Pi3(dZddlZddlZddlmZddlmZddlm Z ddlm Z ddl m Z dd l mZdd lmZmZdd lmZdd lmZmZdd lmZmZgdZdddddejdededededejf dZddddddddddejd dejdededeed eed!ed"e d#eded$ed%eed&ededejfd'Z!d(dd)d*dd+d,ejd-ed.ed/ee"d0ed1edejfd2Z#d(dd)d*dd+d,ejd-ed.ed/ee"d0ed1ededejfd3Z$dS)4zFeature inversionN) get_fftlib)ParameterError) griffinlim) db_to_power)tiny)filters)nnls expand_to) DTypeLike)AnyOptional) _WindowSpec _PadModeSTFT) mel_to_stft mel_to_audio mfcc_to_mel mfcc_to_audioi"Vig@srn_fftpowerMrrrkwargsreturnc tjd|||jd|jd|}t ||}t j|d|z |S)a Approximate STFT magnitude from a Mel power spectrogram. Parameters ---------- M : np.ndarray [shape=(..., n_mels, n), non-negative] The spectrogram as produced by `feature.melspectrogram` sr : number > 0 [scalar] sampling rate of the underlying signal n_fft : int > 0 [scalar] number of FFT components in the resulting STFT power : float > 0 [scalar] Exponent for the magnitude melspectrogram **kwargs : additional keyword arguments for Mel filter bank parameters fmin : float >= 0 [scalar] lowest frequency (in Hz) fmax : float >= 0 [scalar] highest frequency (in Hz). If `None`, use ``fmax = sr / 2.0`` htk : bool [scalar] use HTK formula instead of Slaney norm : {None, 'slaney', or number} [scalar] If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). If numeric, use `librosa.util.normalize` to normalize each filter by to unit l_p norm. See `librosa.util.normalize` for a full description of supported norm values (including `+-np.inf`). Otherwise, leave all the triangles aiming for a peak value of 1.0 dtype : np.dtype The data type of the output basis. By default, uses 32-bit (single-precision) floating point. Returns ------- S : np.ndarray [shape=(..., n_fft, t), non-negative] An approximate linear magnitude spectrogram See Also -------- librosa.feature.melspectrogram librosa.stft librosa.filters.mel librosa.util.nnls Examples -------- >>> y, sr = librosa.load(librosa.ex('trumpet')) >>> S = librosa.util.abs2(librosa.stft(y)) >>> mel_spec = librosa.feature.melspectrogram(S=S, sr=sr) >>> S_inv = librosa.feature.inverse.mel_to_stft(mel_spec, sr=sr) Compare the results visually >>> import matplotlib.pyplot as plt >>> fig, ax = plt.subplots(nrows=3, sharex=True, sharey=True) >>> img = librosa.display.specshow(librosa.amplitude_to_db(S, ref=np.max, top_db=None), ... y_axis='log', x_axis='time', ax=ax[0]) >>> ax[0].set(title='Original STFT') >>> ax[0].label_outer() >>> librosa.display.specshow(librosa.amplitude_to_db(S_inv, ref=np.max, top_db=None), ... y_axis='log', x_axis='time', ax=ax[1]) >>> ax[1].set(title='Reconstructed STFT') >>> ax[1].label_outer() >>> librosa.display.specshow(librosa.amplitude_to_db(np.abs(S_inv - S), ... ref=S.max(), top_db=None), ... vmax=0, y_axis='log', x_axis='time', cmap='magma', ax=ax[2]) >>> ax[2].set(title='Residual error (dB)') >>> fig.colorbar(img, ax=ax, format="%+2.f dB") )rrn_melsdtype?)out)r melshaperr npr)rrrrr mel_basisinverses k/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/librosa/feature/inverse.pyrrseZ  U172;agAGI9a  G 8GS5[g 6 6 66hannTconstant ) rr hop_length win_lengthwindowcenterpad_modern_iterlengthrr-r.r/r0r1r2r3rc Vt|f|||d| } t| | |||||| | | S)a& Invert a mel power spectrogram to audio using Griffin-Lim. This is primarily a convenience wrapper for: >>> S = librosa.feature.inverse.mel_to_stft(M) >>> y = librosa.griffinlim(S) Parameters ---------- M : np.ndarray [shape=(..., n_mels, n), non-negative] The