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| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 15:36:10 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 15:36:10 +0000 |
| commit | 58252e966488a96271875cf7a49928c880265a29 (patch) | |
| tree | 66081e4288e60de635d8571001d2662c57b3cba4 | |
| parent | f664080d3b12ed8aaaa3283efb7a9c6197c01db6 (diff) | |
automatic import of python-ttsopeneuler20.03
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-tts.spec | 1166 | ||||
| -rw-r--r-- | sources | 1 |
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@@ -0,0 +1 @@ +/TTS-0.13.3.tar.gz diff --git a/python-tts.spec b/python-tts.spec new file mode 100644 index 0000000..d5fde32 --- /dev/null +++ b/python-tts.spec @@ -0,0 +1,1166 @@ +%global _empty_manifest_terminate_build 0 +Name: python-TTS +Version: 0.13.3 +Release: 1 +Summary: Deep learning for Text to Speech by Coqui. +License: MPL-2.0 +URL: https://github.com/coqui-ai/TTS +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dc/4d/c39d3cbc089ea6733f7ddf9785b69663759470fa4cd6c12e087827d6dc74/TTS-0.13.3.tar.gz +BuildArch: noarch + +Requires: python3-cython +Requires: python3-scipy +Requires: python3-torch +Requires: python3-torchaudio +Requires: python3-soundfile +Requires: python3-librosa +Requires: python3-inflect +Requires: python3-tqdm +Requires: python3-anyascii +Requires: python3-pyyaml +Requires: python3-fsspec +Requires: python3-aiohttp +Requires: python3-packaging +Requires: python3-flask +Requires: python3-pysbd +Requires: python3-umap-learn +Requires: python3-pandas +Requires: python3-matplotlib +Requires: python3-trainer +Requires: python3-coqpit +Requires: python3-jieba +Requires: python3-pypinyin +Requires: python3-mecab-python3 +Requires: python3-unidic-lite +Requires: python3-gruut[de] +Requires: python3-jamo +Requires: python3-nltk +Requires: python3-g2pkk +Requires: python3-bangla +Requires: python3-bnnumerizer +Requires: python3-bnunicodenormalizer +Requires: python3-numpy +Requires: python3-numba +Requires: python3-numpy +Requires: python3-numba +Requires: python3-black +Requires: python3-coverage +Requires: python3-isort +Requires: python3-nose2 +Requires: python3-pylint +Requires: python3-bokeh +Requires: python3-black +Requires: python3-coverage +Requires: python3-isort +Requires: python3-nose2 +Requires: python3-pylint +Requires: python3-bokeh + +%description + + +## 🐸Coqui.ai News +- 📣 Coqui Studio API is landed on 🐸TTS. You can use the studio voices in combination with 🐸TTS models. [Example](https://github.com/coqui-ai/TTS/edit/dev/README.md#-python-api) +- 📣 Voice generation with prompts - **Prompt to Voice** - is live on Coqui.ai!! [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice) +- 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin) +<br> + +## <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. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. +🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects. + +[](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/) + +📰 [**Subscribe to 🐸Coqui.ai Newsletter**](https://coqui.ai/?subscription=true) + +📢 [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2) + +📄 [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers) + +<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" /> + +## 💬 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#install-tts)| +| 👩💻 **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)| + +## 🥇 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 🐸TTS models +<!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) --> + +## 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. + +## Implemented Models +### 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) + +### End-to-End Models +- VITS: [paper](https://arxiv.org/pdf/2106.06103) +- YourTTS: [paper](https://arxiv.org/abs/2112.02418) + +### 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. + +## Install TTS +🐸TTS is tested on Ubuntu 18.04 with **python >= 3.7, < 3.11.**. + +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 + +```python +from TTS.api import TTS + +# Running a multi-speaker and multi-lingual model + +# List available 🐸TTS models and choose the first one +model_name = TTS.list_models()[0] +# Init TTS +tts = TTS(model_name) +# Run TTS +# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language +# Text to speech with a numpy output +wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0]) +# Text to speech to a file +tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav") + +# Running a single speaker model + +# Init TTS with the target model name +tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False) +# 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, gpu=True) +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 speaker of the `source_wav` to the speaker of the `target_wav` + +tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True) +tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav") + +# Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can +# clone voices by using any model in 🐸TTS. + +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="ouptut.wav" +) + +# Example text to speech using [🐸Coqui Studio](https://coqui.ai) models. You can use all of your available speakers in the studio. +# [🐸Coqui Studio](https://coqui.ai) API token is required. You can get it from the [account page](https://coqui.ai/account). +# You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token. + +# If you have a valid API token set you will see the studio speakers as separate models in the list. +# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio +models = TTS().list_models() +# Init TTS with the target studio speaker +tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False, gpu=False) +# Run TTS +tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH) +# Run TTS with emotion and speed control +tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5) +``` + +### Command line `tts` +#### 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 + ``` + +- Run TTS with default models: + + ``` + $ tts --text "Text for TTS" --out_path output/path/speech.wav + ``` + +- 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 as <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> + ``` + +## 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) +``` + + +%package -n python3-TTS +Summary: Deep learning for Text to Speech by Coqui. +Provides: python-TTS +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-TTS + + +## 🐸Coqui.ai News +- 📣 Coqui Studio API is landed on 🐸TTS. You can use the studio voices in combination with 🐸TTS models. [Example](https://github.com/coqui-ai/TTS/edit/dev/README.md#-python-api) +- 📣 Voice generation with prompts - **Prompt to Voice** - is live on Coqui.ai!! [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice) +- 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin) +<br> + +## <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. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. +🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects. + +[](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/) + +📰 [**Subscribe to 🐸Coqui.ai Newsletter**](https://coqui.ai/?subscription=true) + +📢 [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2) + +📄 [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers) + +<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" /> + +## 💬 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#install-tts)| +| 👩💻 **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)| + +## 🥇 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 🐸TTS models +<!