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dataset_processors.py
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dataset_processors.py
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from abc import ABC, abstractmethod
from typing import Tuple, List
from tqdm import tqdm
import os, csv, glob, json
import pandas as pd
from dataclasses import dataclass
from eval_dataset_configs import VGGSoundConfig, TUTAS2017Config, \
ESC50Config, US8KConfig, AudioCaps16kConfig, Clotho16kConfig
@dataclass
class DatasetProcessor(ABC):
@abstractmethod
def get_filepaths_and_descriptions(self) -> Tuple[List[str], List[List[str]], List[List[str]]]:
pass
class US8KProcessor(DatasetProcessor):
config = US8KConfig()
def get_filepaths_and_descriptions(self, current_split=''):
# init output lists
audio_filepath_list = []
text_dict = {}
synthetic_text_dict = {}
# load audio filepaths
existing_audiopaths = glob.glob(f'{self.config.data_dir}/**/*.wav', recursive=True)
# load meta json file
with open(os.path.join(self.config.data_dir, 'metadata', 'UrbanSound8K.csv'), 'r') as f:
csv_reader = csv.reader(f)
label_dict = {}
for i, row in enumerate(csv_reader):
if i == 0:
continue
label_dict[row[0].split('.wav')[0]] = row[-1].replace('_', ' ')
for audiofile in tqdm(existing_audiopaths[:]):
# get list of text captions
audio_name = audiofile.split('/')[-1].split('.wav')[0]
audio_filepath_list.append(audiofile)
# obtain description item # tags and title+text
text_captions = {}
text_captions['description'] = [label_dict[audio_name]]
text_dict[audio_name] = text_captions
return audio_filepath_list, text_dict, synthetic_text_dict
class ESC50Processor(DatasetProcessor):
# paired wav-json file
config = ESC50Config()
def get_filepaths_and_descriptions(self, current_split=''):
# init output lists
audio_filepath_list = []
text_dict = {}
synthetic_text_dict = {}
# load audio filepaths
existing_audiopaths = glob.glob(f'{self.config.data_dir}/*/*.wav')
# load meta json file
with open(os.path.join(self.config.data_dir, 'esc50.csv'), 'r') as f:
csv_reader = csv.reader(f)
label_dict = {}
for i, row in enumerate(csv_reader):
if i == 0:
continue
label_dict[row[0].split('.wav')[0]] = row[3]
for audiofile in tqdm(existing_audiopaths[:]):
# get list of text captions
audio_name = audiofile.split('/')[-1].split('.wav')[0]
audio_filepath_list.append(audiofile)
# obtain description item # tags and title+text
text_captions = {}
text_captions['description'] = [label_dict[audio_name]]
text_dict[audio_name] = text_captions
return audio_filepath_list, text_dict, synthetic_text_dict
class VGGSoundProcessor(DatasetProcessor):
# paired wav-json file
config = VGGSoundConfig()
def get_filepaths_and_descriptions(self, current_split='test'):
# init output lists
audio_filepath_list = []
text_dict = {}
synthetic_text_dict = {}
# load audio filepaths
existing_audiopaths = glob.glob(f'{self.config.data_dir}/test/*.wav', recursive=True)
# load meta json file
vgg_meta_file = os.path.join(self.config.data_dir, 'vggsound_full.json')
with open(vgg_meta_file, 'r') as f:
vgg_meta_dict = json.load(f)
for audiofile in tqdm(existing_audiopaths[:]):
# get list of text captions
audio_name = audiofile.split('/')[-1].split('.wav')[0]
if audio_name not in vgg_meta_dict:
continue
audio_filepath_list.append(audiofile)
# obtain description item # tags and title+text
text_captions = {}
text_captions['description'] = [vgg_meta_dict[audio_name]]
text_dict[audio_name] = text_captions
return audio_filepath_list, text_dict, synthetic_text_dict
