Initial ComParE commit, Primate Dataset

datasets/metadata_readout.py

@@ -0,0 +1,34 @@
+import numpy as np
+import torch
+import pytorch_lightning as pl
+import librosa
+import pandas as pd
+import variables as v
+from tqdm import tqdm
+
+sr = 16000
+wavs = list((v.PRIMATES_Root/'wav').glob('*.wav'))
+
+if __name__ == '__main__':
+
+    durations = []
+
+    for wav in tqdm(wavs):
+        duration = librosa.get_duration(filename=str(wav), sr=sr)
+        durations.append(duration)
+
+    mean_duration, std_duration, min_duration, max_duration = np.mean(durations), np.std(durations), np.min(durations), np.max(durations)
+
+    print(f'Mean duration: {mean_duration:.3f}s\tstd: {std_duration:.3f}s\tmin: {min_duration:.3f}s\t max: {max_duration:.3f}s')
+
+    primates_train_csv, primates_devel_csv, primates_test_csv = \
+        [pd.read_csv(p) for p in [v.PRIMATES_Root / 'lab' / 'train.csv',
+                                  v.PRIMATES_Root / 'lab' / 'devel.csv',
+                                  v.PRIMATES_Root / 'lab' / 'test.csv']]
+
+    csv = pd.concat((primates_train_csv,
+                     primates_devel_csv,
+                     primates_test_csv))
+
+    print(csv.groupby('label').count())
+    print([np.quantile(durations, q=q) for q in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]])

datasets/primates_librosa_datamodule.py

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+from multiprocessing.pool import ApplyResult
+from pathlib import Path
+from typing import List
+
+from torch.utils.data import DataLoader, ConcatDataset
+from torchvision.transforms import Compose, RandomApply
+from tqdm import tqdm
+
+from ml_lib.audio_toolset.audio_io import NormalizeLocal
+from ml_lib.audio_toolset.audio_to_mel_dataset import LibrosaAudioToMelDataset
+from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipulator, ShiftTime, MaskAug
+from ml_lib.utils._basedatamodule import _BaseDataModule, DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel
+from ml_lib.utils.transforms import ToTensor
+import multiprocessing as mp
+
+
+data_options = [DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel]
+class_names = {key: val for val, key in enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
+
+
+class PrimatesLibrosaDatamodule(_BaseDataModule):
+
+    @property
+    def mel_folder(self):
+        return self.root / 'mel_folder'
+
+    @property
+    def wav_folder(self):
+        return self.root / 'wav'
+
+    def __init__(self, root, batch_size, num_worker, sr, n_mels, n_fft, hop_length,
+                 sample_segment_len=40, sample_hop_len=15):
+        super(PrimatesLibrosaDatamodule, self).__init__()
+        self.sample_hop_len = sample_hop_len
+        self.sample_segment_len = sample_segment_len
+        self.num_worker = num_worker
+        self.batch_size = batch_size
+        self.root = Path(root) / 'primates'
+
+        # Mel Transforms - will be pushed with all other paramters by self.__dict__ to subdataset-class
+        self.mel_kwargs = dict(sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length)
+
+        # Utility
+        self.utility_transforms = Compose([NormalizeLocal(), ToTensor()])
+
+        # Data Augmentations
+        self.mel_augmentations = Compose([
+            # ToDo: HP Search this parameters, make it adjustable from outside
+            RandomApply([NoiseInjection(0.2)], p=0.3),
+            RandomApply([LoudnessManipulator(0.5)], p=0.3),
+            RandomApply([ShiftTime(0.4)], p=0.3),
+            RandomApply([MaskAug(0.2)], p=0.3),
+            self.utility_transforms])
+
+    def train_dataloader(self):
+        return DataLoader(dataset=self.datasets[DATA_OPTION_train], shuffle=True,
+                          batch_size=self.batch_size, pin_memory=True,
+                          num_workers=self.num_worker)
+
+    # Validation Dataloader
+    def val_dataloader(self):
+        return DataLoader(dataset=self.datasets[DATA_OPTION_devel], shuffle=False, pin_memory=True,
+                          batch_size=self.batch_size, num_workers=self.num_worker)
+
+    # Test Dataloader
+    def test_dataloader(self):
+        return DataLoader(dataset=self.datasets[DATA_OPTION_test], shuffle=False,
+                          batch_size=self.batch_size, pin_memory=True,
+                          num_workers=self.num_worker)
+
+    def _build_subdataset(self, row, build=False):
+        slice_file_name, class_name = row.strip().split(',')
+        class_id = class_names.get(class_name, -1)
+        audio_file_path = self.wav_folder / slice_file_name
+        # DATA OPTION DIFFERENTIATION !!!!!!!!!!! - Begin
+        kwargs = self.__dict__
+        if any([x in slice_file_name for x in [DATA_OPTION_devel, DATA_OPTION_test]]):
+            kwargs.update(mel_augmentations=self.utility_transforms)
+        # DATA OPTION DIFFERENTIATION !!!!!!!!!!! - End
+        mel_dataset = LibrosaAudioToMelDataset(audio_file_path, class_id, **kwargs)
+        if build:
+            assert mel_dataset.build_mel()
+        return mel_dataset
+
+    def prepare_data(self, *args, **kwargs):
+        datasets = dict()
+        for data_option in data_options:
+            with open(Path(self.root) / 'lab' / f'{data_option}.csv', mode='r') as f:
+                # Exclude the header
+                _ = next(f)
+                all_rows = list(f)
+            chunksize = len(all_rows) // max(self.num_worker, 1)
+            dataset = list()
+            with mp.Pool(processes=self.num_worker) as pool:
+                pbar = tqdm(total=len(all_rows))
+
+                def update():
+                    pbar.update(chunksize)
+                from itertools import repeat
+                results = pool.starmap_async(self._build_subdataset, zip(all_rows, repeat(True, len(all_rows))),
+                                             chunksize=chunksize)
+                for sub_dataset in results.get():
+                    dataset.append(sub_dataset)
+                    tqdm.update()  # FIXME: will i ever get this to work?
+            datasets[data_option] = ConcatDataset(dataset)
+        self.datasets = datasets
+        return datasets
+
+    def setup(self, stag=None):
+        datasets = dict()
+        for data_option in data_options:
+            with open(Path(self.root) / 'lab' / f'{data_option}.csv', mode='r') as f:
+                # Exclude the header
+                _ = next(f)
+                all_rows = list(f)
+            dataset = list()
+            for row in all_rows:
+                dataset.append(self._build_subdataset(row))
+            datasets[data_option] = ConcatDataset(dataset)
+        self.datasets = datasets
+        return datasets