def test_check_if_extracted():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
path = "./tests/data/features/FeatureExtractor/original"
mkdir_if_not_exists(path, parents=True)
feature_extractor = FeatureExtractor()
json_features = os.path.join(path, "parameters.json")
_clean(json_features)
feature_extractor.set_as_extracted(path)
assert feature_extractor.check_if_extracted(dataset)
def test_check_if_extracted_path():
path = "./tests/data/features"
feature_extractor = FeatureExtractor()
feature_extractor.set_as_extracted(path)
json_features = os.path.join(path, "parameters.json")
_clean(json_features)
feature_extractor.set_as_extracted(path)
assert feature_extractor.check_if_extracted_path(path)
def test_set_as_extracted():
path = "./tests/data/features"
feature_extractor = FeatureExtractor()
feature_extractor.set_as_extracted(path)
json_features = os.path.join(path, "parameters.json")
assert os.path.exists(json_features)
parameters_features = load_json(json_features)
assert len(parameters_features) == 8
default_parameters = {
"sequence_time": 1.0,
"sequence_hop_time": 0.5,
"audio_hop": 680,
"audio_win": 1024,
"sr": 22050,
"sequence_frames": 32,
"sequence_hop": 16,
"features_folder": "features",
}
for key in default_parameters.keys():
assert default_parameters[key] == parameters_features[key]
def test_set_as_extracted():
path = './tests/data/features'
feature_extractor = FeatureExtractor()
feature_extractor.set_as_extracted(path)
json_features = os.path.join(path, "parameters.json")
assert os.path.exists(json_features)
parameters_features = load_json(json_features)
assert len(parameters_features) == 8
default_parameters = {
'sequence_time': 1.0,
'sequence_hop_time': 0.5,
'audio_hop': 680,
'audio_win': 1024,
'sr': 22050,
'sequence_frames': 32,
'sequence_hop': 16,
'features_folder': 'features'
}
for key in default_parameters.keys():
assert default_parameters[key] == parameters_features[key]
def test_load_audio():
audio_file = "./tests/data/audio/40722-8-0-7.wav"
sr = 8000
feature_extractor = FeatureExtractor(sr=8000)
audio = feature_extractor.load_audio(audio_file,
mono=True,
change_sampling_rate=False)
assert len(audio) == 88200
audio = feature_extractor.load_audio(audio_file,
mono=True,
change_sampling_rate=True)
assert len(audio) == 32000
# stereo
audio = feature_extractor.load_audio(audio_file,
mono=False,
change_sampling_rate=False)
assert len(audio) == 88200 # This file is mono
audio_file = "./tests/data/audio/147764-4-7-0.wav"
audio = feature_extractor.load_audio(audio_file,
mono=False,
change_sampling_rate=False)
assert audio.shape[1] == 2
def test_calculate():
feature_extractor = FeatureExtractor()
with pytest.raises(NotImplementedError):
feature_extractor.calculate("file.wav")
def test_convert_to_sequences():
feature_extractor = FeatureExtractor(pad_mode="constant")
audio_rep = np.zeros((feature_extractor.sequence_frames * 2, 2)) # (64, 2)
frames = feature_extractor.convert_to_sequences(audio_rep) # (3, 32, 2)
assert frames.shape == (3, 32, 2)
# Check that ignore last samples
audio_rep = np.zeros(
(feature_extractor.sequence_frames * 2 + 10, 2)) # (74, 2)
frames = feature_extractor.convert_to_sequences(audio_rep) # (3, 32, 2)
assert frames.shape == (3, 32, 2)
feature_extractor = FeatureExtractor(sequence_hop_time=-1,
pad_mode="constant")
frames = feature_extractor.convert_to_sequences(audio_rep) # (1, 74, 2)
assert frames.shape == (1, 32, 2)
feature_extractor = FeatureExtractor(sequence_hop_time=-1,
sequence_time=-1,
pad_mode="constant")
frames = feature_extractor.convert_to_sequences(audio_rep) # (1, 74, 2)
assert frames.shape == (1, 74, 2)
# Test it together with pad_audio
feature_extractor = FeatureExtractor(pad_mode="constant")
n_frames = feature_extractor.sequence_frames
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.zeros(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
