381 lines
12 KiB
Python
381 lines
12 KiB
Python
"""
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.. moduleauthor:: Fabio Manganiello <blacklight86@gmail.com>
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"""
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import enum
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import json
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import math
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class WaveShape(enum.Enum):
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SIN='sin'
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SQUARE='square'
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SAWTOOTH='sawtooth'
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TRIANG='triang'
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class Sound(object):
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"""
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Models a basic synthetic sound that can be played through an audio device
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"""
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STANDARD_A_FREQUENCY = 440.0
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STANDARD_A_MIDI_NOTE = 69
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_DEFAULT_BLOCKSIZE = 1024
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_DEFAULT_SYNTH_BUFSIZE = 2
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_DEFAULT_FILE_BUFSIZE = 20
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_DEFAULT_SAMPLERATE = 44100
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midi_note = None
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frequency = None
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phase = 0.0
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gain = 1.0
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duration = None
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shape = None
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def __init__(self, midi_note=midi_note, frequency=None, phase=phase,
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gain=gain, duration=duration, shape=WaveShape.SIN,
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A_frequency=STANDARD_A_FREQUENCY):
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"""
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You can construct a sound either from a MIDI note or a base frequency
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:param midi_note: MIDI note code, see
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https://newt.phys.unsw.edu.au/jw/graphics/notes.GIF
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:type midi_note: int
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:param frequency: Sound base frequency in Hz
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:type frequency: float
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:param phase: Wave phase shift as a multiple of pi (default: 0.0)
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:type phase: float
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:param gain: Note gain/volume between 0.0 and 1.0 (default: 1.0)
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:type gain: float
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:param duration: Note duration in seconds. Default: keep until
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release/pause/stop
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:type duration: float
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:param shape: Wave shape. Possible values: "``sin``", "``square``",
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"``sawtooth``" or "``triang``" (see :class:`WaveSound`).
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Default: "``sin``"
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:type shape: str
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:param A_frequency: Reference A4 frequency (default: 440 Hz)
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:type A_frequency: float
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"""
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if midi_note and frequency:
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raise RuntimeError('Please specify either a MIDI note or a base ' +
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'frequency')
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if midi_note:
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self.midi_note = midi_note
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self.frequency = self.note_to_freq(midi_note=midi_note,
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A_frequency=A_frequency)
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elif frequency:
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self.frequency = frequency
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self.midi_note = self.freq_to_note(frequency=frequency,
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A_frequency=A_frequency)
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else:
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raise RuntimeError('Please specify either a MIDI note or a base ' +
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'frequency')
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self.phase = phase
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self.gain = gain
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self.duration = duration
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self.shape = WaveShape(shape)
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@classmethod
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def note_to_freq(cls, midi_note, A_frequency=STANDARD_A_FREQUENCY):
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"""
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Converts a MIDI note to its frequency in Hz
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:param midi_note: MIDI note to convert
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:type midi_note: int
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:param A_frequency: Reference A4 frequency (default: 440 Hz)
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:type A_frequency: float
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"""
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return (2.0 ** ((midi_note - cls.STANDARD_A_MIDI_NOTE) / 12.0)) \
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* A_frequency
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@classmethod
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def freq_to_note(cls, frequency, A_frequency=STANDARD_A_FREQUENCY):
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"""
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Converts a frequency in Hz to its closest MIDI note
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:param frequency: Frequency in Hz
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:type midi_note: float
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:param A_frequency: Reference A4 frequency (default: 440 Hz)
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:type A_frequency: float
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"""
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# TODO return also the offset in % between the provided frequency
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# and the standard MIDI note frequency
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return int(12.0 * math.log(frequency/A_frequency, 2)
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+ cls.STANDARD_A_MIDI_NOTE)
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def get_wave(self, t_start=0., t_end=0., samplerate=_DEFAULT_SAMPLERATE):
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"""
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Get the wave binary data associated to this sound
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:param t_start: Start offset for the wave in seconds. Default: 0
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:type t_start: float
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:param t_end: End offset for the wave in seconds. Default: 0
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:type t_end: float
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:param samplerate: Audio sample rate. Default: 44100 Hz
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:type samplerate: int
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:returns: A ``numpy.ndarray[(t_end-t_start)*samplerate, 1]``
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with the raw float values
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"""
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import numpy as np
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x = np.linspace(t_start, t_end, int((t_end-t_start)*samplerate))
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x = x.reshape(len(x), 1)
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if self.shape == WaveShape.SIN or self.shape == WaveShape.SQUARE:
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wave = np.sin((2*np.pi*self.frequency*x) + np.pi*self.phase)
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if self.shape == WaveShape.SQUARE:
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wave[wave < 0] = -1
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wave[wave >= 0] = 1
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elif self.shape == WaveShape.SAWTOOTH or self.shape == WaveShape.TRIANG:
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wave = 2 * (self.frequency*x -
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np.floor(0.5 + self.frequency*x))
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if self.shape == WaveShape.TRIANG:
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wave = 2 * np.abs(wave) - 1
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else:
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raise RuntimeError('Unsupported wave shape: {}'.format(self.shape))
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return self.gain * wave
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def fft(self, t_start=0., t_end=0., samplerate=_DEFAULT_SAMPLERATE,
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freq_range=None, freq_buckets=None):
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"""
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Get the real part of the Fourier transform associated to a time-bounded
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sample of this sound
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:param t_start: Start offset for the wave in seconds. Default: 0
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:type t_start: float
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:param t_end: End offset for the wave in seconds. Default: 0
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:type t_end: float
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:param samplerate: Audio sample rate. Default: 44100 Hz
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:type samplerate: int
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:param freq_range: FFT frequency range. Default: ``(0, samplerate/2)``
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(see `Nyquist-Shannon sampling theorem <https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem>`_)
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:type freq_range: list or tuple with 2 int elements (range)
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:param freq_buckets: Number of buckets to subdivide the frequency range.
