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Repository: aoifemcdonagh/audioset-processing
Branch: master
Commit: 144fbdf19559
Files: 13
Total size: 39.5 MB
Directory structure:
gitextract_4qokgb8g/
├── .gitignore
├── LICENCE
├── README.md
├── core/
│ ├── __init__.py
│ ├── download.sh
│ └── utils.py
├── data/
│ ├── balanced_train_segments.csv
│ ├── class_labels_indices.csv
│ ├── eval_segments.csv
│ └── unbalanced_train_segments.csv
├── demo.ipynb
├── process.py
└── requirements.txt
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
.idea
output/
*.pyc
*.csv#
================================================
FILE: LICENCE
================================================
MIT License
Copyright (c) 2019 Aoife McDonagh
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# audioset-processing [](https://colab.research.google.com/github/aoifemcdonagh/audioset-processing/blob/master/demo.ipynb)
Toolkit for downloading raw audio files from AudioSet.
## Dependencies
- python3
- ffmpeg
- youtube-dl 2019.7.2
## Quick start
To download files from AudioSet for class "bird"
```
python3 process.py download -c "bird"
```
Downloads audio files to a folder `output/bird` in current directory.
Uses CSV files found in `data/` by default. Execute `process.py` in its' own directory.
## process.py Arguments
The following options control how the toolkit operates. The first list of options are the most useful. The second list of options aren't necessary to use but offer more fine grained control if desired.
#### Most useful
- Mode: `download` or `find`
- `-c` or `--classes` List of classes to download (or find). Use quotation marks for class names with spaces, e.g. `"bird song"`. For multiple classes use format `"bird" "flute" "dog" ...`
- `-d` or `--destination_dir` Path to directory for storing downloaded (or found) files. Defaults to `./output`
- `--audio_data_dir` Path to directory containing pre-downloaded AudioSet files. Must be used in `find` mode.
#### Less used
- `-b` or `--blacklist` List of class labels which will exclude a file from being downloaded/found.
- `-fs` or `--sample_rate` Sample rate of audio to download in Hz (not kHz!!). Default is 16000Hz
- `-s` or `--strict` If used, only download/find classes which match exact string arguments passed, i.e. no substring matching. For example, if you wanted to download all instances of class "bird" but not "bird song".
- `--label_file` Path to CSV file containing AudioSet labels for each class. Defaults to `./data/class_labels_indices.csv`
- `--csv_dataset` Path to CSV file containing AudioSet in YouTube-ID/timestamp/class form. Defaults to `./data/balanced_train_segments.csv`
## Project Overview
This toolkit was developed as part of a project for my Master's thesis. This project involved training a WaveGAN model on subsets of the AudioSet dataset.
AudioSet is publicly available in two formats; as a list of YouTube-IDs structured as CSV files, or as 128-dimensional feature vectors stored as TFRecord files.
Neither of these formats could be used as training data for the model I was trying to train.
* The problem with using the dataset's audio feature vectors is that in general, audio feature representations are not invertible.
* The problem with using YouTube-IDs is that they are only references to where the audio can be found online, not the samples themselves.
However, using these identifiers is the only way to obtain raw audio to train a WaveGAN model for this project.
Gathering all samples for an entire class would take an extremely long time and be prone to human error. It would involve a number of lengthy steps which would have to be repeated every time a new data needed to be downloaded;
1. Parsing the CSV dataset for samples labelled with corresponding class identifier
2. Storing YouTube-IDs labelled with class identifier.
3. Putting all IDs into a separate URL addresses.
4. Downloading YouTube video from which a sample originated
5. Extract audio, discard video stream.
6. Using timestamp information in CSV file to retrieve sample.
7. Storing sample on local machine.
Since these steps are repeatable for downloading any target class in AudioSet, it made sense to automate this process. A toolkit for downloading the raw audio samples in AudioSet was developed to solve this problem. The toolkit comprises of a set of Python scripts for taking user input, parsing through the dataset, and downloading the relevant audio clips.
#### Downloading
To download a sub-set of AudioSet, the user can specify target classes they wish to download. Then the csv files distributed for the dataset are parsed for all YouTube-IDs which have a label associated with the given class. Using a number of Python packages, URLs are formed with the YouTube-IDs. Ten second audio clips are downloaded using the generated URLs and corresponding timestamps for each video. Clips are stored locally on the user's machine for future use.
## AudioSet
AudioSet can be downloaded from Google [here](https://research.google.com/audioset/download.html) as a set of CSV files. For each element in the dataset the CSV files list an associated YouTube ID, start time, end time and class labels. The CSV files are used to download AudioSet as raw audio files (WAV).
## Structure
```
audioset-processing
├── procas
| ├── utils.py
| └── download.sh
├── data
| ├── balanced_train_segments.csv
| ├── class_labels_indices.csv
| ├── unbalanced_train_segments.csv
| └── eval_segments.csv
├── src
| └── pictures
├── demo.ipynb
├── LICENCE
├── process.py
├── requirements.txt
└── README.md
```
================================================
FILE: core/__init__.py
================================================
================================================
FILE: core/download.sh
================================================
# NOT USED here for reference
#!/bin/bash
# Script for downloading audio clips (10s) from Youtube using AudioSet CSV datasets
SAMPLE_RATE=22050
# fetch_clip(videoID, startTime, endTime)
fetch_clip() {
echo "Fetching $1 ($2 to $3)..."
outname="$1_$2"
if [ -f "${outname}.wav.gz" ]; then
echo "Already have it."
