[U] Backup unfinished changes

experiment/accuracy_calculation.py

@@ -8,6 +8,8 @@ if __name__ == '__main__':
     with open(r'C:\Datasets\CN-Celeb_flac\ina_pf_map.json', 'r', encoding='UTF-8') as f:
         pf = json.load(f)
 
+    print(len(labels))
+
     correct_f = []
     correct_m = []
     incorrect_f = []

experiment/ina_main.py

@@ -38,8 +38,53 @@ class Result(NamedTuple):
     file: str
 
 
-def segment(file) -> list[ResultFrame]:
-    return [ResultFrame(*s) for s in seg(file)]
+class BatchResults(NamedTuple):
+    results: list[Result]
+    time_full: float
+    time_avg: float
+    successes: int
+    messages: list[tuple[str, int]]
+
+
+def _process_helper(args: tuple[Segmenter, str]) -> Result:
+    seg, f = args
+    lseg = seg(f)
+    return Result([ResultFrame(*s) for s in lseg], f)
+
+
+def process(self: Segmenter, inp: list[str], tmpdir=None, verbose=False, skip_if_exist=False,
+            nbtry=1, try_delay=2.) -> list[Result]:
+    from tqdm.contrib.concurrent import process_map
+    import tqdm
+    results = []
+    for i in tqdm.tqdm(inp):
+        results.append(_process_helper((self, i)))
+    return results
+
+    # return process_map(_process_helper, [(self, i) for i in inp], max_workers=2)
+
+    #
+    # t_batch_start = time.time()
+    #
+    # results: list[Result] = []
+    # lmsg = []
+    # fg = featGenerator(inp.copy(), inp.copy(), tmpdir, self.ffmpeg, skip_if_exist, nbtry, try_delay)
+    # i = 0
+    # for feats, msg in fg:
+    #     lmsg += msg
+    #     i += len(msg)
+    #     if verbose:
+    #         print('%d/%d' % (i, len(inp)), msg)
+    #     if feats is None:
+    #         break
+    #     mspec, loge, diff_len = feats
+    #     lseg = self.segment_feats(mspec, loge, diff_len, 0)
+    #     results.append()
+    #
+    # t_batch_dur = time.time() - t_batch_start
+    # nb_processed = len([e for e in lmsg if e[1] == 0])
+    # avg = t_batch_dur / nb_processed if nb_processed else -1
+    # return BatchResults(results, t_batch_dur, avg, nb_processed, lmsg)
 
 
 def to_wav(file: str, callback: Callable, start_sec: float = 0, stop_sec: float = 0):

experiment/test_fft.py

@@ -0,0 +1,72 @@
+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import parselmouth
+
+import sgs
+
+if __name__ == '__main__':
+    librosa.filters.mel(sr=16000, n_fft=1024, htk=True)
+
+    f = 'Z:/EECS 6414/voice_cnn/test.wav'
+    y, sr = librosa.load(f, sr=16000)
+
+    # Plot waveform
+    # plt.plot(y)
+    # plt.title('Signal')
+    # plt.xlabel('Time (samples)')
+    # plt.ylabel('Amplitude')
+    # plt.show()
+    # plt.clf()
+
+    # Plot frequency domain graph at a single time
+    n_fft = 2048
+    ft = np.abs(librosa.stft(y[:n_fft], hop_length=n_fft + 1))
+
+    # plt.plot(ft)
+    # plt.title('Spectrum')
+    # plt.xlabel('Frequency Bin')
+    # plt.ylabel('Amplitude')
+    # plt.show()
+    # plt.clf()
+
+    # Plot spectrogram
+    spec = np.abs(librosa.stft(y, n_fft=1024, hop_length=512))
+    # spec = librosa.amplitude_to_db(spec, ref=np.max)
+
+    # librosa.display.specshow(spec, sr=sr, x_axis='time', y_axis='log')
+    # plt.colorbar(format='%+2.0f dB')
+    # plt.title('Spectrogram')
+    # plt.show()
+    # plt.clf()
+
+    # Mel transform
+    mel_spect = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=512, htk=True)
+    mel_spect = librosa.power_to_db(mel_spect, ref=np.max)
+
+    print(len(mel_spect))
+    librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time', n_fft=1024, hop_length=512)
+    result, freq_array = sgs.api.calculate_feature_classification(parselmouth.Sound(f))
+
+    pitch_array = freq_array[:, 0]
+    # x_len = len(pitch_array) / len(mel_spect)
+    # x = np.arange(len(mel_spect))
+    # y = []
+    # for x in range(len(mel_spect) // 2):
+    #     y.append(float(np.mean(pitch_array[int(x_len * x):int(x_len * (x + 1))])))
+    # print(len(y))
+    x = np.linspace(0, 4.1)
+    print(x)
+    x_len = len(pitch_array) / len(x)
+    y = []
+    for a in range(len(x)):
+        y.append(np.mean(pitch_array[int(x_len * a):int(x_len * (a + 1))]))
+
+    plt.plot(x, y, color='#7bff4f')
+    plt.plot(x, [100] * len(x), color='#7bff4f')
+    plt.yticks([0,100,200,300,400,500,600,700,800,900,1000,1200,1400,1600])
+    plt.title('Mel Spectrogram')
+    plt.colorbar(format='%+2.0f dB')
+    plt.show()
+    plt.clf()

