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Audio captioning DCASE 2020 baseline system

Welcome to the repository of the audio captioning baseline system for the DCASE challenge of 2020.

Here you can find the complete code of the baseline system, consisting of:

  1. the caption evaluation part,
  2. the dataset pre-processing/feature extraction part,
  3. the data handling part for Pytorch library, and
  4. the deep neural network (DNN) method part

Parts 1, 2, and 3, also exist in separate repositories. Caption evaluation tools for audio captioning can also be found here. Code for dataset pre-processing/feature extraction for Clotho dataset can also be found here. Finally, code for handling the Clotho data (i.e. extracted features and one-hot encoded words) for PyTorch library (i.e. PyTorch DataLoader for Clotho data) can also be found here.

This repository is maintained by K. Drossos.

Table of contents

  1. Too long - Didn't read (TL-DR)
  2. Setting up the code
    1. Using conda
    2. Using PIP
  3. Preparing the data
    1. Getting the data from Zenodo
    2. Settings up the data
  4. Use the baseline system
    1. Create the dataset
    2. Conduct an experiment
    3. Use the pre-trained model
    4. Evaluate predictions
  5. Explanation of settings
    1. Main settings
    2. Settings for directories and files
    3. Settings for the creation of the dataset
    4. Settings for the baseline model
    5. Settings for the baseline method

Too long - Didn't read (TL-DR)

If you are familiar with most of the stuff and you want to use this system fast as possible, do the following:

  1. Install all dependencies from the corresponding files.
  2. Make sure that your system has Java installed and enabled.
  3. Download the data from Zenodo and place them in the data directory.
  4. Run the baseline system.

If you want or need a bit more details, then read the following sections.

Setting up the code

To start using the audio captioning DCASE 2020 baseline system, firstly you have to set-up the code. Please note bold that the code in this repository is tested with Python 3.7.

To set-up the code, you have to do the following:

  1. Clone this repository.
  2. Use either pip or conda to install dependencies

Use the following command to clone this repository at your terminal:

$ git clone [email protected]:audio-captioning/dacse-2020-baseline.git

The above command will create the directory dacse-2020-baseline and populate it with the contents of this repository. The dacse-2020-baseline directory will be called root directory for the rest of this README file.

For installing the dependencies, there are two ways. You can either use conda or pip.

Using conda for installing dependencies

To use conda, you can issue the following command at your terminal (when you are in the root directory):

$ conda create --name audio-captioning-baseline --file requirements_conda.yaml

The above command will create a new environment called audio-captioning-baseline, which will have set-up all the dependencies for the audio captioning DCASE 2020 baseline. To activate the audio-captioning-baseline environment, you can issue the following command"

$ conda activate audio-captioning-baseline

Now, you are ready to proceed to the following steps.

Using pip for installing dependencies

If you do not use anaconda/conda, you can use the default Python package manager to install the dependencies of the audio captioning DCASE 2020 baseline. To do so, you have to issue the following command at the terminal (when you are inside the root directory):

$ pip install -r requirements_pip.txt

The above command will install the required packages using pip. Now you are ready to go to the following steps.

Preparing the data

After setting-up the code for the audio captioning DCASE 2020 baseline system, you have to obtain the Clotho dataset, place it to the proper directory, and do the feature extraction.

Getting the data from Zenodo

Clotho dataset is freely available online at the Zenodo platform. You can find Clotho at DOI

You should download all .7z files and the .csv files with the captions. That is, you have do download the following files from Zenodo:

  1. clotho_audio_development.7z
  2. clotho_audio_evaluation.7z
  3. clotho_captions_development.csv
  4. clotho_captions_evaluation.csv

After downloading the files, you should place them in the data directory, in your root directory.

Setting up the data

You should create two directories in your data directory:

  1. The first is called clotho_audio_files, and
  2. second is called clotho_csv_files.

Then, you have to expand the 7z files. There are many options on how to do this. We do not want to promote different software and/or packages, so you can just search on Google about how to expand 7zip files at your operating system.