spectrogram as produced by `feature.melspectrogram` sr : number > 0 [scalar] sampling rate of the underlying signal n_fft : int > 0 [scalar] number of FFT components in the resulting STFT hop_length : None or int > 0 The hop length of the STFT. If not provided, it will default to ``n_fft // 4`` win_length : None or int > 0 The window length of the STFT. By default, it will equal ``n_fft`` window : string, tuple, number, function, or np.ndarray [shape=(n_fft,)] A window specification as supported by `stft` or `istft` center : boolean If `True`, the STFT is assumed to use centered frames. If `False`, the STFT is assumed to use left-aligned frames. pad_mode : string If ``center=True``, the padding mode to use at the edges of the signal. By default, STFT uses zero padding. power : float > 0 [scalar] Exponent for the magnitude melspectrogram n_iter : int > 0 The number of iterations for Griffin-Lim length : None or int > 0 If provided, the output ``y`` is zero-padded or clipped to exactly ``length`` samples. dtype : np.dtype Real numeric type for the time-domain signal. Default is 32-bit float. **kwargs : additional keyword arguments for Mel filter bank parameters fmin : float >= 0 [scalar] lowest frequency (in Hz) fmax : float >= 0 [scalar] highest frequency (in Hz). If `None`, use ``fmax = sr / 2.0`` htk : bool [scalar] use HTK formula instead of Slaney norm : {None, 'slaney', or number} [scalar] If 'slaney', divide the triangular mel weights by the width of the mel band (area normalization). If numeric, use `librosa.util.normalize` to normalize each filter by to unit l_p norm. See `librosa.util.normalize` for a full description of supported norm values (including `+-np.inf`). Otherwise, leave all the triangles aiming for a peak value of 1.0 Returns ------- y : np.ndarray [shape(..., n,)] time-domain signal reconstructed from ``M`` See Also -------- librosa.griffinlim librosa.feature.melspectrogram librosa.filters.mel librosa.feature.inverse.mel_to_stft r) r2r-r.rr/r0rr3r1)rr)rrrr-r.r/r0r1rr2r3rrstfts r(rrnsX` q DRuE D DV D DD      r)orthor rdct_typenormrefliftermfccrr9r:r;r<c~|dkr|jd}tjdd|z|j}t ||jd}d|dztjtj|z|z zz}tjtj |tj |jj krtj dtd ||t|zz }n|dkrt!d t#} | |d||| } t'| | } | S) aWInvert Mel-frequency cepstral coefficients to approximate a Mel power spectrogram. This inversion proceeds in two steps: 1. The inverse DCT is applied to the MFCCs 2. `librosa.db_to_power` is applied to map the dB-scaled result to a power spectrogram Parameters ---------- mfcc : np.ndarray [shape=(..., n_mfcc, n)] The Mel-frequency cepstral coefficients n_mels : int > 0 The number of Mel frequencies dct_type : {1, 2, 3} Discrete cosine transform (DCT) type By default, DCT type-2 is used. norm : None or 'ortho' If ``dct_type`` is `2 or 3`, setting ``norm='ortho'`` uses an orthonormal DCT basis. Normalization is not supported for `dct_type=1`. ref : float Reference power for (inverse) decibel calculation lifter : number >= 0 If ``lifter>0``, apply inverse liftering (inverse cepstral filtering):: M[n, :] <- M[n, :] / (1 + sin(pi * (n + 1) / lifter) * lifter / 2) Returns ------- M : np.ndarray [shape=(..., n_mels, n)] An approximate Mel power spectrum recovered from ``mfcc`` Warns ----- UserWarning due to critical values in lifter array that invokes underflow. See Also -------- librosa.feature.mfcc librosa.feature.melspectrogram scipy.fft.dct rr)r)ndimaxesg?z@lifter