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()

experiment/vox1.py

@@ -0,0 +1,78 @@
+
+from pathlib import Path
+import os
+import json
+
+import numpy as np
+import pandas as pd
+
+if __name__ == '__main__':
+    cn_celeb_root = Path(r'C:\Datasets\vox1_test_wav\wav')
+    data_dir = cn_celeb_root
+    ids = [id for id in os.listdir(data_dir) if id.startswith('id1')]
+    # ids=[data_dir / id for id in os.listdir(data_dir) if id.startswith('id1')]
+
+    appendix= Path(r'C:\Datasets\VoxCeleb1\wav')
+    ids += [id for id in os.listdir(appendix) if id.startswith('id1')]
+    # ids += [appendix / id for id in os.listdir(appendix) if id.startswith('id1')]
+    with open(r"C:\Datasets\vox1_label.csv") as f:
+        txt = f.read().strip()
+        map = {}
+        for row in txt.split('\n'):
+            id, gender = row.split(',')
+            map[id] = gender
+    # female-> positive, male -> negative
+    f_correct = 0 #tp
+    f_incorrect = 0 #fp
+    m_correct = 0 #tn
+    m_incorrect = 0 #fn
+    for id in ids[:40]:
+        obj = json.loads((data_dir / id / 'total.json').read_text())
+
+        label = map[id] #ground truth
+        if label == 'f':
+            if obj['ratio'] >= 0.5:
+                f_correct += 1
+            else:
+                # f_incorrect += 1 #fn
+                m_incorrect += 1
+        if label == 'm':
+            if obj['ratio'] < 0.5:
+                m_correct += 1
+            else:
+                # m_incorrect += 1 #fp
+                f_incorrect += 1
+    for id in ids[40:]:
+        obj = json.loads((appendix / id / 'total.json').read_text())
+
+        label = map[id] #ground truth
+        if label == 'f':
+            if obj['ratio'] >= 0.5:
+                f_correct += 1
+            else:
+                # f_incorrect += 1 #fn
+                m_incorrect += 1
+        if label == 'm':
+            if obj['ratio'] < 0.5:
+                m_correct += 1
+            else:
+                # m_incorrect += 1 #fp
+                f_incorrect += 1
+
+
+    # print(f_incorrect)
+    # print(m_incorrect)
+
+    f_precision = f_correct / (f_correct + f_incorrect)
+    f_recall = f_correct / (f_correct + m_incorrect)
+
+    m_precision = m_correct / (m_correct + m_incorrect)
+    m_recall = m_correct / (m_correct + f_incorrect)
+
+    print('Precision_f', f_precision)
+    print('Recall_f', f_recall)
+
+    print('Precision_m', m_precision)
+    print('Recall_m', m_recall)
+
+    print('total number: ', f_incorrect+f_correct+m_incorrect+m_correct)