README: EN | 中文

EmotiVoice is a powerful and modern open-source text-to-speech engine. EmotiVoice speaks both English and Chinese, and with over 2000 different voices (refer to the List of Voices for details). 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

• English audio sample

• Chinese-accent English audio sample

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 or Windows WSL2. Then EmotiVoice can be run with,

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

The Docker image was updated on November 21, 2023. If you have an older version, please update it by running the following commands:

docker pull syq163/emoti-voice:latest
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

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

We recommend that users refer to the wiki page How to download the pretrained model files if they encounter any issues.

git lfs install
git lfs clone https://huggingface.co/WangZeJun/simbert-base-chinese WangZeJun/simbert-base-chinese

or, you can run:

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, and run:

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: 8051|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:

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:

pip install streamlit
streamlit run demo_page.py

Wiki page

You may find more information from our wiki page.

Training

Please check Example Recipe

Roadmap & Future work

• Our future plan can be found in the ROADMAP file.
• 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.
• Suggestions are welcome. You can file issues or @ydopensource on twitter.

WeChat group

Welcome to scan the QR code below and join the WeChat group.

Credits

• PromptTTS. The PromptTTS paper is a key basis of this project.
• LibriTTS. The LibriTTS dataset is used in training of EmotiVoice.
• HiFiTTS. The HiFi TTS dataset is used in training of EmotiVoice.
• ESPnet.
• WeTTS
• HiFi-GAN
• Transformers
• tacotron
• KAN-TTS
• StyleTTS
• Simbert
• cn2an. EmotiVoice incorporates cn2an for number processing.

License

EmotiVoice is provided under the Apache-2.0 License - see the LICENSE file for details.

The interactive page is provided under the User Agreement file.