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) --> + +## 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. + +## Implemented Models +### 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) + +### End-to-End Models +- VITS: [paper](https://arxiv.org/pdf/2106.06103) +- YourTTS: [paper](https://arxiv.org/abs/2112.02418) + +### 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. + +## Install TTS +🐸TTS is tested on Ubuntu 18.04 with **python >= 3.7, < 3.11.**. + +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 + +```python +from TTS.api import TTS + +# Running a multi-speaker and multi-lingual model + +# List available 🐸TTS models and choose the first one +model_name = TTS.list_models()[0] +# Init TTS +tts = TTS(model_name) +# Run TTS +# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language +# Text to speech with a numpy output +wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0]) +# Text to speech to a file +tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav") + +# Running a single speaker model + +# Init TTS with the target model name +tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False) +# 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, gpu=True) +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 speaker of the `source_wav` to the speaker of the `target_wav` + +tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True) +tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav") + +# Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can +# clone voices by using any model in 🐸TTS. + +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="ouptut.wav" +) + +# Example text to speech using [🐸Coqui Studio](https://coqui.ai) models. You can use all of your available speakers in the studio. +# [🐸Coqui Studio](https://coqui.ai) API token is required. You can get it from the [account page](https://coqui.ai/account). +# You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token. + +# If you have a valid API token set you will see the studio speakers as separate models in the list. +# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio +models = TTS().list_models() +# Init TTS with the target studio speaker +tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False, gpu=False) +# Run TTS +tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH) +# Run TTS with emotion and speed control +tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5) +``` + +### Command line `tts` +#### 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 + ``` + +- Run TTS with default models: + + ``` + $ tts --text "Text for TTS" --out_path output/path/speech.wav + ``` + +- 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 as <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> + ``` + +## 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) +``` + + +%package help +Summary: Development documents and examples for TTS +Provides: python3-TTS-doc +%description help + + +## 🐸Coqui.ai News +- 📣 Coqui Studio API is landed on 🐸TTS. You can use the studio voices in combination with 🐸TTS models. [Example](https://github.com/coqui-ai/TTS/edit/dev/README.md#-python-api) +- 📣 Voice generation with prompts - **Prompt to Voice** - is live on Coqui.ai!! [Blog Post](https://coqui.ai/blog/tts/prompt-to-voice) +- 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin) +<br> + +## <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. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. +🐸TTS comes with pretrained models, tools for measuring dataset quality and already used in **20+ languages** for products and research projects. + +[](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/) + +📰 [**Subscribe to 🐸Coqui.ai Newsletter**](https://coqui.ai/?subscription=true) + +📢 [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2) + +📄 [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers) + +<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" /> + +## 💬 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#install-tts)| +| 👩💻 **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)| + +## 🥇 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 🐸TTS models +<!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) --> + +## 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. + +## Implemented Models +### 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) + +### End-to-End Models +- VITS: [paper](https://arxiv.org/pdf/2106.06103) +- YourTTS: [paper](https://arxiv.org/abs/2112.02418) + +### 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. + +## Install TTS +🐸TTS is tested on Ubuntu 18.04 with **python >= 3.7, < 3.11.**. + +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 + +```python +from TTS.api import TTS + +# Running a multi-speaker and multi-lingual model + +# List available 🐸TTS models and choose the first one +model_name = TTS.list_models()[0] +# Init TTS +tts = TTS(model_name) +# Run TTS +# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language +# Text to speech with a numpy output +wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0]) +# Text to speech to a file +tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav") + +# Running a single speaker model + +# Init TTS with the target model name +tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False) +# 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, gpu=True) +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 speaker of the `source_wav` to the speaker of the `target_wav` + +tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True) +tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav") + +# Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can +# clone voices by using any model in 🐸TTS. + +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="ouptut.wav" +) + +# Example text to speech using [🐸Coqui Studio](https://coqui.ai) models. You can use all of your available speakers in the studio. +# [🐸Coqui Studio](https://coqui.ai) API token is required. You can get it from the [account page](https://coqui.ai/account). +# You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token. + +# If you have a valid API token set you will see the studio speakers as separate models in the list. +# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio +models = TTS().list_models() +# Init TTS with the target studio speaker +tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False, gpu=False) +# Run TTS +tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH) +# Run TTS with emotion and speed control +tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5) +``` + +### Command line `tts` +#### 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 + ``` + +- Run TTS with default models: + + ``` + $ tts --text "Text for TTS" --out_path output/path/speech.wav + ``` + +- 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 as <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> + ``` + +## 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) +``` + + +%prep +%autosetup -n TTS-0.13.3 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-TTS -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.13.3-1 +- Package Spec generated @@ -0,0 +1 @@ +d916c352a9c6d079574d4588a43c3a44 TTS-0.13.3.tar.gz |