Merge branch 'feature/class_3'

Author: vanpelt

keras-audio/audio.ipynb

@@ -0,0 +1,349 @@
+import scipy.io.wavfile
+from os.path import expanduser
+import os
+import array
+from pylab import *
+import scipy.signal
+import scipy
+import wave
+import numpy as np
+import time
+import sys
+import math
+import matplotlib
+import subprocess
+
+# Author: Brian K. Vogel
+# [email protected]
+
+fft_size = 2048
+iterations = 300
+hopsamp = fft_size // 8
+
+
+def ensure_audio():
+    if not os.path.exists("audio"):
+        print("Downloading audio dataset...")
+        subprocess.check_output(
+            "curl -SL https://storage.googleapis.com/wandb/audio.tar.gz | tar xz", shell=True)
+
+
+def griffin_lim(stft, scale):
+    # Undo the rescaling.
+    stft_modified_scaled = stft / scale
+    stft_modified_scaled = stft_modified_scaled**0.5
+    # Use the Griffin&Lim algorithm to reconstruct an audio signal from the
+    # magnitude spectrogram.
+    x_reconstruct = reconstruct_signal_griffin_lim(stft_modified_scaled,
+                                                   fft_size, hopsamp,
+                                                   iterations)
+    # The output signal must be in the range [-1, 1], otherwise we need to clip or normalize.
+    max_sample = np.max(abs(x_reconstruct))
+    if max_sample > 1.0:
+        x_reconstruct = x_reconstruct / max_sample
+    return x_reconstruct
+
+
+def hz_to_mel(f_hz):
+    """Convert Hz to mel scale.
+
+    This uses the formula from O'Shaugnessy's book.
+    Args:
+        f_hz (float): The value in Hz.
+
+    Returns:
+        The value in mels.
+    """
+    return 2595*np.log10(1.0 + f_hz/700.0)
+
+
+def mel_to_hz(m_mel):
+    """Convert mel scale to Hz.
+
+    This uses the formula from O'Shaugnessy's book.
+    Args:
+        m_mel (float): The value in mels
+
+    Returns:
+        The value in Hz
+    """
+    return 700*(10**(m_mel/2595) - 1.0)
+
+
+def fft_bin_to_hz(n_bin, sample_rate_hz, fft_size):
+    """Convert FFT bin index to frequency in Hz.
+
+    Args:
+        n_bin (int or float): The FFT bin index.
+        sample_rate_hz (int or float): The sample rate in Hz.
+        fft_size (int or float): The FFT size.
+
+    Returns:
+        The value in Hz.
+    """
+    n_bin = float(n_bin)
+    sample_rate_hz = float(sample_rate_hz)
+    fft_size = float(fft_size)
+    return n_bin*sample_rate_hz/(2.0*fft_size)
+
+
+def hz_to_fft_bin(f_hz, sample_rate_hz, fft_size):
+    """Convert frequency in Hz to FFT bin index.
+
+    Args:
+        f_hz (int or float): The frequency in Hz.
+        sample_rate_hz (int or float): The sample rate in Hz.
+        fft_size (int or float): The FFT size.
+
+    Returns:
+        The FFT bin index as an int.
+    """
+    f_hz = float(f_hz)
+    sample_rate_hz = float(sample_rate_hz)
+    fft_size = float(fft_size)
+    fft_bin = int(np.round((f_hz*2.0*fft_size/sample_rate_hz)))
+    if fft_bin >= fft_size:
+        fft_bin = fft_size-1
+    return fft_bin
+
+
+def make_mel_filterbank(min_freq_hz, max_freq_hz, mel_bin_count,
+                        linear_bin_count, sample_rate_hz):
+    """Create a mel filterbank matrix.
+
+    Create and return a mel filterbank matrix `filterbank` of shape (`mel_bin_count`,
+    `linear_bin_couont`). The `filterbank` matrix can be used to transform a
+    (linear scale) spectrum or spectrogram into a mel scale spectrum or
+    spectrogram as follows:
+
+    `mel_scale_spectrum` = `filterbank`*'linear_scale_spectrum'
+
+    where linear_scale_spectrum' is a shape (`linear_bin_count`, `m`) and
+    `mel_scale_spectrum` is shape ('mel_bin_count', `m`) where `m` is the number
+    of spectral time slices.
+
+    Likewise, the reverse-direction transform can be performed as:
+
+    'linear_scale_spectrum' = filterbank.T`*`mel_scale_spectrum`
+
+    Note that the process of converting to mel scale and then back to linear
+    scale is lossy.
+
+    This function computes the mel-spaced filters such that each filter is triangular
+    (in linear frequency) with response 1 at the center frequency and decreases linearly
+    to 0 upon reaching an adjacent filter's center frequency. Note that any two adjacent
+    filters will overlap having a response of 0.5 at the mean frequency of their
+    respective center frequencies.
+
+    Args:
+        min_freq_hz (float): The frequency in Hz corresponding to the lowest
+            mel scale bin.