After you expand the 7z files, you should have two directories created. The first is development and it will be created by teh clotho_audio_development.7z file. The second is evaluation, and it will be created by the clotho_audio_evaluation.7z file. Finally, you should have the following files and directories at your root/data directory:

  1. development directory
  2. evaluation directory
  3. clotho_captions_development.csv file
  4. clotho_captions_evaluation.csv file

The development directory contains 2163 audio files and the evaluation directory 1045 audio files. You should move the development and evaluation directories in the data/clotho_audio_files directory, and the clotho_captions_development.csv and clotho_captions_evaluation.csv files in the data/clotho_csv_files directory. Thus, there should be the following structure in your data directory:

data/
 | - clotho_audio_files/
 |   | - development/
 |   | - evaluation/
 | - clotho_csv_files/
 |   |- clotho_captions_development.csv
 |   |- clotho_captions_evaluation.csv

Now, you can use the baseline system to extract the features and create the dataset.

Use the baseline system

This baseline system implements all the necessary processes in order to use the Clotho data, optimize a deep neural network (DNN), and predict and evaluate captions. Each process has
some corresponding settings that can be modified in order to fine tune the baseline system.

In the following subsection, the default settings will be used.

Create the dataset

To create the dataset, you can either run the script processes/dataset.py using the command:

$ python processes/dataset.py

or run the baseline system using the main.py script. In any case, the dataset creation will start.

The dataset creation is a lengthy process, mainly due to the checking of the data. That is, the dataset creation has two steps:

  1. Firstly a split is created (e.g. development or evaluation), and then
  2. the data for the split are validated.

You can select if you want to have the validation of the data by altering the validate_dataset parameter at the settings/dataset_creation.yaml file.

The result of the dataset creation process will be the creation of the directories:

  1. data/data_splits,
  2. data/data_splits/development,
  3. data/data_splits/evaluation, and
  4. data/pickles

The directories in data/data_splits have the input and output examples for the optimization and assessment of the baseline DNN. The data/pickles directory holds the pickle files that have the frequencies of the words and characters (so one can use weights in the objective function) and the correspondence of words and characters with indices.

Note bold: Once you have created the dataset, there is no need to create it every time. That is, after you create the dataset using the baseline system, then you can set

workflow:
  dataset_creation: No

at the settings/main_settings.yaml file.

Conduct an experiment

To conduct an experiment using the baseline DNN, you can use the main.py script. In case that you do have previously created the input/output examples (using the above mentioned procedure), then you can skip the dataset creation by altering the value of the dataset_creation in the settings/main_settings.yaml file. To use the main.py script, you can issue the command:

$ python main.py

Alternatively, you can use the process/method.py by:

$ python processes/method.py

The above commands will start the process of optimizing the baseline DNN, using the data that were created in the create the dataset section.

Use the pre-trained model

To use the pre-trained model, you have first to obtain the pre-trained weights. The pre-trained weights are freely available online at Zenodo DOI

Evaluate predictions

Note bold: To use the caption evaluate tools you need to have Java installed and enabled.

To evaluate the predictions, you have first to have a optimized (i.e. trained) model (i.e. a DNN). You can obtain this DNN directly from training process (i.e. you do first training and then evaluation) or you can use some pre-trained weights.

To use some pre-trained weights, you have to specify the name of the file having the weights at the settings/dirs_and_files.yaml file. Also, you have to indicate that you will use a pre-trained model (at the settings/model_baseline.yaml file) and indicate that you want to do evaluation of the DNN (at the settings/method_baseline.yaml file).

Please note bold: Before being able to run the code for the evaluation of the predictions, you have first to run the script get_stanford_models.sh in the coco_caption directory.

Explanation of settings

There are different settings for the baseline system, associated with the creation of the dataset, the data, the outputs of the baseline system, and (of course) the optimization of the baseline DNN.

All these settings can be found in the settings directory. This directory has (by default) the following files:

  1. main_settings.yaml
  2. dirs_and_files.yaml
  3. dataset_creation.yaml
  4. model_baseline.yaml
  5. method_baseline.yaml

The parameters in the above .yaml files are explained in the following sections.

Main settings

In the settings/main_settings.yaml file, you can find the following settings:

workflow:
  dataset_creation: Yes
  dnn_training: yes
  dnn_evaluation: yes
dataset_creation_settings: !include dataset_creation.yaml
feature_extraction_settings: !include feature_extraction.yaml
dnn_training_settings: !include method_baseline.yaml
dirs_and_files: !include dirs_and_files.yaml

The settings at the workflow block, correspond to the different processes that the baseline system can do; the creation of the dataset (dataset_creation), the optimization of the DNN (dnn_training), and the evaluation of captions (dnn_evaluation). By indicating a yes or a no, you can switch on (with yes) or off (with no) the processes. For example, the

workflow:
      dataset_creation: no
      dnn_training: yes
      dnn_evaluation: no

means that the baseline system will not create the dataset and will not evaluate captions, but it will do the optimization of the DNN.