return
fi
if [ $? -eq 0 ]; then
# i.e. if we haven't found this file
ffmpeg -loglevel quiet -ss "$2" -t 10 \
-i $(youtube-dl -f 'bestaudio' --get-url https://youtube.com/watch?v=$1) \
-ar $SAMPLE_RATE \
"./$outname.wav"
else
sleep 1
fi
}
# iterate through the input piped into script
grep -E '^[^#]' | while read line
do
fetch_clip $(echo "$line" | sed -E 's/,/ /g')
done
================================================
FILE: core/utils.py
================================================
# This file creates and populates a directory with clips for specified class(es) of Audio Set
# Works on pre-downloaded files. Doesn't download new files
# Input arguments:
# - class labels
# - CSV file of dataset
# - directory where raw audio is stored
# - destination directory to store sorted audio files
import csv
import os
from shutil import copyfile
# defaults
DEFAULT_LABEL_FILE = '../data/class_labels_indices.csv'
DEFAULT_CSV_DATASET = '../data/unbalanced_train_segments.csv'
DEFAULT_DEST_DIR = '../output/'
DEFAULT_FS = 16000
def find(class_name, args):
print("Finding examples for class " + class_name + " in: " + args.audio_data_dir)
# construct path to destination dir
dst_dir = args.destination_dir if args.destination_dir is not None else DEFAULT_DEST_DIR
dst_dir_path = os.path.join(dst_dir, class_name) # Create directory to store found files
csv_dataset = args.csv_dataset if args.csv_dataset is not None else DEFAULT_CSV_DATASET
class_id = get_label_id(class_name, args) # Get ID corresponding to class_name
youtube_ids = get_yt_ids(class_id, csv_dataset) # Find all YouTube IDs which have class_id as a label
find_files(youtube_ids, args.audio_data_dir, dst_dir_path) # Find all files in audio_data_dir which are in the list of YouTube IDs
def download(class_name, args):
new_csv = create_csv(class_name, args)
# construct path to destination dir
dst_dir_root = args.destination_dir if args.destination_dir is not None else DEFAULT_DEST_DIR
dst_dir = os.path.join(dst_dir_root, class_name) # Create directory to store found files
print("dst_dir: " + dst_dir)
if not os.path.isdir(dst_dir):
os.makedirs(dst_dir)
print("dst_dir: " + dst_dir)
with open(new_csv) as dataset:
reader = csv.reader(dataset)
for row in reader:
# print command for debugging
print("ffmpeg -ss " + str(row[1]) + " -t 10 -i $(youtube-dl -f 'bestaudio' -g https://www.youtube.com/watch?v=" +
str(row[0]) + ") -ar " + str(DEFAULT_FS) + " -- \"" + dst_dir + "/" + str(row[0]) + "_" + row[1] + ".wav\"")
os.system(("ffmpeg -ss " + str(row[1]) + " -t 10 -i $(youtube-dl -f 'bestaudio' -g https://www.youtube.com/watch?v=" +
str(row[0]) + ") -ar " + str(DEFAULT_FS) + " -- \"" + dst_dir + "/" + str(row[0]) + "_" + row[1] + ".wav\""))
def create_csv(class_name, args):
"""
Function for creating csv file containing all clips and corresponding info for given class
Blacklisted classes functionality implemented here
:param class_name:
:param args:
:return:
"""
# construct path to destination dir
dst_dir = args.destination_dir if args.destination_dir is not None else DEFAULT_DEST_DIR
csv_dataset = args.csv_dataset if args.csv_dataset is not None else DEFAULT_CSV_DATASET
new_csv_path = os.path.join(str(dst_dir) + "/" + class_name + '.csv')
print(new_csv_path)
# Should check if CSV already exists and possibly return if so? Overwriting for now
if os.path.isfile(new_csv_path):
print("A CSV file for class " + class_name + ' already exists.')
print("*** Overwriting " + str(new_csv_path) + " ***")
label_id = get_label_id(class_name, args) # Get a list of label IDs which match class_name
if args.blacklist != None:
blacklisted_ids = [get_label_id(blacklisted_class, args) for blacklisted_class in args.blacklist ] # Get a list of label IDs for blacklisted classes
blacklisted_ids = [id for blacklist in blacklisted_ids for id in blacklist] # Flatten list of lists into a single list
else:
blacklisted_ids = []
with open(csv_dataset) as dataset, open(new_csv_path, 'w', newline='') as new_csv:
reader = csv.reader(dataset, skipinitialspace=True)
writer = csv.writer(new_csv)
# Include the row if it contains label for desired class and no labels of blacklisted classes
to_write = [row for row in reader for label in label_id if label in row[3]
and bool(set(row[3].split(",")).intersection(blacklisted_ids)) is False] # added check for blacklisted classes
writer.writerows(to_write)
print("Finished writing CSV file for " + class_name)
return new_csv_path
def get_label_id(class_name, args):
"""
Function for getting corresponding label for a class name
The function looks for class name in the CSV file of label indices. It performs sub-string searching rather than
matching so that class labels are more reliably found. Problematic naming convention of classes in AudioSet
forces this to be the case. For example, inputting "female speech" as 'label' will not find class
"Female speech, woman speaking" if exact string matching is performed. Also problems with exact string matching if
there are spaces in class names.
Sub-string matching will result in multiple class labels being found for certain input stings. e.g. "dog"
28/06/18: implemented 'strict' option
:param class_name: label to search for
:param strict:
:return: ID for given label. Given as a list as there can be multiple matching IDs found (e.g. "dog")
"""
label_file_path = args.label_file if args.label_file is not None else DEFAULT_LABEL_FILE
with open(label_file_path) as label_file:
reader = csv.DictReader(label_file)
index, id, display_name, = reader.fieldnames
if args.strict:
label_ids = [row[id] for row in reader if (class_name.lower() == row[display_name].lower())]
else:
label_ids = [row[id] for row in reader if (class_name.lower() in row[display_name].lower())]
if label_ids is None:
print("No id for class " + class_name)
elif len(label_ids) > 1: # If there is more than one class containing the specified string
print("Multiple labels found for " + class_name)
print(label_ids)
else:
print("Label ID for \"" + class_name + "\": " + str(label_ids))
return label_ids # Return a list of matching label IDs
def get_yt_ids(label_ids, csv):
"""
Function for getting the youtube IDs for all clips where the specified classes are present
:param label_ids: list of label IDs (will most often only contain 1 label)
:param csv: path to csv file containing dataset info (i.e. youtube ids and labels)
:return: dictionary containing label-YouTubeID pairs
"""
yt_ids = {label: [] for label in label_ids} # Empty dictionary with class labels as keys and empty lists as values
with open(csv) as dataset:
reader = csv.reader(dataset, skipinitialspace=True)
# Add youtube id to list for label if corresponding audio contains that label/class
[(yt_ids[label].append(row[0])) for row in reader for label in label_ids if label in row[3]]
for label in yt_ids:
if not yt_ids[label]:
print("No clips found for " + label)
yt_ids.pop(label) # remove dict entry for label which doesn't have any clips
print("Youtube ids for label " + label)
print(yt_ids[label])
print("Total number of labels for label " + label + ": " + str(len(yt_ids[label])))
return yt_ids # return dict containing label-yt-id pairs
def find_files(yt_ids, file_dir, dst_dir=None):
"""
Function for getting all wav files associated with given label/class
Name of function was originally 'sort_wav_files' but 'wav' was removed to avoid confusion. Script can be used to
sort archive files, or any other type of file, no distinction is made.
