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README.md

Graphcore

Deep Voice 3

This PopART application implements the Deep Voice 3 model for Text-To-Speech synthesis as described in this paper:

Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.

We also use the guided attention loss as described in this paper:

Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention.

Benchmarking

To reproduce the published Mk2 throughput benchmarks, please follow the setup instructions in this README, and then follow the instructions in README_Benchmarks.md

How to train the Deep Voice-3 model

  1. Prepare the environment.

    Install the Poplar SDK following the instructions in the Getting Started guide for your IPU system. Make sure to source the enable.sh scripts for Poplar and PopART.

  2. Setup a virtual environment

virtualenv venv -p python3.6
source venv/bin/activate
  1. Install the required pip packages:
pip install -r requirements.txt

Install the required apt packages. On Ubuntu:

sudo apt install $(< required_apt_packages.txt)
  1. Dataset: Currently, we use the VCTK dataset which is a multi-speaker dataset. For more details see: https://datashare.is.ed.ac.uk/handle/10283/3443

Download and unzip the dataset to a suitable location. The size of the downloaded file is big (around 11GB).

wget https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/VCTK-Corpus-0.92.zip
unzip VCTK-Corpus-0.92.zip -d VCTK-Corpus-0.92

Also, to download the Carnegie Mellon Pronouncing Dictionary, just run:

python3 -c "import nltk; nltk.download('cmudict')"
  1. Build custom ops to facilitate gradient-clipping
make all

If you want to do training or run tests without gradient-clipping, do not build the custom ops or remove it by doing make clean.

  1. Run the training program. Use the --data_dir option to specify the path to the data and use the --model_dir option to specify a path to save the trained model. For a default batch-size of 2:
python3 deep_voice_train.py --data_dir /path/to/dataset/ --model_dir /path/to/trained/model

The dataset path provided as input must be the parent folder of the VCTK-Corpus-0.92 folder to which the data was extracted. To use multiple IPUs in a data-parallel fashion for higher effective batch-sizes, one can use the --num_ipus and --replication-factor options. For e.g. to use two IPUs for a batch-size of 4, one can do:

python3 deep_voice_train.py --data_dir /path/to/dataset/ --model_dir /path/to/trained/model --num_ipus 2 --replication_factor 2 --batch_size 4

Here, the batch-size must be a multiple of the replication factor. One can also specifiy the IPU to use with the --select_ipu option. For e.g. to use the MultiIPU 18 for data-parallel training, use:

python3 deep_voice_train.py --data_dir /path/to/dataset/ --model_dir /path/to/trained/model --num_ipus 2 --replication_factor 2 --batch_size 4 --select_ipu 18

Synthesize audio using the forward pass of the training graph

One can synthesize audio from text by calling the forward pass of the training graph multiple times and simulating the auto-regressive process during inference. For truly auto-regressive (and efficient) synthesis, a separate graph has to be constructed and scripts for this will be added to this code-base soon. Meanwhile, one can synthesize audio by using the deep_voice_synthesis_ar_sim.py script and use the --sentence argument to specify the text to synthesize.

python3 deep_voice_synthesis_ar_sim.py --trained_model_file /path/to/trained/model.onnx --sentence "It is a beautiful day" --results_path /path/to/audio

Synthesized audio samples for the specified sentence in different speaker voices will be saved in the .wav format in the directory specified by --results_path. In addition, the output linear-scale spectrograms, mel-scale spectrograms and attention maps for each attention block will be saved to the same location in files with the .npz format.

Benchmarking with randomly generated data

To obtain throughput numbers for training, one can use the --generated_data option with the training script.

python3 deep_voice_train.py --data_dir TEST --model_dir TEST --generated_data --num_epochs 5

or

python3 deep_voice_train.py --data_dir TEST --model_dir TEST --generated_data --num_epochs 5 --num_ipus 2 --replication_factor 2 --batch_size 4

Run unit-tests for the Deep Voice model

To run unit-tests, simply do:

pytest test_popart_deep_voice.py

Options

Use --help to show the available options. Here are a few important options:

--batch-size the batch size used to build the training graph.

--num_epochs the number of epochs to run for training.

--num_ipus the number of IPUs for the session.

--replication_factor specifies the number of graph replicas to execute in parallel. The number of samples processed on each IPU will be (batch_size / replication_factor). Note that the number of IPUs must be equal to or a multiple of the replication factor.

--select_ipu specifies the ID of the IPU or MultiIPU to use for the session.

License

This application is licensed under the MIT license - see the LICENSE file at the top-level of this repository.

The code for this application is based on the Carnegie Mellon Pronouncing Dictionary. For license information, see here: http://svn.code.sf.net/p/cmusphinx/code/trunk/cmudict/00README_FIRST.txt

The VCTK dataset used here is licensed under the Creative Commons License: Attribution 4.0 International. See https://datashare.is.ed.ac.uk/bitstream/handle/10283/3443/license_text