The rest settings serve to indicate the files that hold the settings for each of the processes. For example, the dataset_creation.yaml file holds the settings for the dataset creation. An exception is the dirs_and_files field, which indicates which file holds the settings for inputs and outputs of the baseline system.

Settings for directories and files

The settings/dirs_and_files.yaml file, holds the following settings:

root_dirs:
  outputs: 'outputs'
  data: 'data'
dataset:
  development: &dev 'development'
  evaluation: &eva 'evaluation'
  features_dirs:
    output: 'data_splits'
    development: *dev
    evaluation: *eva
  audio_dirs:
    downloaded: 'clotho_audio_files'
    output: 'data_splits_audio'
    development: *dev
    evaluation: *eva
  annotations_dir: 'clotho_csv_files'
  pickle_files_dir: 'pickles'
  files:
    np_file_name_template: 'clotho_file_{audio_file_name}_{caption_index}.npy'
    words_list_file_name: 'words_list.p'
    words_counter_file_name: 'words_frequencies.p'
    characters_list_file_name: 'characters_list.p'
    characters_frequencies_file_name: 'characters_frequencies.p'
model:
  model_dir: 'models'
  checkpoint_model_name: 'dcase_model_baseline.pt'
  pre_trained_model_name: 'dcase_model_pre_trained.pytorch'
logging:
  logger_dir: 'logging'
  caption_logger_file: 'captions_baseline.txt'

These are the necessary settings, to specify the input and output directories for the baseline system. Specifically, the root_dirs has the root directories for the outputs (outputs) and the data (data). The root directory of the outputs will be used in order to output the checkpoints of the baseline DNN and the logging files. The root directory for the data will be used as the root directory where the data are, e.g. the data (at root/data) directory mentioned in section setting up the data.

The dataset has the directory and file names used when creating and accessing the dataset files. The development and evaluation are the name of the directories that will hold the corresponding (in each case) development and evaluation (respectively) data. These names can be overridden in the corresponding entries (for example, in the dataset/audio_dirs for the audio directories).

The dataset/features_dirs has the directory names that:

  • parent directory for the ready-to-use features - output
  • the development features - development
  • the evaluation features - evaluation

The dataset/audio has the directory names that:

  • the downloaded audio will be - downloaded
  • the dataset files (i.e. the input/output examples) will be - output
  • the name of the directory that will hold the development data - development
  • the name of the directory that will hold the evaluation data - evaluation

The annotations_dir and pickle_files_dir hold the names of the directories where the csv files will be (annotations_dir) and where the pickle files will be placed (pickle_files_dir).

The files entry at the dataset block, has:

  • the file names of the pickle files
    • words_list_file_name,
    • words_counter_file_name,
    • characters_list_file_name, and
    • characters_frequencies_file_name)
  • and the template string of the file name of the input/output examples of the dataset (np_file_name_template).

The model entry, has:

  • the name of the directory (in the root_dirs/outputs) where the checkpoints of the baseline DNN will be saved - model_dir,
  • the template file name of the checkpoint file - checkpoint_model_name, and
  • the file name that the baseline DNN will look as pre-trained model - pre_trained_model_name.

Finally, the logging entry has:

  • the directory where the logging files will be placed - logger_dir,
  • and the base file name of the logging - caption_logger_file.

Settings for the creation of the dataset

The file settings/dataset_creation.yaml has:

workflow:
  create_dataset: Yes
  validate_dataset: No
annotations:
  development_file: 'clotho_captions_development.csv'
  evaluation_file: 'clotho_captions_evaluation.csv'
  audio_file_column: 'file_name'
  captions_fields_prefix: 'caption_{}'
  use_special_tokens: Yes
  nb_captions: 5
  keep_case: No
  remove_punctuation_words: Yes
  remove_punctuation_chars: Yes
  use_unique_words_per_caption: No
  use_unique_chars_per_caption: No
audio:
  sr: 44100
  to_mono: Yes
  max_abs_value: 1.

