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