[O] Separate dependencies
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wuliaozhiji
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from __future__ import annotations
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import csv
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import json
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import os
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from json import JSONDecodeError
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from multiprocessing import Pool
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from os import PathLike
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from pathlib import Path
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from typing import Iterable, Literal, Callable
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import jsonpickle as jsonpickle
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import matplotlib.pyplot as plt
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import numpy
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import numpy as np
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import pandas as pd
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import parselmouth
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import seaborn as sns
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import tqdm
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from matplotlib.patches import Patch
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from calculations import calculate_tilt, calculate_freq_info, FrequencyStats, calc_col_stats, calculate_freq_statistics, \
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Statistics
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ASAB = Literal['f', 'm']
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COLOR_PINK = '#F5A9B8'
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COLOR_BLUE = '#5BCEFA'
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CPU_CORES = 36
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def load_vox_celeb_asab_dict(path: PathLike) -> dict[str, ASAB]:
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"""
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Load voxCeleb 1 or 2's metadata to gather a dictionary mapping id to assigned sex at birth.
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:param path: CSV path (Tab separated)
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:return: {id: ASAB}
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"""
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with open(path, 'r', newline='') as f:
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return {row[0]: row[2] for row in csv.reader(f, delimiter='\t') if row[0].startswith('id')}
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def loop_id_dirs() -> Iterable[tuple[str, Path]]:
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# Loop through all ids
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for id in agab:
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id_dir = vox_celeb_dir.joinpath(id)
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# Check if directory exists
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if not id_dir.is_dir():
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continue
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yield id, id_dir
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def get_audio_paths(id_dir: Path, audio_suffix: str = 'wav') -> list[str]:
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"""
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Get all audio paths under one person
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:param id_dir: Person ID directory
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:param audio_suffix: Select only files with this suffix
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:return: audio paths
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"""
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audios = []
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# Loop through all videos
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for vid in os.listdir(id_dir):
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vid_dir = id_dir.joinpath(vid)
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# Check if it's a video directory
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if not vid_dir.is_dir():
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continue
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# Loop through all audios
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for aud in os.listdir(vid_dir):
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aud_dir = vid_dir.joinpath(aud)
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# Check if end with suffix
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if not aud.endswith(audio_suffix):
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continue
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# Add
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audios.append(str(aud_dir))
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return audios
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def compute_audio_freq(aud_dir: str):
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"""
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Compute and save the frequency info of one audio file
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"""
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array = calculate_freq_info(parselmouth.Sound(aud_dir))
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numpy.save(aud_dir, array)
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def compute_audio_tilt(aud_dir: str):
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"""
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Compute and save the tilt info of one audio file
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"""
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spectral_tilt = calculate_tilt(parselmouth.Sound(aud_dir))
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with open(Path(aud_dir).with_suffix('.json'), 'w', encoding='utf-8') as f:
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json.dump({'tilt': spectral_tilt}, f)
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def compute_audio_vox_celeb(func: Callable[[str], None]) -> None:
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"""
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Compute a function for each audio file in the vox celeb dataset
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:param func: The function to compute - func(aud_dir) -> None
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"""
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print('Finding audio files...')
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queue: list[str] = []
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# Loop through all ids
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for id, id_dir in loop_id_dirs():
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queue += get_audio_paths(id_dir)
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print(f'There are {len(queue)} audio files to process.')
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print('Starting processing...')
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# Compute audio files in a cpu pool
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with Pool(CPU_CORES) as pool:
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for _ in tqdm.tqdm(pool.imap(func, queue), total=len(queue)):
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pass
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def combine_id_freq(id_dir: Path):
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"""
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Combine frequency data of all audio files under one person
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"""
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# Load all files
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cumulative: np.ndarray = np.concatenate([np.load(f) for f in get_audio_paths(id_dir, 'npy')])
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# Remove out NaN values
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cumulative = cumulative[~np.isnan(cumulative).any(axis=1), :]
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result = calculate_freq_statistics(cumulative)
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# Write results
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with open(id_dir.joinpath('stats.json'), 'w') as jsonfile:
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jsonfile.write(jsonpickle.encode(result, jsonfile, indent=1))
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def combine_id_tilt(id_dir: Path):
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"""
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Combine tilt data of all audio files under one person
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"""
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# Load all calculated files
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cumulative = []
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for f in get_audio_paths(id_dir, 'json'):
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try:
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cumulative.append(json.loads(Path(f).read_text('utf-8'))['tilt'])
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except JSONDecodeError:
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print(f'Error in {f}')
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# Remove out NaN values
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cumulative = [c for c in cumulative if c is not None]
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result = calc_col_stats(np.array(cumulative))
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# Write results
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with open(id_dir.joinpath('tilt.json'), 'w') as jsonfile:
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jsonfile.write(jsonpickle.encode(result, jsonfile, indent=1))
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def call_id_vox_celeb(func: Callable[[Path], None]) -> None:
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"""
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Call a function for each person's id in the vox celeb dataset.
