2021-08-07 11:56:00 +08:00
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[](http://choosealicense.com/licenses/mit/)
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> This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English.
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> English | [中文](README-CN.md)
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## Features
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🌍 **Chinese** supported mandarin and tested with dataset: aidatatang_200zh
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🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060
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🌍 **Windows + Linux** tested in both Windows OS and linux OS after fixing nits
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🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder
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## Quick Start
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### 1. Install Requirements
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> Follow the original repo to test if you got all environment ready.
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**Python 3.7 or higher ** is needed to run the toolbox.
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* Install [PyTorch](https://pytorch.org/get-started/locally/).
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* Install [ffmpeg](https://ffmpeg.org/download.html#get-packages).
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* Run `pip install -r requirements.txt` to install the remaining necessary packages.
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### 2. Train synthesizer with aidatatang_200zh
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* Download aidatatang_200zh dataset and unzip: make sure you can access all .wav in *train* folder
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* Preprocess with the audios and the mel spectrograms:
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`python synthesizer_preprocess_audio.py <datasets_root>`
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* Preprocess the embeddings:
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`python synthesizer_preprocess_embeds.py <datasets_root>/SV2TTS/synthesizer`
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* Train the synthesizer:
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`python synthesizer_train.py mandarin <datasets_root>/SV2TTS/synthesizer`
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* Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*.
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> FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.
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2021-08-07 12:02:34 +08:00
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2021-08-07 11:56:00 +08:00
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### 3. Launch the Toolbox
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You can then try the toolbox:
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`python demo_toolbox.py -d <datasets_root>`
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or
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`python demo_toolbox.py`
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## TODO
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- Add demo video
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- Add support for more dataset
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- Upload pretrained model
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- 🙏 Welcome to add more
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