{"id":16877232,"url":"https://github.com/timsainb/python_spectrograms_and_inversion","last_synced_at":"2025-03-17T06:31:29.654Z","repository":{"id":86221995,"uuid":"70114185","full_name":"timsainb/python_spectrograms_and_inversion","owner":"timsainb","description":"Spectrograms, MFCCs, and Inversion Demo in a jupyter notebook","archived":false,"fork":false,"pushed_at":"2019-07-15T13:39:08.000Z","size":6741,"stargazers_count":166,"open_issues_count":3,"forks_count":66,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-02-27T19:03:22.343Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/timsainb.png","metadata":{"files":{"readme":"readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2016-10-06T01:44:57.000Z","updated_at":"2025-02-26T13:43:52.000Z","dependencies_parsed_at":"2023-03-08T00:45:10.190Z","dependency_job_id":null,"html_url":"https://github.com/timsainb/python_spectrograms_and_inversion","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timsainb%2Fpython_spectrograms_and_inversion","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timsainb%2Fpython_spectrograms_and_inversion/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timsainb%2Fpython_spectrograms_and_inversion/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/timsainb%2Fpython_spectrograms_and_inversion/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/timsainb","download_url":"https://codeload.github.com/timsainb/python_spectrograms_and_inversion/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243847062,"owners_count":20357317,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-13T15:42:27.534Z","updated_at":"2025-03-17T06:31:28.002Z","avatar_url":"https://github.com/timsainb.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n### Spectrograms, mel scaling, and Inversion demo in jupyter/ipython\nThis is just a bit of code that shows you how to make a spectrogram/sonogram in python using numpy, scipy, and a few functions written by \u003ca href=\"https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe\"\u003eKyle Kastner\u003c/a\u003e. I also show you how to invert those spectrograms back into wavform, filter those spectrograms to be mel-scaled, and invert those spectrograms as well. This should prove to be a useful tool for those interested in generative modelling (as I am). For example, running spectrograms through an LSTM, VAE, GAN, \u003ca href=\"https://timsainb.github.io/multi-gpu-vae-gan-in-tensorflow.html\"\u003eVAE-GAN\u003c/a\u003e, or experimenting with your own audio/waveform models. Check out the \u003ca href=\"http://www.openslr.org/12/\"\u003eLibriSpeech dataset.\u003c/a\u003e for a 1000 hr dataset of transcripted speech from open source audio books. \n- Made by Tim Sainburg and Marvin Thielk\n\n\n```python\n# iPython specific stuff\n%matplotlib inline\nimport IPython.display\nfrom ipywidgets import interact, interactive, fixed\n\n# Packages we're using\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport copy\nfrom scipy.io import wavfile\nfrom scipy.signal import butter, lfilter\nimport scipy.ndimage\n```\n\n### Make Spectrogram\n(Skip over this for now and take a look at the output below)\n\n\n```python\n# Most of the Spectrograms and Inversion are taken from: https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe\n\ndef butter_bandpass(lowcut, highcut, fs, order=5):\n    nyq = 0.5 * fs\n    low = lowcut / nyq\n    high = highcut / nyq\n    b, a = butter(order, [low, high], btype='band')\n    return b, a\n\ndef butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n    b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n    y = lfilter(b, a, data)\n    return y\n\ndef overlap(X, window_size, window_step):\n    \"\"\"\n    Create an overlapped version of X\n    Parameters\n    ----------\n    X : ndarray, shape=(n_samples,)\n        Input signal to window and overlap\n    window_size : int\n        Size of windows to take\n    window_step : int\n        Step size between windows\n    Returns\n    -------\n    X_strided : shape=(n_windows, window_size)\n        2D array of overlapped X\n    \"\"\"\n    window_size, window_step = map(int, (window_size, window_step))\n    if window_size % 2 != 0:\n        raise ValueError(\"Window size must be even!