:param yt_ids: dict containing label-yt_id pairs
:param file_dir: directory where files are stored
:param dst_dir:
:return:
"""
dst_dir = file_dir if dst_dir is None else dst_dir
for class_name in yt_ids: # keys in yt_ids are class names
if not os.path.exists(dst_dir + "/" + class_name):
os.makedirs(dst_dir + "/" + class_name)
print("Created directory for class: " + class_name)
print(dst_dir + "/" + class_name)
for file in os.listdir(file_dir): # Iterate through all files in dir
for class_name, yt_id_list in yt_ids.items(): # Iterate through label-yt_id_list pairs
if any(yt_id in file for yt_id in yt_id_list): # if the file name in list of yt_ids
src = file_dir + "/" + file # source file
dst = (dst_dir + "/" + class_name + "/" + file) # destination of file
copyfile(src, dst) # copy file into directory for current label
print("Finished sorting files")
"""
Don't call this file directly from terminal....
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-l', '--labels', nargs='+', type=str, help='list of classes')
parser.add_argument('-c', '--csv_dataset', type=str, help='csv file containing dataset info')
parser.add_argument('-r', '--raw_audio_dir', type=str, help='directory containing dataset as wav files')
parser.add_argument('-d', '--destination_dir', type=str, help='directory to put sorted files into')
args = parser.parse_args()
print(args.labels)
class_ids = [get_label_id(label) for label in args.labels]
#class_ids = get_label_ids(args.labels)
class_ids = dict(zip(args.labels, class_ids))
print(class_ids)
# yt_ids = [get_yt_ids(label_id, args.csv_dataset) for label_id in class_ids]
youtube_ids = get_yt_ids(class_ids, args.csv_dataset)
# print(youtube_ids)
find_files(youtube_ids, args.raw_audio_dir, args.destination_dir)
"""
================================================
FILE: data/balanced_train_segments.csv
================================================
# num_ytids=22160, num_segs=22160, num_unique_labels=527, num_positive_labels=52882
# YTID, start_seconds, end_seconds, positive_labels
--PJHxphWEs,30,40,"/m/09x0r,/t/dd00088"
--ZhevVpy1s,50,60,/m/012xff
--aE2O5G5WE,0,10,"/m/03fwl,/m/04rlf,/m/09x0r"
--aO5cdqSAg,30,40,"/t/dd00003,/t/dd00005"
--aaILOrkII,200,210,"/m/032s66,/m/073cg4"
--cB2ZVjpnA,30,40,/m/01y3hg
--ekDLDTUXA,30,40,"/m/015lz1,/m/07pws3f"
-0DLPzsiXXE,30,40,"/m/04rlf,/m/07qwdck"
-0DdlOuIFUI,50,60,"/m/0130jx,/m/02jz0l,/m/0838f"
-0FHUc78Gqo,30,40,"/m/02w4v,/m/04rlf"
-0O3e95y4gE,100,110,"/m/07r4wb8,/t/dd00125"
-0SdAVK79lg,30,40,"/m/0155w,/m/01lyv,/m/0342h,/m/042v_gx,/m/04rlf,/m/04szw,/m/07s0s5r,/m/0fx80y,/m/0gg8l"
-0Vl4HyWRk8,410,420,"/m/085jw,/m/0l14l2"
-0mG4W5Hlq8,270,280,"/m/04rlf,/m/05fw6t,/m/07r4k75,/m/09x0r,/m/0ytgt"
-0mjrMposBM,80,90,/m/04zmvq
-11LhdJgBb8,30,40,"/m/04rlf,/m/07qn4z3"
-1LrH01Ei1w,30,40,"/m/02p0sh1,/m/04rlf"
-1TLtjPtnms,10,20,"/m/03lty,/m/04rlf,/m/07szfh9"
-1iKLvsRBbE,80,90,"/m/015lz1,/m/07pws3f"
-24dqQM_rDk,30,40,"/m/04rlf,/t/dd00003"
-275_wTLm-4,7,17,/m/07q6cd_
-28U1_qW0sU,30,40,/m/07qwdck
-2PDE7hUArE,30,40,"/m/02mk9,/m/07pb8fc,/t/dd00066"
-2X03mO3T_U,30,40,"/m/04k94,/m/0dv3j"
-2hQKCE-oTI,30,40,/m/07pbtc8
-30H9V1IKps,6,16,"/m/07yv9,/m/09x0r,/m/0gvgw0"
-38Qgsbh7NQ,100,110,"/m/013y1f,/m/03xq_f"
-3HYdaJyF4U,30,40,"/m/04rlf,/m/06rqw"
