README.md

STR_CLIP_ID = 'clip_id' STR_AUDIO_SIGNAL = 'audio_signal' STR_TARGET_VECTOR = 'target_vector'

STR_CH_FIRST = 'channels_first' STR_CH_LAST = 'channels_last'

import io import os import tqdm import logging import subprocess from typing import Tuple from pathlib import Path

import librosa

import numpy as np import soundfile as sf

import itertools from numpy.fft import irfft

def _resample_load_ffmpeg(path: str, sample_rate: int, downmix_to_mono: bool) -> Tuple[np.ndarray, int]: """ Decoding, downmixing, and downsampling by librosa. Returns a channel-first audio signal. Args: path: sample_rate: downmix_to_mono: Returns: (audio signal, sample rate) """

def _decode_resample_by_ffmpeg(filename, sr):
    """decode, downmix, and resample audio file"""
    channel_cmd = '-ac 1 ' if downmix_to_mono else ''  # downmixing option
    resampling_cmd = f'-ar {str(sr)}' if sr else ''  # downsampling option
    cmd = f"ffmpeg -i \"{filename}\" {channel_cmd} {resampling_cmd} -f wav -"
    p = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
    out, err = p.communicate()
    return out

src, sr = sf.read(io.BytesIO(_decode_resample_by_ffmpeg(path, sr=sample_rate)))
return src.T, sr

def _resample_load_librosa(path: str, sample_rate: int, downmix_to_mono: bool, **kwargs) -> Tuple[np.ndarray, int]: """ Decoding, downmixing, and downsampling by librosa. Returns a channel-first audio signal. """ src, sr = librosa.load(path, sr=sample_rate, mono=downmix_to_mono, **kwargs) return src, sr

def load_audio( path: str or Path, ch_format: str, sample_rate: int = None, downmix_to_mono: bool = False, resample_by: str = 'ffmpeg', **kwargs, ) -> Tuple[np.ndarray, int]: """A wrapper of librosa.load that: - forces the returned audio to be 2-dim, - defaults to sr=None, and - defaults to downmix_to_mono=False. The audio decoding is done by audioread or soundfile package and ultimately, often by ffmpeg. The resampling is done by librosa's child package resampy. Args: path: audio file path ch_format: one of 'channels_first' or 'channels_last' sample_rate: target sampling rate. if None, use the rate of the audio file downmix_to_mono: resample_by (str): 'librosa' or 'ffmpeg'. it decides backend for audio decoding and resampling. **kwargs: keyword args for librosa.load - offset, duration, dtype, res_type. Returns: (audio, sr) tuple """ if ch_format not in (STR_CH_FIRST, STR_CH_LAST): raise ValueError(f'ch_format is wrong here -> {ch_format}')

if os.stat(path).st_size > 8000:
    if resample_by == 'librosa':
        src, sr = _resample_load_librosa(path, sample_rate, downmix_to_mono, **kwargs)
    elif resample_by == 'ffmpeg':
        src, sr = _resample_load_ffmpeg(path, sample_rate, downmix_to_mono)
    else:
        raise NotImplementedError(f'resample_by: "{resample_by}" is not supposred yet')
else:
    raise ValueError('Given audio is too short!')
return src, sr

# if src.ndim == 1:
#     src = np.expand_dims(src, axis=0)
# # now always 2d and channels_first

# if ch_format == STR_CH_FIRST:
#     return src, sr
# else:
#     return src.T, sr

def ms(x): """Mean value of signal x squared. :param x: Dynamic quantity. :returns: Mean squared of x. """ return (np.abs(x)**2.0).mean()

def normalize(y, x=None): """normalize power in y to a (standard normal) white noise signal. Optionally normalize to power in signal x. #The mean power of a Gaussian with :math:\\mu=0 and :math:\\sigma=1 is 1. """ if x is not None: x = ms(x) else: x = 1.0 return y * np.sqrt(x / ms(y))

def noise(N, color='white', state=None): """Noise generator. :param N: Amount of samples. :param color: Color of noise. :param state: State of PRNG. :type state: :class:np.random.RandomState """ try: return _noise_generators[color](N, state) except KeyError: raise ValueError("Incorrect color.")

def white(N, state=None): """ White noise. :param N: Amount of samples. :param state: State of PRNG. :type state: :class:np.random.RandomState White noise has a constant power density. It's narrowband spectrum is therefore flat. The power in white noise will increase by a factor of two for each octave band, and therefore increases with 3 dB per octave. """ state = np.random.RandomState() if state is None else state return state.randn(N)

def pink(N, state=None): """ Pink noise. :param N: Amount of samples. :param state: State of PRNG. :type state: :class:np.random.RandomState Pink noise has equal power in bands that are proportionally wide. Power density decreases with 3 dB per octave. """ state = np.random.RandomState() if state is None else state uneven = N % 2 X = state.randn(N // 2 + 1 + uneven) + 1j * state.randn(N // 2 + 1 + uneven) S = np.sqrt(np.arange(len(X)) + 1.) # +1 to avoid divide by zero y = (irfft(X / S)).real if uneven: y = y[:-1] return normalize(y)

def blue(N, state=None): """ Blue noise. :param N: Amount of samples. :param state: State of PRNG. :type state: :class:np.random.RandomState Power increases with 6 dB per octave. Power density increases with 3 dB per octave. """ state = np.random.RandomState() if state is None else state uneven = N % 2 X = state.randn(N // 2 + 1 + uneven) + 1j * state.randn(N // 2 + 1 + uneven) S = np.sqrt(np.arange(len(X))) # Filter y = (irfft(X * S)).real if uneven: y = y[:-1] return normalize(y)

def brown(N, state=None): """ Violet noise. :param N: Amount of samples. :param state: State of PRNG. :type state: :class:np.random.RandomState Power decreases with -3 dB per octave. Power density decreases with 6 dB per octave. """ state = np.random.RandomState() if state is None else state uneven = N % 2 X = state.randn(N // 2 + 1 + uneven) + 1j * state.randn(N // 2 + 1 + uneven) S = (np.arange(len(X)) + 1) # Filter y = (irfft(X / S)).real if uneven: y = y[:-1] return normalize(y)

def violet(N, state=None): """ Violet noise. Power increases with 6 dB per octave. :param N: Amount of samples. :param state: State of PRNG. :type state: :class:np.random.RandomState Power increases with +9 dB per octave. Power density increases with +6 dB per octave. """ state = np.random.RandomState() if state is None else state uneven = N % 2 X = state.randn(N // 2 + 1 + uneven) + 1j * state.randn(N // 2 + 1 + uneven) S = (np.arange(len(X))) # Filter y = (irfft(X * S)).real if uneven: y = y[:-1] return normalize(y)

_noise_generators = { 'white': white, 'pink': pink, 'blue': blue, 'brown': brown, 'violet': violet, }

def noise_generator(N=44100, color='white', state=None): """Noise generator. :param N: Amount of unique samples to generate. :param color: Color of noise. Generate N amount of unique samples and cycle over these samples. """ #yield from itertools.cycle(noise(N, color)) # Python 3.3 for sample in itertools.cycle(noise(N, color, state)): yield sample

def heaviside(N): """Heaviside. Returns the value 0 for x < 0, 1 for x > 0, and 1/2 for x = 0. """ return 0.5 * (np.sign(N) + 1)