base on 🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time > 🚧 While I no longer actively update this repo, you can find me continuously pushing this tech forward to good side and open-source. I'm also building an optimized and cloud hosted version: https://noiz.ai/ and it's free but not ready for commersial use now. >  [](http://choosealicense.com/licenses/mit/) > English | [中文](README-CN.md)| [中文Linux](README-LINUX-CN.md) ## Features 🌍 **Chinese** supported mandarin and tested with multiple datasets: aidatatang_200zh, magicdata, aishell3, data_aishell, and etc. 🤩 **PyTorch** worked for pytorch, tested in version of 1.9.0(latest in August 2021), with GPU Tesla T4 and GTX 2060 🌍 **Windows + Linux** run in both Windows OS and linux OS (even in M1 MACOS) 🤩 **Easy & Awesome** effect with only newly-trained synthesizer, by reusing the pretrained encoder/vocoder 🌍 **Webserver Ready** to serve your result with remote calling ### [DEMO VIDEO](https://www.bilibili.com/video/BV17Q4y1B7mY/) ## Quick Start ### 1. Install Requirements #### 1.1 General Setup > Follow the original repo to test if you got all environment ready. **Python 3.7 or higher ** is needed to run the toolbox. * Install [PyTorch](https://pytorch.org/get-started/locally/). > If you get an `ERROR: Could not find a version that satisfies the requirement torch==1.9.0+cu102 (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2 )` This error is probably due to a low version of python, try using 3.9 and it will install successfully * Install [ffmpeg](https://ffmpeg.org/download.html#get-packages). * Run `pip install -r requirements.txt` to install the remaining necessary packages. > The recommended environment here is `Repo Tag 0.0.1` `Pytorch1.9.0 with Torchvision0.10.0 and cudatoolkit10.2` `requirements.txt` `webrtcvad-wheels` because `requirements. txt` was exported a few months ago, so it doesn't work with newer versions * Install webrtcvad `pip install webrtcvad-wheels`(If you need) or - install dependencies with `conda` or `mamba` ```conda env create -n env_name -f env.yml``` ```mamba env create -n env_name -f env.yml``` will create a virtual environment where necessary dependencies are installed. Switch to the new environment by `conda activate env_name` and enjoy it. > env.yml only includes the necessary dependencies to run the project，temporarily without monotonic-align. You can check the official website to install the GPU version of pytorch. #### 1.2 Setup with a M1 Mac > The following steps are a workaround to directly use the original `demo_toolbox.py`without the changing of codes. > > Since the major issue comes with the PyQt5 packages used in `demo_toolbox.py` not compatible with M1 chips, were one to attempt on training models with the M1 chip, either that person can forgo `demo_toolbox.py`, or one can try the `web.py` in the project. ##### 1.2.1 Install `PyQt5`, with [ref](https://stackoverflow.com/a/68038451/20455983) here. * Create and open a Rosetta Terminal, with [ref](https://dev.to/courier/tips-and-tricks-to-setup-your-apple-m1-for-development-547g) here. * Use system Python to create a virtual environment for the project ``` /usr/bin/python3 -m venv /PathToMockingBird/venv source /PathToMockingBird/venv/bin/activate ``` * Upgrade pip and install `PyQt5` ``` pip install --upgrade pip pip install pyqt5 ``` ##### 1.2.2 Install `pyworld` and `ctc-segmentation` > Both packages seem to be unique to this project and are not seen in the original [Real-Time Voice Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) project. When installing with `pip install`, both packages lack wheels so the program tries to directly compile from c code and could not find `Python.h`. * Install `pyworld` * `brew install python` `Python.h` can come with Python installed by brew * `export CPLUS_INCLUDE_PATH=/opt/homebrew/Frameworks/Python.framework/Headers` The filepath of brew-installed `Python.h` is unique to M1 MacOS and listed above. One needs to manually add the path to the environment variables. * `pip install pyworld` that should do. * Install`ctc-segmentation` > Same method does not apply to `ctc-segmentation`, and one needs to compile it from the source code on [github](https://github.com/lumaku/ctc-segmentation). * `git clone https://github.com/lumaku/ctc-segmentation.git` * `cd ctc-segmentation` * `source /PathToMockingBird/venv/bin/activate` If the virtual environment hasn't been deployed, activate it. * `cythonize -3 ctc_segmentation/ctc_segmentation_dyn.pyx` * `/usr/bin/arch -x86_64 python setup.py build` Build with x86 architecture. * `/usr/bin/arch -x86_64 python setup.py install --optimize=1 --skip-build`Install with x86 architecture. ##### 1.2.3 Other dependencies * `/usr/bin/arch -x86_64 pip install torch torchvision torchaudio` Pip installing `PyTorch` as an example, articulate that it's installed with x86 architecture * `pip install ffmpeg` Install ffmpeg * `pip install -r requirements.txt` Install other requirements. ##### 1.2.4 Run the Inference Time (with Toolbox) > To run the project on x86 architecture. [ref](https://youtrack.jetbrains.com/issue/PY-46290/Allow-running-Python-under-Rosetta-2-in-PyCharm-for-Apple-Silicon). * `vim /PathToMockingBird/venv/bin/pythonM1` Create an executable file `pythonM1` to condition python interpreter at `/PathToMockingBird/venv/bin`. * Write in the following content: ``` #!