\")\n    # Make sure there are an even number of windows before stridetricks\n    append = np.zeros((window_size - len(X) % window_size))\n    X = np.hstack((X, append))\n\n    ws = window_size\n    ss = window_step\n    a = X\n\n    valid = len(a) - ws\n    nw = (valid) // ss\n    out = np.ndarray((nw,ws),dtype = a.dtype)\n\n    for i in range(nw):\n        # \"slide\" the window along the samples\n        start = i * ss\n        stop = start + ws\n        out[i] = a[start : stop]\n\n    return out\n\n\ndef stft(X, fftsize=128, step=65, mean_normalize=True, real=False,\n         compute_onesided=True):\n    \"\"\"\n    Compute STFT for 1D real valued input X\n    \"\"\"\n    if real:\n        local_fft = np.fft.rfft\n        cut = -1\n    else:\n        local_fft = np.fft.fft\n        cut = None\n    if compute_onesided:\n        cut = fftsize // 2\n    if mean_normalize:\n        X -= X.mean()\n\n    X = overlap(X, fftsize, step)\n    \n    size = fftsize\n    win = 0.54 - .46 * np.cos(2 * np.pi * np.arange(size) / (size - 1))\n    X = X * win[None]\n    X = local_fft(X)[:, :cut]\n    return X\n\ndef pretty_spectrogram(d,log = True, thresh= 5, fft_size = 512, step_size = 64):\n    \"\"\"\n    creates a spectrogram\n    log: take the log of the spectrgram\n    thresh: threshold minimum power for log spectrogram\n    \"\"\"\n    specgram = np.abs(stft(d, fftsize=fft_size, step=step_size, real=False,\n        compute_onesided=True))\n  \n    if log == True:\n        specgram /= specgram.max() # volume normalize to max 1\n        specgram = np.log10(specgram) # take log\n        specgram[specgram \u003c -thresh] = -thresh # set anything less than the threshold as the threshold\n    else:\n        specgram[specgram \u003c thresh] = thresh # set anything less than the threshold as the threshold\n    \n    return specgram\n\n# Also mostly modified or taken from https://gist.github.com/kastnerkyle/179d6e9a88202ab0a2fe\ndef invert_pretty_spectrogram(X_s, log = True, fft_size = 512, step_size = 512/4, n_iter = 10):\n    \n    if log == True:\n        X_s = np.power(10, X_s)\n\n    X_s = np.concatenate([X_s, X_s[:, ::-1]], axis=1)\n    X_t = iterate_invert_spectrogram(X_s, fft_size, step_size, n_iter=n_iter)\n    return X_t\n\ndef iterate_invert_spectrogram(X_s, fftsize, step, n_iter=10, verbose=False):\n    \"\"\"\n    Under MSR-LA License\n    Based on MATLAB implementation from Spectrogram Inversion Toolbox\n    References\n    ----------\n    D. Griffin and J. Lim. Signal estimation from modified\n    short-time Fourier transform. IEEE Trans. Acoust. Speech\n    Signal Process., 32(2):236-243, 1984.\n    Malcolm Slaney, Daniel Naar and Richard F. Lyon. Auditory\n    Model Inversion for Sound Separation. Proc. IEEE-ICASSP,\n    Adelaide, 1994, II.77-80.\n    Xinglei Zhu, G. Beauregard, L. Wyse. Real-Time Signal\n    Estimation from Modified Short-Time Fourier Transform\n    Magnitude Spectra. IEEE Transactions on Audio Speech and\n    Language Processing, 08/2007.\n    \"\"\"\n    reg = np.max(X_s) / 1E8\n    X_best = copy.deepcopy(X_s)\n    for i in range(n_iter):\n        if verbose:\n            print(\"Runnning iter %i\" % i)\n        if i == 0:\n            X_t = invert_spectrogram(X_best, step, calculate_offset=True,\n                                     set_zero_phase=True)\n        else:\n            # Calculate offset was False in the MATLAB version\n            # but in mine it massively improves the result\n            # Possible bug in my impl?\n            X_t = invert_spectrogram(X_best, step, calculate_offset=True,\n                                     set_zero_phase=False)\n        est = stft(X_t, fftsize=fftsize, step=step, compute_onesided=False)\n        phase = est / np.maximum(reg, np.abs(est))\n        X_best = X_s * phase[:len(X_s)]\n    X_t = invert_spectrogram(X_best, step, calculate_offset=True,\n                             set_zero_phase=False)\n    return np.real(X_t)\n\ndef invert_spectrogram(X_s, step, calculate_offset=True, set_zero_phase=True):\n    \"\"\"\n    Under MSR-LA License\n    Based on MATLAB implementation from Spectrogram Inversion Toolbox\n    References\n    ----------\n    D. Griffin and J. Lim. Signal estimation from modified\n    short-time Fourier transform. IEEE Trans. Acoust. Speech\n    Signal Process., 32(2):236-243, 1984.