+        max_freq_hz (flloat): The frequency in Hz corresponding to the highest
+            mel scale bin.
+        mel_bin_count (int): The number of mel scale bins.
+        linear_bin_count (int): The number of linear scale (fft) bins.
+        sample_rate_hz (float): The sample rate in Hz.
+
+    Returns:
+        The mel filterbank matrix as an 2-dim Numpy array.
+    """
+    min_mels = hz_to_mel(min_freq_hz)
+    max_mels = hz_to_mel(max_freq_hz)
+    # Create mel_bin_count linearly spaced values between these extreme mel values.
+    mel_lin_spaced = np.linspace(min_mels, max_mels, num=mel_bin_count)
+    # Map each of these mel values back into linear frequency (Hz).
+    center_frequencies_hz = np.array([mel_to_hz(n) for n in mel_lin_spaced])
+    mels_per_bin = float(max_mels - min_mels)/float(mel_bin_count - 1)
+    mels_start = min_mels - mels_per_bin
+    hz_start = mel_to_hz(mels_start)
+    fft_bin_start = hz_to_fft_bin(hz_start, sample_rate_hz, linear_bin_count)
+    #print('fft_bin_start: ', fft_bin_start)
+    mels_end = max_mels + mels_per_bin
+    hz_stop = mel_to_hz(mels_end)
+    fft_bin_stop = hz_to_fft_bin(hz_stop, sample_rate_hz, linear_bin_count)
+    #print('fft_bin_stop: ', fft_bin_stop)
+    # Map each center frequency to the closest fft bin index.
+    linear_bin_indices = np.array([hz_to_fft_bin(
+        f_hz, sample_rate_hz, linear_bin_count) for f_hz in center_frequencies_hz])
+    # Create filterbank matrix.
+    filterbank = np.zeros((mel_bin_count, linear_bin_count))
+    for mel_bin in range(mel_bin_count):
+        center_freq_linear_bin = int(linear_bin_indices[mel_bin].item())
+        # Create a triangular filter having the current center freq.
+        # The filter will start with 0 response at left_bin (if it exists)
+        # and ramp up to 1.0 at center_freq_linear_bin, and then ramp
+        # back down to 0 response at right_bin (if it exists).
+
+        # Create the left side of the triangular filter that ramps up
+        # from 0 to a response of 1 at the center frequency.
+        if center_freq_linear_bin > 1:
+            # It is possible to create the left triangular filter.
+            if mel_bin == 0:
+                # Since this is the first center frequency, the left side
+                # must start ramping up from linear bin 0 or 1 mel bin before the center freq.
+                left_bin = max(0, fft_bin_start)
+            else:
+                # Start ramping up from the previous center frequency bin.
+                left_bin = int(linear_bin_indices[mel_bin - 1].item())
+            for f_bin in range(left_bin, center_freq_linear_bin+1):
+                if (center_freq_linear_bin - left_bin) > 0:
+                    response = float(f_bin - left_bin) / \
+                        float(center_freq_linear_bin - left_bin)
+                    filterbank[mel_bin, f_bin] = response
+        # Create the right side of the triangular filter that ramps down
+        # from 1 to 0.
+        if center_freq_linear_bin < linear_bin_count-2:
+            # It is possible to create the right triangular filter.
+            if mel_bin == mel_bin_count - 1:
+                # Since this is the last mel bin, we must ramp down to response of 0
+                # at the last linear freq bin.
+                right_bin = min(linear_bin_count - 1, fft_bin_stop)
+            else:
+                right_bin = int(linear_bin_indices[mel_bin + 1].item())
+            for f_bin in range(center_freq_linear_bin, right_bin+1):
+                if (right_bin - center_freq_linear_bin) > 0:
+                    response = float(right_bin - f_bin) / \
+                        float(right_bin - center_freq_linear_bin)
+                    filterbank[mel_bin, f_bin] = response
+        filterbank[mel_bin, center_freq_linear_bin] = 1.0
+
+    return filterbank
+
+
+def stft_for_reconstruction(x, fft_size, hopsamp):
+    """Compute and return the STFT of the supplied time domain signal x.
+
+    Args:
+        x (1-dim Numpy array): A time domain signal.
+        fft_size (int): FFT size. Should be a power of 2, otherwise DFT will be used.
+        hopsamp (int):
+
+    Returns:
+        The STFT. The rows are the time slices and columns are the frequency bins.
+    """
+    window = np.hanning(fft_size)
+    fft_size = int(fft_size)
+    hopsamp = int(hopsamp)
+    return np.array([np.fft.rfft(window*x[i:i+fft_size])
+                     for i in range(0, len(x)-fft_size, hopsamp)])
+
+
+def istft_for_reconstruction(X, fft_size, hopsamp):
+    """Invert a STFT into a time domain signal.
+
+    Args:
+        X (2-dim Numpy array): Input spectrogram. The rows are the time slices and columns are the frequency bins.
