XPhoneBERT : A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech

XPhoneBERT is the first pre-trained multilingual model for phoneme representations for text-to-speech(TTS). XPhoneBERT has the same model architecture as BERT-base, trained using the RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100 languages and locales. Experimental results show that employing XPhoneBERT as an input phoneme encoder significantly boosts the performance of a strong neural TTS model in terms of naturalness and prosody and also helps produce fairly high-quality speech with limited training data.

The general architecture and experimental results of XPhoneBERT can be found in our INTERSPEECH 2023 paper:

@inproceedings{xphonebert,
title     = {{XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech}},
author    = {Linh The Nguyen and Thinh Pham and Dat Quoc Nguyen},
booktitle = {Proceedings of the 24th Annual Conference of the International Speech Communication Association (INTERSPEECH)},
year      = {2023}
}

Please CITE our paper when XPhoneBERT is used to help produce published results or is incorporated into other software.

Using XPhoneBERT with transformers

Installation

• Install transformers with pip: pip install transformers, or install transformers from source.

• Install text2phonemesequence: pip install text2phonemesequence
  Our text2phonemesequence package is to convert text sequences into phoneme-level sequences, employed to construct our multilingual phoneme-level pre-training data. We build text2phonemesequence by incorporating the CharsiuG2P and the segments toolkits that perform text-to-phoneme conversion and phoneme segmentation, respectively.

• Notes

  • Initializing text2phonemesequence for each language requires its corresponding ISO 639-3 code. The ISO 639-3 codes of supported languages are available at HERE.

  • text2phonemesequence takes a word-segmented sequence as input. And users might also perform text normalization on the word-segmented sequence before feeding into text2phonemesequence. When creating our pre-training data, we perform word and sentence segmentation on all text documents in each language by using the spaCy toolkit, except for Vietnamese where we employ the VnCoreNLP toolkit. We also use the text normalization component from the NVIDIA NeMo toolkit for English, German, Spanish and Chinese, and the Vinorm text normalization package for Vietnamese.

Pre-trained model

Model	#params	Arch.	Max length	Pre-training data
vinai/xphonebert-base	88M	base	512	330M phoneme-level sentences from nearly 100 languages and locales

Example usage

from transformers import AutoModel, AutoTokenizer
from text2phonemesequence import Text2PhonemeSequence

# Load XPhoneBERT model and its tokenizer
xphonebert = AutoModel.from_pretrained("vinai/xphonebert-base")
tokenizer = AutoTokenizer.from_pretrained("vinai/xphonebert-base")

# Load Text2PhonemeSequence
# text2phone_model = Text2PhonemeSequence(language='eng-us', is_cuda=True)
text2phone_model = Text2PhonemeSequence(language='jpn', is_cuda=True)

# Input sequence that is already WORD-SEGMENTED (and text-normalized if applicable)
# sentence = "That is , it is a testing text ."  
sentence = "これ は 、 テスト テキスト です ."

input_phonemes = text2phone_model.infer_sentence(sentence)

input_ids = tokenizer(input_phonemes, return_tensors="pt")

with torch.no_grad():
    features = xphonebert(**input_ids)

License

MIT License

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