Metadata-Version: 2.4
Name: e2-tts-pytorch
Version: 2.2.1
Summary: E2-TTS in Pytorch
Project-URL: Homepage, https://pypi.org/project/e2-tts-pytorch/
Project-URL: Repository, https://github.com/lucidrains/e2-tts-pytorch
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Phil Wang
        
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License-File: LICENSE
Keywords: artificial intelligence,attention mechanism,deep learning,text to speech,transformers
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Requires-Dist: accelerate>=0.33.0
Requires-Dist: adam-atan2-pytorch>=0.1.12
Requires-Dist: beartype
Requires-Dist: einops>=0.8.0
Requires-Dist: einx>=0.3.0
Requires-Dist: ema-pytorch>=0.5.2
Requires-Dist: g2p-en
Requires-Dist: hl-gauss-pytorch>=0.1.7
Requires-Dist: hyper-connections>=0.0.10
Requires-Dist: jaxtyping
Requires-Dist: loguru
Requires-Dist: pydantic<2
Requires-Dist: tensorboard
Requires-Dist: torch>=2.0
Requires-Dist: torchaudio>=2.3.1
Requires-Dist: torchdiffeq
Requires-Dist: tqdm>=4.65.0
Requires-Dist: vocos
Requires-Dist: x-transformers>=1.42.23
Provides-Extra: examples
Requires-Dist: datasets; extra == 'examples'
Description-Content-Type: text/markdown


<img src="./e2-tts.png" width="400px"></img>

## E2 TTS - Pytorch

Implementation of E2-TTS, <a href="https://arxiv.org/abs/2406.18009v1">Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS</a>, in Pytorch

The repository differs from the paper in that it uses a <a href="https://arxiv.org/abs/2107.10342">multistream transformer</a> for text and audio, with conditioning done every transformer block in the E2 manner.

It also includes an improvisation that was proven out by Manmay, where the text is simply interpolated to the length of the audio for conditioning. You can try this by setting `interpolated_text = True` on `E2TTS`

## Appreciation

- <a href="https://github.com/manmay-nakhashi">Manmay</a> for contributing <a href="https://github.com/lucidrains/e2-tts-pytorch/pull/1">working end-to-end training code</a>!

- <a href="https://github.com/lucasnewman">Lucas Newman</a> for the code contributions, helpful feedback, and for sharing the first set of positive experiments!

- <a href="https://github.com/JingRH">Jing</a> for sharing the second positive result with a multilingual (English + Chinese) dataset!

- <a href="https://github.com/Coice">Coice</a> and <a href="https://github.com/manmay-nakhashi">Manmay</a> for reporting the third and fourth successful runs. Farewell alignment engineering

## Install

```bash
$ pip install e2-tts-pytorch
```

## Usage

```python
import torch

from e2_tts_pytorch import (
    E2TTS,
    DurationPredictor
)

duration_predictor = DurationPredictor(
    transformer = dict(
        dim = 512,
        depth = 8,
    )
)

mel = torch.randn(2, 1024, 100)
text = ['Hello', 'Goodbye']

loss = duration_predictor(mel, text = text)
loss.backward()

e2tts = E2TTS(
    duration_predictor = duration_predictor,
    transformer = dict(
        dim = 512,
        depth = 8        
    ),
)

out = e2tts(mel, text = text)
out.loss.backward()

sampled = e2tts.sample(mel[:, :5], text = text)

```

## Related Works

- [Nanospeech](https://github.com/lucasnewman/nanospeech) by [Lucas Newman](https://github.com/lucasnewman), which contains training code, working examples, as well as interoperable MLX version!