-3IGxVTJvgI,30,40,"/m/06bz3,/m/09x0r"
-3IYpJfLVJk,30,40,"/m/03k3r,/m/07q5rw0,/m/0jbk"
-3hKkjKmIGE,30,40,"/m/019jd,/m/06q74"
-3pPrlCm6gg,30,40,/m/01wy6
-4NLarMj4xU,30,40,"/m/04rlf,/t/dd00034"
-4RWqM0UCCY,0,10,"/m/012n7d,/m/02mfyn,/m/03j1ly,/m/03kmc9"
-4hZyOdm7EI,200,210,"/m/015lz1,/m/015vgc"
-4xJv59_zcA,30,40,"/m/07pl1bw,/m/083vt"
-5-vmt2iKT0,30,40,"/m/02zsn,/m/09x0r"
-5DdYkYOjy0,30,40,/m/06h7j
-5GhUbDLYkQ,16,26,"/m/0130jx,/m/02jz0l,/m/0838f"
-5Jlimvsuwo,90,100,"/m/011k_j,/m/04rlf"
-5Kk-m7BmiE,21,31,"/m/07pyf11,/m/07qmpdm"
-5S70zOSw30,30,40,"/m/030rvx,/m/07pzfmf,/m/09x0r"
-5f6hjZf9Yw,30,40,"/m/02w4v,/m/04rlf"
-5xOcMJpTUk,70,80,"/m/018vs,/m/0342h,/m/042v_gx,/m/04rlf,/m/04szw,/m/09x0r,/m/0fx80y"
-60XojQWWoc,30,40,"/m/02rhddq,/m/07r04,/m/07yv9"
-64xnyOswXA,120,130,/m/0cdnk
-693sNKI3iM,30,40,"/m/015lz1,/m/01swy6,/m/04rlf"
-6QGvxvaTkI,280,290,"/m/01p970,/m/026t6,/m/0l14md"
-6RN8skIHU4,20,30,"/m/09x0r,/m/0dv5r,/t/dd00125"
-6fxJZ7CW00,30,40,/m/07pjjrj
-6gC_nlUBfA,60,70,"/m/02y_763,/m/09x0r"
-6krAYK2LLo,30,40,"/m/04rlf,/m/06j64v,/m/0l14gg"
-6pcgdLfb_A,110,120,"/m/04rlf,/t/dd00035"
-6sgMgtW91g,20,30,"/m/04rlf,/m/05rj2,/m/09x0r"
-6x2PtSRfJU,30,40,"/m/01yrx,/m/068hy,/m/07qrkrw,/m/09x0r"
-70MzkgSsHQ,30,40,"/m/04rlf,/m/04rzd"
-70wVF5u-gg,30,40,/m/07pn_8q
-75IwDlkzDQ,30,40,"/m/01h82_,/m/02mk9"
-76P3VHPuus,10,20,/m/01jg1z
-7J4109yM7w,30,40,"/m/02mk9,/m/07yv9,/t/dd00130"
-7QVE8o9uk4,30,40,"/m/01yg9g,/m/04rlf,/m/07yv9"
-7WpTk5AHfk,30,40,"/m/04rlf,/m/07pws3f,/m/09x0r"
-7aibH1LKwI,90,100,/t/dd00121
-7wUQP6G5EQ,30,40,"/m/02cjck,/m/02w4v,/m/04rlf"
-7ye24UFRng,0,10,"/m/032n05,/m/04rlf"
-7z662AsuTE,380,390,/m/0mbct
-82vJ9bcar4,40,50,/m/0642b4
-8CrCItCO24,30,40,/m/07q7njn
-8Ezw9946g8,100,110,/m/0fqfqc
-8H4GKg-mYQ,30,40,"/m/04rlf,/m/07gxw"
-8S_tLKfeJg,200,210,"/m/04fgwm,/m/09x0r,/t/dd00125"
-8_HpHg6nCw,30,40,"/m/07ptzwd,/m/0838f"
-8fQePz4FcE,30,40,"/m/04rlf,/m/07xzm,/t/dd00126"
-8n2NqDFRko,30,40,"/m/02rhddq,/m/07r04,/m/07yv9,/m/09x0r"
-8wQV7VJnmM,30,40,"/m/04rlf,/m/04wptg,/t/dd00031"
-92cYmTgIjM,190,200,/m/02fxyj
-99daJhXYJY,30,40,"/m/019jd,/m/02rlv9,/m/03m9d0z,/t/dd00092"
-9ME9qMgkeY,270,280,"/m/08j51y,/m/09ct_"
-9Nl3LZBCOo,200,210,"/m/01s0vc,/m/09x0r"
-9c1c8G_WO8,24,34,"/m/07c52,/m/09x0r"
-9ek6eO0RtI,260,270,/m/03wwcy
-9wxcZf43a4,530,540,/m/0cj0r
-A0MTi2NMGI,30,40,/t/dd00134
-AIrHVeCgtM,30,40,"/m/02rhddq,/m/07r04,/m/07yv9,/m/09x0r"
-ASYwidRD7M,30,40,/m/01d3sd
-AgbNy9f2sg,80,90,"/m/019jd,/m/06q74"
-Ar7D-GARhI,30,40,"/m/07bgp,/m/07q0h5t,/m/09x0r"
-AswFDuOptQ,0,10,"/m/04k94,/m/04rlf,/m/09x0r"
-AvC89aikTw,180,190,/m/0242l
-B3_mFE3pog,30,40,/m/07qfgpx
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8SYLYWR47EE,30,40,/t/dd00134
8ShW4vN7ads,120,130,"/m/016cjb,/m/04rlf"
8StkM09rkCw,30,40,"/m/04rlf,/m/07pp_mv,/m/09x0r"
8T-ElyQVDAI,60,70,/m/07r4gkf
8T0FDAMUfjU,30,40,"/m/04rlf,/m/05r6t,/m/07sbbz2,/m/0dl5d,/m/0dls3,/m/0l14gg,/t/dd00032"
8TVgx-oXkWA,10,20,"/m/04rlf,/m/064t9,/m/06j6l,/m/07p78v5"
8Tgad6VT_x8,440,450,"/m/09x0r,/t/dd00013,/t/dd00126"
8Ti-qa2gvoc,29,39,"/m/06mb1,/m/07r10fb,/t/dd00038"
8TkXXqFWNWQ,240,250,"/m/01j3sz,/m/09x0r,/m/0lyf6"
8TnjXTtWinU,1,11,"/m/01jg02,/m/07p_0gm"
8TodOGHkHIY,210,220,"/m/02z32qm,/m/09x0r"
8TyBaWkeSUw,160,170,"/m/01jt3m,/m/0838f"
