[U] Backup unfinished changes

experiment/validate_cn_celeb.py

@@ -1,69 +1,81 @@
 import json
 import os
+# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 import warnings
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
-import pygame
+import tensorflow as tf
 from inaSpeechSegmenter import Segmenter
 
 from ina_main import process, get_result_percentages
 from utils import color, printc
 
 
+gpu_devices = tf.config.experimental.list_physical_devices('GPU')
+for device in gpu_devices:
+    tf.config.experimental.set_memory_growth(device, True)
+
+
 def segment_all():
     # Create segmenter
     seg = Segmenter()
     np.seterr(invalid='ignore')
 
     # Loop through all celebrities
-    for id in ids:
-        id_dir = data_dir.joinpath(id)
+    for id in ids[559:]:
+        id_dir = data_dir / id
+
+        if (id_dir / 'total.json').is_file():
+            continue
 
         # Loop through all recordings (Exclude singing for now)
-        utters = [r for r in os.listdir(id_dir) if r.endswith('.flac')
-                  and not r.startswith('singing')]
+        utters = audio_files[id]
 
         # Exclude existing
-        utters = [id_dir.joinpath(u) for u in utters]
-        utters = [u for u in utters if not u.with_suffix('.json').exists()]
+        utters = [id_dir.joinpath(u) for u in utters if u.endswith('.wav')]
+        # utters = [u for u in utters if not u.with_suffix('.json').exists()]
+
 
         if len(utters) == 0:
             continue
 
         # Analyze
+        print(f'Processing {id}')
         results = process(seg, [str(u) for u in utters], verbose=True)
 
         # Write results
-        total = [0, 0, 0, 0, 0]
-        type_totals = {}
-        for result in results.results:
+        # total = [0, 0, 0, 0, 0]
+        # type_totals = {}
+        total = []
+        for result in results:
             file = Path(result.file).with_suffix('.json')
 
             # Get results
             # f: Frames, r: Ratios
-            ratios = [round(r, 3) for r in get_result_percentages(result)]
-            stored = {'f': result.frames, 'r': ratios}
+            _, _, _, pf = get_result_percentages(result)
+            total.append(pf)
 
             # Count type total (type_totals[utter_type][-1] is the count)
-            file_name = file.name
-            utter_type = file_name[:file_name.index('-')]
-            type_totals.setdefault(utter_type, [0, 0, 0, 0, 0])
-            for i in range(4):
-                type_totals[utter_type][i] += ratios[i]
-                total[i] += ratios[i]
-            type_totals[utter_type][-1] += 1
-            total[-1] += 1
+            # file_name = file.name
+            # utter_type = file_name[:file_name.index('-')]
+            # type_totals.setdefault(utter_type, [0, 0, 0, 0, 0])
+            # for i in range(4):
+            #     type_totals[utter_type][i] += ratios[i]
+            #     total[i] += ratios[i]
+            # type_totals[utter_type][-1] += 1
+            # total[-1] += 1
 
             # Write result
-            file.write_text(json.dumps(stored))
+            # file.write_text(json.dumps(ratios))
 
         # Write type averages
-        type_averages = {t: [r / type_totals[t][-1] for r in type_totals[t][:-1]] for t in type_totals}
-        total_average = [r / total[-1] for r in total[:-1]]
-        obj = {'type_averages': type_averages, 'total_averages': total_average}
-        id_dir.joinpath('total.json').write_text(json.dumps(obj))
+        # type_averages = {t: [r / type_totals[t][-1] for r in type_totals[t][:-1]] for t in type_totals}
+        # total_average = [r / total[-1] for r in total[:-1]]
+        # obj = {'type_averages': type_averages, 'total_averages': total_average}
+        # id_dir.joinpath('total.json').write_text(json.dumps(obj))
+        id_dir.joinpath('total.json').write_text(json.dumps({'ratio': np.nanmean(total)}))
 
 
 def graph_histogram():
@@ -107,7 +119,7 @@ def manually_label_data():
     Since CN-Celeb isn't labelled with the speaker's gender, this script is used to manually label
     them.
     """
-    pygame.mixer.init()
+    # pygame.mixer.init()
 
     # Load existing labels
     labels_json = data_dir.joinpath('id_labels.json')
@@ -132,13 +144,13 @@ def manually_label_data():
         tracks = [f for f in os.listdir(id_dir) if f.endswith('.flac')]
         for track_i, audio in enumerate(tracks):
             # Play track
-            sound = pygame.mixer.Sound(id_dir.joinpath(audio))
-            sound.play()
+            # sound = pygame.mixer.Sound(id_dir.joinpath(audio))
+            # sound.play()
             i = input(color(
                 f'\n&7Playing speaker {id[-3:]}/{len(ids)} - track {track_i}/{len(tracks)} - {audio}&r'
-                f'\n- Press f / m, or anything else to play next track: '))\
+                f'\n- Press f / m, or anything else to play next track: ')) \
                 .lower().strip()
-            sound.stop()
+            # sound.stop()
 
             # Skip
             if i == 's':
@@ -159,10 +171,19 @@ def manually_label_data():
 
 
 if __name__ == '__main__':
-    cn_celeb_root = Path('C:/Users/me/Workspace/Data/CN-Celeb_flac')
-    data_dir = cn_celeb_root.joinpath('data')
-    ids = [id for id in os.listdir(data_dir) if id.startswith('id0')]
+    cn_celeb_root = Path(r'C:\Datasets\VoxCeleb1\wav')
 
-    # segment_all()
+    data_dir = cn_celeb_root
+    ids = [id for id in os.listdir(data_dir) if id.startswith('id1')]
+
+    # Get all audio files for each id
+    audio_files = {}
+    for id in ids[559:]:
+        audio_files[id] = []
+        for dirpath, dirnames, filenames in os.walk(data_dir / id):
+            audio_files[id] += [os.path.join(dirpath, file) for file in filenames if file.endswith('.wav')]
+    # print(audio_files.keys())
+
+    segment_all()
     # graph_histogram()
-    manually_label_data()
+    # manually_label_data()