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63 lines
3.1 KiB
Markdown
63 lines
3.1 KiB
Markdown
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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 multiple datasets: aidatatang_200zh, SLR68
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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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### [DEMO VIDEO](https://www.bilibili.com/video/BV1sA411P7wM/)
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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. Reuse the pretrained encoder/vocoder
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* Download the following models and extract the encoder and vocoder models to the according directory of this project. Don't use the synthesizer
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https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Pretrained-models
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> Note that we need to specify the newly trained synthesizer model, since the original model is incompatible with the Chinese sympols. It means the demo_cli is not working at this moment.
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### 3. Train synthesizer with your dataset
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* Download aidatatang_200zh or SLR68 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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Allow parameter `--dataset {dataset}` to support adatatang_200zh, SLR68
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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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> A link to my early trained model: [Baidu Yun](https://pan.baidu.com/s/10t3XycWiNIg5dN5E_bMORQ)
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Code：aid4
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### 4. 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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- [x] Add demo video
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- [X] Add support for more dataset
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- [X] Upload pretrained model
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- 🙏 Welcome to add more
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