8UG0gvIYeIE,130,140,"/m/04rlf,/m/07q7njn"
8UPNmrGfsAY,450,460,"/m/01280g,/m/0cdnk,/m/0jbk"
8UhdwnsckJ8,30,40,"/m/0342h,/m/042v_gx,/m/04rlf,/m/04szw,/m/07s0s5r,/m/09x0r,/m/0fx80y"
8UkHc9FLm9U,380,390,"/m/01lsmm,/m/09x0r"
8UxgJtO6gU4,0,10,"/m/01p970,/m/026t6,/m/0l14md"
8V66GlcieTE,20,30,"/m/05zc1,/t/dd00077"
8VQiP0-YvLk,0,10,/m/04fq5q
8VZvVvWZKCA,30,40,"/m/016cjb,/m/04rlf"
8VbJJQXwX4o,30,40,"/m/06mb1,/m/07qwyj0,/m/07r10fb"
8W0KcQLImuo,190,200,"/m/014yck,/m/07yv9,/m/0cmf2,/m/0k5j,/t/dd00128"
8WOO6qoEcgo,140,150,"/m/013y1f,/m/03gvt"
8WPG0dD20lg,540,550,"/m/01b9nn,/m/0342h,/m/04rlf,/m/04szw,/m/0fx80y"
8WfOH_ej_RU,30,40,"/m/02mk9,/m/04rlf,/m/07yv9,/m/09x0r,/t/dd00130"
8X4_2bcXKvM,310,320,"/m/09x0r,/m/0g6b5"
8XGXHDMIJw4,150,160,/m/01hgjl
8XIlsK3qZF0,20,30,"/m/07p_0gm,/m/07rcgpl"
8XIsw5VBfLk,240,250,"/m/015lz1,/m/04rlf,/m/07q4ntr,/m/09x0r,/t/dd00126"
8XT_S36xHCA,0,10,/m/0gg8l
8XUVAOMzjFY,30,40,"/m/01bns_,/m/0d8_n"
8XoY6gxDwLE,30,40,"/m/01x3z,/m/07qjznl,/m/07qjznt"
8XogbE2OMek,20,30,"/m/04rlf,/m/09x0r,/m/0ytgt,/t/dd00121"
8XuDQnw8jTY,30,40,/m/07qmpdm
8Y0FRjkX9xI,100,110,"/m/02w4v,/m/04rlf"
8Y3pAqq5s0E,150,160,"/m/07pzfmf,/m/09x0r"
8Ymf53VWNyU,20,30,"/m/07qcx4z,/m/09x0r"
8YoDTfC4oqw,560,570,/m/0j2kx
8Yt55huZGZc,30,40,"/m/04rlf,/t/dd00031"
8Z2GLhocH9A,30,40,"/m/01qbl,/m/026t6,/m/02hnl,/m/03qtq,/m/03t3fj,/m/06rvn,/m/0bm02,/m/0l14md"
8Z3AE-grMhs,90,100,/m/03r5q_
8Z5xnSxUmGE,60,70,"/m/02w4v,/m/04rlf"
8ZC27l1aqiQ,0,10,/m/07rqsjt
8ZK1ajW598M,110,120,"/m/04rlf,/m/06j64v"
8ZUrVD1DSUk,30,40,"/m/04rlf,/t/dd00018"
8ZuLPszCoKA,300,310,/m/07hvw1
8_19ZcMbd0M,450,460,"/m/09x0r,/m/0k65p"
8_2T2cPxZ_M,30,40,/m/07ptzwd
8_6sazVJmlE,120,130,"/m/06xkwv,/m/07rcgpl"
8_SNB66-wBA,0,8,/m/07pczhz
8_Xx1mOhHys,60,70,/t/dd00066
8__OAtLb8wo,60,70,"/m/01xq0k1,/m/07rpkh9,/m/0ch8v"
8_dg_Npjkp8,210,220,"/m/011k_j,/m/04rlf,/m/09x0r"
8_lTwzNaIxc,100,110,"/m/07p9k1k,/m/07ptfmf,/m/0dxrf"
8a2OeWS3G-A,50,60,"/m/014zdl,/m/04rlf,/m/09x0r"
8a9bzBiund8,70,80,"/m/03p19w,/m/04rlf,/m/09x0r,/t/dd00129"
8aaG9UHDKbw,0,10,"/m/01g50p,/m/0284vy3,/m/06d_3,/m/07jdr"
8akKhUw9nKo,7,17,"/m/01rd7k,/m/025rv6n,/m/07svc2k"
8b3PWpYU8Ns,30,40,"/m/07bjf,/m/09x0r"
8bY9H7ec6_0,30,40,"/m/04rlf,/m/0b_fwt"
8bogiM0sCGs,330,340,"/m/03cczk,/t/dd00125"
8cC4mv-9Res,70,80,"/m/07p6mqd,/m/09x0r,/m/0ytgt,/t/dd00128"
8cDXwosw9nA,30,40,"/m/05tny_,/m/068hy,/m/07qf0zm,/m/07r_k2n,/m/0bt9lr,/m/0jbk"
8cM-69gMf5g,8,18,/m/0dl9sf8
8c_lJaK1-W8,30,40,"/m/04rlf,/m/0xzly"
8cb9HKbKRmA,5,15,/m/07q0yl5
8d3WbkLC7Gw,30,40,"/m/03k3r,/m/07rv9rh,/m/09x0r,/m/0jbk"
8dE1x--TuF8,10,20,"/m/03qjg,/m/07qfgpx,/m/085jw"
8dFDn_ECt6c,380,390,"/m/0326g,/m/04rlf"
8daqHY_fpO8,30,40,"/m/07rv9rh,/m/09x0r"
8dy0NkwIT-A,100,110,/m/03wvsk
8e7vDFSTXlk,40,50,"/m/04_sv,/m/07hvw1,/m/07yv9"
8eQxKfJApNM,10,20,/m/07rv4dm
8ezpb8VoZ9Q,30,40,"/m/04rlf,/t/dd00034"
8f3TpJL3XKE,0,10,/m/01g90h
8f8WYvAo-RA,30,40,/m/07pkxdp
8fFN09mPlfY,21,31,"/m/04rlf,/m/07p6mqd,/m/09x0r"
8fInAz_GICs,0,10,"/m/026t6,/m/02k_mr,/m/03t3fj,/m/06rvn,/m/0bm02,/m/0l14md"
8fibq2VXibw,90,100,"/m/04rlf,/m/05fw6t,/m/074ft"
8fxzeFCUwWs,30,40,"/m/04rlf,/m/07pkxdp"
8g-QrSnITN8,250,260,/m/07ptfmf
8g07TLllL8Y,430,440,"/m/03vt0,/m/09xqv"
8g2Uv6QqI_Y,70,80,"/m/02bm9n,/m/05zc1,/m/0l7xg,/t/dd00077"
8grUwE1WFUM,10,20,"/m/04rlf,/m/07pqn27"
