mirror of https://github.com/coqui-ai/TTS.git
373 lines
17 KiB
Markdown
373 lines
17 KiB
Markdown
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## 🐸Coqui.ai News
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- 📣 You can use [~1100 Fairseq models](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS.
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- 📣 🐸TTS now supports 🐢Tortoise with faster inference.
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- 📣 **Coqui Studio API** is landed on 🐸TTS. - [Example](https://github.com/coqui-ai/TTS/blob/dev/README.md#-python-api)
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- 📣 [**Coqui Studio API**](https://docs.coqui.ai/docs) is live.
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- 📣 Voice generation with prompts - **Prompt to Voice** - is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin)!! - [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice)
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- 📣 Voice generation with fusion - **Voice fusion** - is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin).
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- 📣 Voice cloning is live on [**Coqui Studio**](https://app.coqui.ai/auth/signin).
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## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
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🐸TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality.
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🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects.
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[](https://discord.gg/5eXr5seRrv)
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[](https://opensource.org/licenses/MPL-2.0)
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[](https://badge.fury.io/py/TTS)
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[](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md)
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[](https://pepy.tech/project/tts)
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[](https://zenodo.org/badge/latestdoi/265612440)
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[](https://tts.readthedocs.io/en/latest/)
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📰 [**Subscribe to 🐸Coqui.ai Newsletter**](https://coqui.ai/?subscription=true)
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📢 [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2)
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📄 [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers)
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<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" />
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## 💬 Where to ask questions
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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.
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| Type | Platforms |
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| ------------------------------- | --------------------------------------- |
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| 🚨 **Bug Reports** | [GitHub Issue Tracker] |
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| 🎁 **Feature Requests & Ideas** | [GitHub Issue Tracker] |
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| 👩💻 **Usage Questions** | [GitHub Discussions] |
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| 🗯 **General Discussion** | [GitHub Discussions] or [Discord] |
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[github issue tracker]: https://github.com/coqui-ai/tts/issues
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[github discussions]: https://github.com/coqui-ai/TTS/discussions
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[discord]: https://discord.gg/5eXr5seRrv
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[Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials
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## 🔗 Links and Resources
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| Type | Links |
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| ------------------------------- | --------------------------------------- |
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| 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
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| 💾 **Installation** | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#install-tts)|
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| 👩💻 **Contributing** | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)|
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| 📌 **Road Map** | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378)
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| 🚀 **Released Models** | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)|
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## 🥇 TTS Performance
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<p align="center"><img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/TTS-performance.png" width="800" /></p>
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Underlined "TTS*" and "Judy*" are 🐸TTS models
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<!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) -->
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## Features
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- High-performance Deep Learning models for Text2Speech tasks.
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- Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
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- Speaker Encoder to compute speaker embeddings efficiently.
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- Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN)
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- Fast and efficient model training.
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- Detailed training logs on the terminal and Tensorboard.
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- Support for Multi-speaker TTS.
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- Efficient, flexible, lightweight but feature complete `Trainer API`.
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- Released and ready-to-use models.
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- Tools to curate Text2Speech datasets under```dataset_analysis```.
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- Utilities to use and test your models.
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- Modular (but not too much) code base enabling easy implementation of new ideas.
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## Implemented Models
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### Spectrogram models
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- Tacotron: [paper](https://arxiv.org/abs/1703.10135)
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- Tacotron2: [paper](https://arxiv.org/abs/1712.05884)
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- Glow-TTS: [paper](https://arxiv.org/abs/2005.11129)
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- Speedy-Speech: [paper](https://arxiv.org/abs/2008.03802)
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- Align-TTS: [paper](https://arxiv.org/abs/2003.01950)
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- FastPitch: [paper](https://arxiv.org/pdf/2006.06873.pdf)
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- FastSpeech: [paper](https://arxiv.org/abs/1905.09263)
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- FastSpeech2: [paper](https://arxiv.org/abs/2006.04558)
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- SC-GlowTTS: [paper](https://arxiv.org/abs/2104.05557)
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- Capacitron: [paper](https://arxiv.org/abs/1906.03402)
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- OverFlow: [paper](https://arxiv.org/abs/2211.06892)
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- Neural HMM TTS: [paper](https://arxiv.org/abs/2108.13320)
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### End-to-End Models
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- VITS: [paper](https://arxiv.org/pdf/2106.06103)
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- YourTTS: [paper](https://arxiv.org/abs/2112.02418)
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- Tortoise: [orig. repo](https://github.com/neonbjb/tortoise-tts)
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### Attention Methods
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- Guided Attention: [paper](https://arxiv.org/abs/1710.08969)
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- Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006)
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- Graves Attention: [paper](https://arxiv.org/abs/1910.10288)
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- Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/)
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- Dynamic Convolutional Attention: [paper](https://arxiv.org/pdf/1910.10288.pdf)
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- Alignment Network: [paper](https://arxiv.org/abs/2108.10447)
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### Speaker Encoder
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- GE2E: [paper](https://arxiv.org/abs/1710.10467)
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- Angular Loss: [paper](https://arxiv.org/pdf/2003.11982.pdf)
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### Vocoders
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- MelGAN: [paper](https://arxiv.org/abs/1910.06711)
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- MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106)
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- ParallelWaveGAN: [paper](https://arxiv.org/abs/1910.11480)
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- GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646)
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- WaveRNN: [origin](https://github.com/fatchord/WaveRNN/)
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- WaveGrad: [paper](https://arxiv.org/abs/2009.00713)
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- HiFiGAN: [paper](https://arxiv.org/abs/2010.05646)
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- UnivNet: [paper](https://arxiv.org/abs/2106.07889)
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### Voice Conversion
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- FreeVC: [paper](https://arxiv.org/abs/2210.15418)
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You can also help us implement more models.
