Text-to-speech Python* Demo

The text to speech demo show how to run the ForwardTacotron and WaveRNN models or modified ForwardTacotron and MelGAN models to produce an audio file for a given input text file. The demo is based on https://github.com/seungwonpark/melgan, https://github.com/as-ideas/ForwardTacotron and https://github.com/fatchord/WaveRNN repositories.

How It Works

On startup, the demo application reads command-line parameters and loads four or three networks to the Inference Engine plugin. The demo pipeline reads text file by lines and divides every line to parts by punctuation marks. The heuristic algorithm chooses punctuation near to the some threshold by sentence length. When inference is done, the application outputs the audio to the WAV file with 22050 Hz sample rate.

Preparing to Run

The list of models supported by the demo is in <omz_dir>/demos/text_to_speech_demo/python/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO Inference Engine format (*.xml + *.bin).

An example of using the Model Downloader:

python3 <omz_dir>/tools/downloader/downloader.py --list models.lst

An example of using the Model Converter:

python3 <omz_dir>/tools/downloader/converter.py --list models.lst

Supported Models

  • forward-tacotron-duration-prediction
  • forward-tacotron-regression
  • wavernn-rnn
  • wavernn-upsampler
  • text-to-speech-en-0001-duration-prediction
  • text-to-speech-en-0001-generation
  • text-to-speech-en-0001-regression
  • text-to-speech-en-multi-0001-duration-prediction
  • text-to-speech-en-multi-0001-generation
  • text-to-speech-en-multi-0001-regression

NOTE: Refer to the tables Intel's Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.


Running the application with the -h option yields the following usage message:

usage: text_to_speech_demo.py [-h] -m_duration MODEL_DURATION -m_forward
[-m_upsample MODEL_UPSAMPLE] [-m_rnn MODEL_RNN]
[--upsampler_width UPSAMPLER_WIDTH]
[-m_melgan MODEL_MELGAN] [-s_id SPEAKER_ID]
[-a ALPHA]
-h, --help Show this help message and exit.
-m_duration MODEL_DURATION, --model_duration MODEL_DURATION
Required. Path to ForwardTacotron`s duration
prediction part (*.xml format).
-m_forward MODEL_FORWARD, --model_forward MODEL_FORWARD
Required. Path to ForwardTacotron`s mel-spectrogram
regression part (*.xml format).
-i INPUT, --input INPUT
Text file with text.
-o OUT, --out OUT Required. Path to an output .wav file
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, HDDL, MYRIAD or HETERO is acceptable. The
demo will look for a suitable plugin for device
specified. Default value is CPU
-m_upsample MODEL_UPSAMPLE, --model_upsample MODEL_UPSAMPLE
Path to WaveRNN`s part for mel-spectrogram upsampling
by time axis (*.xml format).
-m_rnn MODEL_RNN, --model_rnn MODEL_RNN
Path to WaveRNN`s part for waveform autoregression
(*.xml format).
--upsampler_width UPSAMPLER_WIDTH
Width for reshaping of the model_upsample in WaveRNN
vocoder. If -1 then no reshape. Do not use with FP16
-m_melgan MODEL_MELGAN, --model_melgan MODEL_MELGAN
Path to model of the MelGAN (*.xml format).
-s_id SPEAKER_ID, --speaker_id SPEAKER_ID
Ordinal number of the speaker in embeddings array for
multi-speaker model. If -1 then activates the multi-
speaker TTS model parameters selection window.
-a ALPHA, --alpha ALPHA
Coefficient for controlling of the speech time
(inversely proportional to speed).

Running the application with the empty list of options yields the usage message and an error message.

Example for Running with Arguments

Speech synthesis with ForwardTacotron and WaveRNN models

python3 text_to_speech_demo.py \
--input <path_to_file>/text.txt \
-o <path_to_audio>/audio.wav \
--model_duration <path_to_model>/forward_tacotron_duration_prediction.xml \
--model_forward <path_to_model>/forward_tacotron_regression.xml \
--model_upsample <path_to_model>/wavernn_upsampler.xml \
--model_rnn <path_to_model>/wavernn_rnn.xml

NOTE: You can use --upsampler_width parameter for this demo for the purpose of control width of the time axis in the input mel-spectrogram for the wavernn_upsampler network. This option can help you improve the speed of the pipeline inference on the long sentences.

Speech synthesis with text-to-speech-en-0001 models

python3 text_to_speech_demo.py \
-i <path_to_file>/text.txt \
-o <path_to_audio>/audio.wav \
-m_duration <path_to_model>/text-to-speech-en-0001-duration-prediction.xml \
-m_forward <path_to_model>/text-to-speech-en-0001-regression.xml \
-m_melgan <path_to_model>/text-to-speech-en-0001-generation.xml

Speech synthesis with multi-speaker text-to-speech-en-multi-0001 models

python3 text_to_speech_demo.py \
-i <path_to_file>/text.txt \
-o <path_to_audio>/audio.wav \
-s_id 19 \
-m_duration <path_to_model>/text-to-speech-en-multi-0001-duration-prediction.xml \
-m_forward <path_to_model>/text-to-speech-en-multi-0001-regression.xml \
-m_melgan <path_to_model>/text-to-speech-en-multi-0001-generation.xml

NOTE: s_id defines the style of the speaker utterance. You can choose it equal to -1 to activate the multi-speaker TTS model parameters selection window. This window provides an opportunity to choose the gender of the speaker, index number of the speaker or calculate PCA based speaker embedding. The s_id is available only for text-to-speech-en-multi-0001 models.

Demo Output

The application outputs WAV file with generated audio.

See Also