8guASCeupec,180,190,"/m/01y3hg,/m/09x0r"
8h-M9SHTV1I,50,60,"/m/07ptzwd,/m/0838f"
8hE_fU9F8-s,140,150,"/m/0dwsp,/m/0dwtp,/m/0j45pbj"
8hT2G2ew6-c,30,40,"/m/04rlf,/m/06j64v"
8hlrmVlVwQk,210,220,/m/07pyy8b
8hvL1kH7Im8,20,30,"/m/04rlf,/m/07s8j8t,/m/07sbbz2"
8iHywI5rtQQ,10,20,"/m/03k3r,/m/07rv9rh"
8ih6wxRFx2A,30,40,"/m/04rlf,/m/09x0r,/m/0b9m1"
8iizS8kZaAo,40,50,"/m/03m5k,/m/0d8_n"
8itgAad5TdE,110,120,"/m/07qnq_y,/m/09x0r"
8iy3RjnVats,60,70,"/m/07szfh9,/m/07yv9,/m/096m7z,/m/0k4j"
8izN-5rfb84,90,100,"/m/03v3yw,/m/07yv9,/m/09x0r"
8jMGRSWGEUc,130,140,/m/05_wcq
8jO08bKvvNQ,30,40,"/m/018j2,/m/04rlf"
8jW0gmW9SAY,30,40,"/m/07s0dtb,/m/09x0r"
8jjlkOmWGUo,30,40,"/m/02mk9,/m/07q2z82,/t/dd00130"
8jt-1-EgB6Q,30,40,"/m/04rlf,/m/07qlf79"
8jtKYSQKCpY,160,170,"/m/07qcx4z,/m/09x0r,/t/dd00125"
8kGi5lkxZWc,90,100,"/m/0155w,/m/01glhc,/m/02sgy,/m/0342h,/m/04rlf,/m/04szw,/m/09x0r,/m/0fx80y"
8kH-dzSBthI,250,260,"/m/085jw,/m/0l14j_"
8kIMIXYRLRo,490,500,"/m/04rlf,/m/07qqyl4"
8kRkchBWPWU,19,29,/m/03v3yw
8kZ1ZTlricI,90,100,/m/02c8p
8klyoflIeTc,30,40,"/m/04rlf,/m/07s72n,/m/08cyft,/m/09x0r,/m/0c1dj"
8kx5ST65Fog,90,100,/m/05pd6
8l-TWnwTapo,30,40,"/m/039jq,/m/04rlf,/m/07q7njn,/m/09x0r"
8l8ghk2CDbE,190,200,"/m/01b_21,/m/09x0r"
8lDKvwgGVVQ,440,450,/m/04fgwm
8lq-ftmI2CU,20,30,"/m/04rlf,/m/07phhsh,/t/dd00126"
8ltlZZLuf4k,0,10,"/m/02mk9,/m/07pb8fc,/m/07yv9,/t/dd00066"
8mFghVOSazM,150,160,"/m/015y_n,/m/04rlf"
8mJLpttMP4A,100,110,/m/06bxc
8mLGZjZsNtU,410,420,"/t/dd00112,/t/dd00125"
8mNRc0NGZzc,160,170,/m/0c2wf
8mcBJ6Szz_M,0,10,"/m/028v0c,/m/04rlf,/m/09x0r,/t/dd00126"
8mcFQSVAE0Y,370,380,"/m/06_fw,/m/09x0r"
8mgRjPyCf1E,30,40,"/m/025rv6n,/m/07st89h,/m/09b5t"
8mix3gmzPa0,130,140,"/m/015lz1,/m/07sbbz2"
8mrFvoLINIM,30,40,/m/0fx9l
8n1eXN6YGAU,280,290,"/m/04rlf,/m/0m0jc"
8n2adsU18BU,140,150,"/m/07plz5l,/m/07qcpgn,/m/09x0r"
8n5sXWT9HGs,30,40,/m/081rb
8nC5PrbKbfk,0,10,"/m/04rlf,/m/0gy1t2s"
8nJ2MhvhnJ0,30,40,"/m/0hdsk,/m/0l14t7"
8nMsIB8SeDc,70,80,/m/023pjk
8nUPwvuTkEc,14,24,/m/07pczhz
8nXNlU9Ghbw,0,10,"/m/04rlf,/m/07gxw,/m/07lnk"
8nggB29pv1Q,30,40,"/m/02mk9,/m/07pb8fc,/m/07yv9,/m/0k4j,/t/dd00066"
8numls0lm4Q,410,420,"/m/07pbtc8,/m/09x0r"
8o2UyEYbNmY,90,100,/m/0l14t7
8oBmYWGuVF0,30,40,/m/07plct2
8oBxzje16HU,30,40,"/m/03q5_w,/m/04rlf"
8oN13PMMPbY,0,10,/m/0l156k
8oTTgXIO0-I,90,100,"/m/02lkt,/m/04rlf,/m/07qb_dv,/m/09x0r,/t/dd00126"
8oUI02eK3SM,30,40,"/m/0155w,/m/04rlf"
8oa7QzW-IBc,90,100,/m/014zdl
8ob7Y6spUW8,30,40,"/m/07qqyl4,/m/09x0r"
8ofrO5oTmu4,280,290,"/m/02l6bg,/m/07yv9,/m/09ct_,/m/09x0r,/m/0k5j,/t/dd00129"
8olHAhUKkuk,200,210,"/m/01p970,/m/0l14md"
8ozUxamIQo8,20,30,"/m/01m2v,/m/0316dw"
8pAigLVDuns,20,30,"/m/01g50p,/m/0284vy3,/m/06d_3,/m/07jdr,/t/dd00048"
8pAlUegRP_0,0,10,"/m/07r4k75,/m/09x0r"
8pHUBnRV8qc,10,20,"/m/0239kh,/m/0ch8v"
8pJUJvPfIx0,110,120,"/m/06mb1,/m/07qwyj0,/m/0ngt1"
8pSWaTLZI_Y,30,40,"/m/02l6bg,/m/09x0r"
8pdkn_RRmtc,28,38,"/m/02dgv,/m/07r4wb8,/m/09x0r"
8pit9UV69S8,0,10,"/m/04rlf,/m/06by7,/m/07pt_g0"
8pl1D6oG38k,30,40,"/m/01hnzm,/m/04rlf,/m/06j6l"
8ppYXPDTw0E,30,40,"/m/07bjf,/m/09x0r"
8q0An6WY7_c,10,20,"/m/04rlf,/m/04szw,/m/07kc_"
8qL-HSodIVA,30,40,"/m/04rlf,/m/06h7j,/m/09x0r,/t/dd00126"
8qOaUShkI3A,50,60,/m/07p7b8y
8qeTEfOqB0A,80,90,"/m/02lkt,/m/04rlf,/m/08cyft,/t/dd00126"
8qwSJsL781E,20,30,"/m/07ppn3j,/m/09x0r"