stft = librosa.core.stft(
audio_pad,
n_fft=1024,
hop_length=feature_extractor.audio_hop,
win_length=feature_extractor.audio_win,
center=False,
)
spectrogram = np.abs(stft)**2
spectrogram = spectrogram.T
frames = feature_extractor.convert_to_sequences(spectrogram)
assert frames.shape == (1, feature_extractor.sequence_frames, 513)
n_frames = 2 * feature_extractor.sequence_frames
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.zeros(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
stft = librosa.core.stft(
audio_pad,
n_fft=1024,
hop_length=feature_extractor.audio_hop,
win_length=feature_extractor.audio_win,
center=False,
)
spectrogram = np.abs(stft)**2
spectrogram = spectrogram.T
frames = feature_extractor.convert_to_sequences(spectrogram)
assert frames.shape == (3, feature_extractor.sequence_frames, 513)
def test_pad_audio():
# No sequence slicing
# sequence_frames = 32
feature_extractor = FeatureExtractor(sequence_hop_time=-1,
pad_mode="constant")
n_frames = feature_extractor.sequence_frames - 1
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.ones(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
assert (len(audio_pad) == feature_extractor.sequence_frames *
feature_extractor.audio_hop + feature_extractor.audio_win)
# No sequence slicing, audio larger than sequence_time
n_frames = feature_extractor.sequence_frames + 1
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.ones(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
assert (len(audio_pad) == feature_extractor.sequence_frames *
feature_extractor.audio_hop + feature_extractor.audio_win)
# Sequence slicing, audio shorter than one sequence
# sequence_frames = 32
# sequence_hop = 16
feature_extractor = FeatureExtractor(pad_mode="constant")
n_frames = feature_extractor.sequence_frames - 1
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.ones(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
assert (len(audio_pad) == feature_extractor.sequence_frames *
feature_extractor.audio_hop + feature_extractor.audio_win)
# Sequence slicing, audio larger than one sequence
# sequence_frames = 32
# sequence_hop = 16
feature_extractor = FeatureExtractor(pad_mode="constant")
n_frames = feature_extractor.sequence_frames + 1
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.ones(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
assert (len(audio_pad) == (feature_extractor.sequence_frames +
feature_extractor.sequence_hop) *
feature_extractor.audio_hop + feature_extractor.audio_win)
# Sequence slicing, audio length equal to two sequences
feature_extractor = FeatureExtractor(pad_mode="constant")
n_frames = 2 * feature_extractor.sequence_frames
n_samples = n_frames * feature_extractor.audio_hop + feature_extractor.audio_win
audio = np.ones(n_samples)
audio_pad = feature_extractor.pad_audio(audio)
assert (len(audio_pad) == (2 * feature_extractor.sequence_frames) *
feature_extractor.audio_hop + feature_extractor.audio_win)
def test_get_features_path():
dataset_path = "./tests/data"
dataset = Dataset(dataset_path)
feature_extractor = FeatureExtractor()
path = "./tests/data/features/FeatureExtractor"
assert feature_extractor.get_features_path(dataset) == path
from .figures import generate_figure_metrics
import os
import numpy as np
import ast
import soundfile as sf
from tensorflow.compat.v1 import get_default_graph
from sklearn.decomposition import PCA
import base64
import dash
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output, State
dataset = Dataset("")
feature_extractor = FeatureExtractor()
data_generator_train = DataGenerator(dataset, feature_extractor, [])
data_generator_val = DataGenerator(dataset, feature_extractor, [])
data_generator_test = DataGenerator(dataset, feature_extractor, [])
X_pca = np.zeros((1, 4))
X = np.zeros((1, 128, 64))
Y = np.zeros((1, 10))
file_names = []
graph = get_default_graph()
# VIS TAB
def conv_path(file_or_folder):