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.
$ pip install audiolm-pytorchFirst, 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,
)
trainer = SoundStreamTrainer(
soundstream,
folder = '/path/to/librispeech',
batch_size = 4,
data_max_length = 320 * 32,
num_train_steps = 10000
).cuda()
trainer.train()Then three separate transformers (SemanticTransformer, CoarseTransformer, FineTransformer) need to be trained
ex. SemanticTransformer
import torch
from audiolm_pytorch import HubertWithKmeans, SemanticTransformer
wav2vec = HubertWithKmeans(
checkpoint_path = './hubert/hubert_base_ls960.pt',
kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)
semantic_transformer = SemanticTransformer(
wav2vec = wav2vec,
dim = 1024,
depth = 6
).cuda()
wave = torch.randn(1, 320 * 512).cuda()
loss = semantic_transformer(
raw_wave = wave,
return_loss = True
)
loss.backward()
# after much training above
sample = semantic_transformer.generate(max_length = 128) # (1, < 128) - may terminate early if it detects [eos]ex. CoarseTransformer
import torch
from audiolm_pytorch import HubertWithKmeans, SoundStream, CoarseTransformer, CoarseTransformerWrapper
wav2vec = HubertWithKmeans(
checkpoint_path = './hubert/hubert_base_ls960.pt',
kmeans_path = './hubert/hubert_base_ls960_L9_km500.bin'
)
soundstream = SoundStream(
codebook_size = 1024,
rq_num_quantizers = 8,
)
coarse_transformer = CoarseTransformer(
wav2vec = wav2vec,
codebook_size = 1024,
num_coarse_quantizers = 3,
dim = 512,
depth = 6
)
coarse_wrapper = CoarseTransformerWrapper(
wav2vec = wav2vec,
soundstream = soundstream,
transformer = coarse_transformer
).cuda()
wave = torch.randn(1, 32 * 320).cuda()
loss = coarse_wrapper(
raw_wave = wave,
return_loss = True
)
loss.backward()ex. FineTransformer
import torch
from audiolm_pytorch import SoundStream, FineTransformer, FineTransformerWrapper
soundstream = SoundStream(
codebook_size = 1024,
rq_num_quantizers = 8,
)
soundstream.load('/path/to/trained/soundstream.pt')
transformer = FineTransformer(
num_coarse_quantizers = 3,
num_fine_quantizers = 5,
codebook_size = 1024,
dim = 512,
depth = 6
)
train_wrapper = FineTransformerWrapper(
soundstream = soundstream,
transformer = transformer
).cuda()
wave = torch.randn(1, 320 * 512).cuda()
loss = train_wrapper(
raw_wave = wave,
return_loss = True
)
loss.backward()- show how to generate from prompt tensor or file
-
Stability.ai for the generous sponsorship to work and open source cutting edge artificial intelligence research
-
🤗 Huggingface for their amazing accelerate and transformers libraries
-
@eonglints for offering his professional advice and expertise
-
complete CoarseTransformer
-
use fairseq vq-wav2vec for embeddings
-
add conditioning
-
add classifier free guidance
-
add unique consecutive for
-
incorporate ability to use hubert intermediate features as semantic tokens, recommended by eonglints
-
accommodate variable lengthed audio, bring in eos token
-
make sure unique consecutive works with coarse transformer
-
pretty printing all discriminator losses to log
-
handle when generating semantic tokens, that last logits may not be necessarily the last in the sequence given unique consecutive processing
-
complete full training code for soundstream, taking care of discriminator training
-
figure out how to do the normalization across each dimension mentioned in the paper, but ignore it for v1 of the framework
-
complete sampling code for both Coarse and Fine Transformers, which will be tricky
-
full transformer training code for all three transformers
-
make sure full inference with or without prompting works on the
AudioLMclass -
offer option to weight tie coarse, fine, and semantic embeddings across the 3 hierarchical transformers
-
DRY a little at the end
-
figure out how to suppress logging in fairseq
-
test with speech synthesis for starters
-
abstract out conditioning + classifier free guidance into external module or potentially a package
-
add option to use flash attention
-
simplify training even more within AudioLM class
@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}
}
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