## Citations

```bibtex
@inproceedings{Eskimez2024E2TE,
    title   = {E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS},
    author  = {Sefik Emre Eskimez and Xiaofei Wang and Manthan Thakker and Canrun Li and Chung-Hsien Tsai and Zhen Xiao and Hemin Yang and Zirun Zhu and Min Tang and Xu Tan and Yanqing Liu and Sheng Zhao and Naoyuki Kanda},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:270738197}
}
```

```bibtex
@inproceedings{Darcet2023VisionTN,
    title   = {Vision Transformers Need Registers},
    author  = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:263134283}
}
```

```bibtex
@article{Bao2022AllAW,
    title   = {All are Worth Words: A ViT Backbone for Diffusion Models},
    author  = {Fan Bao and Shen Nie and Kaiwen Xue and Yue Cao and Chongxuan Li and Hang Su and Jun Zhu},
    journal = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year    = {2022},
    pages   = {22669-22679},
    url     = {https://api.semanticscholar.org/CorpusID:253581703}
}
```

```bibtex
@article{Burtsev2021MultiStreamT,
    title     = {Multi-Stream Transformers},
    author    = {Mikhail S. Burtsev and Anna Rumshisky},
    journal   = {ArXiv},
    year      = {2021},
    volume    = {abs/2107.10342},
    url       = {https://api.semanticscholar.org/CorpusID:236171087}
}
```

```bibtex
@inproceedings{Sadat2024EliminatingOA,
    title   = {Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models},
    author  = {Seyedmorteza Sadat and Otmar Hilliges and Romann M. Weber},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273098845}
}
```

```bibtex
@article{Gulati2020ConformerCT,
    title   = {Conformer: Convolution-augmented Transformer for Speech Recognition},
    author  = {Anmol Gulati and James Qin and Chung-Cheng Chiu and Niki Parmar and Yu Zhang and Jiahui Yu and Wei Han and Shibo Wang and Zhengdong Zhang and Yonghui Wu and Ruoming Pang},
    journal = {ArXiv},
    year    = {2020},
    volume  = {abs/2005.08100},
    url     = {https://api.semanticscholar.org/CorpusID:218674528}
}
```

```bibtex
@article{Yang2024ConsistencyFM,
    title   = {Consistency Flow Matching: Defining Straight Flows with Velocity Consistency},
    author  = {Ling Yang and Zixiang Zhang and Zhilong Zhang and Xingchao Liu and Minkai Xu and Wentao Zhang and Chenlin Meng and Stefano Ermon and Bin Cui},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2407.02398},
    url     = {https://api.semanticscholar.org/CorpusID:270878436}
}
```

```bibtex
@article{Li2024SwitchEA,
    title   = {Switch EMA: A Free Lunch for Better Flatness and Sharpness},
    author  = {Siyuan Li and Zicheng Liu and Juanxi Tian and Ge Wang and Zedong Wang and Weiyang Jin and Di Wu and Cheng Tan and Tao Lin and Yang Liu and Baigui Sun and Stan Z. Li},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2402.09240},
    url     = {https://api.semanticscholar.org/CorpusID:267657558}
}
```

```bibtex
@inproceedings{Zhou2024ValueRL,
    title   = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
    author  = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273532030}
}
```

```bibtex
@inproceedings{Duvvuri2024LASERAW,
    title   = {LASER: Attention with Exponential Transformation},
    author  = {Sai Surya Duvvuri and Inderjit S. Dhillon},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273849947}
}
```

```bibtex
@article{Zhu2024HyperConnections,
    title   = {Hyper-Connections},
    author  = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2409.19606},
    url     = {https://api.semanticscholar.org/CorpusID:272987528}
}
```

```bibtex
@inproceedings{Lu2023MusicSS,
    title   = {Music Source Separation with Band-Split RoPE Transformer},
    author  = {Wei-Tsung Lu and Ju-Chiang Wang and Qiuqiang Kong and Yun-Ning Hung},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:261556702}
}
```

```bibtex
@inproceedings{Dong2025FANformerIL,
    title   = {FANformer: Improving Large Language Models Through Effective Periodicity Modeling},
    author  = {Yi Dong and Ge Li and Xue Jiang and Yongding Tao and Kechi Zhang and Hao Zhu and Huanyu Liu and Jiazheng Ding and Jia Li and Jinliang Deng and Hong Mei},
    year    = {2025},
    url     = {https://api.semanticscholar.org/CorpusID:276724636}
}
```

```bibtex
@article{Karras2024GuidingAD,
    title   = {Guiding a Diffusion Model with a Bad Version of Itself},
    author  = {Tero Karras and Miika Aittala and Tuomas Kynk{\"a}{\"a}nniemi and Jaakko Lehtinen and Timo Aila and Samuli Laine},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2406.02507},
    url     = {https://api.semanticscholar.org/CorpusID:270226598}
}
```
