Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning

Overview

Unet-TTS: Improving Unseen Speaker and Style Transfer in One-shot Voice Cloning

MIT License

English | 中文

Now we provide inferencing code and pre-training models. You could generate any text sounds you want.

The model training only uses the corpus of neutral emotion, and does not use any strongly emotional speech.

There are still great challenges in out-of-domain style transfer. Limited by the training corpus, it is difficult for the speaker-embedding or unsupervised style learning (like GST) methods to imitate the unseen data.

With the help of Unet network and AdaIN layer, our proposed algorithm has powerful speaker and style transfer capabilities.

Infer code or Colab notebook

Demo results

Paper link


😄 The authors are preparing simple, clear, and well-documented training process of Unet-TTS based on Aishell3. It contains:

  • MFA-based duration alignment
  • Multi-speaker TTS with speaker_embedding-Instance-Normalization, and this model provides pre-training Content Encoder.
  • Unet-TTS training
  • One-shot Voice cloning inference
  • C++ inference

Stay tuned!


Install Requirements

  • Install the appropriate TensorFlow and tensorflow-addons versions according to CUDA version.
  • The default is TensorFlow 2.6 and tensorflow-addons 0.14.0.
pip install TensorFlowTTS

Usage

  • see file UnetTTS_syn.py or notebook
CUDA_VISIBLE_DEVICES=0 python UnetTTS_syn.py
from UnetTTS_syn import UnetTTS

models_and_params = {"duration_param": "train/configs/unetts_duration.yaml",
                    "duration_model": "models/duration4k.h5",
                    "acous_param": "train/configs/unetts_acous.yaml",
                    "acous_model": "models/acous12k.h5",
                    "vocoder_param": "train/configs/multiband_melgan.yaml",
                    "vocoder_model": "models/vocoder800k.h5"}

feats_yaml = "train/configs/unetts_preprocess.yaml"

text2id_mapper = "models/unetts_mapper.json"

Tts_handel = UnetTTS(models_and_params, text2id_mapper, feats_yaml)

#text: input text
#src_audio: reference audio
#dur_stat: phoneme duration statistis to contraol speed rate
syn_audio, _, _ = Tts_handel.one_shot_TTS(text, src_audio, dur_stat)

Reference

https://github.com/TensorSpeech/TensorFlowTTS

https://github.com/CorentinJ/Real-Time-Voice-Cloning

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