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from glob import glob
from pathlib import Path
from types import SimpleNamespace
from typing import Union
import librosa
import numpy as np
import yaml
CLASSES = ["tru", "sax", "vio", "gac", "org", "cla", "flu", "voi", "gel", "cel", "pia"]
def get_wav_files(base_path):
"""
Function to recursively get all the .wav files in a directory.
:param base_path: The base path of the directory to search.
:type base_path: str or pathlib.Path
:return: A list of paths to .wav files found in the directory.
:rtype: List[str]
"""
return glob(f"{base_path}/**/*.wav", recursive=True)
def parse_config(config_path):
"""
Parse a YAML configuration file and return the configuration as a SimpleNamespace object.
:param config_path: The path to the YAML configuration file.
:type config_path: str or pathlib.Path
:return: A SimpleNamespace object representing the configuration.
:rtype: types.SimpleNamespace
"""
with open(config_path) as file:
return SimpleNamespace(**yaml.safe_load(file))
def init_transforms(fn_dict, module):
"""
Initialize a list of transforms from a dictionary of function names and their parameters.
:param fn_dict: A dictionary where keys are the names of transform functions
and values are dictionaries of parameters.
:type fn_dict: Dict[str, Dict[str, Any]]
:param module: The module where the transform functions are defined.
:type module: module
:return: A list of transform functions.
:rtype: List[Callable]
"""
transforms = init_objs(fn_dict, module)
if transforms is not None:
transforms = ComposeTransforms(transforms)
return transforms
def init_objs(fn_dict, module):
"""
Initialize a list of objects from a dictionary of object names and their parameters.
:param fn_dict: A dictionary where keys are the names of object classes and values are dictionaries of parameters.
:type fn_dict: Dict[str, Dict[str, Any]]
:param module: The module where the object classes are defined.
:type module: module
:return: A list of objects.
:rtype: List[Any]
"""
if fn_dict is None:
return None
transforms = []
for transform in fn_dict.keys():
fn = getattr(module, transform)
if fn is None:
raise NotImplementedError(
"The attribute '{}' is not implemented in the module '{}'.".format(transform, module.__name__)
)
fn_args = fn_dict[transform]
if fn_args is None:
transforms.append(fn())
else:
transforms.append(fn(**fn_args))
return transforms
def init_obj(fn_dict, module, *args, **kwargs):
"""
Initialize an object by calling a function with the provided arguments.
:param fn_dict: A dictionary that maps the function name to its arguments.
:type fn_dict: dict or None
:param module: The module containing the function.
:type module: module
:param args: The positional arguments for the function.
:type args: tuple
:param kwargs: The keyword arguments for the function.
:type kwargs: dict
:raises AssertionError: If a keyword argument is already specified in fn_dict.
:return: The result of calling the function with the provided arguments.
:rtype: Any
"""
if fn_dict is None:
return None
name = list(fn_dict.keys())[0]
fn = getattr(module, name)
if fn is None:
raise NotImplementedError(
"The attribute '{}' is not implemented in the module '{}'.".format(name, module.__name__)
)
fn_args = fn_dict[name]
if fn_args is not None:
assert all(k not in fn_args for k in kwargs)
fn_args.update(kwargs)
return fn(*args, **fn_args)
else:
return fn(*args, **kwargs)
class ComposeTransforms:
"""
Composes a list of transforms to be applied in sequence to input data.
:param transforms: A list of transforms to be applied.
:type transforms: List[callable]
"""
def __init__(self, transforms: list):
self.transforms = transforms
def __call__(self, data, *args):
for t in self.transforms:
data = t(data, *args)
return data
def load_raw_file(path: Union[str, Path]):
"""
Loads an audio file from disk and returns its raw waveform and sample rate.
:param path: The path to the audio file to load.
:type path: Union[str, Path]
:return: A tuple containing the raw waveform and sample rate.
:rtype: tuple
"""
return librosa.load(path, sr=None, mono=False)
def get_onset(signal, sr):
"""
Computes the onset of an audio signal.
:param signal: The audio signal.
:type signal: np.ndarray
:param sr: The sample rate of the audio signal.
:type sr: int
:return: The onset of the audio signal in seconds.
:rtype: float
"""
onset = librosa.onset.onset_detect(y=signal, sr=sr, units="time")[0]
return onset
def get_bpm(signal, sr):
"""
Computes the estimated beats per minute (BPM) of an audio signal.
:param signal: The audio signal.
:type signal: np.ndarray
:param sr: The sample rate of the audio signal.
:type sr: int
:return: The estimated BPM of the audio signal, or None if the BPM cannot be computed.
