soundstorm pytorch ดาวน์โหลด - soundstorm pytorch ดาวน์โหลดซอร์สโค้ด

การใช้งาน SoundStorm การสร้างเสียงคู่ขนานที่มีประสิทธิภาพจาก Google Deepmind ใน Pytorch

โดยทั่วไปแล้ว พวกเขาใช้ MaskGiT กับโค้ดเวกเตอร์เชิงปริมาณที่เหลือจาก Soundstream สถาปัตยกรรมหม้อแปลงที่พวกเขาเลือกใช้นั้นเป็นสถาปัตยกรรมที่เหมาะกับโดเมนเสียงที่ชื่อว่า Conformer

$ pip install soundstorm-pytorch

 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)

หากต้องการฝึกเสียง Raw โดยตรง คุณจะต้องส่ง SoundStream ที่ได้รับการฝึกมาแล้วไปยัง SoundStorm คุณสามารถฝึก SoundStream ของคุณเองได้ที่ 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)

การแปลงข้อความเป็นคำพูดที่สมบูรณ์จะขึ้นอยู่กับตัวแปลงตัวเข้ารหัส / ตัวถอดรหัส TextToSemantic ที่ได้รับการฝึกอบรม จากนั้นคุณจะโหลดตุ้มน้ำหนักและส่งผ่านไปยัง SoundStorm เป็น spear_tts_text_to_semantic

นี่เป็นงานที่อยู่ระหว่างดำเนินการ เนื่องจาก spear-tts-pytorch มีเฉพาะสถาปัตยกรรมโมเดลที่สมบูรณ์เท่านั้น และไม่ใช่ตรรกะ pretraining + pseudo-labeling + backtranslation

 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

 @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 }
}