audiolm-pytorch: A Python repository from pranavmalikk

AudioLM - Pytorch

Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch

It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper. Yes, this means VALL-E can be trained from this repository. It is essentially the same.

Please join if you are interested in replicating this work in the open

This repository now also contains a MIT licensed version of SoundStream. It is also compatible with EnCodec, however, be aware that it has a more restrictive non-commercial license, if you choose to use it.

Update: AudioLM was essentially used to 'solve' music generation in the new MusicLM

In the future, this movie clip would no longer make any sense. You would just prompt an AI instead.

Appreciation

Stability.ai for the generous sponsorship to work and open source cutting edge artificial intelligence research
🤗 Huggingface for their amazing accelerate and transformers libraries
MetaAI for Fairseq and the liberal license
@eonglints and Joseph for offering their professional advice and expertise as well as pull requests!
@djqualia, @yigityu, @inspirit, and @BlackFox1197 for helping with the debugging of soundstream
Allen and LWprogramming for reviewing the code and submitting bug fixes!
Ilya for finding an issue with multi-scale discriminator downsampling and for soundstream trainer improvements
Andrey for identifying a missing loss in soundstream and guiding me through the proper mel spectrogram hyperparameters
Alejandro and Ilya for sharing their results with training soundstream, and for working through a few issues with the local attention positional embeddings
LWprogramming for adding Encodec compatibility!

Install

$ pip install audiolm-pytorch

Usage

SoundStream & Encodec

There are two options for the neural codec. If you want to use the pretrained 24kHz Encodec, just create an Encodec object as follows:

from audiolm_pytorch import EncodecWrapper
encodec = EncodecWrapper()
# Now you can use the encodec variable in the same way you'd use the soundstream variables below.

Otherwise, to stay more true to the original paper, you can use SoundStream. First, SoundStream needs to be trained on a large corpus of audio data

from audiolm_pytorch import SoundStream, SoundStreamTrainer

soundstream = SoundStream(
    codebook_size = 1024,
    rq_num_quantizers = 8,
    attn_window_size = 128,       # local attention receptive field at bottleneck
    attn_depth = 2                # 2 local attention transformer blocks - the soundstream folks were not experts with attention, so i took the liberty to add some. encodec went with lstms, but attention should be better
)

trainer = SoundStreamTrainer(
    soundstream,
    folder = '/path/to/audio/files',
    batch_size = 4,
    grad_accum_every = 8,         # effective batch size of 32
    data_max_length_seconds = 2,  # train on 2 second audio
    num_train_steps = 1_000_000
).cuda()

trainer.train()

# after a lot of training, you can test the autoencoding as so

audio = torch.randn(10080).cuda()
recons = soundstream(audio, return_recons_only = True) # (1, 10080) - 1 channel

You can also use soundstreams that are specific to AudioLM and MusicLM by importing AudioLMSoundStream and MusicLMSoundStream respectively

from audiolm_pytorch import AudioLMSoundStream, MusicLMSoundStream

soundstream = AudioLMSoundStream(...) # say you want the hyperparameters as in Audio LM paper

# rest is the same as above

As of version 0.17.0, you can now invoke the class method on SoundStream to load from checkpoint files, without having to remember your configurations.

from audiolm_pytorch import SoundStream

soundstream = SoundStream.init_and_load_from('./path/to/checkpoint.pt')

Hierarchical Transformers

Then three separate transformers (SemanticTransformer, CoarseTransformer, FineTransformer) need to be trained

ex. SemanticTransformer

import torch
from audiolm_pytorch import HubertWithKmeans, SemanticTransformer, SemanticTransformerTrainer