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## Install TTS
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🐸TTS is tested on Ubuntu 18.04 with **python >= 3.7, < 3.11.**.
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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.
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```bash
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pip install TTS
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```
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If you plan to code or train models, clone 🐸TTS and install it locally.
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```bash
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git clone https://github.com/coqui-ai/TTS
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pip install -e .[all,dev,notebooks] # Select the relevant extras
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```
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If you are on Ubuntu (Debian), you can also run following commands for installation.
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```bash
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$ make system-deps # intended to be used on Ubuntu (Debian). Let us know if you have a different OS.
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$ make install
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```
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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).
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## Docker Image
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You can also try TTS without install with the docker image.
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Simply run the following command and you will be able to run TTS without installing it.
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```bash
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docker run --rm -it -p 5002:5002 --entrypoint /bin/bash ghcr.io/coqui-ai/tts-cpu
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python3 TTS/server/server.py --list_models #To get the list of available models
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python3 TTS/server/server.py --model_name tts_models/en/vctk/vits # To start a server
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```
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You can then enjoy the TTS server [here](http://[::1]:5002/)
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More details about the docker images (like GPU support) can be found [here](https://tts.readthedocs.io/en/latest/docker_images.html)
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## Synthesizing speech by 🐸TTS
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### 🐍 Python API
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```python
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from TTS.api import TTS
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# Running a multi-speaker and multi-lingual model
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# List available 🐸TTS models and choose the first one
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model_name = TTS.list_models()[0]
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# Init TTS
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tts = TTS(model_name)
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# Run TTS
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# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language
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# Text to speech with a numpy output
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wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
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# Text to speech to a file
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tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")
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# Running a single speaker model
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# Init TTS with the target model name
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tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
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# Run TTS
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tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)
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# Example voice cloning with YourTTS in English, French and Portuguese
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tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True)
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tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
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tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr-fr", file_path="output.wav")
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tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt-br", file_path="output.wav")
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# Example voice conversion converting speaker of the `source_wav` to the speaker of the `target_wav`
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tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True)
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tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav")
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# Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can
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# clone voices by using any model in 🐸TTS.
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tts = TTS("tts_models/de/thorsten/tacotron2-DDC")
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tts.tts_with_vc_to_file(
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"Wie sage ich auf Italienisch, dass ich dich liebe?",
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speaker_wav="target/speaker.wav",
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file_path="ouptut.wav"
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)
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# Example text to speech using [🐸Coqui Studio](https://coqui.ai) models.
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# You can use all of your available speakers in the studio.
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# [🐸Coqui Studio](https://coqui.ai) API token is required. You can get it from the [account page](https://coqui.ai/account).
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# You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token.
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# If you have a valid API token set you will see the studio speakers as separate models in the list.
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# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio
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models = TTS().list_models()
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# Init TTS with the target studio speaker
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tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False, gpu=False)
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# Run TTS
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tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH)
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# Run TTS with emotion and speed control
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tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5)
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#Example text to speech using **Fairseq models in ~1100 languages** 🤯.
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#For these models use the following name format: `tts_models/<lang-iso_code>/fairseq/vits`.
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#You can find the list of 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).
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# TTS with on the fly voice conversion
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api = TTS("tts_models/deu/fairseq/vits")
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api.tts_with_vc_to_file(
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"Wie sage ich auf Italienisch, dass ich dich liebe?",
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speaker_wav="target/speaker.wav",
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file_path="ouptut.wav"
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)
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```
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### Command line `tts`
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#### Single Speaker Models
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- List provided models:
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```
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$ tts --list_models
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```
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- Get model info (for both tts_models and vocoder_models):
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- Query by type/name:
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The model_info_by_name uses the name as it from the --list_models.
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```
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$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
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```
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For example:
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```
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$ tts --model_info_by_name tts_models/tr/common-voice/glow-tts
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```
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```
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$ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
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```
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- Query by type/idx:
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The model_query_idx uses the corresponding idx from --list_models.
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```
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$ tts --model_info_by_idx "<model_type>/<model_query_idx>"
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```
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For example:
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```
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$ tts --model_info_by_idx tts_models/3
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```
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- Run TTS with default models:
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```
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$ tts --text "Text for TTS" --out_path output/path/speech.wav
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```
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- Run a TTS model with its default vocoder model:
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```
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$ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
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```
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For example:
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```
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$ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav
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```
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- Run with specific TTS and vocoder models from the list:
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```
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$ 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
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```
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For example:
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```
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$ 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
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```
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- Run your own TTS model (Using Griffin-Lim Vocoder):
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```
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$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
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```
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- Run your own TTS and Vocoder models:
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```
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$ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
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--vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json
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```
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#### Multi-speaker Models
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- List the available speakers and choose a <speaker_id> among them:
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```
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$ tts --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs
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```
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- Run the multi-speaker TTS model with the target speaker ID:
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```
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$ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --speaker_idx <speaker_id>
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```
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- Run your own multi-speaker TTS model:
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```
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$ 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>
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```
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## Directory Structure
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```
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|- notebooks/ (Jupyter Notebooks for model evaluation, parameter selection and data analysis.)
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|- utils/ (common utilities.)
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|- TTS
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|- bin/ (folder for all the executables.)
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|- train*.py (train your target model.)
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|- ...
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|- tts/ (text to speech models)
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|- layers/ (model layer definitions)
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|- models/ (model definitions)
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|- utils/ (model specific utilities.)
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|- speaker_encoder/ (Speaker Encoder models.)
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|- (same)
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|- vocoder/ (Vocoder models.)
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|- (same)
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```
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