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import numpy as np
import pandas as pd
import librosa
from pathlib import Path
from typing import Callable, Literal, Optional
def load_dataset(
paths: list,
remove_label: list = [""],
sr: int = 22050,
method = "fix_length",
max_time: float = 4.0):
"""Folder dataset in memory loader (return fully loaded pandas dataframe).
- For sklearn, load the whole dataset if possible otherwise use `proportion` to only load a part of the dataset.
- For pytorch, load the whole dataset if possible otherwise use `proportion` to only load a part of the dataset.
And convert output to Tensor on the fly.
Use `to_numpy(df.y)` to extract a numpy matrix with a (n_row, ...) shape.
Expect a dataset folder structure as: paths = [paths1, paths2, ...]
- paths1
- sub1
- blabla_GroundTruth1.wav
- blabla_GroundTruth2.wav
- sub2
- ...
...
- ...
Args:
paths (list[Path]): list of dataset directory to parse.
remove_label (list, optional): list of label to remove. Defaults to None.. Defaults to [""].
shuffle (bool, optional): True to suffle the dataframe. Defaults to True.
proportion (float, optional): Proportion of file to load. Defaults to 1.0.
sr (int, optional): Sample Rate to resample audio file. Defaults to 22050.
method (Literal['fix_length';, 'time_stretch'], optional): uniformization method to apply. Defaults to "fix_length".
max_time (float, optional): Common audio duration . Defaults to 4.0.
Returns:
df (pd.DataFrame): A pd.DataFrame with such define column:
- absolute_path (str): file-system absolute path of the .wav file.
- labels (list): list of labels defining the sound file (ie, subdirectories and post _ filename).
- ground_truth (str): ground_truth label meaning the last one after _ in the sound filename.
- y_original_signal (np.ndarray): sound signal normalize as `float64` and resample with the given sr by `librosa.load`
- y_original_duration (float): y_original_signal signal duration.
- y_uniform (np.ndarray): uniformized sound signal compute from y_original_signal using the chosen uniform method.
uniform_transform (Callable[[np.ndarray, int], np.ndarray]]): A lambda function to uniformized an audio signal as the same in df.
"""
data = []
uniform_transform = lambda y, sr: uniformize(y, sr, method, max_time)
for path in paths:
path = Path(path)
for wav_file in path.rglob("*.wav"):
wav_file_dict = dict()
absolute_path = wav_file.absolute()
*labels, label = absolute_path.relative_to(path.absolute()).parts
label = label.replace(".wav", "").split("_")
labels.extend(label)
ground_truth = labels[-1]
if ground_truth not in remove_label:
y_original, sr = librosa.load(path=absolute_path, sr=sr)
# WARINING : Convert the sampling rate to 22.05 KHz,
# normalize the bit depth between -1 and 1 and convert stereo to mono
wav_file_dict["absolute_path"] = absolute_path
wav_file_dict["labels"] = labels
wav_file_dict["ground_truth"] = ground_truth
## Save original sound signal
wav_file_dict["y_original_signal"] = y_original
duration = librosa.get_duration(y=y_original, sr=sr)
wav_file_dict["y_original_duration"] = duration
## Save uniformized sound signal
wav_file_dict["y_uniform"] = uniform_transform(y_original, sr)
data.append(wav_file_dict)
df = pd.DataFrame(data)
return df, uniform_transform
def uniformize(
audio: np.ndarray,
sr: int,
method = "fix_length",
max_time: float = 4.0
):
if method == "fix_length":
return librosa.util.fix_length(audio, size=int(np.ceil(max_time*sr)))
elif method == "time_stretch":
duration = librosa.get_duration(y=audio, sr=sr)
return librosa.effects.time_stretch(audio, rate=duration/max_time)
def to_numpy(ds: pd.Series) -> np.ndarray:
"""Transform a pd.Series (ie columns slice) in a numpy array with the shape (n_row, cell_array.flatten()).
Args:
df (pd.Series): Columns to transform in numpy.
Returns:
np.ndarray: resulting np.array from the ds pd.Series.
"""
numpy_df = np.stack([*ds.to_numpy()])
C, *o = numpy_df.shape
if o:
return numpy_df.reshape(numpy_df.shape[0], np.prod(o))
else:
return numpy_df.reshape(numpy_df.shape[0])
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