e2 tts pytorch下载 - e2 tts pytorch源码下载

E2 TTS - Pytorch

在 Pytorch 中实现 E2-TTS，极其简单的完全非自回归零样本 TTS

该存储库与论文的不同之处在于它使用多流转换器来处理文本和音频，并以 E2 方式对每个转换器块进行调节。

它还包括经 Manmay 证明的即兴创作，其中文本只是简单地插入到音频的长度中以进行调节。您可以通过在E2TTS上设置interpolated_text = True来尝试此操作

欣赏

Manmay 贡献了有效的端到端培训代码！
Lucas Newman 贡献了代码，提供了有用的反馈，并分享了第一组积极的实验！
Jing 分享了多语言（英语+中文）数据集的第二个阳性结果！
科伊斯和曼梅报告了第三次和第四次成功运行。告别对准工程

安装

$ pip install e2-tts-pytorch

用法

 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 )

引文

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}

 @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 }
}