class TUTAS2017Processor(DatasetProcessor):
config = TUTAS2017Config()
def get_filepaths_and_descriptions(self, current_split=''):
# init output lists
audio_filepath_list = []
text_dict = {}
synthetic_text_dict = {}
# load audio filepaths
audio_files = glob.glob(f'{self.config.data_dir}/*/*.wav')
train_json_path = os.path.join(self.config.data_dir, 'meta_train.json')
eval_json_path = os.path.join(self.config.data_dir, 'meta_eval.json')
with open(train_json_path) as f:
train_dict = json.load(f)
with open(eval_json_path) as f:
eval_dict = json.load(f)
# load meta files
for audio_filepath in tqdm(audio_files[:]):
# load audio filepaths
audio_filepath_list.append(audio_filepath)
audio_name = audio_filepath.split('/')[-1].split('.wav')[0]
# get list of text captions
split = audio_filepath.split('/')[-2]
ref_dict = train_dict if split == 'train' else eval_dict
# collecting captions
text_captions = {}
text_captions['description'] = []
text_captions['description'] = [ref_dict[audio_name + '.wav']]
text_dict[audio_name] = text_captions
# obtain computer description item
return audio_filepath_list, text_dict, synthetic_text_dict
class AudioCaps16kProcessor(DatasetProcessor):
# AudioCaps uses a master cvs for each datasplit
config = AudioCaps16kConfig()
def get_filepaths_and_descriptions(self, current_split='test'):
# init output lists
audio_filepath_list = []
text_dict = {}
synthetic_text_dict = {}
# load audio filepaths
audio_files = glob.glob(f'{self.config.data_dir}/{current_split}/*.wav')
with open(os.path.join(self.config.data_dir, current_split + '.csv'), 'r') as f:
csv_reader = csv.reader(f)
meta_info_dict = {}
for i, row in enumerate(csv_reader):
if i == 0:
continue
if row[1] not in meta_info_dict:
meta_info_dict[row[1]] = [row[-1]]
else:
meta_info_dict[row[1]].append(row[-1])
# load meta files
for audio_filepath in tqdm(audio_files[:]):
# load audio filepaths
audio_name = audio_filepath.split('/')[-1].split('.wav')[0]
# get list of text captions
if audio_name not in meta_info_dict:
continue
audio_filepath_list.append(audio_filepath)
# collecting captions
text_captions = {}
text_captions['description'] = meta_info_dict[audio_name]
text_dict[audio_name] = text_captions
# obtain computer description item
return audio_filepath_list, text_dict, synthetic_text_dict
class Clotho16kProcessor(DatasetProcessor):
# clothov2 uses a master cvs for each datasplit instead of paired wav-json
config = Clotho16kConfig()
def get_filepaths_and_descriptions(self, current_split=''):
# init output lists
audio_filepath_list = []
text_dict = {}
synthetic_text_dict = {}
# load audio filepaths
audio_files = glob.glob(f'{self.config.data_dir}/{current_split}/*.wav')
# load meta files
for audio_filepath in tqdm(audio_files[:]):
# load audio filepaths
audio_filepath_list.append(audio_filepath)
audio_name = audio_filepath.split('/')[-1].split('.wav')[0]
# get list of text captions
audio_filename = audio_filepath.split('/')[-1]
split = audio_filepath.split('/')[-2]
if split != current_split:
continue
caption_filename = 'clotho_captions_' + split + '.csv'
caption_path = os.path.join(self.config.data_dir, caption_filename)
split_df = pd.read_csv(caption_path)
data_slice = split_df.loc[split_df['file_name'] == audio_filename]
# collecting captions
text_captions = {}
text_captions['description'] = []
for i in range(5):
text_captions['description'] += data_slice['caption_'+str(i+1)].tolist()
text_dict[audio_name] = text_captions
# obtain computer description item
return audio_filepath_list, text_dict, synthetic_text_dict