\n    Malcolm Slaney, Daniel Naar and Richard F. Lyon. Auditory\n    Model Inversion for Sound Separation. Proc. IEEE-ICASSP,\n    Adelaide, 1994, II.77-80.\n    Xinglei Zhu, G. Beauregard, L. Wyse. Real-Time Signal\n    Estimation from Modified Short-Time Fourier Transform\n    Magnitude Spectra. IEEE Transactions on Audio Speech and\n    Language Processing, 08/2007.\n    \"\"\"\n    step = int(step)\n    size = int(X_s.shape[1] // 2)\n    wave = np.zeros((X_s.shape[0] * step + size))\n    # Getting overflow warnings with 32 bit...\n    wave = wave.astype('float64')\n    total_windowing_sum = np.zeros((X_s.shape[0] * step + size))\n    win = 0.54 - .46 * np.cos(2 * np.pi * np.arange(size) / (size - 1))\n\n    est_start = int(size // 2) - 1\n    est_end = est_start + size\n    for i in range(X_s.shape[0]):\n        wave_start = int(step * i)\n        wave_end = wave_start + size\n        if set_zero_phase:\n            spectral_slice = X_s[i].real + 0j\n        else:\n            # already complex\n            spectral_slice = X_s[i]\n\n        # Don't need fftshift due to different impl.\n        wave_est = np.real(np.fft.ifft(spectral_slice))[::-1]\n        if calculate_offset and i \u003e 0:\n            offset_size = size - step\n            if offset_size \u003c= 0:\n                print(\"WARNING: Large step size \u003e50\\% detected! \"\n                      \"This code works best with high overlap - try \"\n                      \"with 75% or greater\")\n                offset_size = step\n            offset = xcorr_offset(wave[wave_start:wave_start + offset_size],\n                                  wave_est[est_start:est_start + offset_size])\n        else:\n            offset = 0\n        wave[wave_start:wave_end] += win * wave_est[\n            est_start - offset:est_end - offset]\n        total_windowing_sum[wave_start:wave_end] += win\n    wave = np.real(wave) / (total_windowing_sum + 1E-6)\n    return wave\n\ndef xcorr_offset(x1, x2):\n    \"\"\"\n    Under MSR-LA License\n    Based on MATLAB implementation from Spectrogram Inversion Toolbox\n    References\n    ----------\n    D. Griffin and J. Lim. Signal estimation from modified\n    short-time Fourier transform. IEEE Trans. Acoust. Speech\n    Signal Process., 32(2):236-243, 1984.\n    Malcolm Slaney, Daniel Naar and Richard F. Lyon. Auditory\n    Model Inversion for Sound Separation. Proc. IEEE-ICASSP,\n    Adelaide, 1994, II.77-80.\n    Xinglei Zhu, G. Beauregard, L. Wyse. Real-Time Signal\n    Estimation from Modified Short-Time Fourier Transform\n    Magnitude Spectra. IEEE Transactions on Audio Speech and\n    Language Processing, 08/2007.\n    \"\"\"\n    x1 = x1 - x1.mean()\n    x2 = x2 - x2.mean()\n    frame_size = len(x2)\n    half = frame_size // 2\n    corrs = np.convolve(x1.astype('float32'), x2[::-1].astype('float32'))\n    corrs[:half] = -1E30\n    corrs[-half:] = -1E30\n    offset = corrs.argmax() - len(x1)\n    return offset\n\ndef make_mel(spectrogram, mel_filter, shorten_factor = 1):\n    mel_spec =np.transpose(mel_filter).dot(np.transpose(spectrogram))\n    mel_spec = scipy.ndimage.zoom(mel_spec.astype('float32'), [1, 1./shorten_factor]).astype('float16')\n    mel_spec = mel_spec[:,1:-1] # a little hacky but seemingly needed for clipping \n    return mel_spec\n\ndef mel_to_spectrogram(mel_spec, mel_inversion_filter, spec_thresh, shorten_factor):\n    \"\"\"\n    takes in an mel spectrogram and returns a normal spectrogram for inversion \n    \"\"\"\n    mel_spec = (mel_spec+spec_thresh)\n    uncompressed_spec = np.transpose(np.transpose(mel_spec).dot(mel_inversion_filter))\n    uncompressed_spec = scipy.ndimage.zoom(uncompressed_spec.astype('float32'), [1,shorten_factor]).astype('float16')\n    uncompressed_spec = uncompressed_spec -4\n    return uncompressed_spec\n\n```\n\n\n```python\n# From https://github.com/jameslyons/python_speech_features\n\ndef hz2mel(hz):\n    \"\"\"Convert a value in Hertz to Mels\n    :param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.