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@inproceedings{Meseguer-Brocal_2018, Author = {Meseguer-Brocal, Gabriel and Cohen-Hadria, Alice and Peeters, Geoffroy}, Booktitle = {19th International Society for Music Information Retrieval Conference}, Editor = {ISMIR}, Month = {September}, Title = {DALI: a large Dataset of synchronized Audio, LyrIcs and notes, automatically created using teacher-student machine learning paradigm.}, Year = {2018}}

Here's an example of the kind of information DALI contains:

DALI has two main elements:

1- The dataset - dali_data

The dataset itself. It is denoted as dali_data and it is presented as a collection of gz files. You can find the different DALI_data versions in here.

2- The code for working with DALI - dali_code

The code, denoted as dali_code, for reading and working with dali_data. It is stored in this repository and presented as a python package. Dali_code has its own versions controlled by this github. The release and stable versions can be found at pypi.

repository
├── code
│   ├── DALI
│   │   ├── __init__.py
│   │   ├── Annotations.py
│   │   ├── main.py
│   │   ├── utilities.py
│   │   ├── extra.py
│   │   ├── download.py
│   │   ├── vizualization.py
│   └── setup.py

NEWS:

Ground-Truth for version 1.0 updated with 105 songs. Remember that DALI is a ongoing project. There are many things to solve.

Currently we are working in:

the second generation for the Singing voice detection system.
solving errors for indivual notes.
solving errors global notas errors (songs where all the notes are place off by the same certain interval)
errors in local note alignments.

Please, if you have any suggestion our improvement please contact us at: dali [dot] dataset [at] gmail [dot] com

For any problem with the package that deal with the annotations open an issue in this repository.

Thank you.

TUTORIAL:

First of all, download your Dali_data version and clone this repository.

0- Installing Dali_code.

For the release and stable versions just run the command:

pip install dali-dataset

For non-release and unstable versions you can install them manually going to folder DALI/code and running:

pip install .

You can upgrade DALI for future version with:

pip install dali-dataset --upgrade

DALI can be uninstalled with:

pip uninstall dali-dataset

Requirements: numpy and youtube_dl

NOTE: the version of the code in pip only refers to the code itself. The different versions of the Dali_data can be found above.

1- Loading DALI_data.

DALI is presented as a set of gz files. Each gz contains the annotations of a particular song. We use a unique id for each entry. You can download your dali_data version as follow:

import DALI as dali_code
dali_data_path = 'full_path_to_your_dali_data'
dali_data = dali_code.get_the_DALI_dataset(dali_data_path, skip=[], keep=[])

This function can also be used to load a subset of the DALI dataset by providing the ids of the entries you either want to skip or to keep.

NOTE: Loading DALI might take some minutes depending on your computer and python version. Python3 is faster than python2.

Additionally, each DALI version contains a DALI_DATA_INFO.gz:

dali_info = dali_code.get_info(dali_data_path + 'info/DALI_DATA_INFO.gz')
print(dali_info[0]) -> array(['DALI_ID', 'NAME', 'YOUTUBE', 'WORKING'])

This file matches the unique DALI id with the artist_name-song_tile, the url to youtube and a bool that says if the youtube link is working or not.

1.1- An annotation instance.

dali_data is a dictionary where each key is a unique id and the value is an instance of the class DALI/Annotations namely an annotation instance of the class Annotations.

entry = dali_data['a_dali_unique_id']
type(entry) -> DALI.Annotations.Annotations

Each annotation instance has two attributes: info and annotations.

entry.info --> {'id': 'a_dali_unique_id',
                'artist': 'An Artist',
                'title': 'A song title',
                'dataset_version': 1.0,     **# dali_data version**
                'ground-truth': False,     
                'scores': {'NCC': 0.8098520072498807,
                           'manual': 0.0},  **# Not ready yet**
                'audio': {'url': 'a youtube url',
                          'path': 'None',   
                          **# To you to modify it to point to your local audio file**
                          'working': True},
                'metadata': {'album': 'An album title',
                             'release_date': 'A year',
                             'cover': 'link to a image with the cover',
                             'genres': ['genre_0', ... , 'genre_n'],
                             # The n of genre depends on the song
                             'language': 'a language'}}

entry.annotations --> {'annot': {'the annotations themselves'},
                       'type': 'horizontal' or 'vertical',
                       'annot_param': {'fr': float(frame rate used in the annotation process),
                                      'offset': float(offset value)}}

1.2- Saving as json.

