audiosegment.py
def spectrogram(self, start_s=None, duration_s=None, start_sample=None, num_samples=None,
                    window_length_s=None, window_length_samples=None, overlap=0.5, window=('tukey', 0.25)):
        """
        Does a series of FFTs from `start_s` or `start_sample` for `duration_s` or `num_samples`.
        Effectively, transforms a slice of the AudioSegment into the frequency domain across different
        time bins.

        .. code-block:: python

            # Example for plotting a spectrogram using this function
            import audiosegment
            import matplotlib.pyplot as plt

            #...
            seg = audiosegment.from_file("somebodytalking.wav")
            freqs, times, amplitudes = seg.spectrogram(window_length_s=0.03, overlap=0.5)
            amplitudes = 10 * np.log10(amplitudes + 1e-9)

            # Plot
            plt.pcolormesh(times, freqs, amplitudes)
            plt.xlabel("Time in Seconds")
            plt.ylabel("Frequency in Hz")
            plt.show()

        .. image:: images/spectrogram.png

        :param start_s: The start time. Starts at the beginning if neither this nor `start_sample` is specified.
        :param duration_s: The duration of the spectrogram in seconds. Goes to the end if neither this nor
                           `num_samples` is specified.
        :param start_sample: The index of the first sample to use. Starts at the beginning if neither this nor
                             `start_s` is specified.
        :param num_samples: The number of samples in the spectrogram. Goes to the end if neither this nor
                            `duration_s` is specified.
        :param window_length_s: The length of each FFT in seconds. If the total number of samples in the spectrogram
                                is not a multiple of the window length in samples, the last window will be zero-padded.
        :param window_length_samples: The length of each FFT in number of samples. If the total number of samples in the
                                spectrogram is not a multiple of the window length in samples, the last window will
                                be zero-padded.
        :param overlap: The fraction of each window to overlap.
        :param window: See Scipy's spectrogram-function_.
                       This parameter is passed as-is directly into the Scipy spectrogram function. It's documentation is reproduced here:
                       Desired window to use. If window is a string or tuple, it is passed to get_window to generate the window values,
                       which are DFT-even by default. See get_window for a list of windows and required parameters.
                       If window is array_like it will be used directly as the window and its length must be
                       `window_length_samples`.
                       Defaults to a Tukey window with shape parameter of 0.25.
        :returns: Three np.ndarrays: The frequency values in Hz (the y-axis in a spectrogram), the time values starting
                  at start time and then increasing by `duration_s` each step (the x-axis in a spectrogram), and
                  the dB of each time/frequency bin as a 2D array of shape [len(frequency values), len(duration)].
        :raises ValueError: If `start_s` and `start_sample` are both specified, if `duration_s` and `num_samples` are both
                            specified, if the first window's duration plus start time lead to running off the end
                            of the AudioSegment, or if `window_length_s` and `window_length_samples` are either
                            both specified or if they are both not specified.

        .. _spectrogram-function: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html
        """
        if start_s is not None and start_sample is not None:
            raise ValueError("Only one of start_s and start_sample may be specified.")
        if duration_s is not None and num_samples is not None:
            raise ValueError("Only one of duration_s and num_samples may be specified.")
        if window_length_s is not None and window_length_samples is not None:
            raise ValueError("Only one of window_length_s and window_length_samples may be specified.")
        if window_length_s is None and window_length_samples is None:
            raise ValueError("You must specify a window length, either in window_length_s or in window_length_samples.")

        # Determine the start sample
        if start_s is None and start_sample is None:
            start_sample = 0
        elif start_s is not None:
            start_sample = int(round(start_s * self.frame_rate))

        # Determine the number of samples
        if duration_s is None and num_samples is None:
            num_samples = len(self.get_array_of_samples()) - int(start_sample)
        elif duration_s is not None:
            num_samples = int(round(duration_s * self.frame_rate))

        # Determine the number of samples per window
        if window_length_s is not None:
            window_length_samples = int(round(window_length_s * self.frame_rate))

        # Check validity of number of samples
        if start_sample + num_samples > len(self.get_array_of_samples()):
            raise ValueError("The combination of start and duration will run off the end of the AudioSegment object.")

        # Create a Numpy Array out of the correct samples
        arr = self.to_numpy_array()[start_sample:start_sample+num_samples]

        # Use Scipy spectrogram and return
        fs, ts, sxx = signal.spectrogram(arr, self.frame_rate, scaling='spectrum', nperseg=window_length_samples,
                                             noverlap=int(round(overlap * window_length_samples)),
                                             mode='magnitude', window=window)
        return fs, ts, sxx
def spectrogram(self, start_s=None, duration_s=None, start_sample=None, num_samples=None,
                    window_length_s=None, window_length_samples=None, overlap=0.5, window=('tukey', 0.25)):
        if start_s is not None and start_sample is not None:
            raise ValueError("Only one of start_s and start_sample may be specified.")
        if duration_s is not None and num_samples is not None:
            raise ValueError("Only one of duration_s and num_samples may be specified.")
        if window_length_s is not None and window_length_samples is not None:
            raise ValueError("Only one of window_length_s and window_length_samples may be specified.")
        if window_length_s is None and window_length_samples is None:
            raise ValueError("You must specify a window length, either in window_length_s or in window_length_samples.")

