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21 KiB
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506 lines
21 KiB
Plaintext
Metadata-Version: 2.1
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Name: TTS
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Version: 0.22.0
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Summary: Deep learning for Text to Speech by Coqui.
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Home-page: https://github.com/coqui-ai/TTS
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Author: Eren Gölge
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Author-email: egolge@coqui.ai
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License: MPL-2.0
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Project-URL: Documentation, https://github.com/coqui-ai/TTS/wiki
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Project-URL: Tracker, https://github.com/coqui-ai/TTS/issues
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Project-URL: Repository, https://github.com/coqui-ai/TTS
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Project-URL: Discussions, https://github.com/coqui-ai/TTS/discussions
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Development Status :: 3 - Alpha
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Classifier: Intended Audience :: Science/Research
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Classifier: Intended Audience :: Developers
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Classifier: Operating System :: POSIX :: Linux
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Classifier: License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)
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Classifier: Topic :: Software Development
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Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Classifier: Topic :: Multimedia :: Sound/Audio :: Speech
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Classifier: Topic :: Multimedia :: Sound/Audio
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Classifier: Topic :: Multimedia
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Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
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Requires-Python: >=3.9.0, <3.12
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Description-Content-Type: text/markdown
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License-File: LICENSE.txt
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Requires-Dist: cython >=0.29.30
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Requires-Dist: scipy >=1.11.2
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Requires-Dist: torch >=2.1
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Requires-Dist: torchaudio
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Requires-Dist: soundfile >=0.12.0
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Requires-Dist: librosa >=0.10.0
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Requires-Dist: scikit-learn >=1.3.0
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Requires-Dist: inflect >=5.6.0
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Requires-Dist: fsspec >=2023.6.0
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Requires-Dist: packaging >=23.1
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Requires-Dist: flask >=2.0.1
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Requires-Dist: pysbd >=0.3.4
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Requires-Dist: umap-learn >=0.5.1
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Requires-Dist: pandas <2.0,>=1.4
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Requires-Dist: matplotlib >=3.7.0
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Requires-Dist: trainer >=0.0.32
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Requires-Dist: coqpit >=0.0.16
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Requires-Dist: jieba
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Requires-Dist: pypinyin
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Requires-Dist: hangul-romanize
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Requires-Dist: gruut[de,es,fr] ==2.2.3
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Requires-Dist: jamo
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Requires-Dist: nltk
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Requires-Dist: g2pkk >=0.1.1
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Requires-Dist: bangla
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Requires-Dist: bnnumerizer
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Requires-Dist: bnunicodenormalizer
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Requires-Dist: einops >=0.6.0
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Requires-Dist: transformers >=4.33.0
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Requires-Dist: encodec >=0.1.1
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Requires-Dist: unidecode >=1.3.2
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Requires-Dist: num2words
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Requires-Dist: spacy[ja] >=3
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Requires-Dist: numba ==0.55.1 ; python_version < "3.9"
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Requires-Dist: numpy ==1.22.0 ; python_version <= "3.10"
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Requires-Dist: numpy >=1.24.3 ; python_version > "3.10"
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Requires-Dist: numba >=0.57.0 ; python_version >= "3.9"
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Provides-Extra: all
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Requires-Dist: black ; extra == 'all'
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Requires-Dist: coverage ; extra == 'all'
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Requires-Dist: isort ; extra == 'all'
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Requires-Dist: nose2 ; extra == 'all'
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Requires-Dist: pylint ==2.10.2 ; extra == 'all'
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Requires-Dist: bokeh ==1.4.0 ; extra == 'all'
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Requires-Dist: mecab-python3 ==1.0.6 ; extra == 'all'
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Provides-Extra: dev
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Provides-Extra: ja
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Requires-Dist: mecab-python3 ==1.0.6 ; extra == 'ja'
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Requires-Dist: unidic-lite ==1.0.8 ; extra == 'ja'
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Requires-Dist: cutlet ; extra == 'ja'
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Provides-Extra: notebooks
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Requires-Dist: bokeh ==1.4.0 ; extra == 'notebooks'
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## 🐸Coqui.ai News
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- 📣 ⓍTTSv2 is here with 16 languages and better performance across the board.
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- 📣 ⓍTTS fine-tuning code is out. Check the [example recipes](https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech).
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- 📣 ⓍTTS can now stream with <200ms latency.
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- 📣 Ⓧ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)
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- 📣 [🐶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)
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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. [Docs](https://tts.readthedocs.io/en/dev/models/tortoise.html)
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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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<div align="center">
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<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" />
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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.**
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🚀 Pretrained models in +1100 languages.
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🛠️ Tools for training new models and fine-tuning existing models in any language.
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📚 Utilities for dataset analysis and curation.
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______________________________________________________________________
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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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</div>
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______________________________________________________________________
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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#installation)|
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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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| 📰 **Papers** | [TTS Papers](https://github.com/erogol/TTS-papers)|
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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 **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.
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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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## Model Implementations
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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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- Delightful TTS: [paper](https://arxiv.org/abs/2110.12612)
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### End-to-End Models
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- ⓍTTS: [blog](https://coqui.ai/blog/tts/open_xtts)
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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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- 🐶 Bark: [orig. repo](https://github.com/suno-ai/bark)
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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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## Installation
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🐸TTS is tested on Ubuntu 18.04 with **python >= 3.9, < 3.12.**.
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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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#### Running a multi-speaker and multi-lingual model
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```python
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import torch
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from TTS.api import TTS
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# Get device
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# List available 🐸TTS models
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print(TTS().list_models())
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# Init TTS
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tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
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# Run TTS
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# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
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# Text to speech list of amplitude values as output
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wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en")
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# Text to speech to a file
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tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
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```
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#### Running a single speaker model
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```python
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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).to(device)
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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).to(device)
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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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```
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#### Example voice conversion
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Converting the voice in `source_wav` to the voice of `target_wav`
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```python
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tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False).to("cuda")
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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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```
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#### Example voice cloning together with the voice conversion model.
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This way, you can clone voices by using any model in 🐸TTS.
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```python
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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="output.wav"
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)
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```
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#### Example text to speech using **Fairseq models in ~1100 languages** 🤯.
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For Fairseq models, use the following name format: `tts_models/<lang-iso_code>/fairseq/vits`.
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You can find the language ISO codes [here](https://dl.fbaipublicfiles.com/mms/tts/all-tts-languages.html)
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and learn about the Fairseq models [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms).
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```python
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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="output.wav"
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)
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```
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### Command-line `tts`
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<!-- begin-tts-readme -->
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Synthesize speech on command line.
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You can either use your trained model or choose a model from the provided list.
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If you don't specify any models, then it uses LJSpeech based English model.
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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
|
|
$ 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)
|
|
```
|