base on 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production ## 🐸Coqui.ai News - 📣 ⓍTTSv2 is here with 16 languages and better performance across the board. - 📣 ⓍTTS fine-tuning code is out. Check the [example recipes](https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech). - 📣 ⓍTTS can now stream with <200ms latency. - 📣 ⓍTTS, our production TTS model that can speak 13 languages, is released [Blog Post](https://coqui.ai/blog/tts/open_xtts), [Demo](https://huggingface.co/spaces/coqui/xtts), [Docs](https://tts.readthedocs.io/en/dev/models/xtts.html) - 📣 [🐶Bark](https://github.com/suno-ai/bark) is now available for inference with unconstrained voice cloning. [Docs](https://tts.readthedocs.io/en/dev/models/bark.html) - 📣 You can use [~1100 Fairseq models](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS. - 📣 🐸TTS now supports 🐢Tortoise with faster inference. [Docs](https://tts.readthedocs.io/en/dev/models/tortoise.html) <div align="center"> <img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" /> ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/> **🐸TTS is a library for advanced Text-to-Speech generation.** 🚀 Pretrained models in +1100 languages. 🛠️ Tools for training new models and fine-tuning existing models in any language. 📚 Utilities for dataset analysis and curation. ______________________________________________________________________ [](https://discord.gg/5eXr5seRrv) [](https://opensource.org/licenses/MPL-2.0) [](https://badge.fury.io/py/TTS) [](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md) [](https://pepy.tech/project/tts) [](https://zenodo.org/badge/latestdoi/265612440)            [](https://tts.readthedocs.io/en/latest/) </div> ______________________________________________________________________ ## 💬 Where to ask questions Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it. | Type | Platforms | | ------------------------------- | --------------------------------------- | | 🚨 **Bug Reports** | [GitHub Issue Tracker] | | 🎁 **Feature Requests & Ideas** | [GitHub Issue Tracker] | | 👩‍💻 **Usage Questions** | [GitHub Discussions] | | 🗯 **General Discussion** | [GitHub Discussions] or [Discord] | [github issue tracker]: https://github.com/coqui-ai/tts/issues [github discussions]: https://github.com/coqui-ai/TTS/discussions [discord]: https://discord.gg/5eXr5seRrv [Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials ## 🔗 Links and Resources | Type | Links | | ------------------------------- | --------------------------------------- | | 💼 **Documentation** | [ReadTheDocs](https://tts.readthedocs.io/en/latest/) | 💾 **Installation** | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#installation)| | 👩‍💻 **Contributing** | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)| | 📌 **Road Map** | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378) | 🚀 **Released Models** | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)| | 📰 **Papers** | [TTS Papers](https://github.com/erogol/TTS-papers)| ## 🥇 TTS Performance <p align="center"><img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/TTS-performance.png" width="800" /></p> Underlined "TTS*" and "Judy*" are **internal** 🐸TTS models that are not released open-source. They are here to show the potential. Models prefixed with a dot (.Jofish .Abe and .Janice) are real human voices. ## Features - High-performance Deep Learning models for Text2Speech tasks. - Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). - Speaker Encoder to compute speaker embeddings efficiently. - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN) - Fast and efficient model training. - Detailed training logs on the terminal and Tensorboard. - Support for Multi-speaker TTS. - Efficient, flexible, lightweight but feature complete `Trainer API`. - Released and ready-to-use models. - Tools to curate Text2Speech datasets under```dataset_analysis```. - Utilities to use and test your models. - Modular (but not too much) code base enabling easy implementation of new ideas. ## Model Implementations ### Spectrogram models - Tacotron: [paper](https://arxiv.org/abs/1703.10135) - Tacotron2: [paper](https://arxiv.org/abs/1712.05884) - Glow-TTS: [paper](https://arxiv.org/abs/2005.11129) - Speedy-Speech: [paper](https://arxiv.org/abs/2008.03802) - Align-TTS: [paper](https://arxiv.org/abs/2003.01950) - FastPitch: [paper](https://arxiv.org/pdf/2006.06873.pdf) - FastSpeech: [paper](https://arxiv.org/abs/1905.09263) - FastSpeech2: [paper](https://arxiv.org/abs/2006.04558) - SC-GlowTTS: [paper](https://arxiv.org/abs/2104.05557) - Capacitron: [paper](https://arxiv.org/abs/1906.03402) - OverFlow: [paper](https://arxiv.org/abs/2211.06892) - Neural HMM TTS: [paper](https://arxiv.org/abs/2108.13320) - Delightful TTS: [paper](https://arxiv.org/abs/2110.12612) ### End-to-End Models - ⓍTTS: [blog](https://coqui.ai/blog/tts/open_xtts) - VITS: [paper](https://arxiv.org/pdf/2106.06103) - 🐸 YourTTS: [paper](https://arxiv.org/abs/2112.02418) - 🐢 Tortoise: [orig. repo](https://github.com/neonbjb/tortoise-tts) - 🐶 Bark: [orig. repo](https://github.com/suno-ai/bark) ### Attention Methods - Guided Attention: [paper](https://arxiv.org/abs/1710.08969) - Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006) - Graves Attention: [paper](https://arxiv.org/abs/1910.10288) - Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) - Dynamic Convolutional Attention: [paper](https://arxiv.org/pdf/1910.10288.pdf) - Alignment Network: [paper](https://arxiv.org/abs/2108.10447) ### Speaker Encoder - GE2E: [paper](https://arxiv.org/abs/1710.10467) - Angular Loss: [paper](https://arxiv.org/pdf/2003.11982.pdf) ### Vocoders - MelGAN: [paper](https://arxiv.org/abs/1910.06711) - MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106) - ParallelWaveGAN: [paper](https://arxiv.org/abs/1910.11480) - GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646) - WaveRNN: [origin](https://github.com/fatchord/WaveRNN/) - WaveGrad: [paper](https://arxiv.org/abs/2009.00713) - HiFiGAN: [paper](https://arxiv.org/abs/2010.05646) - UnivNet: [paper](https://arxiv.org/abs/2106.07889) ### Voice Conversion - FreeVC: [paper](https://arxiv.org/abs/2210.15418) You can also help us implement more models. ## Installation 🐸TTS is tested on Ubuntu 18.04 with **python >= 3.9, < 3.12.**. If you are only interested in [synthesizing speech](https://tts.readthedocs.io/en/latest/inference.html) with the released 🐸TTS models, installing from PyPI is the easiest option. ```bash pip install TTS ``` If you plan to code or train models, clone 🐸TTS and install it locally. ```bash git clone https://github.com/coqui-ai/TTS pip install -e .[all,dev,notebooks] # Select the relevant extras ``` If you are on Ubuntu (Debian), you can also run following commands for installation. ```bash $ make system-deps # intended to be used on Ubuntu (Debian). Let us know if you have a different OS. $ make install ``` If you are on Windows, 👑@GuyPaddock wrote installation instructions [here](https://stackoverflow.com/questions/66726331/how-can-i-run-mozilla-tts-coqui-tts-training-with-cuda-on-a-windows-system). ## Docker Image You can also try TTS without install with the docker image. Simply run the following command and you will be able to run TTS without installing it. ```bash docker run --rm -it -p 5002:5002 --entrypoint /bin/bash ghcr.io/coqui-ai/tts-cpu python3 TTS/server/server.py --list_models #To get the list of available models python3 TTS/server/server.py --model_name tts_models/en/vctk/vits # To start a server ``` You can then enjoy the TTS server [here](http://[::1]:5002/) More details about the docker images (like GPU support) can be found [here](https://tts.readthedocs.io/en/latest/docker_images.html) ## Synthesizing speech by 🐸TTS ### 🐍 Python API #### Running a multi-speaker and multi-lingual model ```python import torch from TTS.api import TTS # Get device device = "cuda" if torch.cuda.is_available() else "cpu" # List available 🐸TTS models print(TTS().list_models()) # Init TTS tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device) # Run TTS # ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language # Text to speech list of amplitude values as output wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en") # Text to speech to a file tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav") ``` #### Running a single speaker model ```python # Init TTS with the target model name tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False).to(device) # Run TTS tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH) # Example voice cloning with YourTTS in English, French and Portuguese tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False).to(device) tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav") tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr-fr", file_path="output.wav") tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt-br", file_path="output.wav") ``` #### Example voice conversion Converting the voice in `source_wav` to the voice of `target_wav` ```python tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False).to("cuda") tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav") ``` #### Example voice cloning together with the voice conversion model. This way, you can clone voices by using any model in 🐸TTS. ```python tts = TTS("tts_models/de/thorsten/tacotron2-DDC") tts.tts_with_vc_to_file( "Wie sage ich auf Italienisch, dass ich dich liebe?", speaker_wav="target/speaker.wav", file_path="output.wav" ) ``` #### Example text to speech using **Fairseq models in ~1100 languages** 🤯. For Fairseq models, use the following name format: `tts_models/<lang-iso_code>/fairseq/vits`. You can find the language ISO codes [here](https://dl.fbaipublicfiles.com/mms/tts/all-tts-languages.html) and learn about the Fairseq models [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms). ```python # TTS with on the fly voice conversion api = TTS("tts_models/deu/fairseq/vits") api.tts_with_vc_to_file( "Wie sage ich auf Italienisch, dass ich dich liebe?", speaker_wav="target/speaker.wav", file_path="output.wav" ) ``` ### Command-line `tts` <!-- begin-tts-readme --> Synthesize speech on command line. You can either use your trained model or choose a model from the provided list. If you don't specify any models, then it uses LJSpeech based English model. #### Single Speaker Models - List provided models: ``` $ tts --list_models ``` - Get model info (for both tts_models and vocoder_models): - Query by type/name: The model_info_by_name uses the name as it from the --list_models. ``` $ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>" ``` For example: ``` $ tts --model_info_by_name tts_models/tr/common-voice/glow-tts $ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2 ``` - Query by type/idx: The model_query_idx uses the corresponding idx from --list_models. ``` $ tts --model_info_by_idx "<model_type>/<model_query_idx>" ``` For example: ``` $ tts --model_info_by_idx tts_models/3 ``` - Query info for model info by full name: ``` $ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>" ``` - Run TTS with default models: ``` $ tts --text "Text for TTS" --out_path output/path/speech.wav ``` - Run TTS and pipe out the generated TTS wav file data: ``` $ tts --text "Text for TTS" --pipe_out --out_path output/path/speech.wav | aplay ``` - Run a TTS model with its default vocoder model: ``` $ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav ``` For example: ``` $ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav ``` - Run with specific TTS and vocoder models from the list: ``` $ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --vocoder_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav ``` For example: ``` $ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --vocoder_name "vocoder_models/en/ljspeech/univnet" --out_path output/path/speech.wav ``` - Run your own TTS model (Using Griffin-Lim Vocoder): ``` $ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav ``` - Run your own TTS and Vocoder models: ``` $ tts --text "Text for TTS" --model_path path/to/model.pth --config_path path/to/config.json --out_path output/path/speech.wav --vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json ``` #### Multi-speaker Models - List the available speakers and choose a <speaker_id> among them: ``` $ tts --model_name "<language>/<dataset>/<model_name>" --list_speaker_idxs ``` - Run the multi-speaker TTS model with the target speaker ID: ``` $ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --speaker_idx <speaker_id> ``` - Run your own multi-speaker TTS model: ``` $ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/model.pth --config_path path/to/config.json --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id> ``` ### Voice Conversion Models ``` $ tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --source_wav <path/to/speaker/wav> --target_wav <path/to/reference/wav> ``` <!-- end-tts-readme --> ## Directory Structure ``` |- notebooks/ (Jupyter Notebooks for model evaluation, parameter selection and data analysis.) |- utils/ (common utilities.) |- TTS |- bin/ (folder for all the executables.) |- train*.py (train your target model.) |- ... |- tts/ (text to speech models) |- layers/ (model layer definitions) |- models/ (model definitions) |- utils/ (model specific utilities.) |- speaker_encoder/ (Speaker Encoder models.) |- (same) |- vocoder/ (Vocoder models.) |- (same) ``` ", Assign "at most 3 tags" to the expected json: {"id":"1590","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"