        # Determine the start sample
        if start_s is None and start_sample is None:
            start_sample = 0
        elif start_s is not None:
            start_sample = int(round(start_s * self.frame_rate))

        # Determine the number of samples
        if duration_s is None and num_samples is None:
            num_samples = len(self.get_array_of_samples()) - int(start_sample)
        elif duration_s is not None:
            num_samples = int(round(duration_s * self.frame_rate))

        # Determine the number of samples per window
        if window_length_s is not None:
            window_length_samples = int(round(window_length_s * self.frame_rate))

        # Check validity of number of samples
        if start_sample + num_samples > len(self.get_array_of_samples()):
            raise ValueError("The combination of start and duration will run off the end of the AudioSegment object.")

        # Create a Numpy Array out of the correct samples
        arr = self.to_numpy_array()[start_sample:start_sample+num_samples]

        # Use Scipy spectrogram and return
        fs, ts, sxx = signal.spectrogram(arr, self.frame_rate, scaling='spectrum', nperseg=window_length_samples,
                                             noverlap=int(round(overlap * window_length_samples)),
                                             mode='magnitude', window=window)
        return fs, ts, sxx
def spectrogram ( self , start_s = None , duration_s = None , start_sample = None , num_samples = None , window_length_s = None , window_length_samples = None , overlap = 0.5 , window = ( 'tukey' , 0.25 ) ) : if start_s is not None and start_sample is not None : raise ValueError ( "Only one of start_s and start_sample may be specified." ) if duration_s is not None and num_samples is not None : raise ValueError ( "Only one of duration_s and num_samples may be specified." ) if window_length_s is not None and window_length_samples is not None : raise ValueError ( "Only one of window_length_s and window_length_samples may be specified." ) if window_length_s is None and window_length_samples is None : raise ValueError ( "You must specify a window length, either in window_length_s or in window_length_samples." ) # Determine the start sample if start_s is None and start_sample is None : start_sample = 0 elif start_s is not None : start_sample = int ( round ( start_s * self . frame_rate ) ) # Determine the number of samples if duration_s is None and num_samples is None : num_samples = len ( self . get_array_of_samples ( ) ) - int ( start_sample ) elif duration_s is not None : num_samples = int ( round ( duration_s * self . frame_rate ) ) # Determine the number of samples per window if window_length_s is not None : window_length_samples = int ( round ( window_length_s * self . frame_rate ) ) # Check validity of number of samples if start_sample + num_samples > len ( self . get_array_of_samples ( ) ) : raise ValueError ( "The combination of start and duration will run off the end of the AudioSegment object." ) # Create a Numpy Array out of the correct samples arr = self . to_numpy_array ( ) [ start_sample : start_sample + num_samples ] # Use Scipy spectrogram and return fs , ts , sxx = signal . spectrogram ( arr , self . frame_rate , scaling = 'spectrum' , nperseg = window_length_samples , noverlap = int ( round ( overlap * window_length_samples ) ) , mode = 'magnitude' , window = window ) return fs , ts , sxx
Does a series of FFTs from `start_s` or `start_sample` for `duration_s` or `num_samples`.
        Effectively, transforms a slice of the AudioSegment into the frequency domain across different
        time bins.

        .. code-block:: python

            # Example for plotting a spectrogram using this function
            import audiosegment
            import matplotlib.pyplot as plt

            #...
            seg = audiosegment.from_file("somebodytalking.wav")
            freqs, times, amplitudes = seg.spectrogram(window_length_s=0.03, overlap=0.5)
            amplitudes = 10 * np.log10(amplitudes + 1e-9)

            # Plot
            plt.pcolormesh(times, freqs, amplitudes)
            plt.xlabel("Time in Seconds")
            plt.ylabel("Frequency in Hz")
            plt.show()

        .. image:: images/spectrogram.png

        :param start_s: The start time. Starts at the beginning if neither this nor `start_sample` is specified.
        :param duration_s: The duration of the spectrogram in seconds. Goes to the end if neither this nor
                           `num_samples` is specified.
        :param start_sample: The index of the first sample to use. Starts at the beginning if neither this nor
                             `start_s` is specified.
        :param num_samples: The number of samples in the spectrogram. Goes to the end if neither this nor
                            `duration_s` is specified.
        :param window_length_s: The length of each FFT in seconds. If the total number of samples in the spectrogram
                                is not a multiple of the window length in samples, the last window will be zero-padded.
        :param window_length_samples: The length of each FFT in number of samples. If the total number of samples in the
                                spectrogram is not a multiple of the window length in samples, the last window will
                                be zero-padded.
        :param overlap: The fraction of each window to overlap.
        :param window: See Scipy's spectrogram-function_.
                       This parameter is passed as-is directly into the Scipy spectrogram function. It's documentation is reproduced here:
                       Desired window to use. If window is a string or tuple, it is passed to get_window to generate the window values,
                       which are DFT-even by default. See get_window for a list of windows and required parameters.
                       If window is array_like it will be used directly as the window and its length must be
                       `window_length_samples`.
                       Defaults to a Tukey window with shape parameter of 0.25.
        :returns: Three np.ndarrays: The frequency values in Hz (the y-axis in a spectrogram), the time values starting
                  at start time and then increasing by `duration_s` each step (the x-axis in a spectrogram), and
                  the dB of each time/frequency bin as a 2D array of shape [len(frequency values), len(duration)].
        :raises ValueError: If `start_s` and `start_sample` are both specified, if `duration_s` and `num_samples` are both
                            specified, if the first window's duration plus start time lead to running off the end
                            of the AudioSegment, or if `window_length_s` and `window_length_samples` are either
                            both specified or if they are both not specified.

        .. _spectrogram-function: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html
Does a series of FFTs from start_s or start_sample for duration_s or num_samples . Effectively transforms a slice of the AudioSegment into the frequency domain across different time bins .