8r4t3_KEhtg,80,90,"/m/02y_763,/m/07yv9"
8rS14kaupdE,0,10,"/m/07sq110,/m/09x0r"
8rZEOvULYzo,30,40,"/m/03qc9zr,/m/09x0r"
8rjeBrriB0c,30,40,"/m/06h7j,/m/09x0r,/t/dd00127,/t/dd00128"
8ryZVuSlM_Q,120,130,"/m/09x0r,/m/0llzx"
8ryifCe_sIU,20,30,/m/02_41
8sBAZm6SPQM,30,40,"/m/02dgv,/m/07r4wb8"
8sSV_vqOlS4,250,260,"/m/01kcd,/m/06ncr"
8sXu3FjHPps,30,40,"/m/04rlf,/m/06h7j,/m/09x0r"
8shbCFdh37w,10,20,/m/0j2kx
8sqewUGPr-w,300,310,"/m/02rhddq,/m/07yv9,/t/dd00129"
8stTQGZ0RAw,0,10,/m/0j2kx
8tAXbkGR_-c,30,40,"/m/04rlf,/m/07qjznl"
8tY6nioUQIw,220,230,"/m/085jw,/m/0l14j_"
8t_2JvyuPn0,30,40,"/m/04rlf,/m/07gxw"
8tbm8V9vyMk,120,130,"/m/039jq,/m/07k1x,/m/09x0r"
8u5lBv4gHaA,40,50,/m/0j2kx
8u_gw7ZUpwU,30,40,"/m/04rlf,/t/dd00018"
8udxyD8JwZw,30,40,"/m/07r_80w,/m/09d5_"
8ulLeRTvMKE,290,300,"/m/04rlf,/m/06_y0by"
8uoUtlHC8J0,40,50,/m/015y_n
8vF49L0IiS8,30,40,"/m/04rlf,/m/07qcpgn"
8vOBijliMRc,30,40,"/m/04s8yn,/m/07r5c2p,/m/09x0r,/m/0jbk"
8wIUhbLvkrA,10,20,"/m/015lz1,/t/dd00005"
8wMzUIkaZ-I,510,520,"/m/01jnbd,/m/02rr_,/m/0342h,/m/04rlf,/m/04szw"
8wepi7llBVI,30,40,/m/07n_g
8wpzhcVh5wM,28,38,/t/dd00018
8wty3wJfmEY,330,340,"/m/04rlf,/m/06cqb"
8wzXl3ssAaU,320,330,"/m/09x0r,/t/dd00088"
8xA1afzXvQY,540,550,"/m/02c8p,/m/09x0r"
8xlfb4H0iww,10,20,"/m/04rlf,/m/0l14gg"
8xtkDTOnyrs,160,170,"/m/04rlf,/m/06bxc"
8y36aT4C_c0,30,40,"/m/01kcd,/m/01wy6,/m/07c6l"
8yJDa6Jb_1Y,200,210,"/m/028v0c,/m/04rlf"
8yRROnG0-lA,30,40,/t/dd00134
8ySFFOeBGEs,30,40,/m/01j4z9
8yiJOImd6k0,30,40,"/m/03q5t,/m/05148p4"
8z-7VW_4-zM,300,310,"/m/023pjk,/m/04brg2"
8z2yFtX66C4,30,40,"/m/04rlf,/m/07p6fty,/m/09x0r,/t/dd00127"
8zGJ9N7c6pE,20,30,"/m/01qbl,/m/026t6,/m/02hnl,/m/02k_mr,/m/03qtq,/m/03t3fj,/m/06rvn,/m/0bm02,/m/0l14md"
8zJv0X4EZPI,180,190,/m/0642b4
8zcogfmAD_o,180,190,"/m/01kcd,/m/06ncr"
8zn9L4SRjCs,0,10,"/m/04rlf,/m/07p6fty,/t/dd00126"
8zronW6HcmU,20,30,"/m/02cz_7,/m/04rlf,/t/dd00125"
9-9J6nPdCN4,100,110,"/m/04rlf,/m/0ln16"
9-ZM0E1btNo,60,70,/m/07ryjzk
9-ymRm0OHgY,580,590,"/m/02mk9,/m/07qv_d5,/m/07yv9,/m/09x0r,/t/dd00129"
90Cb7iPCE_4,30,40,"/m/04rlf,/m/07qdb04,/m/09x0r"
90Ffw3_Veq4,90,100,"/m/023vsd,/m/07pdjhy"
90JreF-DDsw,30,40,"/m/02mk9,/m/07pb8fc,/t/dd00066"
90WuJr2Me10,110,120,"/m/01z47d,/m/07cx4"
90pIXbNzT-M,30,40,"/m/04rlf,/m/0dls3"
90rgiP6MPM8,0,10,"/m/07qyrcz,/m/09x0r"
90wsfIQ0kHs,30,40,"/m/01h3n,/m/03vt0,/m/0h2mp"
910QkCqm70g,30,40,"/m/07qwdck,/m/09x0r"
919RleFXcNM,380,390,"/m/01y3hg,/m/030rvx,/m/09x0r"
91AmOKytRUM,0,10,"/m/04rlf,/m/09x0r,/t/dd00033"
91FtvAfmI6s,540,550,"/m/028sqc,/m/04rlf,/m/04szw,/m/0jtg0"
91Gqo2d-Ito,280,290,"/m/07q0yl5,/m/09x0r"
91J13TaWA54,480,490,"/m/07p6mqd,/t/dd00129"
91JI0Z4XZAM,140,150,/m/032n05
91Qp8XUskXo,20,30,"/m/085jw,/m/0l14l2"
91RY97QQ-oI,160,170,"/m/04rlf,/m/06w87"
92QYLjfo68Q,30,40,"/m/04rlf,/m/0g293,/m/0ln16"
9332PjiYkhI,210,220,"/m/04k94,/m/09x0r"
93VErcb3CiI,40,50,"/m/04rlf,/m/04wptg"
93X6stMF9xU,30,40,"/m/07st88b,/m/09ld4"
93b0scnqSOw,30,40,"/m/015p6,/m/020bb7,/m/07r_25d,/m/0h0rv"
93vYcBbPDwQ,90,100,"/m/02_nn,/m/07q5rw0"
940MNbCeobw,0,10,"/m/0239kh,/m/0l14md"
949zqB0GlGE,460,470,"/m/04rlf,/m/07pqn27,/m/09x0r"
94MQpuIA1ls,17,27,"/m/07bgp,/m/07q0h5t"
94Y_QnedxB8,11,21,/m/0c2wf
94v040UK918,260,270,"/m/0dwsp,/m/0dwtp,/m/0f8s22,/m/0j45pbj"
94xXQbgs9Fo,40,50,/m/0cj0r