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Default: None
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:type freq_buckets: int
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:returns: A numpy.ndarray[freq_range,1] with the raw float values
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"""
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import numpy as np
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if not freq_range:
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freq_range = (0, int(samplerate/2))
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wave = self.get_wave(t_start=t_start, t_end=t_end, samplerate=samplerate)
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fft = np.fft.fft(wave.reshape(len(wave)))
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fft = fft.real[freq_range[0]:freq_range[1]]
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if freq_buckets is not None:
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fft = np.histogram(fft, bins=freq_buckets)
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return fft
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def __iter__(self):
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for attr in ['midi_note', 'frequency', 'gain', 'duration']:
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yield (attr, getattr(self, attr))
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def __str__(self):
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return json.dumps(dict(self))
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@classmethod
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def build(cls, *args, **kwargs):
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"""
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Construct a sound object either from a JSON representation or a
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key-value representation
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"""
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if args:
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if isinstance(args[0], cls):
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return args[0]
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if isinstance(args[0], str):
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kwargs = json.loads(args[0])
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elif isinstance(args[0], dict):
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kwargs = args[0]
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if kwargs:
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return Sound(**kwargs)
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raise RuntimeError('Usage: {}'.format(__doc__))
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class Mix(object):
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"""
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This class models a set of mixed :class:`Sound` instances that can be played
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through an audio stream to an audio device
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"""
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_sounds = None
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def __init__(self, *sounds):
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self._sounds = []
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for sound in sounds:
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self.add(sound)
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def __iter__(self):
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for sound in self._sounds:
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yield dict(sound)
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def __str__(self):
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return json.dumps(list(self))
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def add(self, sound):
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self._sounds.append(Sound.build(sound))
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def remove(self, sound_index):
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if sound_index >= len(self._sounds):
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self.logger.error('No such sound index: {} in mix {}'.format(
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sound_index, list(self)))
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return
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self._sounds.pop(sound_index)
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def get_wave(self, t_start=0., t_end=0., normalize_range=(-1.0, 1.0),
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on_clip='scale', samplerate=Sound._DEFAULT_SAMPLERATE):
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"""
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Get the wave binary data associated to this mix
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:param t_start: Start offset for the wave in seconds. Default: 0
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:type t_start: float
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:param t_end: End offset for the wave in seconds. Default: 0
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:type t_end: float
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:param normalize_range: Normalization range. If set the gain values of the
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wave will be normalized to fit into the specified range if it
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"clips" above or below. Default: ``(-1.0, 1.0)``
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:type normalize_range: list[float]
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:param on_clip: Action to take on wave clipping if ``normalize_range``
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is set. Possible values: "``scale``" (scale down the frame to remove
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the clipping) or "``clip``" (saturate the values above/below range).
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Default: "``scale``".
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:type on_clip: str
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:param samplerate: Audio sample rate. Default: 44100 Hz
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:type samplerate: int
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:returns: A numpy.ndarray[n,1] with the raw float values
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"""
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wave = None
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for sound in self._sounds:
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sound_wave = sound.get_wave(t_start=t_start, t_end=t_end,
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samplerate=samplerate)
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if wave is None:
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wave = sound_wave
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else:
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wave += sound_wave
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if normalize_range and len(wave):
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scale_factor = (normalize_range[1]-normalize_range[0]) / \
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(wave.max()-wave.min())
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if scale_factor < 1.0: # Wave clipping
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if on_clip == 'scale':
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wave = scale_factor * wave
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elif on_clip == 'clip':
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wave[wave < normalize_range[0]] = normalize_range[0]
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wave[wave > normalize_range[1]] = normalize_range[1]
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else:
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raise RuntimeError('Supported values for "on_clip": ' +
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'"scale" or "clip"')
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return wave
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def fft(self, t_start=0., t_end=0., samplerate=Sound._DEFAULT_SAMPLERATE,
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freq_range=None, freq_buckets=None):
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"""
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Get the real part of the Fourier transform associated to a time-bounded
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sample of this mix
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:param t_start: Start offset for the wave in seconds. Default: 0
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:type t_start: float
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:param t_end: End offset for the wave in seconds. Default: 0
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:type t_end: float
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:param samplerate: Audio sample rate. Default: 44100 Hz
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:type samplerate: int
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:param freq_range: FFT frequency range. Default: ``(0, samplerate/2)``
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(see `Nyquist-Shannon sampling theorem <https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem>`_)
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:type freq_range: list or tuple with 2 int elements (range)
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:param freq_buckets: Number of buckets to subdivide the frequency range.
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Default: None
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:type freq_buckets: int
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:returns: A numpy.ndarray[freq_range,1] with the raw float values
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"""
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import numpy as np
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if not freq_range:
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freq_range = (0, int(samplerate/2))
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wave = self.get_wave(t_start=t_start, t_end=t_end, samplerate=samplerate)
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fft = np.fft.fft(wave.reshape(len(wave)))
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fft = fft.real[freq_range[0]:freq_range[1]]
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if freq_buckets is not None:
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fft = np.histogram(fft, bins=freq_buckets)
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return fft
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def duration(self):
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"""
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:returns: The duration of the mix in seconds as duration of its longest
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sample, or None if the mixed sample have no duration set
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"""
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duration = 0
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for sound in self._sounds:
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if sound.duration is None:
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return None
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duration = max(duration, sound.duration)
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return duration
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# vim:sw=4:ts=4:et:
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