Lyrics Generator

This is a small experiment in generating lyrics with a recurrent neural network, trained with Keras and Tensorflow 2.

It works in the browser with Tensorflow.js! Try it here.

The model can be trained at both word- and character level which each have their own pros and cons.

Pre-trained models

A few pre-trained models can be found here.

Train the model

Install dependencies

Requires Python 3.7+.

pip install -r requirements.txt

The requirement file has been reduced in size so if any of the scripts fail, just install the missing packages :-)

Get the data

Create a song dataset. See "Create your own song dataset" below.
- Save the dataset as songdata.csv file in a data sub-directory.
- Alternatively, you can name it anything you like and use the --songdata-file parameter when training.
Download the Glove embeddings
- Save the glove.6B.50d.txt file in a data sub-directory.
- Alternatively, you can create your a word2vec embedding (see below)

Create your own song dataset

The code expects an input dataset to be stored at date/songdata.csv by default (this can be changed in config.py or via CLI parameter --songdata-file).

The file should be in CSV format with the following columns (case sensitive):

artist
- A string, e.g. "The Beatles"
text
- A string with the entire lyrics for one song, including newlines.

You can have any number of other columns, they will just be ignored.

A sample dataset with a simple text is provided in sample.csv. To test things are working, you can train using that file:

python -m lyrics.train --songdata-file sample.csv --early-stopping-patience 50 --artists '*'

Dataset suggestions

Download billboardHot100_1999-2019.csv file from the Data on Songs from Billboard 1999-2019
- Put it into the data/ folder and run python scripts/billboard.py script which will prepare the file for training.
- (Optional) pip install fasttext to detect language. If it's not installed, language is not detected.

(Optional) Create a word2vec embedding matrix

If you have the songdata.csv file from above, you can simply create the word2vec vectors like this:

python -m lyrics.embedding --name-suffix _myembedding

This will create word2vec_myembedding.model and word2vec_myembedding.txt files in the default data directory data/. Use -h to see other options like artists and custom songdata file.

Run the training

python -m lyrics.train -h

This command by default takes care of all the training. Warning: it takes a very long time on a normal CPU!

Check -h for options. For example, if you want to use a different embedding than the glove embedding:

python -m lyrics.train --embedding-file ./embeddings.txt

The embeddings are still assumed to be 50 dimensional.

The output model and tokenizer is stored in a timestamped folder like export/2020-01-01T010203 by default.

Note: During experimentation, I found that raising the batch size to something like 2048 speeds up processing, but it depends on your hardware resources whether this is feasible of course.

Training on GPU

I have found it easier to train on GPU by using Docker and nvidia-docker, rather than try to install CUDA myself. To do this, first make sure you have nvidia-docker set up correct, and then:

docker build -t lyrics-gpu .
docker run --rm -it --gpus all -v $PWD:/tf/src -u $(id -u):$(id -g) lyrics-gpu bash

Then run the normal commands from there, e.g. python -m lyrics.train.

Tip: You might want to use the parameter --gpu-speedup! Just note that this will disable the Tensorflowjs compatibility, regardless of whether you have set the --tfjs-compatible flag.

Tip: If you get a cryptic Tensorflow error like errors_impl.CancelledError: [_Derived_]RecvAsync is cancelled. while training on GPU, try pre-pending the train command with TF_FORCE_GPU_ALLOW_GROWTH=true, e.g.:

TF_FORCE_GPU_ALLOW_GROWTH=true python -m lyrics.train --transform-words --num-lines-to-include=10 --artists '*' --gpu-speedup

Use transformer network

To use the universal sentence encoder or BERT architecture use the --transformer-network parameter:

python -m lyrics.train --transformer-network [use|bert]

Note: These models are not going to work in Tensorflow JS currently, so it should only be used from the command-line.

Note: I have not been able to get any result with BERT. Only included for illustration purposes.

Character-level predictions

In the default training mode, the model predicts the next word, given a sequence of words. Changing the model to predict the next character can be done using the --char-level flag.

python -m lyrics.train --char-level

Create new lyrics

python -m cli lyrics model.h5 tokenizer.pickle

Try python -m cli lyrics -h to find out more. For example, using --randomness and --text can be recommended.

If you want to add newlines to the seed text via --text, you need to add a space on each side. For example, this works in Bash:

--text $'you are my fire \n the one desire'

Export to Tensorflow JS (used for the app)

Note: Make sure to use the --tfjs-compatible flag during training!

python -m cli export model.h5 tokenizer.pickle

This creates a sub-directory export/js with the relevant files (can be used for the app).

Single-page "app" for creating lyrics

Note: Make sure to use the --tfjs-compatible flag during training!

The lyrics-tfjs sub-directory has a simple web-page that can be used to create lyrics in the browser. The code expects data to be found in a data/ sub-directory. This includes the words.json file, model.json and any extra files generated by the Tensorflow export.

Demo.

Development

Make sure to get all dependencies:

pip install -r requirements_dev.txt

Testing

python -m pytest --cov=lyrics tests/

dlebech / lyrics-generator