:rtype: Union[float, None]
"""
bpm, _ = librosa.beat.beat_track(y=signal, sr=sr)
return bpm if bpm != 0 else None
def get_pitch(signal, sr):
"""
Computes the estimated pitch of an audio signal.
:param signal: The audio signal.
:type signal: np.ndarray
:param sr: The sample rate of the audio signal.
:type sr: int
:return: The estimated pitch of the audio signal in logarithmic scale, or None if the pitch cannot be computed.
:rtype: Union[float, None]
"""
eps = 1e-8
fmin = librosa.note_to_hz("C2")
fmax = librosa.note_to_hz("C7")
pitch, _, _ = librosa.pyin(y=signal, sr=sr, fmin=fmin, fmax=fmax)
if not np.isnan(pitch).all():
mean_log_pitch = np.nanmean(np.log(pitch + eps))
else:
mean_log_pitch = None
return mean_log_pitch
def get_file_info(path: Union[str, Path], extract_music_features: bool):
"""
Loads an audio file and computes some basic information about it,
such as pitch, BPM, onset time, duration, sample rate, and number of channels.
:param path: The path to the audio file.
:type path: Union[str, Path]
:param extract_music_features: Whether to extract music features such as pitch, BPM, and onset time.
:type extract_music_features: bool
:return: A dictionary containing information about the audio file.
:rtype: dict
"""
path = str(path) if isinstance(path, Path) else path
signal, sr = load_raw_file(path)
channels = signal.shape[0]
signal = librosa.to_mono(signal)
duration = len(signal) / sr
pitch, bpm, onset = None, None, None
if extract_music_features:
pitch = get_pitch(signal, sr)
bpm = get_bpm(signal, sr)
onset = get_onset(signal, sr)
return {
"path": path,
"pitch": pitch,
"bpm": bpm,
"onset": onset,
"sample_rate": sr,
"duration": duration,
"channels": channels,
}
def sync_pitch(file_to_sync: np.ndarray, sr: int, pitch_base: float, pitch: float):
"""
Shift the pitch of an audio file to match a new pitch value.
:param file_to_sync: The input audio file as a NumPy array.
:type file_to_sync: np.ndarray
:param sr: The sample rate of the input file.
:type sr: int
:param pitch_base: The pitch value of the original file.
:type pitch_base: float
:param pitch: The pitch value to synchronize the input file to.
:type pitch: float
:return: The synchronized audio file as a NumPy array.
:rtype: np.ndarray
"""
assert np.ndim(file_to_sync) == 1, "Input array has more than one dimensions"
if any(np.isnan(x) for x in [pitch_base, pitch]):
return file_to_sync
steps = np.round(12 * np.log2(np.exp(pitch_base) / np.exp(pitch)), 0)
return librosa.effects.pitch_shift(y=file_to_sync, sr=sr, n_steps=steps)
def sync_bpm(file_to_sync: np.ndarray, sr: int, bpm_base: float, bpm: float):
"""
Stretch or compress the duration of an audio file to match a new tempo.
:param file_to_sync: The input audio file as a NumPy array.
:type file_to_sync: np.ndarray
:param sr: The sample rate of the input file.
:type sr: int
:param bpm_base: The tempo of the original file.
:type bpm_base: float
:param bpm: The tempo to synchronize the input file to.
:type bpm: float
:return: The synchronized audio file as a NumPy array.
:rtype: np.ndarray
"""
assert np.ndim(file_to_sync) == 1, "Input array has more than one dimensions"
if any(np.isnan(x) for x in [bpm_base, bpm]):
return file_to_sync
return librosa.effects.time_stretch(y=file_to_sync, rate=bpm_base / bpm)
def sync_onset(file_to_sync: np.ndarray, sr: int, onset_base: float, onset: float):
"""
Sync the onset of an audio signal by adding or removing silence at the beginning.
:param file_to_sync: The audio signal to synchronize.
:type file_to_sync: np.ndarray
:param sr: The sample rate of the audio signal.
:type sr: int
:param onset_base: The onset of the reference signal in seconds.
:type onset_base: float
:param onset: The onset of the signal to synchronize in seconds.
:type onset: float
:raises AssertionError: If the input array has more than one dimension.
:return: The synchronized audio signal.
:rtype: np.ndarray
"""
assert np.ndim(file_to_sync) == 1, "Input array has more than one dimensions"
if any(np.isnan(x) for x in [onset_base, onset]):
return file_to_sync
diff = int(round(abs(onset_base * sr - onset * sr), 0))
if onset_base > onset:
return np.pad(file_to_sync, (diff, 0), mode="constant", constant_values=0)
else:
return file_to_sync[diff:]
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