\n    :returns: a value in Mels. If an array was passed in, an identical sized array is returned.\n    \"\"\"\n    return 2595 * np.log10(1+hz/700.)\n    \ndef mel2hz(mel):\n    \"\"\"Convert a value in Mels to Hertz\n    :param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise.\n    :returns: a value in Hertz. If an array was passed in, an identical sized array is returned.\n    \"\"\"\n    return 700*(10**(mel/2595.0)-1)\n\ndef get_filterbanks(nfilt=20,nfft=512,samplerate=16000,lowfreq=0,highfreq=None):\n    \"\"\"Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond\n    to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1)\n    :param nfilt: the number of filters in the filterbank, default 20.\n    :param nfft: the FFT size. Default is 512.\n    :param samplerate: the samplerate of the signal we are working with. Affects mel spacing.\n    :param lowfreq: lowest band edge of mel filters, default 0 Hz\n    :param highfreq: highest band edge of mel filters, default samplerate/2\n    :returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter.\n    \"\"\"\n    highfreq= highfreq or samplerate/2\n    assert highfreq \u003c= samplerate/2, \"highfreq is greater than samplerate/2\"\n    \n    # compute points evenly spaced in mels\n    lowmel = hz2mel(lowfreq)\n    highmel = hz2mel(highfreq)\n    melpoints = np.linspace(lowmel,highmel,nfilt+2)\n    # our points are in Hz, but we use fft bins, so we have to convert\n    #  from Hz to fft bin number\n    bin = np.floor((nfft+1)*mel2hz(melpoints)/samplerate)\n\n    fbank = np.zeros([nfilt,nfft//2])\n    for j in range(0,nfilt):\n        for i in range(int(bin[j]), int(bin[j+1])):\n            fbank[j,i] = (i - bin[j]) / (bin[j+1]-bin[j])\n        for i in range(int(bin[j+1]), int(bin[j+2])):\n            fbank[j,i] = (bin[j+2]-i) / (bin[j+2]-bin[j+1])\n    return fbank\n\ndef create_mel_filter(fft_size, n_freq_components = 64, start_freq = 300, end_freq = 8000, samplerate=44100):\n    \"\"\"\n    Creates a filter to convolve with the spectrogram to get out mels\n\n    \"\"\"\n    mel_inversion_filter = get_filterbanks(nfilt=n_freq_components, \n                                           nfft=fft_size, samplerate=samplerate, \n                                           lowfreq=start_freq, highfreq=end_freq)\n    # Normalize filter\n    mel_filter = mel_inversion_filter.T / mel_inversion_filter.sum(axis=1)\n\n    return mel_filter, mel_inversion_filter\n```\n\n### Parameters\n\n\n```python\n### Parameters ###\nfft_size = 2048 # window size for the FFT\nstep_size = fft_size/16 # distance to slide along the window (in time)\nspec_thresh = 4 # threshold for spectrograms (lower filters out more noise)\nlowcut = 500 # Hz # Low cut for our butter bandpass filter\nhighcut = 15000 # Hz # High cut for our butter bandpass filter\n# For mels\nn_mel_freq_components = 64 # number of mel frequency channels\nshorten_factor = 10 # how much should we compress the x-axis (time)\nstart_freq = 300 # Hz # What frequency to start sampling our melS from \nend_freq = 8000 # Hz # What frequency to stop sampling our melS from \n```\n\n### Loading the WAV\n\n\n```python\n# Grab your wav and filter it\nmywav = 'bushOffersPeace.wav'\nrate, data = wavfile.read(mywav)\ndata = butter_bandpass_filter(data, lowcut, highcut, rate, order=1)\n# Only use a short clip for our demo\nif np.shape(data)[0]/float(rate) \u003e 10:\n    data = data[0:rate*10] \nprint ('Length in time (s): ', np.shape(data)[0]/float(rate))\n```\n\n    Length in time (s):  4.547437641723356\n\n\n\n```python\n# Play the audio\nIPython.display.Audio(data=data, rate=rate)\n```\n\n\n\n\n\n                \u003caudio  controls=\"controls\" \u003e\n                    \u003csource 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