soikat1139/soundstorm-pytorch

Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch

Soundstorm - Pytorch (wip)

Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch.

They basically applied MaskGiT to the residual vector quantized codes from Soundstream. The transformer architecture they chose to use is one that fits well with the audio domain, named Conformer

Project Page

Appreciation

• Stability and 🤗 Huggingface for their generous sponsorships to work on and open source cutting edge artificial intelligence research

• 🤗 Accelerate for providing a simple and powerful solution for training

• Einops for the indispensable abstraction that makes building neural networks fun, easy, and uplifting

• Steven Hillis for submitting the correct masking strategy and for verifying that the repository works! 🙏

Install

$ pip install soundstorm-pytorch

Usage

import torch
from soundstorm_pytorch import SoundStorm, ConformerWrapper

conformer = ConformerWrapper(
    codebook_size = 1024,
    num_quantizers = 4,
    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))

# 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)

To directly train on raw audio, you need to pass in your pretrained SoundStream into SoundStorm. You can train your own SoundStream at audiolm-pytorch.

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

conformer = ConformerWrapper(
    codebook_size = 1024,
    num_quantizers = 4,
    conformer = dict(
        dim = 512,
        depth = 2
    ),
)

soundstream = SoundStream(
    codebook_size = 1024,
    rq_num_quantizers = 4,
    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)

Todo

• integrate soundstream

• when generating, and length can be defined in seconds (takes into sampling freq etc)

• make sure grouped rvq is supported. concat embeddings rather than sum across group dimension

• just copy conformer over and redo shaw's relative positional embedding with rotary embedding. nobody uses shaw anymore.

• default flash attention to true

• remove batchnorm, and just use layernorm, but after the swish (as in normformer paper)

• option to return list of audio files when generating

• turn it into a command line tool

• add cross attention and adaptive layernorm conditioning

• trainer with accelerate

Citations

@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}
}