wuliaozhiji
169 lines
5.1 KiB
Python
169 lines
5.1 KiB
Python
import json
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import os
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import warnings
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from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import pygame
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from inaSpeechSegmenter import Segmenter
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from ina_main import process, get_result_percentages
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from utils import color, printc
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def segment_all():
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# Create segmenter
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seg = Segmenter()
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np.seterr(invalid='ignore')
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# Loop through all celebrities
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for id in ids:
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id_dir = data_dir.joinpath(id)
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# Loop through all recordings (Exclude singing for now)
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utters = [r for r in os.listdir(id_dir) if r.endswith('.flac')
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and not r.startswith('singing')]
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# Exclude existing
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utters = [id_dir.joinpath(u) for u in utters]
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utters = [u for u in utters if not u.with_suffix('.json').exists()]
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if len(utters) == 0:
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continue
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# Analyze
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results = process(seg, [str(u) for u in utters], verbose=True)
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# Write results
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total = [0, 0, 0, 0, 0]
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type_totals = {}
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for result in results.results:
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file = Path(result.file).with_suffix('.json')
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# Get results
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# f: Frames, r: Ratios
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ratios = [round(r, 3) for r in get_result_percentages(result)]
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stored = {'f': result.frames, 'r': ratios}
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# Count type total (type_totals[utter_type][-1] is the count)
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file_name = file.name
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utter_type = file_name[:file_name.index('-')]
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type_totals.setdefault(utter_type, [0, 0, 0, 0, 0])
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for i in range(4):
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type_totals[utter_type][i] += ratios[i]
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total[i] += ratios[i]
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type_totals[utter_type][-1] += 1
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total[-1] += 1
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# Write result
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file.write_text(json.dumps(stored))
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# Write type averages
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type_averages = {t: [r / type_totals[t][-1] for r in type_totals[t][:-1]] for t in type_totals}
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total_average = [r / total[-1] for r in total[:-1]]
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obj = {'type_averages': type_averages, 'total_averages': total_average}
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id_dir.joinpath('total.json').write_text(json.dumps(obj))
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def graph_histogram():
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closest_to_half = 1000
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closest_to_half_id = ''
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id_pf_map = {}
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for id in ids:
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id_dir = data_dir.joinpath(id)
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json_path = id_dir.joinpath('total.json')
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if not json_path.exists():
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continue
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obj = json.loads(json_path.read_text())
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f, m, o, pf = obj['total_averages']
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# Recalculate pf (pf is actually calculated incorrectly)
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if f + m == 0:
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continue
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pf = f / (f + m)
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id_pf_map[id] = pf
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# Save fixed json
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obj['total_averages'][3] = pf
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json_path.write_text(json.dumps(obj))
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# Find pf closest to .5
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dist = abs(pf - .5)
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if dist < closest_to_half:
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closest_to_half = dist
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closest_to_half_id = id
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data_dir.joinpath('id_pf_map.json').write_text(json.dumps(id_pf_map))
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plt.hist(id_pf_map.values(), bins=50)
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plt.show()
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print(closest_to_half_id)
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def manually_label_data():
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"""
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Since CN-Celeb isn't labelled with the speaker's gender, this script is used to manually label
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them.
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"""
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pygame.mixer.init()
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# Load existing labels
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labels_json = data_dir.joinpath('id_labels.json')
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id_labels = json.loads(labels_json.read_text()) if labels_json.exists() else {}
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# Load pf table
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id_pfs = json.loads(data_dir.joinpath('id_pf_map.json').read_text())
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# Loop through all speaker
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for id in sorted(ids):
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id_dir = data_dir.joinpath(id)
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# Skip already identified labels
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if id in id_labels:
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continue
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# Get ina choice
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pf = id_pfs.get(id, -1)
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ina_choice = 'f' if pf > 0.5 else 'm'
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# Loop through all tracks until identified
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tracks = [f for f in os.listdir(id_dir) if f.endswith('.flac')]
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for track_i, audio in enumerate(tracks):
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# Play track
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sound = pygame.mixer.Sound(id_dir.joinpath(audio))
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sound.play()
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i = input(color(
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f'\n&7Playing speaker {id[-3:]}/{len(ids)} - track {track_i}/{len(tracks)} - {audio}&r'
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f'\n- Press f / m, or anything else to play next track: '))\
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.lower().strip()
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sound.stop()
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# Skip
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if i == 's':
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break
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# Labeled
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if i == 'f' or i == 'm':
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id_labels[id] = i
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labels_json.write_text(json.dumps(id_labels))
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# Print choice match
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if pf != -1:
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agree = '&aINA agrees' if ina_choice == i else '&cINA disagree'
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printc(f'{agree} with confidence {abs(pf - 0.5) * 200:.0f}%')
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else:
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printc(f"&7INA didn't identify any voice")
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break
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if __name__ == '__main__':
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cn_celeb_root = Path('C:/Users/me/Workspace/Data/CN-Celeb_flac')
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data_dir = cn_celeb_root.joinpath('data')
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ids = [id for id in os.listdir(data_dir) if id.startswith('id0')]
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# segment_all()
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# graph_histogram()
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manually_label_data()
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