/usr/bin/env zsh mydir=${0:a:h} /usr/bin/arch -x86_64 $mydir/python "$@" ``` * `chmod +x pythonM1` Set the file as executable. * If using PyCharm IDE, configure project interpreter to `pythonM1`([steps here](https://www.jetbrains.com/help/pycharm/configuring-python-interpreter.html#add-existing-interpreter)), if using command line python, run `/PathToMockingBird/venv/bin/pythonM1 demo_toolbox.py` ### 2. Prepare your models > Note that we are using the pretrained encoder/vocoder but not synthesizer, since the original model is incompatible with the Chinese symbols. It means the demo_cli is not working at this moment, so additional synthesizer models are required. You can either train your models or use existing ones: #### 2.1 Train encoder with your dataset (Optional) * Preprocess with the audios and the mel spectrograms: `python encoder_preprocess.py <datasets_root>` Allowing parameter `--dataset {dataset}` to support the datasets you want to preprocess. Only the train set of these datasets will be used. Possible names: librispeech_other, voxceleb1, voxceleb2. Use comma to sperate multiple datasets. * Train the encoder: `python encoder_train.py my_run <datasets_root>/SV2TTS/encoder` > For training, the encoder uses visdom. You can disable it with `--no_visdom`, but it's nice to have. Run "visdom" in a separate CLI/process to start your visdom server. #### 2.2 Train synthesizer with your dataset * Download dataset and unzip: make sure you can access all .wav in folder * Preprocess with the audios and the mel spectrograms: `python pre.py <datasets_root>` Allowing parameter `--dataset {dataset}` to support aidatatang_200zh, magicdata, aishell3, data_aishell, etc.If this parameter is not passed, the default dataset will be aidatatang_200zh. * Train the synthesizer: `python train.py --type=synth mandarin <datasets_root>/SV2TTS/synthesizer` * Go to next step when you see attention line show and loss meet your need in training folder *synthesizer/saved_models/*. #### 2.3 Use pretrained model of synthesizer > Thanks to the community, some models will be shared: | author | Download link | Preview Video | Info | | --- | ----------- | ----- |----- | | @author | https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g [Baidu](https://pan.baidu.com/s/1iONvRxmkI-t1nHqxKytY3g) 4j5d | | 75k steps trained by multiple datasets | @author | https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw [Baidu](https://pan.baidu.com/s/1fMh9IlgKJlL2PIiRTYDUvw) code：om7f | | 25k steps trained by multiple datasets, only works under version 0.0.1 |@FawenYo | https://yisiou-my.sharepoint.com/:u:/g/personal/lawrence_cheng_fawenyo_onmicrosoft_com/EWFWDHzee-NNg9TWdKckCc4BC7bK2j9cCbOWn0-_tK0nOg?e=n0gGgC | [input](https://github.com/babysor/MockingBird/wiki/audio/self_test.mp3) [output](https://github.com/babysor/MockingBird/wiki/audio/export.wav) | 200k steps with local accent of Taiwan, only works under version 0.0.1 |@miven| https://pan.baidu.com/s/1PI-hM3sn5wbeChRryX-RCQ code: 2021 https://www.aliyundrive.com/s/AwPsbo8mcSP code: z2m0 | https://www.bilibili.com/video/BV1uh411B7AD/ | only works under version 0.0.1 #### 2.4 Train vocoder (Optional) > note: vocoder has little difference in effect, so you may not need to train a new one. * Preprocess the data: `python vocoder_preprocess.py <datasets_root> -m <synthesizer_model_path>` > `<datasets_root>` replace with your dataset root，`<synthesizer_model_path>`replace with directory of your best trained models of sythensizer, e.g. *sythensizer\saved_mode\xxx* * Train the wavernn vocoder: `python vocoder_train.py mandarin <datasets_root>` * Train the hifigan vocoder `python vocoder_train.py mandarin <datasets_root> hifigan` ### 3. Launch #### 3.1 Using the web server You can then try to run:`python web.py` and open it in browser, default as `http://localhost:8080` #### 3.2 Using the Toolbox You can then try the toolbox: `python demo_toolbox.py -d <datasets_root>` #### 3.3 Using the command line You can then try the command: `python gen_voice.py <text_file.txt> your_wav_file.wav` you may need