mirror of https://github.com/coqui-ai/TTS.git
424 lines
16 KiB
Markdown
424 lines
16 KiB
Markdown
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## 🐸Coqui TTS News
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- 📣 Fork of the [original, unmaintained repository](https://github.com/coqui-ai/TTS). New PyPI package: [coqui-tts](https://pypi.org/project/coqui-tts)
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- 📣 [OpenVoice](https://github.com/myshell-ai/OpenVoice) models now available for voice conversion.
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- 📣 Prebuilt wheels are now also published for Mac and Windows (in addition to Linux as before) for easier installation across platforms.
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- 📣 ⓍTTSv2 is here with 17 languages and better performance across the board. ⓍTTS can stream with <200ms latency.
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- 📣 ⓍTTS fine-tuning code is out. Check the [example recipes](https://github.com/idiap/coqui-ai-TTS/tree/dev/recipes/ljspeech).
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- 📣 [🐶Bark](https://github.com/suno-ai/bark) is now available for inference with unconstrained voice cloning. [Docs](https://coqui-tts.readthedocs.io/en/latest/models/bark.html)
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- 📣 You can use [Fairseq models in ~1100 languages](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS.
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## <img src="https://raw.githubusercontent.com/idiap/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/coqui-tts)
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[](https://pepy.tech/project/coqui-tts)
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[](https://zenodo.org/badge/latestdoi/265612440)
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[](https://coqui-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, 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/idiap/coqui-ai-TTS/issues
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[github discussions]: https://github.com/idiap/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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The [issues](https://github.com/coqui-ai/TTS/issues) and
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[discussions](https://github.com/coqui-ai/TTS/discussions) in the original
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repository are also still a useful source of information.
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## 🔗 Links and Resources
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| Type | Links |
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| ------------------------------- | --------------------------------------- |
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| 💼 **Documentation** | [ReadTheDocs](https://coqui-tts.readthedocs.io/en/latest/)
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| 💾 **Installation** | [TTS/README.md](https://github.com/idiap/coqui-ai-TTS/tree/dev#installation)|
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| 👩💻 **Contributing** | [CONTRIBUTING.md](https://github.com/idiap/coqui-ai-TTS/blob/main/CONTRIBUTING.md)|
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| 🚀 **Released Models** | [Standard models](https://github.com/idiap/coqui-ai-TTS/blob/dev/TTS/.models.json) and [Fairseq models in ~1100 languages](https://github.com/idiap/coqui-ai-TTS#example-text-to-speech-using-fairseq-models-in-1100-languages-)|
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## Features
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- High-performance Deep Learning models for Text2Speech tasks. See lists of models below.
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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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- OpenVoice: [technical report](https://arxiv.org/abs/2312.01479)
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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 22.04 with **python >= 3.9, < 3.13.**.
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If you are only interested in [synthesizing speech](https://coqui-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 coqui-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/idiap/coqui-ai-TTS
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cd coqui-ai-TTS
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pip install -e .
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```
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### Optional dependencies
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The following extras allow the installation of optional dependencies:
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| Name | Description |
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|------|-------------|
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| `all` | All optional dependencies |
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| `notebooks` | Dependencies only used in notebooks |
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| `server` | Dependencies to run the TTS server |
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| `bn` | Bangla G2P |
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| `ja` | Japanese G2P |
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| `ko` | Korean G2P |
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| `zh` | Chinese G2P |
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| `languages` | All language-specific dependencies |
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You can install extras with one of the following commands:
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```bash
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pip install coqui-tts[server,ja]
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pip install -e .[server,ja]
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```
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### Platforms
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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
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[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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(note that these are out of date, e.g. you need to have at least Python 3.9).
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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
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[here](https://coqui-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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Other available voice conversion models:
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- `voice_conversion_models/multilingual/multi-dataset/openvoice_v1`
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- `voice_conversion_models/multilingual/multi-dataset/openvoice_v2`
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#### Example voice cloning together with the default voice conversion model.
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This way, you can clone voices by using any model in 🐸TTS. The FreeVC model is
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used for voice conversion after synthesizing speech.
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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 fairseq models
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api = TTS("tts_models/deu/fairseq/vits")
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api.tts_to_file(
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"Wie sage ich auf Italienisch, dass ich dich liebe?",
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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
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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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- Query info for model info by full name:
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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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- 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 TTS and pipe out the generated TTS wav file data:
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```
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$ tts --text "Text for TTS" --pipe_out --out_path output/path/speech.wav | aplay
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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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### Voice Conversion Models
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```
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$ 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>
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```
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<!-- end-tts-readme -->
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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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|- vc/ (Voice conversion models.)
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|- (same)
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```
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