Unduh soundstorm pytorch - pengunduhan kode sumber soundstorm pytorch

Badai Suara - Pytorch

Implementasi SoundStorm, Pembuatan Audio Paralel yang Efisien dari Google Deepmind, di Pytorch.

Mereka pada dasarnya menerapkan MaskGiT ke kode terkuantisasi vektor sisa dari Soundstream. Arsitektur transformator yang mereka pilih untuk digunakan adalah arsitektur yang cocok dengan domain audio, bernama Conformer

Halaman Proyek

Apresiasi

Stabilitas dan? Huggingface atas sponsorship mereka yang murah hati untuk mengerjakan dan membuka sumber penelitian kecerdasan buatan yang canggih
Lucas Newman atas berbagai kontribusinya, termasuk kode pelatihan awal, logika dorongan akustik, decoding kuantizer per level!
? Mempercepat penyediaan solusi sederhana dan kuat untuk pelatihan
Terima kasih atas abstraksi yang sangat diperlukan yang membuat pembangunan jaringan saraf menjadi menyenangkan, mudah, dan membangkitkan semangat
Steven Hillis karena mengirimkan strategi masking yang benar dan memverifikasi bahwa repositori berfungsi!
Lucas Newman pada dasarnya melatih Soundstorm kecil yang berfungsi dengan model di berbagai repositori, menunjukkan semuanya berfungsi secara end-to-end. Modelnya termasuk SoundStream, Text-to-Semantic T5, dan terakhir transformator SoundStorm di sini.
@Jiang-Stan karena mengidentifikasi bug kritis dalam pembukaan kedok berulang!

Memasang

$ pip install soundstorm-pytorch

Penggunaan

 import torch
from soundstorm_pytorch import SoundStorm , ConformerWrapper

conformer = ConformerWrapper (
    codebook_size = 1024 ,
    num_quantizers = 12 ,
    conformer = dict (
        dim = 512 ,
        depth = 2
    ),
)

model = SoundStorm (
    conformer ,
    steps = 18 ,          # 18 steps, as in original maskgit paper
    schedule = 'cosine'  # currently the best schedule is cosine
)

# get your pre-encoded codebook ids from the soundstream from a lot of raw audio

codes = torch . randint ( 0 , 1024 , ( 2 , 1024 , 12 )) # (batch, seq, num residual VQ)

# do the below in a loop for a ton of data

loss , _ = model ( codes )
loss . backward ()

# model can now generate in 18 steps. ~2 seconds sounds reasonable

generated = model . generate ( 1024 , batch_size = 2 ) # (2, 1024)

Untuk langsung melatih audio mentah, Anda harus meneruskan SoundStream yang telah dilatih sebelumnya ke SoundStorm . Anda dapat melatih SoundStream Anda sendiri di audiolm-pytorch.

 import torch
from soundstorm_pytorch import SoundStorm , ConformerWrapper , Conformer , SoundStream

conformer = ConformerWrapper (
    codebook_size = 1024 ,
    num_quantizers = 12 ,
    conformer = dict (
        dim = 512 ,
        depth = 2
    ),
)

soundstream = SoundStream (
    codebook_size = 1024 ,
    rq_num_quantizers = 12 ,
    attn_window_size = 128 ,
    attn_depth = 2
)

model = SoundStorm (
    conformer ,
    soundstream = soundstream   # pass in the soundstream
)

# find as much audio you'd like the model to learn

audio = torch . randn ( 2 , 10080 )

# course it through the model and take a gazillion tiny steps

loss , _ = model ( audio )
loss . backward ()

# and now you can generate state-of-the-art speech

generated_audio = model . generate ( seconds = 30 , batch_size = 2 )  # generate 30 seconds of audio (it will calculate the length in seconds based off the sampling frequency and cumulative downsamples in the soundstream passed in above)

Text-to-speech yang lengkap akan bergantung pada transformator encoder/decoder TextToSemantic yang terlatih. Anda kemudian akan memuat bobot dan meneruskannya ke SoundStorm sebagai spear_tts_text_to_semantic

Ini masih dalam proses, karena spear-tts-pytorch hanya memiliki arsitektur model yang lengkap, dan bukan logika pra-pelatihan + pelabelan semu + terjemahan balik.

 from spear_tts_pytorch import TextToSemantic

text_to_semantic = TextToSemantic (
    dim = 512 ,
    source_depth = 12 ,
    target_depth = 12 ,
    num_text_token_ids = 50000 ,
    num_semantic_token_ids = 20000 ,
    use_openai_tokenizer = True
)

# load the trained text-to-semantic transformer

text_to_semantic . load ( '/path/to/trained/model.pt' )

# pass it into the soundstorm

model = SoundStorm (
    conformer ,
    soundstream = soundstream ,
    spear_tts_text_to_semantic = text_to_semantic
). cuda ()

# and now you can generate state-of-the-art speech

generated_speech = model . generate (
    texts = [
        'the rain in spain stays mainly in the plain' ,
        'the quick brown fox jumps over the lazy dog'
    ]
) # (2, n) - raw waveform decoded from soundstream

Semua yang harus dilakukan

Kutipan

 @misc { borsos2023soundstorm ,
    title   = { SoundStorm: Efficient Parallel Audio Generation } , 
    author  = { Zalán Borsos and Matt Sharifi and Damien Vincent and Eugene Kharitonov and Neil Zeghidour and Marco Tagliasacchi } ,
    year    = { 2023 } ,
    eprint  = { 2305.09636 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.SD }
}

 @inproceedings { dao2022flashattention ,
    title   = { Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness } ,
    author  = { Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{'e}, Christopher } ,
    booktitle = { Advances in Neural Information Processing Systems } ,
    year    = { 2022 }
}

 @article { Chang2022MaskGITMG ,
    title   = { MaskGIT: Masked Generative Image Transformer } ,
    author  = { Huiwen Chang and Han Zhang and Lu Jiang and Ce Liu and William T. Freeman } ,
    journal = { 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) } ,
    year    = { 2022 } ,
    pages   = { 11305-11315 }
}

 @article { Lezama2022ImprovedMI ,
    title   = { Improved Masked Image Generation with Token-Critic } ,
    author  = { Jos{'e} Lezama and Huiwen Chang and Lu Jiang and Irfan Essa } ,
    journal = { ArXiv } ,
    year    = { 2022 } ,
    volume  = { abs/2209.04439 }
}

 @inproceedings { Nijkamp2021SCRIPTSP ,
    title   = { SCRIPT: Self-Critic PreTraining of Transformers } ,
    author  = { Erik Nijkamp and Bo Pang and Ying Nian Wu and Caiming Xiong } ,
    booktitle = { North American Chapter of the Association for Computational Linguistics } ,
    year    = { 2021 }
}

 @inproceedings { rogozhnikov2022einops ,
    title   = { Einops: Clear and Reliable Tensor Manipulations with Einstein-like Notation } ,
    author  = { Alex Rogozhnikov } ,
    booktitle = { International Conference on Learning Representations } ,
    year    = { 2022 } ,
    url     = { https://openreview.net/forum?id=oapKSVM2bcj }
}

 @misc { su2021roformer ,
    title   = { RoFormer: Enhanced Transformer with Rotary Position Embedding } ,
    author  = { Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu } ,
    year    = { 2021 } ,
    eprint  = { 2104.09864 } ,
    archivePrefix = { arXiv } ,
    primaryClass = { cs.CL }
}

 @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 }
}