# hubert checkpoints can be downloaded at
# https://github.com/facebookresearch/fairseq/tree/main/examples/hubert

wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    dim = 1024,
    depth = 6
).cuda()


trainer = SemanticTransformerTrainer(
    transformer = semantic_transformer,
    wav2vec = wav2vec,
    folder ='/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1
)

trainer.train()

ex. CoarseTransformer

import torch
from audiolm_pytorch import HubertWithKmeans, SoundStream, CoarseTransformer, CoarseTransformerTrainer

wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

soundstream = SoundStream.init_and_load_from('/path/to/trained/soundstream.pt')

coarse_transformer = CoarseTransformer(
    num_semantic_tokens = wav2vec.codebook_size,
    codebook_size = 1024,
    num_coarse_quantizers = 3,
    dim = 512,
    depth = 6
)

trainer = CoarseTransformerTrainer(
    transformer = coarse_transformer,
    codec = soundstream,
    wav2vec = wav2vec,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()

ex. FineTransformer

import torch
from audiolm_pytorch import SoundStream, FineTransformer, FineTransformerTrainer

soundstream = SoundStream.init_and_load_from('/path/to/trained/soundstream.pt')

fine_transformer = FineTransformer(
    num_coarse_quantizers = 3,
    num_fine_quantizers = 5,
    codebook_size = 1024,
    dim = 512,
    depth = 6
)

trainer = FineTransformerTrainer(
    transformer = fine_transformer,
    codec = soundstream,
    folder = '/path/to/audio/files',
    batch_size = 1,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()

All together now

from audiolm_pytorch import AudioLM

audiolm = AudioLM(
    wav2vec = wav2vec,
    codec = soundstream,
    semantic_transformer = semantic_transformer,
    coarse_transformer = coarse_transformer,
    fine_transformer = fine_transformer
)

generated_wav = audiolm(batch_size = 1)

# or with priming

generated_wav_with_prime = audiolm(prime_wave = torch.randn(1, 320 * 8))

# or with text condition, if given

generated_wav_with_text_condition = audiolm(text = ['chirping of birds and the distant echos of bells'])

Text Conditioned Audio Synthesis

Update: Looks like this will work, given 'VALL-E'

ex. Semantic Transformer

import torch
from audiolm_pytorch import HubertWithKmeans, SemanticTransformer, SemanticTransformerTrainer

wav2vec = HubertWithKmeans(
    checkpoint_path = './hubert/hubert_base_ls960.pt',
    kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)

semantic_transformer = SemanticTransformer(
    num_semantic_tokens = 500,
    dim = 1024,
    depth = 6,
    has_condition = True,               # this will have to be set to True
    cond_as_self_attn_prefix = True     # whether to condition as prefix to self attention, instead of cross attention, as was done in 'VALL-E' paper
).cuda()

# mock text video dataset (as an example)

# you will have to extend your own from `Dataset`, and return an audio tensor as well as a string (the audio description) in any order (the framework will autodetect and route it into the transformer)

from torch.utils.data import Dataset

class MockTextAudioDataset(Dataset):
    def __init__(self, length = 100, audio_length = 320 * 32):
        super().__init__()
        self.audio_length = audio_length
        self.len = length

    def __len__(self):
        return self.len

    def __getitem__(self, idx):
        mock_audio = torch.randn(self.audio_length)
        mock_caption = 'audio caption'
        return mock_caption, mock_audio

dataset = MockTextAudioDataset()

# instantiate semantic transformer trainer and train

trainer = SemanticTransformerTrainer(
    transformer = semantic_transformer,
    wav2vec = wav2vec,
    dataset = dataset,
    batch_size = 4,
    grad_accum_every = 8,
    data_max_length = 320 * 32,
    num_train_steps = 1_000_000
)

trainer.train()

# after much training above

sample = trainer.generate(text = ['sound of rain drops on the rooftops'], batch_size = 1, max_length = 2) # (1, < 128) - may terminate early if it detects [eos]

Multi-GPU

Because all the trainer classes uses 🤗 Accelerator, you can easily do multi gpu training by using the accelerate command as so

At the project root

$ accelerate config

Then, in the same directory

$ accelerate launch train.py

Todo

Citations

@inproceedings{Borsos2022AudioLMAL,
  title  = {AudioLM: a Language Modeling Approach to Audio Generation},
  author = {Zal{\'a}n Borsos and Rapha{\"e}l Marinier and Damien Vincent and Eugene Kharitonov and Olivier Pietquin and Matthew Sharifi and Olivier Teboul and David Grangier and Marco Tagliasacchi and Neil Zeghidour},
  year   = {2022}
}