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:param func: func(id_dir) -> None
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"""
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id_dirs = [id_dir for id, id_dir in loop_id_dirs()]
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# Loop through all ids
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with Pool(CPU_CORES) as pool:
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for _ in tqdm.tqdm(pool.imap(func, id_dirs), total=len(id_dirs)):
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pass
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def subplots(**kwargs) -> tuple[plt.Figure, plt.Axes]:
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return plt.subplots(**kwargs)
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def collect_visualize_freq():
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"""
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Collect statistics and draw interesting visualizations from its results
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"""
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# Read stats
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stats_list: list[tuple[FrequencyStats, ASAB]] = []
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for id, id_dir in loop_id_dirs():
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stats_dir = id_dir.joinpath('stats.json')
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if not stats_dir.is_file():
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continue
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stats_list.append((jsonpickle.decode(stats_dir.read_text()), agab[id]))
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# Get AFAB and AMAB means
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headers = ['Pitch\n(Fundamental\nFrequency)', 'Formant F1', 'Formant F2', 'Formant F3']
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f_means = np.array([[t.mean for t in [s.pitch, s.f1, s.f2, s.f3]]
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for s, ag in stats_list if ag == 'f'])
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m_means = np.array([[t.mean for t in [s.pitch, s.f1, s.f2, s.f3]]
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for s, ag in stats_list if ag == 'm'])
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# Plot bar chart
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sns.set_theme(style="ticks")
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fig, ax = subplots(figsize=(10, 5))
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df = pd.DataFrame({headers[i]: f_means[:, i] for i in range(4)})
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dm = pd.DataFrame({headers[i]: m_means[:, i] for i in range(4)})
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args = dict(orient='h', scale='width', inner='quartile', linewidth=0.5)
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sns.violinplot(data=df, color=COLOR_PINK, **args)
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sns.violinplot(data=dm, color=COLOR_BLUE, **args)
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[c.set_alpha(0.7) for c in ax.collections]
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# Create legend
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legend_elements = [
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Patch(facecolor=COLOR_PINK, edgecolor='r', label='Feminine'),
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Patch(facecolor=COLOR_BLUE, edgecolor='b', label='Masculine'),
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]
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plt.legend(handles=legend_elements)
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ax.set_title("Distribution of Pitch and Formant Based on Gender")
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ax.xaxis.grid(True)
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ax.set_ylabel('')
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ax.set_xlabel('Frequency (Hz)')
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sns.despine(fig, ax)
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plt.show()
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# Write JSON
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data = {val: {'f': f_means[:, i].tolist(), 'm': m_means[:, i].tolist()} for i, val in enumerate(['Pitch', 'F1', 'F2', 'F3'])}
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Path('results/frequency-data.json').write_text(json.dumps(data), 'utf-8')
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def collect_visualize_tilt():
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"""
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Collect statistics and draw interesting visualizations from its results
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"""
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# Read stats
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stats_list: list[tuple[Statistics, ASAB]] = []
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for id, id_dir in loop_id_dirs():
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stats_dir = id_dir.joinpath('tilt.json')
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if not stats_dir.is_file():
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continue
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stats_list.append((jsonpickle.decode(stats_dir.read_text()), agab[id]))
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# Get AFAB and AMAB means
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f_means = np.array([s.mean for s, ag in stats_list if ag == 'f'])
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m_means = np.array([s.mean for s, ag in stats_list if ag == 'm'])
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# Plot bar chart
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sns.set_theme(style="ticks")
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fig, ax = subplots(figsize=(10, 5))
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df = pd.DataFrame({"Tilt": f_means})
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dm = pd.DataFrame({"Tilt": m_means})
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args = dict(orient='h', scale='width', inner='quartile', linewidth=0.5)
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sns.violinplot(data=df, color=COLOR_PINK, **args)
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sns.violinplot(data=dm, color=COLOR_BLUE, **args)
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[c.set_alpha(0.7) for c in ax.collections]
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# Create legend
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legend_elements = [
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Patch(facecolor=COLOR_PINK, edgecolor='r', label='Feminine'),
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Patch(facecolor=COLOR_BLUE, edgecolor='b', label='Masculine'),
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]
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plt.legend(handles=legend_elements)
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ax.set_title("Distribution of Spectral Tilt on Gender")
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ax.xaxis.grid(True)
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ax.set_ylabel('')
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ax.set_xlabel('Tilt Value')
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sns.despine(fig, ax)
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plt.show()
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# Write JSON
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data = {'tilt': {'f': f_means.tolist(), 'm': m_means.tolist()}}
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Path('results/tilt-data.json').write_text(json.dumps(data), 'utf-8')
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if __name__ == '__main__':
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vox_celeb_dir = Path('../Datasets/VoxCeleb1/wav')
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agab = load_vox_celeb_asab_dict(vox_celeb_dir.joinpath('../vox1_meta.csv'))
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############
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# 1. Compute and save all the frequency (pitch, f0, f1, f2) for vox1
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# For each audio, a file <audio-name>.npy will be saved, with each row representing 10ms data
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# compute_audio_vox_celeb(compute_audio_freq)
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# 2. Combine and save statistics for each person in vox1
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# For each person, stats.json will be saved, containing statistics of all of their audios
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# call_id_vox_celeb(combine_id_freq)
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# 3. Collect statistics and draw visualizations
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# collect_visualize_freq()
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###########
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# 1. Compute and save all the spectral tilt for vox1
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# For each audio, a file <audio-name>.json will be saved with tilt value in it
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# compute_audio_vox_celeb(compute_audio_tilt)
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# 2. Combine statistics for each person in vox1
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# call_id_vox_celeb(combine_id_tilt)
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# 3. Collect statistics and draw visualizations
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collect_visualize_tilt()
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# print(calculate_freq_info(parselmouth.Sound('../00001.wav')))
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# print(calculate_freq_info(parselmouth.Sound('D:/Downloads/Vowels-Extract-Z-44kHz.flac')))
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# print(calculate_freq_info(parselmouth.Sound('D:/Downloads/Vowels-Azalea.flac')))
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