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toktts

toktts is an exploration in building a Text-to-Speech (TTS) system using a sequence-to-sequence transformer architecture with discrete tokens.

For some quick examples, see the example Notebook here: sample_from_model.ipynb

The goal is to create a system that is easy to play with, and with which it is easy to get interesting results, without an excessive GPU budget and without excessive hacking time. The following design decisions support the above simplicity goal:

  • Use a pure discrete token transformer architecture rather than directly generating mel/linear spectrograms or time domain waveforms. This is not a new idea, there are several projects that also use Meta's Encodec for this purpose. (TODO REFS) Directly working with tokens means that we don't have to worry about finicky details about losses in the spectral/time domain.
  • Use an encoder decoder architecture, rather than a purely decoder transformer in order to avoid the bookkeeping of having both text and audio tokens in the same sequence.
  • Using a pure transformer architecture allows us to more easily get a decent utilization of the GPU (provided that features are computed efficiently, which is the case due to caching and small sequence length) and hence a better use of our GPU budget.
  • Generate only the first two seconds of audio to save compute budget. toktts does not attempt to shorten the input text to match the smaller audio size, probably it would be a good idea to do so as it appears that short text inputs have low performance in fitted models.
  • Try to keep feature engineering simple, no separate preprocessing data script, all processing in a single machine, no phonemizer, also no input text tokenization (one character is one token).
  • Also there is no text normalization (e.g. 1 to one, it would be great to add it)

Setup

Some technical details about the implementation:

  • The model is an encoder-decoder architecture. The encoder encodes the input characters (no phonemizer) into a latent representation which the decoder consumes using cross-attention. The decoder models the audio tokens in a causal way.
  • The audio tokens are obtained using Meta's Encodec from the first two seconds of the audio waveforms. We use the lowest fidelity of encoding (1.5kbps) which encodes audio at a rate of 75Hz with two discrete tokens each out of a vocabulary of size 1024. We arrive at the 1.5kpbs bit rate by 75 * 2 * 10 which yields 1500 bits per second. Taking into account the two seconds of audio that we use, the TTS model needs to generate a sequence of 300 audio tokens.
  • To keep feature engineering simple, we prefer to use a large machine for generating datasets and use caching from Hugging Face's datasets package as much as possible.

Installation

A reasonable machine to train the models in this repository would require at least 16 CPU cores, 120Gb of disk space, 150Gb of RAM and a GPU like the RTX4090. The amount of RAM and CPU cores are required to allow fast generation of the training dataset. There seems to be a memory leak, possibly in Encodec. One can sidestep the need for RAM due to the leak using the shards option in the configuration.

Assuming a machine like the above, the model in config/two_secs_augmented/ex_small.yaml would train in about 7 hours with about 1 additional hour to generate the training and validation datasets.

One can create a conda training environment using conda-environment.yaml. It's important to have the right versions of packages, otherwise the dataset generation takes too long for a reason that I haven't yet figured out.

Running

Initially I used Google Cloud Platform but it turned out to be much cheaper to rent a server from https://vast.ai/.

After installation, to start a simple training, try:

python trainer.py --config config/basic/small_train.yaml

This invocation will download LJSpeech (~13K training examples) and generate datasets for a small amount of examples.

It is highly recommended to log training runs in wandb by modifying and using the template file in config/templates/wandb_TEMPLATE.yaml.

After running the trainer one can generate samples using parts of the training and validation datasets using the generation script:

python generate_samples.py --config config/basic/small_train.yaml

Alternatively one can use custom text passing the --text option or even better one can use the provided pipeline from within a Notebook, for example:

pipeline = text_to_speech_pipeline(model_path)
audio = pipeline("I am happy you are here", forward_params=forward_params)
IPython.display.Audio(audio["audio"], rate=audio["sampling_rate"])

See the example notebook for an example on how to set forward_params.

The model that is used in the example notebook was trained from config/two_secs_augmented/ex_small.yaml. The model was trained for 89 epochs (interrupted), uses an augmentation factor of 4 and a batch size of 64. This model produces interesting examples but unfortunately the augmentation appears to generate a faint clicking background that is absent without augmentation (and yes, augmentation prevents overfitting for that model).

Example Notebook

The example Notebook can be viewed here: sample_from_model.ipynb