@misc{https://doi.org/10.48550/arxiv.2107.03312,
  title  = {SoundStream: An End-to-End Neural Audio Codec},
  author = {Zeghidour, Neil and Luebs, Alejandro and Omran, Ahmed and Skoglund, Jan and Tagliasacchi, Marco},
  publisher = {arXiv},
  url    = {https://arxiv.org/abs/2107.03312},
  year   = {2021}
}

@misc{shazeer2020glu,
    title   = {GLU Variants Improve Transformer},
    author  = {Noam Shazeer},
    year    = {2020},
    url     = {https://arxiv.org/abs/2002.05202}
}

@article{Shazeer2019FastTD,
    title   = {Fast Transformer Decoding: One Write-Head is All You Need},
    author  = {Noam M. Shazeer},
    journal = {ArXiv},
    year    = {2019},
    volume  = {abs/1911.02150}
}

@article{Ho2022ClassifierFreeDG,
    title   = {Classifier-Free Diffusion Guidance},
    author  = {Jonathan Ho},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2207.12598}
}

@misc{crowson2022,
    author  = {Katherine Crowson},
    url     = {https://twitter.com/rivershavewings}
}

@misc{ding2021cogview,
    title   = {CogView: Mastering Text-to-Image Generation via Transformers},
    author  = {Ming Ding and Zhuoyi Yang and Wenyi Hong and Wendi Zheng and Chang Zhou and Da Yin and Junyang Lin and Xu Zou and Zhou Shao and Hongxia Yang and Jie Tang},
    year    = {2021},
    eprint  = {2105.13290},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@article{Liu2022FCMFC,
    title   = {FCM: Forgetful Causal Masking Makes Causal Language Models Better Zero-Shot Learners},
    author  = {Hao Liu and Xinyang Geng and Lisa Lee and Igor Mordatch and Sergey Levine and Sharan Narang and P. Abbeel},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2210.13432}
}

@inproceedings{anonymous2022normformer,
    title   = {NormFormer: Improved Transformer Pretraining with Extra Normalization},
    author  = {Anonymous},
    booktitle = {Submitted to The Tenth International Conference on Learning Representations },
    year    = {2022},
    url     = {https://openreview.net/forum?id=GMYWzWztDx5},
    note    = {under review}
}

@article{Li2021LocalViTBL,
    title   = {LocalViT: Bringing Locality to Vision Transformers},
    author  = {Yawei Li and K. Zhang and Jie Cao and Radu Timofte and Luc Van Gool},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2104.05707}
}

@misc{liu2021swin,
    title   = {Swin Transformer V2: Scaling Up Capacity and Resolution},
    author  = {Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
    year    = {2021},
    eprint  = {2111.09883},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@inproceedings{Ma2022MegaMA,
    title   = {Mega: Moving Average Equipped Gated Attention},
    author  = {Xuezhe Ma and Chunting Zhou and Xiang Kong and Junxian He and Liangke Gui and Graham Neubig and Jonathan May and Luke Zettlemoyer},
    year    = {2022}
}

@misc{gilmer2023intriguing
    title  = {Intriguing Properties of Transformer Training Instabilities},
    author = {Justin Gilmer, Andrea Schioppa, and Jeremy Cohen},
    year   = {2023},
    status = {to be published - one attention stabilization technique is circulating within Google Brain, being used by multiple teams}
}

@article{Defossez2022HighFN,
    title   = {High Fidelity Neural Audio Compression},
    author  = {Alexandre D'efossez and Jade Copet and Gabriel Synnaeve and Yossi Adi},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2210.13438}
}

@article{Hu2017SqueezeandExcitationN,
    title   = {Squeeze-and-Excitation Networks},
    author  = {Jie Hu and Li Shen and Gang Sun},
    journal = {2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year    = {2017},
    pages   = {7132-7141}
}

pranavmalikk/audiolm-pytorch