The workflow block is to indicate the execution or not of the dataset creation and validation. That is:

  • if the dataset will be created - create_dataset, and
  • if the data for each split will be validated (this is a lengthy process) - validate_dataset

The annotations block holds the settings needed for accessing and processing the annotations (i.e. the csv files) of Clotho. That is:

  • the name of the file holding the annotations of the development split - development_file
  • the name of the file holding the annotations of the evaluation split - evaluation_file
  • the name of the column in the annotations file that has the file name of the corresponding audio file - 'audio_file_column'
  • the prefix of the column name of the columns (at the csv files) that have the captions - captions_fields_prefix
  • indication of the special tokens (i.e. <sos> and <eos>) will be used - use_special_tokens
  • the amount of captions per each audio file - nb_captions
  • indication if the letter case. i.e. keep capital letters as capitals (Yes) or turn them to small case (No) will be kept - keep_case
  • if the punctuation will be removed from the word tokens - remove_punctuation_words
  • if the punctuation will be removed from the character tokens - remove_punctuation_chars
  • take into account unique words per audio file when counting the frequency of appearance of each word - use_unique_words_per_caption
  • take into account unique characters per audio file when counting the frequency of appearance of each character - use_unique_chars_per_caption

The audio block holds settings for processing the audio data:

  • the sampling frequency that the audio data will be resampled (if needed) to - sr
  • indication if the audio files should be turned to mono - to_mono
  • maximum absolute value for normalizing the audio data - max_abs_value

Settings for the baseline model

The file settings/model_baseline.yaml holds the settings for the baseline DNN:

use_pre_trained_model: No
encoder:
  input_dim_encoder: 64
  hidden_dim_encoder: 256
  output_dim_encoder: 256
  dropout_p_encoder: .25
decoder:
  output_dim_h_decoder: 256
  nb_classes:  # Empty, to be filled automatically.
  dropout_p_decoder: .25
  max_out_t_steps: 22

The use_pre_trained_model flag indicates if a pre-trained model will be used. If this flag is set to Yes, then the name of the file with the weights of the pre-trained model has to be specified in the settings/dirs_and_files.yaml file.

The encoder block has the settings for the encoder of the baseline DNN:

  • the input dimensionality to the first layer of the encoder - input_dim_encoder
  • the hidden output dimensionality of the first and second layers of the encoder - hidden_dim_encoder
  • the output dimensionality of the third layer of the encoder - output_dim_encoder
  • the dropout probability for the encoder - dropout_p_encoder

Similarly, the decoder block holds the settings for the decoder of the baseline DNN:

  • the output dimensionality of the RNN of the decoder - output_dim_h_decoder
  • the amount of classes for the classifier (it is filled automatically by the baseline system) - nb_classes
  • the dropout probability for the decoder - dropout_p_decoder
  • the maximum output time-steps for the decoder - max_out_t_steps

Settings for the baseline method

The file settings/method_baseline.yaml holds the settings for the training and evaluation procedure of the baseline DNN:

model: !include model_baseline.yaml
data:
  input_field_name: 'features'
  output_field_name: 'words_ind'
  load_into_memory: No
  batch_size: 16
  shuffle: Yes
  num_workers: 0
  drop_last: Yes
training:
  nb_epochs: 300
  patience: 10
  loss_thr: !!float 1e-4
  optimizer:
    lr: !!float 1e-4
  grad_norm:
    value: !!float 1.
    norm: 2
  force_cpu: No
  text_output_every_nb_epochs: !!int 10
  nb_examples_to_sample: 100

The model specifies the file where the settings of the baseline DNN are (i.e. the settings/model_baseline.yaml file).

The data block has the settings for handling the data at the training/evaluation processes:

  • the name of the field of the numpy object that holds the input values - input_field_name
  • the name of the field of the numpy object that holds the output/target values - output_field_name
  • indication if the whole dataset should be loaded into the memory during training/evaluation processes - load_into_memory
  • the size of the batch - batch_size
  • indication for shuffling the training data - shuffle
  • the amount of workers that the PyTorch DataLoader will use - num_workers
  • indication if the last (incomplete) batch will be used or not - drop_last

The training block holds settings for the training process:

  • the maximum amount of epochs - nb_epochs
  • the amount of epochs for patience - patience
  • the threshold at the loss for considering that the loss is the same or changed - loss_thr
  • the settings for the optimizer (just the learning rate) - optimizer
  • settings for clipping the gradient norm - grad_norm
  • indication if the training should necessarily be on the CPU (e.g. for debugging) - force_cpu
  • indication of every how many epochs there should be an output of predicted captions - text_output_every_nb_epochs
  • how many examples to use for outputting the captions - nb_examples_to_sample