wavernn (composite)

Use Case and High-Level Description

WaveRNN is a model for the text-to-speech task originally trained in PyTorch* then converted to ONNX* format. The model was trained on LJSpeech dataset. WaveRNN performs waveform regression from mel-spectrogram. For details see paper, repository.

ONNX Models

We provide pre-trained models in ONNX format for user convenience.

Steps to Reproduce PyTorch to ONNX Conversion

Model is provided in ONNX format, which was obtained by the following steps.

  1. Clone the original repository

git clone https://github.com/as-ideas/ForwardTacotron
cd ForwardTacotron

  1. Checkout the commit that the conversion was tested on:

git checkout 78789c1aa845057bb2f799e702b1be76bf7defd0

  1. Follow README.md and preprocess LJSpeech dataset.

  2. Copy provided script wavernn_to_onnx.py to ForwardTacotron root directory, and apply git patch 0001-Added-batch-norm-fusing-to-conv-layers.patch.

  3. Download WaveRNN model from https://github.com/fatchord/WaveRNN/tree/master/pretrained/ and extract in to pre-trained directory.

mkdir pretrained
wget https://raw.githubusercontent.com/fatchord/WaveRNN/master/pretrained/ljspeech.wavernn.mol.800k.zip
unzip ljspeech.wavernn.mol.800k.zip -d pretrained && mv pretrained/latest_weights.pyt pretrained/wave_800K.pyt

  1. Run provided script for conversion WaveRNN to onnx format

python3 wavernn_to_onnx.py --mel <path_to_preprocessed_dataset>/mel/LJ008-0254.npy --voc_weights pretrained/wave_800K.pyt --hp_file hparams.py --batched

Note: by the reason of autoregressive nature of the network, the model is divided into two parts: wavernn_upsampler.onnx, wavernn_rnn.onnx. The first part expands feature map by the time dimension, and the second one iteratively processes every column in expanded feature map.

Composite model specification

Metric

Value

Source framework

PyTorch*

Accuracy

Subjective

wavernn-upsampler model specification

The wavernn-upsampler model accepts mel-spectrogram and produces two feature map: the first one expands mel-spectrogram in one step using Upsample layer and sequence of convolutions, and the second one expands mel-spectrogram in three steps using sequence of Upsample layers and of convolutions.

Metric

Value

GOPs

0.37

MParams

0.4

Input

Mel-spectrogram, name: mels, shape: 1, 200, 80, format: B, T, C, where:

  • B - batch size

  • T - time in mel-spectrogram

  • C - number of mels in mel-spectrogram

Output

  1. Processed mel-spectrogram, name: aux, shape: 1, 53888, 128, format: B, T, C, where:

    • B - batch size

    • T - time in audio (equal to time in mel spectrogram * hop_length)

    • C - number of features in processed mel-spectrogram.

  2. Upsampled and processed (by time) mel-spectrogram, name: upsample_mels, shape: 1, 55008, 80, format: B, T', C, where:

    • B - batch size

    • T' - time in audio padded with number of samples for crossfading between batches

    • C - number of mels in mel-spectrogram

wavernn-rnn model specification

The wavernn-rnn model accepts two feature maps from wavernn-upsampler and produces parameters for mixture of logistics distribution that is used for audio regression by B samples per forward step, where B is batch size.

Metric

Value

GOps

0.06

MParams

3.83

Input

  1. Time slice in upsampled_mels, name: m_t, shape: B, 80

  2. Time/space slice in aux, name: a1_t, shape: B, 32, where second dimension is 32 = aux.shape[1] / 4

  3. Time/space slice in aux, name: a2_t, shape: B, 32, where second dimension is 32 = aux.shape[1] / 4

  4. Time/space slice in aux, name: a3_t, shape: B, 32, where second dimension is 32 = aux.shape[1] / 4

  5. Time/space slice in aux, name: a4_t, shape: B, 32, where second dimension is 32 = aux.shape[1] / 4

  6. Hidden state for GRU layers in autoregression, name: h1.1, shape: B, 512

  7. Hidden state for GRU layers in autoregression, name: h2.1, shape: B, 512

  8. Previous prediction for autoregression (initially equal to zero), name: x, shape: B, 1

Note: B - batch size.

Output

  1. Hidden state for GRU layers in autoregression, name: h1, shape: B, 512

  2. Hidden state for GRU layers in autoregression, name: h2, shape: B, 512

  3. Parameters for mixture of logistics distribution, name: logits, shape: B, 30. Can be divided to parameters of mixture of logistic distributions: probabilities = logits[:, :10], means = logits[:, 10:20], scales = logits[:, 20:30]

Note: B - batch size.

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: