| ## 🐸Coqui.ai News | |
| - 📣 ⓍTTSv2 is here with 16 languages and better performance across the board. | |
| - 📣 ⓍTTS fine-tuning code is out. Check the [example recipes](https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech). | |
| - 📣 ⓍTTS can now stream with <200ms latency. | |
| - 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released [Blog Post](https://coqui.ai/blog/tts/open_xtts), [Demo](https://huggingface.co/spaces/coqui/xtts), [Docs](https://tts.readthedocs.io/en/dev/models/xtts.html) | |
| - 📣 [🐶Bark](https://github.com/suno-ai/bark) is now available for inference with unconstrained voice cloning. [Docs](https://tts.readthedocs.io/en/dev/models/bark.html) | |
| - 📣 You can use [~1100 Fairseq models](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS. | |
| - 📣 🐸TTS now supports 🐢Tortoise with faster inference. [Docs](https://tts.readthedocs.io/en/dev/models/tortoise.html) | |
| <div align="center"> | |
| <img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" /> | |
| ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/> | |
| **🐸TTS is a library for advanced Text-to-Speech generation.** | |
| 🚀 Pretrained models in +1100 languages. | |
| 🛠️ Tools for training new models and fine-tuning existing models in any language. | |
| 📚 Utilities for dataset analysis and curation. | |
| ______________________________________________________________________ | |
| [](https://discord.gg/5eXr5seRrv) | |
| [](https://opensource.org/licenses/MPL-2.0) | |
| [](https://badge.fury.io/py/TTS) | |
| [](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md) | |
| [](https://pepy.tech/project/tts) | |
| [](https://zenodo.org/badge/latestdoi/265612440) | |
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| [](https://tts.readthedocs.io/en/latest/) | |
| </div> | |
| ______________________________________________________________________ | |
| ## 💬 Where to ask questions | |
| Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it. | |
| | Type | Platforms | | |
| | ------------------------------- | --------------------------------------- | | |
| | 🚨 **Bug Reports** | [GitHub Issue Tracker] | | |
| | 🎁 **Feature Requests & Ideas** | [GitHub Issue Tracker] | | |
| | 👩💻 **Usage Questions** | [GitHub Discussions] | | |
| | 🗯 **General Discussion** | [GitHub Discussions] or [Discord] | | |
| [github issue tracker]: https://github.com/coqui-ai/tts/issues | |
| [github discussions]: https://github.com/coqui-ai/TTS/discussions | |
| [discord]: https://discord.gg/5eXr5seRrv | |
| [Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials | |
| ## 🔗 Links and Resources | |
| | Type | Links | | |
| | ------------------------------- | --------------------------------------- | | |
| | 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/) | |
| | 💾 **Installation** | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#installation)| | |
| | 👩💻 **Contributing** | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)| | |
| | 📌 **Road Map** | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378) | |
| | 🚀 **Released Models** | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)| | |
| | 📰 **Papers** | [TTS Papers](https://github.com/erogol/TTS-papers)| | |
| ## 🥇 TTS Performance | |
| <p align="center"><img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/TTS-performance.png" width="800" /></p> | |
| Underlined "TTS*" and "Judy*" are **internal** 🐸TTS models that are not released open-source. They are here to show the potential. Models prefixed with a dot (.Jofish .Abe and .Janice) are real human voices. | |
| ## Features | |
| - High-performance Deep Learning models for Text2Speech tasks. | |
| - Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). | |
| - Speaker Encoder to compute speaker embeddings efficiently. | |
| - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN) | |
| - Fast and efficient model training. | |
| - Detailed training logs on the terminal and Tensorboard. | |
| - Support for Multi-speaker TTS. | |
| - Efficient, flexible, lightweight but feature complete `Trainer API`. | |
| - Released and ready-to-use models. | |
| - Tools to curate Text2Speech datasets under```dataset_analysis```. | |
| - Utilities to use and test your models. | |
| - Modular (but not too much) code base enabling easy implementation of new ideas. | |
| ## Model Implementations | |
| ### Spectrogram models | |
| - Tacotron: [paper](https://arxiv.org/abs/1703.10135) | |
| - Tacotron2: [paper](https://arxiv.org/abs/1712.05884) | |
| - Glow-TTS: [paper](https://arxiv.org/abs/2005.11129) | |
| - Speedy-Speech: [paper](https://arxiv.org/abs/2008.03802) | |
| - Align-TTS: [paper](https://arxiv.org/abs/2003.01950) | |
| - FastPitch: [paper](https://arxiv.org/pdf/2006.06873.pdf) | |
| - FastSpeech: [paper](https://arxiv.org/abs/1905.09263) | |
| - FastSpeech2: [paper](https://arxiv.org/abs/2006.04558) | |
| - SC-GlowTTS: [paper](https://arxiv.org/abs/2104.05557) | |
| - Capacitron: [paper](https://arxiv.org/abs/1906.03402) | |
| - OverFlow: [paper](https://arxiv.org/abs/2211.06892) | |
| - Neural HMM TTS: [paper](https://arxiv.org/abs/2108.13320) | |
| - Delightful TTS: [paper](https://arxiv.org/abs/2110.12612) | |
| ### End-to-End Models | |
| - ⓍTTS: [blog](https://coqui.ai/blog/tts/open_xtts) | |
| - VITS: [paper](https://arxiv.org/pdf/2106.06103) | |
| - 🐸 YourTTS: [paper](https://arxiv.org/abs/2112.02418) | |
| - 🐢 Tortoise: [orig. repo](https://github.com/neonbjb/tortoise-tts) | |
| - 🐶 Bark: [orig. repo](https://github.com/suno-ai/bark) | |
| ### Attention Methods | |
| - Guided Attention: [paper](https://arxiv.org/abs/1710.08969) | |
| - Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006) | |
| - Graves Attention: [paper](https://arxiv.org/abs/1910.10288) | |
| - Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) | |
| - Dynamic Convolutional Attention: [paper](https://arxiv.org/pdf/1910.10288.pdf) | |
| - Alignment Network: [paper](https://arxiv.org/abs/2108.10447) | |
| ### Speaker Encoder | |
| - GE2E: [paper](https://arxiv.org/abs/1710.10467) | |
| - Angular Loss: [paper](https://arxiv.org/pdf/2003.11982.pdf) | |
| ### Vocoders | |
| - MelGAN: [paper](https://arxiv.org/abs/1910.06711) | |
| - MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106) | |
| - ParallelWaveGAN: [paper](https://arxiv.org/abs/1910.11480) | |
| - GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646) | |
| - WaveRNN: [origin](https://github.com/fatchord/WaveRNN/) | |
| - WaveGrad: [paper](https://arxiv.org/abs/2009.00713) | |
| - HiFiGAN: [paper](https://arxiv.org/abs/2010.05646) | |
| - UnivNet: [paper](https://arxiv.org/abs/2106.07889) | |
| ### Voice Conversion | |
| - FreeVC: [paper](https://arxiv.org/abs/2210.15418) | |
| You can also help us implement more models. | |
| ## Installation | |
| 🐸TTS is tested on Ubuntu 18.04 with **python >= 3.9, < 3.12.**. | |
| If you are only interested in [synthesizing speech](https://tts.readthedocs.io/en/latest/inference.html) with the released 🐸TTS models, installing from PyPI is the easiest option. | |
| ```bash | |
| pip install TTS | |
| ``` | |
| If you plan to code or train models, clone 🐸TTS and install it locally. | |
| ```bash | |
| git clone https://github.com/coqui-ai/TTS | |
| pip install -e .[all,dev,notebooks] # Select the relevant extras | |
| ``` | |
| If you are on Ubuntu (Debian), you can also run following commands for installation. | |
| ```bash | |
| $ make system-deps # intended to be used on Ubuntu (Debian). Let us know if you have a different OS. | |
| $ make install | |
| ``` | |
| If you are on Windows, 👑@GuyPaddock wrote installation instructions [here](https://stackoverflow.com/questions/66726331/how-can-i-run-mozilla-tts-coqui-tts-training-with-cuda-on-a-windows-system). | |
| ## Docker Image | |
| You can also try TTS without install with the docker image. | |
| Simply run the following command and you will be able to run TTS without installing it. | |
| ```bash | |
| docker run --rm -it -p 5002:5002 --entrypoint /bin/bash ghcr.io/coqui-ai/tts-cpu | |
| python3 TTS/server/server.py --list_models #To get the list of available models | |
| python3 TTS/server/server.py --model_name tts_models/en/vctk/vits # To start a server | |
| ``` | |
| You can then enjoy the TTS server [here](http://[::1]:5002/) | |
| More details about the docker images (like GPU support) can be found [here](https://tts.readthedocs.io/en/latest/docker_images.html) | |
| ## Synthesizing speech by 🐸TTS | |
| ### 🐍 Python API | |
| #### Running a multi-speaker and multi-lingual model | |
| ```python | |
| import torch | |
| from TTS.api import TTS | |
| # Get device | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| # List available 🐸TTS models | |
| print(TTS().list_models()) | |
| # Init TTS | |
| tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device) | |
| # Run TTS | |
| # ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language | |
| # Text to speech list of amplitude values as output | |
| wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en") | |
| # Text to speech to a file | |
| tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav") | |
| ``` | |
| #### Running a single speaker model | |
| ```python | |
| # Init TTS with the target model name | |
| tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False).to(device) | |
| # Run TTS | |
| tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH) | |
| # Example voice cloning with YourTTS in English, French and Portuguese | |
| tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False).to(device) | |
| tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav") | |
| tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr-fr", file_path="output.wav") | |
| tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt-br", file_path="output.wav") | |
| ``` | |
| #### Example voice conversion | |
| Converting the voice in `source_wav` to the voice of `target_wav` | |
| ```python | |
| tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False).to("cuda") | |
| tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav") | |
| ``` | |
| #### Example voice cloning together with the voice conversion model. | |
| This way, you can clone voices by using any model in 🐸TTS. | |
| ```python | |
| tts = TTS("tts_models/de/thorsten/tacotron2-DDC") | |
| tts.tts_with_vc_to_file( | |
| "Wie sage ich auf Italienisch, dass ich dich liebe?", | |
| speaker_wav="target/speaker.wav", | |
| file_path="output.wav" | |
| ) | |
| ``` | |
| #### Example text to speech using **Fairseq models in ~1100 languages** 🤯. | |
| For Fairseq models, use the following name format: `tts_models/<lang-iso_code>/fairseq/vits`. | |
| You can find the language ISO codes [here](https://dl.fbaipublicfiles.com/mms/tts/all-tts-languages.html) | |
| and learn about the Fairseq models [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms). | |
| ```python | |
| # TTS with on the fly voice conversion | |
| api = TTS("tts_models/deu/fairseq/vits") | |
| api.tts_with_vc_to_file( | |
| "Wie sage ich auf Italienisch, dass ich dich liebe?", | |
| speaker_wav="target/speaker.wav", | |
| file_path="output.wav" | |
| ) | |
| ``` | |
| ### Command-line `tts` | |
| <!-- begin-tts-readme --> | |
| Synthesize speech on command line. | |
| You can either use your trained model or choose a model from the provided list. | |
| If you don't specify any models, then it uses LJSpeech based English model. | |
| #### Single Speaker Models | |
| - List provided models: | |
| ``` | |
| $ tts --list_models | |
| ``` | |
| - Get model info (for both tts_models and vocoder_models): | |
| - Query by type/name: | |
| The model_info_by_name uses the name as it from the --list_models. | |
| ``` | |
| $ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>" | |
| ``` | |
| For example: | |
| ``` | |
| $ tts --model_info_by_name tts_models/tr/common-voice/glow-tts | |
| $ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2 | |
| ``` | |
| - Query by type/idx: | |
| The model_query_idx uses the corresponding idx from --list_models. | |
| ``` | |
| $ tts --model_info_by_idx "<model_type>/<model_query_idx>" | |
| ``` | |
| For example: | |
| ``` | |
| $ tts --model_info_by_idx tts_models/3 | |
| ``` | |
| - Query info for model info by full name: | |
| ``` | |
| $ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>" | |
| ``` | |
| - Run TTS with default models: | |
| ``` | |
| $ tts --text "Text for TTS" --out_path output/path/speech.wav | |
| ``` | |
| - Run TTS and pipe out the generated TTS wav file data: | |
| ``` | |
| $ tts --text "Text for TTS" --pipe_out --out_path output/path/speech.wav | aplay | |
| ``` | |
| - Run a TTS model with its default vocoder model: | |
| ``` | |
| $ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav | |
| ``` | |
| For example: | |
| ``` | |
| $ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav | |
| ``` | |
| - Run with specific TTS and vocoder models from the list: | |
| ``` | |
| $ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --vocoder_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav | |
| ``` | |
| For example: | |
| ``` | |
| $ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --vocoder_name "vocoder_models/en/ljspeech/univnet" --out_path output/path/speech.wav | |
| ``` | |
| - Run your own TTS model (Using Griffin-Lim Vocoder): | |
| ``` | |
| $ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav | |
| ``` | |
| - Run your own TTS and Vocoder models: | |
| ``` | |
| $ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav | |
| --vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json | |
| ``` | |
| #### Multi-speaker Models | |
| - List the available speakers and choose a <speaker_id> among them: | |
| ``` | |
| $ tts --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs | |
| ``` | |
| - Run the multi-speaker TTS model with the target speaker ID: | |
| ``` | |
| $ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --speaker_idx <speaker_id> | |
| ``` | |
| - Run your own multi-speaker TTS model: | |
| ``` | |
| $ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/model.pth --config_path path/to/config.json --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id> | |
| ``` | |
| ### Voice Conversion Models | |
| ``` | |
| $ tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --source_wav <path/to/speaker/wav> --target_wav <path/to/reference/wav> | |
| ``` | |
| <!-- end-tts-readme --> | |
| ## Directory Structure | |
| ``` | |
| |- notebooks/ (Jupyter Notebooks for model evaluation, parameter selection and data analysis.) | |
| |- utils/ (common utilities.) | |
| |- TTS | |
| |- bin/ (folder for all the executables.) | |
| |- train*.py (train your target model.) | |
| |- ... | |
| |- tts/ (text to speech models) | |
| |- layers/ (model layer definitions) | |
| |- models/ (model definitions) | |
| |- utils/ (model specific utilities.) | |
| |- speaker_encoder/ (Speaker Encoder models.) | |
| |- (same) | |
| |- vocoder/ (Vocoder models.) | |
| |- (same) | |
| ``` | |