr, n_steps=-6) Shift up by 3 quarter-tones >>> y_three_qt = librosa.effects.pitch_shift(y, sr=sr, n_steps=3, ... bins_per_octave=24) zbins_per_octave=z must be a positive integer.r#rC)Zorig_srZ target_srrJrKr3)size) rZis_positive_intrfloatrZresamplerZ fix_lengthr:) r/rLrMrIrJrKrDrCZy_shiftr?r?r@rs F   r) align_zeros intervalsrPcCsg}|rt|}tt|d}t|t|g}|D]}|r*|t||}||d|d|dfqtj |ddS)a^Remix an audio signal by re-ordering time intervals. Parameters ---------- y : np.ndarray [shape=(..., t)] Audio time series. Multi-channel is supported. intervals : iterable of tuples (start, end) An iterable (list-like or generator) where the ``i``th item ``intervals[i]`` indicates the start and end (in samples) of a slice of ``y``. align_zeros : boolean If ``True``, interval boundaries are mapped to the closest zero-crossing in ``y``. If ``y`` is stereo, zero-crossings are computed after converting to mono. Returns ------- y_remix : np.ndarray [shape=(..., d)] ``y`` remixed in the order specified by ``intervals`` Examples -------- Load in the example track and reverse the beats >>> y, sr = librosa.load(librosa.ex('choice')) Compute beats >>> _, beat_frames = librosa.beat.beat_track(y=y, sr=sr, ... hop_length=512) Convert from frames to sample indices >>> beat_samples = librosa.frames_to_samples(beat_frames) Generate intervals from consecutive events >>> intervals = librosa.util.frame(beat_samples, frame_length=2, ... hop_length=1).T Reverse the beat intervals >>> y_out = librosa.effects.remix(y, intervals[::-1]) r3.rrZaxis) rZto_mononpnonzeroZzero_crossingsappendlenrZ match_events concatenate)r/rQrPy_outZy_monozerosintervalr?r?r@rs/  ri< frame_lengthtop_dbref aggregatecCsvtj|||d}tj|ddddf|dd}|jdkr6t||t|jd}tj|t t|jdd}|| kS)a?Frame-wise non-silent indicator for audio input. This is a helper function for `trim` and `split`. Parameters ---------- y : np.ndarray Audio signal, mono or stereo frame_length : int > 0 The number of samples per frame hop_length : int > 0 The number of samples between frames top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : callable or float The reference amplitude aggregate : callable [default: np.max] Function to aggregate dB measurements across channels (if y.ndim > 1) Note: for multiple leading axes, this is performed using ``np.apply_over_axes``. Returns ------- non_silent : np.ndarray, shape=(m,), dtype=bool Indicator of non-silent frames )r/r\r*.rN)r^r]rrR) rZrmsrZamplitude_to_dbndimrSZapply_over_axesrangeZsqueezetuple)r/r\r*r]r^r_Zmsedbr?r?r@_signal_to_frame_nonsilent-s )  rd)r]r^r\r*r_c Cst||||||d}t|}|jdkr3ttj|d|d}t|jdttj|dd|d} nd\}} t dg|j } t || | d<|t | t || gfS)aTrim leading and trailing silence from an audio signal. Silence is defined as segments of the audio signal that are `top_db` decibels (or more) quieter than a reference level, `ref`. By default, `ref` is set to the signal's maximum RMS value. It's important to note that if the entire signal maintains a uniform RMS value, there will be no segments considered quieter than the maximum, leading to no trimming. This implies that a completely silent signal will remain untrimmed with the default `ref` setting. In these situations, an explicit value for `ref` (in decibels) should be used instead. Parameters ---------- y : np.ndarray, shape=(..., n) Audio signal. Multi-channel is supported. top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : number or callable The reference amplitude. By default, it uses `np.max` and compares to the peak amplitude in the signal. frame_length : int > 0 The number of samples per analysis frame hop_length : int > 0 The number of samples between analysis frames aggregate : callable [default: np.max] Function to aggregate across channels (if y.ndim > 1) Returns ------- y_trimmed : np.ndarray, shape=(..., m) The trimmed signal index : np.ndarray, shape=(2,) the interval of ``y`` corresponding to the non-silent region: ``y_trimmed = y[index[0]:index[1]]`` (for mono) or ``y_trimmed = y[:, index[0]:index[1]]`` (for stereo). Examples -------- >>> # Load some audio >>> y, sr = librosa.load(librosa.ex('choice')) >>> # Trim the beginning and ending silence >>> yt, index = librosa.effects.trim(y) >>> # Print the durations >>> print(librosa.get_duration(y, sr=sr), librosa.get_duration(yt, sr=sr)) 25.025986394557822 25.007891156462584 r\r*r^r]r_rr*r3r)rrN) rdrS flatnonzerorNrFrframes_to_samplesminr:slicer`rbasarray) r/r]r^r\r*r_ non_silentrTstartendZ full_indexr?r?r@r es&8 r cCst||||||d}tt|t}|dg}|dr(|dtdg|dr7|tt |gt j t ||d}t ||jd}|d}|S)anSplit an audio signal into non-silent intervals. Parameters ---------- y : np.ndarray, shape=(..., n) An audio signal. Multi-channel is supported. top_db : number > 0 The threshold (in decibels) below reference to consider as silence ref : number or callable The reference amplitude. By default, it uses `np.max` and compares to the peak amplitude in the signal. frame_length : int > 0 The number of samples per analysis frame hop_length : int > 0 The number of samples between analysis frames aggregate : callable [default: np.max] Function to aggregate across channels (if y.ndim > 1) Returns ------- intervals : np.ndarray, shape=(m, 2) ``intervals[i] == (start_i, end_i)`` are the start and end time (in samples) of non-silent interval ``i``. rerrr3rf)r3)rdrSrgdiffastyperFinsertarrayrUrVrrhrWminimumr:Zreshape)r/r]r^r\r*r_rledgesr?r?r@r!s$"  r!.)coefzi return_zfrvrwrxcCdSNr?r/rvrwrxr?r?r@ preemphasisr|)rvrwcCryrzr?r{r?r?r@r| r}cCryrzr?r{r?r?r@r|r}g ףp= ?cCstjd| g|jd}tjdg|jd}|dur*d|dddf|dddf}t|}tjj|||tj||jdd\}}|rG||fS|S) a Pre-emphasize an audio signal with a first-order differencing filter: y[n] -> y[n] - coef * y[n-1] Parameters ---------- y : np.ndarray [shape=(..., n)] Audio signal. Multi-channel is supported. coef : positive number Pre-emphasis coefficient. Typical values of ``coef`` are between 0 and 1. At the limit ``coef=0``, the signal is unchanged. At ``coef=1``, the result is the first-order difference of the signal. The default (0.97) matches the pre-emphasis filter used in the HTK implementation of MFCCs [#]_. .. [#] https://htk.eng.cam.ac.uk/ zi : number Initial filter state. When making successive calls to non-overlapping frames, this can be set to the ``zf`` returned from the previous call. (See example below.) By default ``zi`` is initialized as ``2*y[0] - y[1]``. return_zf : boolean If ``True``, return the final filter state. If ``False``, only return the pre-emphasized signal. Returns ------- y_out : np.ndarray pre-emphasized signal zf : number if ``return_zf=True``, the final filter state is also returned Examples -------- Apply a standard pre-emphasis filter >>> import matplotlib.pyplot as plt >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> y_filt = librosa.effects.preemphasis(y) >>> # and plot the results for comparison >>> S_orig = librosa.amplitude_to_db(np.abs(librosa.stft(y)), ref=np.max, top_db=None) >>> S_preemph = librosa.amplitude_to_db(np.abs(librosa.stft(y_filt)), ref=np.max, top_db=None) >>> fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True) >>> librosa.display.specshow(S_orig, y_axis='log', x_axis='time', ax=ax[0]) >>> ax[0].set(title='Original signal') >>> ax[0].label_outer() >>> img = librosa.display.specshow(S_preemph, y_axis='log', x_axis='time', ax=ax[1]) >>> ax[1].set(title='Pre-emphasized signal') >>> fig.colorbar(img, ax=ax, format="%+2.f dB") Apply pre-emphasis in pieces for block streaming. Note that the second block initializes ``zi`` with the final state ``zf`` returned by the first call. >>> y_filt_1, zf = librosa.effects.preemphasis(y[:1000], return_zf=True) >>> y_filt_2, zf = librosa.effects.preemphasis(y[1000:], zi=zf, return_zf=True) >>> np.allclose(y_filt, np.concatenate([y_filt_1, y_filt_2])) True See Also -------- deemphasis r$r5Nro.rrrw)rSrkr5 atleast_1dscipysignallfilter)r/rvrwrxbarXZz_fr?r?r@r|sL$ $cCryrzr?r{r?r?r@ deemphasisr}rcCryrzr?r{r?r?r@rr}cCstjd| g|jd}tjdg|jd}|dur[tjt|jdddg|jd}tjj||||d\}}|d||dd df|dddfd ||t |jd8}nt |}tjj|||| |jd\}}|rv||fS|S) aDe-emphasize an audio signal with the inverse operation of preemphasis(): If y = preemphasis(x, coef=coef, zi=zi), the deemphasis is: >>> x[i] = y[i] + coef * x[i-1] >>> x = deemphasis(y, coef=coef, zi=zi) Parameters ---------- y : np.ndarray [shape=(..., n)] Audio signal. Multi-channel is supported. coef : positive number Pre-emphasis coefficient. Typical values of ``coef`` are between 0 and 1. At the limit ``coef=0``, the signal is unchanged. At ``coef=1``, the result is the first-order difference of the signal. The default (0.97) matches the pre-emphasis filter used in the HTK implementation of MFCCs [#]_. .. [#] https://htk.eng.cam.ac.uk/ zi : number Initial filter state. If inverting a previous preemphasis(), the same value should be used. By default ``zi`` is initialized as ``((2 - coef) * y[0] - y[1]) / (3 - coef)``. This value corresponds to the transformation of the default initialization of ``zi`` in ``preemphasis()``, ``2*x[0] - x[1]``. return_zf : boolean If ``True``, return the final filter state. If ``False``, only return the pre-emphasized signal. Returns ------- y_out : np.ndarray de-emphasized signal zf : number if ``return_zf=True``, the final filter state is also returned Examples -------- Apply a standard pre-emphasis filter and invert it with de-emphasis >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> y_filt = librosa.effects.preemphasis(y) >>> y_deemph = librosa.effects.deemphasis(y_filt) >>> np.allclose(y, y_deemph) True See Also -------- preemphasis r$r~Nr3rrro.r) rSrsr5rYlistr:rrrZarangerrq)r/rvrwrxrrrXzfr?r?r@rs"@$&  )4__doc__numpyrSZ scipy.signalrrrrrZutil.exceptionsrtypingrr r r r r rrZtyping_extensionsrZ numpy.typingrZ_typingrrrrrrrr__all__ZndarrayrOboolrFrrrrstrrrmaxrdr r!r|rr?r?r?r@s!     (  (     i    [    VB [ B  ;  Y  F       `