array includes critical values that may invoke underflow.r)messagecategory stacklevelz1MFCC to mel lifter must be a non-negative number.)axistyper:n)r;)r$r%arangerr r@sinpianyabsfinfoepswarningswarn UserWarningrrridctr) r=rr9r:r;r<n_mfccidx lifter_sinefftlogmelmelspecs r(rrs4hzzBi1v:TZ888$)"555&3, f0D)E)EEE  6"&%%1B(C(C(GG H H  MZ$    {T$ZZ/0 1PQQQ ,,C XXd(X H HF%f#666G Nr)c Ft||||||}t|fi|S)a Convert Mel-frequency cepstral coefficients to a time-domain audio signal This function is primarily a convenience wrapper for the following steps: 1. Convert mfcc to Mel power spectrum (`mfcc_to_mel`) 2. Convert Mel power spectrum to time-domain audio (`mel_to_audio`) Parameters ---------- mfcc : np.ndarray [shape=(..., n_mfcc, n)] The Mel-frequency cepstral coefficients n_mels : int > 0 The number of Mel frequencies dct_type : {1, 2, 3} Discrete cosine transform (DCT) type By default, DCT type-2 is used. norm : None or 'ortho' If ``dct_type`` is `2 or 3`, setting ``norm='ortho'`` uses an orthonormal DCT basis. Normalization is not supported for ``dct_type=1``. ref : float Reference power for (inverse) decibel calculation lifter : number >= 0 If ``lifter>0``, apply inverse liftering (inverse cepstral filtering):: M[n, :] <- M[n, :] / (1 + sin(pi * (n + 1) / lifter)) * lifter / 2 **kwargs : additional keyword arguments to pass through to `mel_to_audio` M : np.ndarray [shape=(..., n_mels, n), non-negative] The spectrogram as produced by `feature.melspectrogram` sr : number > 0 [scalar] sampling rate of the underlying signal n_fft : int > 0 [scalar] number of FFT components in the resulting STFT hop_length : None or int > 0 The hop length of the STFT. If not provided, it will default to ``n_fft // 4`` win_length : None or int > 0 The window length of the STFT. By default, it will equal ``n_fft`` window : string, tuple, number, function, or np.ndarray [shape=(n_fft,)] A window specification as supported by `stft` or `istft` center : boolean If `True`, the STFT is assumed to use centered frames. If `False`, the STFT is assumed to use left-aligned frames. pad_mode : string If ``center=True``, the padding mode to use at the edges of the signal. By default, STFT uses zero padding. power : float > 0 [scalar] Exponent for the magnitude melspectrogram n_iter : int > 0 The number of iterations for Griffin-Lim length : None or int > 0 If provided, the output ``y`` is zero-padded or clipped to exactly ``length`` samples. dtype : np.dtype Real numeric type for the time-domain signal. Default is 32-bit float. **kwargs : additional keyword arguments for Mel filter bank parameters fmin : float >= 0 [scalar] lowest frequency (in Hz) fmax : float >= 0 [scalar] highest frequency (in Hz). If `None`, use ``fmax = sr / 2.0`` htk : bool [scalar] use HTK formula instead of Slaney Returns ------- y : np.ndarray [shape=(..., n)] A time-domain signal reconstructed from `mfcc` See Also -------- mfcc_to_mel mel_to_audio librosa.feature.mfcc librosa.griffinlim scipy.fft.dct r8)rr)r=rr9r:r;r<rmel_specs r(rrs>j VhTs6H  + +F + ++r))%__doc__rOnumpyr%core.fftrutil.exceptionsr core.spectrumrr util.utilsrr utilr r numpy.typingr typingr r_typingrr__all__ndarrayfloatintrfloat32boolrstrrrr"r)r(rms!!!!!!,,,,,,&&&&&&''''''"""""""""""""" //////// I I I  U7U7U7 zU7 U7  U7  U7  U7ZU7U7U7U7v $ $ ' z]]] z] ]  ]  ]  ] ] ]] ] ] SM] ]]Z]]]]F!KKK *K K K 3- K  K KZKKKKb!Y,Y,Y, *Y, Y, Y, 3- Y,  Y, Y,Y,ZY,Y,Y,Y,Y,Y,r)