![]() Mock_caption = 'audio caption' return mock_caption, mock_audio dataset = MockTextAudioDataset() data import Dataset class MockTextAudioDataset( Dataset):ĭef _init_( self, length = 100, audio_length = 320 * 32): ![]() # 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. 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 Semantic_transformer = SemanticTransformer( Import torch from audiolm_pytorch import HubertWithKmeans, SemanticTransformer, SemanticTransformerTrainer wav2vec = HubertWithKmeans(Ĭheckpoint_path = './hubert/hubert_base_ls960.pt', You can also use soundstreams that are specific to AudioLM and MusicLM by importing AudioLMSoundStream and MusicLMSoundStream respectively Recons = soundstream( audio, return_recons_only = True) # (1, 10080) - 1 channel ![]() # after a lot of training, you can test the autoencoding as so audio = torch. 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 ![]() encodec went with lstms, but attention should be better Rq_groups = 2, # this paper proposes using multi-headed residual vector quantization - 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. Hayden for pointing out some discrepancies in the multi-scale discriminator for Soundstreamįrom audiolm_pytorch import SoundStream, SoundStreamTrainer soundstream = SoundStream( LWprogramming for finding an issue with handling of the EOS token when sampling from the for identifying a big bug in the 1d causal convolution for soundstream related to padding not accounting for strides! LWprogramming for adding Encodec compatibility! Ilya for finding an issue with multi-scale discriminator downsampling and for soundstream trainer improvementsĪndrey for identifying a missing loss in soundstream and guiding me through the proper mel spectrogram hyperparametersĪlejandro and Ilya for sharing their results with training soundstream, and for working through a few issues with the local attention positional embeddings MetaAI for Fairseq and the liberal and Joseph for offering their professional advice and expertise as well as pull and for helping with the debugging of soundstreamĪllen and LWprogramming for reviewing the code and submitting bug fixes! □ Huggingface for their amazing accelerate and transformers libraries Stability.ai for the generous sponsorship to work and open source cutting edge artificial intelligence research In the future, this movie clip would no longer make any sense. Update: AudioLM was essentially used to 'solve' music generation in the new MusicLM It is also compatible with EnCodec, which is also MIT-licensed at the time of writing. This repository now also contains a MIT licensed version of SoundStream. Please join if you are interested in replicating this work in the open Yes, this means VALL-E can be trained from this repository. This allows for one to do text-to-audio or TTS, not offered in the paper. It also extends the work for conditioning with classifier free guidance with T5. ![]() Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch ![]()
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