/probing-TTS-models

Link to paper: https://www.isca-speech.org/archive/SpeechProsody_2020/pdfs/51.pdf

Primary LanguageJupyter NotebookBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Probing the phonetic and phonological knowledge of tones in Mandarin TTS models

link to pdf

Data

Audio samples can be found here: online demo

All synthesized stimuli can be accessed here.

Traning data can be found here.

Demo

Online Colab demo.

You can directly run the TTS models (Tacotron2 and WaveGlow) on Google Colab (with a powerful GPU).
Open In Colab

Runing locally.

torch == 1.1.0

  1. Download pre-trained Mandarin models at this folder.
  2. Download pre-trained Chinese BERT (BERT-wwm-ext, Chinese).
  3. Run ``inference_bert.ipynb''
    Or:
    Use the following command line.
python synthesize.py --text ./stimuli/tone3_stimuli --use_bert --bert_folder path_to_bert_folder 
--tacotron_path path_to_pre-trained_tacotron2 --waveglow_path path_to_pre-trained_waveglow 
--out_dir path_output_dir

Note. The current implementation is based on the Nvidia's public implementation of Tacotron2 and Waveglow

References

This project has benefited immensely from the following works.
Pre-Trained Chinese BERT with Whole Word Masking
Tacotron 2 - PyTorch implementation with faster-than-realtime inference
WaveGlow: a Flow-based Generative Network for Speech Synthesis
A Demo of MTTS Mandarin/Chinese Text to Speech FrontEnd
Open-source mandarin speech synthesis data
只用同一声调的字可以造出哪些句子?