PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

DATASET refers to the names of datasets such as VCTK in the following documents.

You can install the Python dependencies with

pip3 install -r requirements.txt

Also, Dockerfile is provided for Docker users.

You have to download the pretrained models and put them in output/ckpt/DATASET/.

For a multi-speaker TTS, run

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --speaker_id SPEAKER_ID --restore_step RESTORE_STEP --mode single --dataset DATASET --ref_audio REF_AUDIO

to synthesize speech with the style of input audio at REF_AUDIO. The generated utterances will be put in output/result/.

Batch inference is also supported, try

python3 synthesize.py --source preprocessed_data/DATASET/val.txt --restore_step RESTORE_STEP --mode batch --dataset DATASET

to synthesize all utterances consuming themselves as a reference audio in preprocessed_data/DATASET/val.txt.

The pitch/volume/speaking rate of the synthesized utterances can be controlled by specifying the desired pitch/energy/duration ratios. For example, one can increase the speaking rate by 20 % and decrease the volume by 20 % by

python3 synthesize.py --text "YOUR_DESIRED_TEXT" --speaker_id SPEAKER_ID --restore_step RESTORE_STEP --mode single --dataset DATASET --ref_audio REF_AUDIO --duration_control 0.8 --energy_control 0.8

The supported datasets are

• VCTK: The CSTR VCTK Corpus includes speech data uttered by 110 English speakers (multi-speaker TTS) with various accents. Each speaker reads out about 400 sentences, which were selected from a newspaper, the rainbow passage and an elicitation paragraph used for the speech accent archive.
• Any of multi-speaker TTS dataset (e.g., LibriTTS) can be added following VCTK.

• For a multi-speaker TTS with external speaker embedder, download ResCNN Softmax+Triplet pretrained model of philipperemy's DeepSpeaker for the speaker embedding and locate it in ./deepspeaker/pretrained_models/.

• Run

  python3 prepare_align.py --dataset DATASET
  

  for some preparations.

  For the forced alignment, Montreal Forced Aligner (MFA) is used to obtain the alignments between the utterances and the phoneme sequences. Pre-extracted alignments for the datasets are provided here. You have to unzip the files in preprocessed_data/DATASET/TextGrid/. Alternately, you can run the aligner by yourself.

  After that, run the preprocessing script by

  python3 preprocess.py --dataset DATASET
  

Train your model with

python3 train.py --dataset DATASET

Use

tensorboard --logdir output/log

to serve TensorBoard on your localhost.

• RangeParameterPredictor is built with BiLSTM rather than a single linear layer with softplus() activation (it is however implemented and named as 'range_param_predictor_paper' in GaussianUpsampling).
• Use 32 batch size instead of 48 due to memory issues.
• Use log duration instead of normal duration.
• Follow FastSpeech2 for the preprocess of pitch and energy.
• Two options for embedding for the multi-speaker TTS setting: training speaker embedder from scratch or using a pre-trained philipperemy's DeepSpeaker model (as STYLER did). You can toggle it by setting the config (between 'none' and 'DeepSpeaker').
• DeepSpeaker on VCTK dataset shows clear identification among speakers. The following figure shows the T-SNE plot of extracted speaker embedding.

• For vocoder, HiFi-GAN and MelGAN are supported.

@misc{lee2021daft_exprt,
  author = {Lee, Keon},
  title = {Daft-Exprt},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/keonlee9420/Daft-Exprt}}
}

• keonlee9420's WaveGrad2 for GaussianUpsampling and RangeParameterPredictor
• keonlee9420's STYLER for the (domain) adversarial training of SpeakerClassifier
• keonlee9420's StyleSpeech for reference auido interface
• FiLM: Visual Reasoning with a General Conditioning Layer
• TADAM: Task dependent adaptive metric for improved few-shot learning