e2 tts pytorch

E2 TTS - Pytorch

Implementasi E2-TTS, TTS Zero-Shot Non-Autoregresif yang Sangat Mudah dan Memalukan, di Pytorch

Repositori berbeda dari kertas karena menggunakan trafo multistream untuk teks dan audio, dengan pengkondisian dilakukan setiap blok trafo dengan cara E2.

Ini juga mencakup improvisasi yang dibuktikan oleh Manmay, di mana teks hanya diinterpolasi sepanjang audio untuk pengkondisian. Anda dapat mencobanya dengan mengatur interpolated_text = True pada E2TTS

Apresiasi

• Manmay karena telah menyumbangkan kode pelatihan ujung ke ujung yang berfungsi!

• Lucas Newman atas kontribusi kode, masukan yang bermanfaat, dan berbagi rangkaian eksperimen positif pertama!

• Jing karena telah membagikan hasil positif kedua dengan kumpulan data multibahasa (Inggris + Mandarin)!

• Coice dan Manmay karena melaporkan keberhasilan ketiga dan keempat. Rekayasa penyelarasan perpisahan

Memasang

$ pip install e2-tts-pytorch

Penggunaan

 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 )

Kutipan

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    title   = { E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS } ,
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    year    = { 2024 } ,
    url     = { https://api.semanticscholar.org/CorpusID:270738197 }
}

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}

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}

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}

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}

 @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 } ,
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    volume  = { abs/2407.02398 } ,
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}

 @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 } ,
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}

 @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 } ,
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}

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    title   = { Hyper-Connections } ,
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}