to install cn2an by "pip install cn2an" for better digital number result. ## Reference > This repository is forked from [Real-Time-Voice-Cloning](https://github.com/CorentinJ/Real-Time-Voice-Cloning) which only support English. | URL | Designation | Title | Implementation source | | --- | ----------- | ----- | --------------------- | | [1803.09017](https://arxiv.org/abs/1803.09017) | GlobalStyleToken (synthesizer)| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis | This repo | | [2010.05646](https://arxiv.org/abs/2010.05646) | HiFi-GAN (vocoder)| Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis | This repo | | [2106.02297](https://arxiv.org/abs/2106.02297) | Fre-GAN (vocoder)| Fre-GAN: Adversarial Frequency-consistent Audio Synthesis | This repo | |[**1806.04558**](https://arxiv.org/pdf/1806.04558.pdf) | **SV2TTS** | **Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis** | This repo | |[1802.08435](https://arxiv.org/pdf/1802.08435.pdf) | WaveRNN (vocoder) | Efficient Neural Audio Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) | |[1703.10135](https://arxiv.org/pdf/1703.10135.pdf) | Tacotron (synthesizer) | Tacotron: Towards End-to-End Speech Synthesis | [fatchord/WaveRNN](https://github.com/fatchord/WaveRNN) |[1710.10467](https://arxiv.org/pdf/1710.10467.pdf) | GE2E (encoder)| Generalized End-To-End Loss for Speaker Verification | This repo | ## F Q&A #### 1.Where can I download the dataset? | Dataset | Original Source | Alternative Sources | | --- | ----------- | ---------------| | aidatatang_200zh | [OpenSLR](http://www.openslr.org/62/) | [Google Drive](https://drive.google.com/file/d/110A11KZoVe7vy6kXlLb6zVPLb_J91I_t/view?usp=sharing) | | magicdata | [OpenSLR](http://www.openslr.org/68/) | [Google Drive (Dev set)](https://drive.google.com/file/d/1g5bWRUSNH68ycC6eNvtwh07nX3QhOOlo/view?usp=sharing) | | aishell3 | [OpenSLR](https://www.openslr.org/93/) | [Google Drive](https://drive.google.com/file/d/1shYp_o4Z0X0cZSKQDtFirct2luFUwKzZ/view?usp=sharing) | | data_aishell | [OpenSLR](https://www.openslr.org/33/) | | > After unzip aidatatang_200zh, you need to unzip all the files under `aidatatang_200zh\corpus\train` #### 2.What is`<datasets_root>`? If the dataset path is `D:\data\aidatatang_200zh`,then `<datasets_root>` is`D:\data` #### 3.Not enough VRAM Train the synthesizer：adjust the batch_size in `synthesizer/hparams.py` ``` //Before tts_schedule = [(2, 1e-3, 20_000, 12), # Progressive training schedule (2, 5e-4, 40_000, 12), # (r, lr, step, batch_size) (2, 2e-4, 80_000, 12), # (2, 1e-4, 160_000, 12), # r = reduction factor (# of mel frames (2, 3e-5, 320_000, 12), # synthesized for each decoder iteration) (2, 1e-5, 640_000, 12)], # lr = learning rate //After tts_schedule = [(2, 1e-3, 20_000, 8), # Progressive training schedule (2, 5e-4, 40_000, 8), # (r, lr, step, batch_size) (2, 2e-4, 80_000, 8), # (2, 1e-4, 160_000, 8), # r = reduction factor (# of mel frames (2, 3e-5, 320_000, 8), # synthesized for each decoder iteration) (2, 1e-5, 640_000, 8)], # lr = learning rate ``` Train Vocoder-Preprocess the data：adjust the batch_size in `synthesizer/hparams.py` ``` //Before ### Data Preprocessing max_mel_frames = 900, rescale = True, rescaling_max = 0.9, synthesis_batch_size = 16, # For vocoder preprocessing and inference. //After ### Data Preprocessing max_mel_frames = 900, rescale = True, rescaling_max = 0.9, synthesis_batch_size = 8, # For vocoder preprocessing and inference. ``` Train Vocoder-Train the vocoder：adjust the batch_size in `vocoder/wavernn/hparams.py` ``` //Before # Training voc_batch_size = 100 voc_lr = 1e-4 voc_gen_at_checkpoint = 5 voc_pad = 2 //After # Training voc_batch_size = 6 voc_lr = 1e-4 voc_gen_at_checkpoint = 5 voc_pad =2 ``` #### 4.If it happens `RuntimeError: Error(s) in loading state_dict for Tacotron: size mismatch for encoder.embedding.weight: copying a param with shape torch.Size([70, 512]) from checkpoint, the shape in current model is torch.Size([75, 512]).` Please refer to issue [#37](https://github.com/babysor/MockingBird/issues/37) #### 5. How to improve CPU and GPU occupancy rate? Adjust the batch_size as appropriate to improve #### 6. What if it happens `the page file is too small to complete the operation` Please refer to this [video](https://www.youtube.com/watch?v=Oh6dga-Oy10&ab_channel=CodeProf) and change the virtual memory to 100G (102400), for example : When the file is placed in the D disk, the virtual memory of the D disk is changed. #### 7. When should I stop during training? FYI, my attention came after 18k steps and loss became lower than 0.4 after 50k steps.   ", Assign "at most 3 tags" to the expected json: {"id":"3869","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"