You can export and import annotations a json file.

    path_save = 'my_full_save_path'
    name = 'my_annot_name'
    # export
    entry.write_json(path_save, name)
    # import
    my_json_entry = dali_code.Annotations()
    my_json_entry.read_json(os.path.join(path_save, name+'.json'))

1.3- Ground-truth.

Each dali_data has its own ground-truth ground-truth file. The annotations that are part of the ground-truth are entries of the dali_data with the offset and fr parameters manually annotated.

You can easily load a ground-truth file:

gt_file = 'full_path_to_my_ground_truth_file'
# you can load the ground-truth
gt = dali_code.utilities.read_gzip(gt_file)
type(gt) --> dict
gt['a_dali_unique_id'] --> {'offset': float(a_number),
                            'fr': float(a_number)}

You can also load a dali_gt with all the entries of the dali_data that are part of the ground-truth with their annotations updated to the offset and fr parameters manually annotated:

# dali_gt only with ground_truth songs
gt = dali_code.utilities.read_gzip(gt_file)
dali_gt = dali_code.get_the_DALI_dataset(dali_data_path, gt_file, keep=gt.keys())
len(dali_gt) -> == len(gt)

You can also load the whole dali_data and update the songs that are part of the ground truth with the offset and fr parameters manually verified.

# Two options:
# 1- once you have your dali_data
dali_data = dali_code.update_with_ground_truth(dali_data, gt_file)

# 2- while reading the dataset
dali_data = dali_code.get_the_DALI_dataset(dali_data_path, gt_file=gt_file)

NOTE 1: Please be sure you have the last ground truth version.

2- Getting the audio.

You can retrieve the audio for each annotation (if avilable) using the function dali_code.get_audio():

path_audio = 'full_path_to_store_the_audio'
errors = dali_code.get_audio(dali_info, path_audio, skip=[], keep=[])
errors -> ['dali_id', 'youtube_url', 'error']

This function can also be used to download a subset of the DALI dataset by providing the ids of the entries you either want to skip or to keep.

3- Working with DALI.

Annotations are in:

entry.annotations['annot']

and they are presented in two different formats: 'horizontal' or 'vertical'. You can easily change the format using the functions:

  entry.horizontal2vertical()
  entry.vertical2horizontal()

3.1- Horizontal.

In this format each level of granularity is stored individually. It is the default format.

entry.vertical2horizontal() --> 'Annot are already in a vertical format'
entry.annotations['type'] --> 'horizontal'
entry.annotations['annot'].keys() --> ['notes', 'lines', 'words', 'paragraphs']

Each level contains a list of annotation where each element has:

my_annot = entry.annotations['annot']['notes']
my_annot[0] --> {'text': 'wo', # the annotation itself.
                 'time': [12.534, 12.659], # the begining and end of the  segment in seconds.
                 'freq': [466.1637615180899, 466.1637615180899], # The range of frequency the text information is covering. At the lowest level, syllables, it corresponds to the vocal note.
                 'index': 0} # link with the upper level. For example, index 0 at the 'words' level means that that particular word below to first line ([0]). The paragraphs level has no index key.

3.1.1- Vizualizing an annotation file.

You can export the annotations of each individual level to a xml or text file to vizualize them with Audacity or AudioSculpt. The pitch information is only presented in the xml files for AudioSculpt.

    my_annot = entry.annotations['annot']['notes']
    path_save = 'my_save_path'
    name = 'my_annot_name'
    dali_code.write_annot_txt(my_annot, name, path_save)
    # import the txt file in your Audacity
    dali_code.write_annot_xml(my_annot, name, path_save)
    # import Rythm XML file in AudioSculpt

3.1.2- Examples.

This format is ment to be use for working with each level individually.

Example 1: recovering the main vocal melody.

