voices/readme.md

Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions

Yinghao Aaron Li, Cong Han, Xilin Jiang, Nima Mesgarani

Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.

Paper: https://arxiv.org/abs/2301.08810

Audio samples: https://pl-bert.github.io/

Pre-requisites

1. Python >= 3.7
2. Clone this repository:

git clone https://github.com/yl4579/PL-BERT.git
cd PL-BERT

3. Create a new environment (recommended):

conda create --name BERT python=3.8
conda activate BERT
python -m ipykernel install --user --name BERT --display-name "BERT"

4. Install python requirements:

pip install pandas singleton-decorator datasets "transformers<4.33.3" accelerate nltk phonemizer sacremoses pebble

Preprocessing

Please refer to the notebook preprocess.ipynb for more details. The preprocessing is for English Wikipedia dataset only. I will make a new branch for Japanese if I have extra time to demostrate training on other languages. You may also refer to #6 for preprocessing in other languages like Japanese.

Trianing

Please run each cell in the notebook train.ipynb. You will need to change the line config_path = "Configs/config.yml" in cell 2 if you wish to use a different config file. The training code is in Jupyter notebook primarily because the initial epxeriment was conducted in Jupyter notebook, but you can easily make it a Python script if you want to.

Finetuning

Here is an example of how to use it for StyleTTS finetuning. You can use it for other TTS models by replacing the text encoder with the pre-trained PL-BERT.

1. Modify line 683 in models.py with the following code to load BERT model in to StyleTTS:

from transformers import AlbertConfig, AlbertModel

log_dir = "YOUR PL-BERT CHECKPOINT PATH"
config_path = os.path.join(log_dir, "config.yml")
plbert_config = yaml.safe_load(open(config_path))

albert_base_configuration = AlbertConfig(**plbert_config['model_params'])
bert = AlbertModel(albert_base_configuration)

files = os.listdir(log_dir)
ckpts = []
for f in os.listdir(log_dir):
    if f.startswith("step_"): ckpts.append(f)

iters = [int(f.split('_')[-1].split('.')[0]) for f in ckpts if os.path.isfile(os.path.join(log_dir, f))]
iters = sorted(iters)[-1]
        
checkpoint = torch.load(log_dir + "/step_" + str(iters) + ".t7", map_location='cpu')
state_dict = checkpoint['net']
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
    name = k[7:] # remove `module.`
    if name.startswith('encoder.'):
        name = name[8:] # remove `encoder.`
        new_state_dict[name] = v
bert.load_state_dict(new_state_dict)

nets = Munch(bert=bert,
  # linear projection to match the hidden size (BERT 768, StyleTTS 512)
  bert_encoder=nn.Linear(plbert_config['model_params']['hidden_size'], args.hidden_dim),
  predictor=predictor,
    decoder=decoder,
             pitch_extractor=pitch_extractor,
                 text_encoder=text_encoder,
                 style_encoder=style_encoder,
             text_aligner = text_aligner,
            discriminator=discriminator)

2. Modify line 126 in train_second.py with the following code to adjust the learning rate of BERT model:

# for stability
for g in optimizer.optimizers['bert'].param_groups:
    g['betas'] = (0.9, 0.99)
    g['lr'] = 1e-5
    g['initial_lr'] = 1e-5
    g['min_lr'] = 0
    g['weight_decay'] = 0.01

3. Modify line 211 in train_second.py with the following code to replace text encoder with BERT encoder:

            bert_dur = model.bert(texts, attention_mask=(~text_mask).int()).last_hidden_state
            d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
            d, _ = model.predictor(d_en, s, 
                                                    input_lengths, 
                                                    s2s_attn_mono, 
                                                    m)

line 257:

            _, p = model.predictor(d_en, s, 
                                                    input_lengths, 
                                                    s2s_attn_mono, 
                                                    m)

and line 415:

                bert_dur = model.bert(texts, attention_mask=(~text_mask).int()).last_hidden_state
                d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
                d, p = model.predictor(d_en, s, 
                                                    input_lengths, 
                                                    s2s_attn_mono, 
                                                    m)

4. Modify line 347 in train_second.py with the following code to make sure parameters of BERT model are updated:

            optimizer.step('bert_encoder')
            optimizer.step('bert')

The pre-trained PL-BERT on Wikipedia for 1M steps can be downloaded at: PL-BERT link.

The demo on LJSpeech dataset along with the pre-modified StyleTTS repo and pre-trained models can be downloaded here: StyleTTS Link. This zip file contains the code modification above, the pre-trained PL-BERT model listed above, pre-trained StyleTTS w/ PL-BERT, pre-trained StyleTTS w/o PL-BERT and pre-trained HifiGAN on LJSpeech from the StyleTTS repo.

References

• NVIDIA/NeMo-text-processing
• tomaarsen/TTSTextNormalization