94yctzPL91s,380,390,/m/02x984l
95MSkigqNQE,210,220,"/m/01j3sz,/m/04rlf,/m/07rgt08,/m/09x0r"
95ThRxhl-ug,170,180,"/m/04rlf,/m/05rwpb"
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9C
gitextract_4qokgb8g/ ├── .gitignore ├── LICENCE ├── README.md ├── core/ │ ├── __init__.py │ ├── download.sh │ └── utils.py ├── data/ │ ├── balanced_train_segments.csv │ ├── class_labels_indices.csv │ ├── eval_segments.csv │ └── unbalanced_train_segments.csv ├── demo.ipynb ├── process.py └── requirements.txt
SYMBOL INDEX (8 symbols across 2 files) FILE: core/utils.py function find (line 20) | def find(class_name, args): function download (line 34) | def download(class_name, args): function create_csv (line 57) | def create_csv(class_name, args): function get_label_id (line 99) | def get_label_id(class_name, args): function get_yt_ids (line 141) | def get_yt_ids(label_ids, csv): function find_files (line 169) | def find_files(yt_ids, file_dir, dst_dir=None): FILE: process.py function find (line 11) | def find(args): function download (line 24) | def download(args):
Condensed preview — 13 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (2,036K chars).
[
{
"path": ".gitignore",
"chars": 27,
"preview": ".idea\noutput/\n*.pyc\n*.csv#\n"
},
{
"path": "LICENCE",
"chars": 1071,
"preview": "MIT License\n\nCopyright (c) 2019 Aoife McDonagh\n\nPermission is hereby granted, free of charge, to any person obtaining a "
},
{
"path": "README.md",
"chars": 5062,
"preview": "# audioset-processing [](https://colab.researc"
},
{
"path": "core/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "core/download.sh",
"chars": 749,
"preview": "# NOT USED here for reference\n\n#!/bin/bash\n\n# Script for downloading audio clips (10s) from Youtube using AudioSet CSV d"
},
{
"path": "core/utils.py",
"chars": 9765,
"preview": "# This file creates and populates a directory with clips for specified class(es) of Audio Set\n# Works on pre-downloaded "
},
{
"path": "data/balanced_train_segments.csv",
"chars": 957155,
"preview": "# num_ytids=22160, num_segs=22160, num_unique_labels=527, num_positive_labels=52882\n# YTID, start_seconds, end_seconds, "
},
{
"path": "data/class_labels_indices.csv",
"chars": 14675,
"preview": "index,mid,display_name\n0,/m/09x0r,\"Speech\"\n1,/m/05zppz,\"Male speech, man speaking\"\n2,/m/02zsn,\"Female speech, woman spea"
},
{
"path": "data/eval_segments.csv",
"chars": 909708,
"preview": "# YTID, start_seconds, end_seconds, positive_labels\n--4gqARaEJE,0,10,\"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk\"\n--BfvyPmVMo"
},
{
"path": "demo.ipynb",
"chars": 23997,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"l"
},
{
"path": "process.py",
"chars": 2778,
"preview": "# Processing utility for AudioSet dataset\n# Sort, find and download the entire, or subsets of, AudioSet\n\n# Aoife McDonag"
},
{
"path": "requirements.txt",
"chars": 59,
"preview": "altgraph==0.17\nfuture==0.18.2\npefile==2019.4.18\nyoutube-dl\n"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the aoifemcdonagh/audioset-processing GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 13 files (39.5 MB), approximately 1.3M tokens, and a symbol index with 8 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.