Let's used the extra function dali_code.annot2vector() that transforms the annotations into a vector. There are two types of vector:

type='voice': each frame has a value 1 or 0 for voice or not voice.

type='melody': each frame has the freq value of the main vocal melody.

my_annot = entry.annotations['annot']['notes']
time_resolution = 0.014
# the value dur is just an example you should use the end of your audio file
end_of_the_song =  entry.annotations['annot']['notes'][-1]['time'][1] + 10
melody = dali_code.annot2vector(my_annot, end_of_the_song, time_resolution, type='melody')

NOTE: have a look to dali_code.annot2vector_chopping() for computing a vector chopped with respect to a given window and hop size.

Example 2: find the audio frames that define each paragraph.

Let's used the other extra function dali_code.annot2frames() that transforms time in seconds into time in frames.

  my_annot = entry.annotations['annot']['paragraphs']
  paragraphs = [i['time'] for i in dali_code.annot2frames(my_annot, time_resolution)]
  paragraphs --> [(49408, 94584), ..., (3080265, 3299694)]

NOTE: dali_code.annot2frames() can also be used in the vertical format but not dali_code.annot2vector().

3.2- Vertical.

In this format the different levels of granularity are hierarchically connected:

  entry.horizontal2vertical()
  entry.annotations['type'] --> 'vertical'
  entry.annotations['annot'].keys() --> ['hierarchical']
  my_annot = entry.annotations['annot']['hierarchical']

Each element of the list is a paragraph.

  my_annot[0] --> {'freq': [277.1826309768721, 493.8833012561241], # The range of frequency the text information is covering
                   'time': [12.534, 41.471500000000006], # the begining and end of the time segment.
                   'text': [line_0, line_1, ..., line_n]}

where 'text' contains all the lines of the paragraph. Each line follows the same format:

  lines_1paragraph = my_annot[0]['text']
  lines_1paragraph[0] --> {'freq': [...], 'time': [...],
                           'text': [word_0, word_1, ..., word_n]}

again, each word contains all the notes for that word to be sung:

  words_1line_1paragraph = lines_1paragraph[0]['text']
  words_1line_1paragraph[0] --> {'freq': [...], 'time': [...],
                                 'text': [note_0, note_1, ..., note_n]}

Only the deepest level directly has the text information.

  notes_1word_1line_1paragraph = words_1line_1paragraph[1]['text']
  notes_1word_1line_1paragraph[0] --> {'freq': [...], 'time': [...],
                                       'text': 'note text'}

You can always get the text at specific point with dali_code.get_text(), i.e:

  dali_code.get_text(lines_1paragraph) --> ['text word_0', 'text word_1', ..., text_word_n]
  # words in the first line of the first paragraph

  dali_code.get_text(my_annot[0]['text']) --> ['text word_0', 'text word_1', ..., text_word_n]
  # words in the first paragraph

3.2.2- Examples.

This organization is meant to be used for working with specific hierarchical blocks.

Example 1: working only with the third paragraph.

  my_paragraph = my_annot[3]['text']
  text_paragraph = dali_code.get_text(my_paragraph)

Additionally, you can easily retrieve all its individual information with the function dali_code.unroll():

  lines_in_paragraph, _ = dali_code.unroll(my_paragraph, depth=0, output=[])
  words_in_paragraph, _ = dali_code.unroll(my_paragraph, depth=1, output=[])
  notes_in_paragraph, _ = dali_code.unroll(my_paragraph, depth=2, output=[])

Example 2: working only with the first line of the third paragraph

  my_line = my_annot[3]['text'][0]['text']
  text_line = dali_code.get_text(my_line)
  words_in_line, _ = dali_code.unroll(my_line, depth=0, output=[])
  notes_in_line, _ = dali_code.unroll(my_line, depth=1, output=[])

4- Correcting Annotations.

Up to now, we are facing only global alignment problems. You can change this alignment by modifying the offset and frame rate parameters. The original ones are stored at:

  print(entry.annotations['annot_param'])
  {'offset': float(a_number), 'fr': float(a_number)}

If you find a better parameters set you can modified the annotations using the function dali_code.change_time():

  dali_code.change_time(entry, new_offset, new_fr)
  # The default new_offset and new_fr are entry.annotations['annot_param']

We encourage you to send us your parameters in order to improve DALI.

You can contact us at:

dali dot dataset at gmail dot com

This research has received funding from the French National Research Agency under the contract ANR-16-CE23-0017-01 (WASABI project)

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].

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