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18 changes: 18 additions & 0 deletions Dockerfile
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# syntax=docker/dockerfile:1
FROM ubuntu:22.04

# install app dependencies
RUN apt-get update && apt-get install -y python3 python3-pip libsndfile1
RUN python3 -m pip install torch==1.11.0 torchaudio numpy numba scipy transformers==4.26.1 soundfile yacs
RUN python3 -m pip install pypinyin jieba

# install app
RUN mkdir /EmotiVoice
COPY . /EmotiVoice/

# final configuration
EXPOSE 8501
RUN python3 -m pip install streamlit g2p_en
WORKDIR /EmotiVoice
RUN python3 frontend_en.py
CMD streamlit run demo_page.py --server.port 8501
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243 changes: 243 additions & 0 deletions LICENSE
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[Pytorch]
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106 changes: 106 additions & 0 deletions README.md
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# EmotiVoice - a Multi-Voice and Prompt-Controlled TTS Engine

[中文](README.zh.md)

**EmotiVoice** is a powerful and modern open-source text-to-speech engine. EmotiVoice speaks both English and Chinese, and with over 2000 different voices. The most prominent feature is **emotional synthesis**, allowing you to create speech with a wide range of emotions, including happy, excited, sad, angry and others.

An easy-to-use web interface is provided. There is also a scripting interface for batch generation of results.

Here are a few samples that EmotiVoice generates:


- ![Chinese audio sample](./assets/audio/emotivoice_intro_cn.wav)

- ![English audio sample](./assets/audio/emotivoice_intro_en.wav)


# Quickstart

## EmotiVoice Docker image

The easiest way to try EmotiVoice is by running the docker image. You need a machine with a NVidia GPU. If you have not done so, set up NVidia container toolkit by following the instructions for [Linux](https://www.server-world.info/en/note?os=Ubuntu_22.04&p=nvidia&f=2) or [Windows WSL2](https://github.com/nyp-sit/it3103/blob/main/nvidia-docker-wsl2.md). Then EmotiVoice can be run with,

```sh
docker run -dp 127.0.0.1:8501:8501 syq163/emoti-voice:latest
```

Now open your browser and navigate to http://localhost:8501 to start using EmotiVoice's powerful TTS capabilities.

## Full installation

```sh
conda create -n EmotiVoice python=3.8 -y
conda activate EmotiVoice
pip install torch torchaudio
pip install numpy numba scipy transformers==4.26.1 soundfile yacs g2p_en jieba pypinyin
```

## Prepare model files

```sh
git lfs install
git lfs clone https://huggingface.co/WangZeJun/simbert-base-chinese WangZeJun/simbert-base-chinese
```
or, you can run:
```sh
mkdir -p WangZeJun/simbert-base-chinese
wget https://huggingface.co/WangZeJun/simbert-base-chinese/resolve/main/config.json -P WangZeJun/simbert-base-chinese
wget https://huggingface.co/WangZeJun/simbert-base-chinese/resolve/main/pytorch_model.bin -P WangZeJun/simbert-base-chinese
wget https://huggingface.co/WangZeJun/simbert-base-chinese/resolve/main/vocab.txt -P WangZeJun/simbert-base-chinese
```

## Inference

1. You have to download the [pretrained models](https://drive.google.com/drive/folders/1y6Xwj_GG9ulsAonca_unSGbJ4lxbNymM?usp=sharing), and run:
```sh
mkdir -p outputs/style_encoder/ckpt
mkdir -p outputs/prompt_tts_open_source_joint/ckpt
```
2. And place `g_*`, `do_*` under `outputs/prompt_tts_open_source_joint/ckpt` and put `checkpoint_*` in `outputs/style_encoder/ckpt`.
3. The inference text format is `<speaker>|<style_prompt/emotion_prompt/content>|<phoneme>|<content>`.
- inference text example: `Maria_Kasper|Happy|<sos/eos> [IH0] [M] [AA1] [T] engsp4 [V] [OY1] [S] engsp4 [AH0] engsp1 [M] [AH1] [L] [T] [IY0] engsp4 [V] [OY1] [S] engsp1 [AE1] [N] [D] engsp1 [P] [R] [AA1] [M] [P] [T] engsp4 [K] [AH0] [N] [T] [R] [OW1] [L] [D] engsp1 [T] [IY1] engsp4 [T] [IY1] engsp4 [EH1] [S] engsp1 [EH1] [N] [JH] [AH0] [N] . <sos/eos>|Emoti-Voice - a Multi-Voice and Prompt-Controlled T-T-S Engine`.
4. You can get phonemes by `python frontend_en.py data/my_text.txt > data/my_text_for_tts.txt`.

5. Then run:
```sh
TEXT=data/inference/text
python inference_am_vocoder_joint.py \
--logdir prompt_tts_open_source_joint \
--config_folder config/joint \
--checkpoint g_00140000 \
--test_file $TEXT
```
the synthesized speech is under `outputs/prompt_tts_open_source_joint/test_audio`.

1. Or if you just want to use the interactive TTS demo page, run:
```sh
pip install streamlit
streamlit run demo_page.py
```

# Training

To be released.

# Future work

* The current implementation focuses on emotion/style control by prompts. It uses only pitch, speed, energy, and emotion as style factors, and does not use gender. But it is not complicated to change it to style/timbre control, similar to the original close-source implementation.

# Credits

- [PromptTTS](https://speechresearch.github.io/prompttts/). The PromptTTS paper is a key basis of this project.
- [LibriTTS](https://www.openslr.org/60/). The LibriTTS dataset is used in training of EmotiVoice.
- [HiFiTTS](https://www.openslr.org/109/). The HiFi TTS dataset is used in training of EmotiVoice.
- [ESPnet](https://github.com/espnet/espnet).
- [WeTTS](https://github.com/wenet-e2e/wetts)
- [HiFi-GAN](https://github.com/jik876/hifi-gan)
- [Transformers](https://github.com/huggingface/transformers)
- [tacotron](https://github.com/keithito/tacotron)
- [KAN-TTS](https://github.com/alibaba-damo-academy/KAN-TTS)
- [StyleTTS](https://github.com/yl4579/StyleTTS)

# License

EmotiVoice is provided under the Apache-2.0 License - see the [LICENSE](./LICENSE) file for details.

The interactive page is provided under the [User Agreement](./EmotiVoice_UserAgreement_易魔声用户协议.pdf) file.
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