+        fft_size (int):
+        hopsamp (int): The hop size, in samples.
+
+    Returns:
+        The inverse STFT.
+    """
+    fft_size = int(fft_size)
+    hopsamp = int(hopsamp)
+    window = np.hanning(fft_size)
+    time_slices = X.shape[0]
+    len_samples = int(time_slices*hopsamp + fft_size)
+    x = np.zeros(len_samples)
+    for n, i in enumerate(range(0, len(x)-fft_size, hopsamp)):
+        x[i:i+fft_size] += window*np.real(np.fft.irfft(X[n]))
+    return x
+
+
+def get_signal(in_file, expected_fs=44100):
+    """Load a wav file.
+
+    If the file contains more than one channel, return a mono file by taking
+    the mean of all channels.
+
+    If the sample rate differs from the expected sample rate (default is 44100 Hz),
+    raise an exception.
+
+    Args:
+        in_file: The input wav file, which should have a sample rate of `expected_fs`.
+        expected_fs (int): The expected sample rate of the input wav file.
+
+    Returns:
+        The audio siganl as a 1-dim Numpy array. The values will be in the range [-1.0, 1.0]. fixme ( not yet)
+    """
+    fs, y = scipy.io.wavfile.read(in_file)
+    num_type = y[0].dtype
+    if num_type == 'int16':
+        y = y*(1.0/32768)
+    elif num_type == 'int32':
+        y = y*(1.0/2147483648)
+    elif num_type == 'float32':
+        # Nothing to do
+        pass
+    elif num_type == 'uint8':
+        raise Exception('8-bit PCM is not supported.')
+    else:
+        raise Exception('Unknown format.')
+    if fs != expected_fs:
+        raise Exception('Invalid sample rate.')
+    if y.ndim == 1:
+        return y
+    else:
+        return y.mean(axis=1)
+
+
+def reconstruct_signal_griffin_lim(magnitude_spectrogram, fft_size, hopsamp, iterations):
+    """Reconstruct an audio signal from a magnitude spectrogram.
+
+    Given a magnitude spectrogram as input, reconstruct
+    the audio signal and return it using the Griffin-Lim algorithm from the paper:
+    "Signal estimation from modified short-time fourier transform" by Griffin and Lim,
+    in IEEE transactions on Acoustics, Speech, and Signal Processing. Vol ASSP-32, No. 2, April 1984.
+
+    Args:
+        magnitude_spectrogram (2-dim Numpy array): The magnitude spectrogram. The rows correspond to the time slices
+            and the columns correspond to frequency bins.
+        fft_size (int): The FFT size, which should be a power of 2.
+        hopsamp (int): The hope size in samples.
+        iterations (int): Number of iterations for the Griffin-Lim algorithm. Typically a few hundred
+            is sufficient.
+
+    Returns:
+        The reconstructed time domain signal as a 1-dim Numpy array.
+    """
+    time_slices = magnitude_spectrogram.shape[0]
+    len_samples = int(time_slices*hopsamp + fft_size)
+    # Initialize the reconstructed signal to noise.
+    x_reconstruct = np.random.randn(len_samples)
+    n = iterations  # number of iterations of Griffin-Lim algorithm.
+    while n > 0:
+        n -= 1
+        reconstruction_spectrogram = stft_for_reconstruction(
+            x_reconstruct, fft_size, hopsamp)
+        reconstruction_angle = np.angle(reconstruction_spectrogram)
+        # Discard magnitude part of the reconstruction and use the supplied magnitude spectrogram instead.
+        proposal_spectrogram = magnitude_spectrogram * \
+            np.exp(1.0j*reconstruction_angle)
+        prev_x = x_reconstruct
+        x_reconstruct = istft_for_reconstruction(
+            proposal_spectrogram, fft_size, hopsamp)
+        diff = sqrt(sum((x_reconstruct - prev_x)**2)/x_reconstruct.size)
+        #print('Reconstruction iteration: {}/{} RMSE: {} '.format(iterations - n, iterations, diff))
+    return x_reconstruct
+
+
+def save_audio_to_file(x, sample_rate, outfile='out.wav'):
+    """Save a mono signal to a file.
+
+    Args:
+        x (1-dim Numpy array): The audio signal to save. The signal values should be in the range [-1.0, 1.0].
+        sample_rate (int): The sample rate of the signal, in Hz.
+        outfile: Name of the file to save.
+
+    """
+    x_max = np.max(abs(x))
+    assert x_max <= 1.0, 'Input audio value is out of range. Should be in the range [-1.0, 1.0].'
+    x = x*32767.0
+    data = array.array('h')
+    for i in range(len(x)):
+        cur_samp = int(round(x[i]))
+        data.append(cur_samp)
+    f = wave.open(outfile, 'w')
+    f.setparams((1, 2, sample_rate, 0, "NONE", "Uncompressed"))
+    f.writeframes(data.tostring())
+    f.close()