o ei@sBdZddlZddlZddlmZddlmZGdddejZdS)a&This lobe enables the integration of pretrained WavTokenizer. Note that you need to pip install `git+https://github.com/Tomiinek/WavTokenizer` to use this module. Repository: https://github.com/jishengpeng/WavTokenizer/ Paper: https://arxiv.org/abs/2408.16532 Authors * Pooneh Mousavi 2024 N)snapshot_downloadcsTeZdZdZ     dfdd Zd d Zed d Zd dZ ddZ Z S) WavTokenizeraThis lobe enables the integration of pretrained WavTokenizer model, a discrete codec models with single codebook for Audio Language Modeling. Source paper: https://arxiv.org/abs/2408.16532 You need to pip install `git+https://github.com/Tomiinek/WavTokenizer` to use this module. The code is adapted from the official WavTokenizer repository: https://github.com/jishengpeng/WavTokenizer/ Arguments --------- source : str A HuggingFace repository identifier or a path save_path : str The location where the pretrained model will be saved config : str The name of the HF config file. checkpoint : str The name of the HF checkpoint file. sample_rate : int (default: 24000) The audio sampling rate freeze : bool whether the model will be frozen (e.g. not trainable if used as part of training another model) Example ------- >>> model_hub = "novateur/WavTokenizer" >>> save_path = "savedir" >>> config="wavtokenizer_smalldata_frame40_3s_nq1_code4096_dim512_kmeans200_attn.yaml" >>> checkpoint="WavTokenizer_small_600_24k_4096.ckpt" >>> model = WavTokenizer(model_hub, save_path,config=config,checkpoint=checkpoint) >>> audio = torch.randn(4, 48000) >>> length = torch.tensor([1.0, .5, .75, 1.0]) >>> tokens, embs= model.encode(audio) >>> tokens.shape torch.Size([4, 1, 80]) >>> embs.shape torch.Size([4, 80, 512]) >>> rec = model.decode(tokens) >>> rec.shape torch.Size([4, 48000]) NIwavtokenizer_smalldata_frame40_3s_nq1_code4096_dim512_kmeans200_attn.yaml$WavTokenizer_small_600_24k_4096.ckpt]Tc sz ddl}||_Wn tytdwtt||d}tj||} tj||} |jj | | |_ | |_ ||_ dS)NrzhPlease install the WavTokenizer module using: `pip install git+https://github.com/Tomiinek/WavTokenizer`)repo_id cache_dir) wavtokenizer ImportErrorsuper__init__rospathjoinrfrom_pretrained0802model_compute_embedding embeddings sample_rate) selfsource save_pathconfig checkpointrfreezer rcheckpoint_path config_path __class__l/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/lobes/models/discrete/wavtokenizer.pyr As"       zWavTokenizer.__init__cCs"||\}}||}|||fS)aEncodes the input audio as tokens and embeddings and decodes audio from tokens Arguments --------- inputs : torch.Tensor A (Batch x Samples) tensor of audio Returns ------- tokens : torch.Tensor A (Batch x Tokens x Heads) tensor of audio tokens emb : torch.Tensor Raw vector embeddings from the model's quantizers audio : torch.Tensor the reconstructed audio )encodedecode)rinputstokens embeddingaudiorrr forward`s  zWavTokenizer.forwardcCs|jjjjjjdj}|S)Nr)rfeature_extractorencodec quantizervqlayerscodebook)rembsrrr rxs zWavTokenizer._compute_embeddingcCs,|jj|dd\}}|dd|ddfS)aEncodes the input audio as tokens and embeddings Arguments --------- inputs : torch.Tensor A (Batch x Samples) or (Batch x Channel x Samples) tensor of audio Returns ------- tokens : torch.Tensor A (Batch x NQ x Length) tensor of audio tokens emb : torch.Tensor Raw vector embeddings from the model's quantizers r bandwidth_id)rr!movedim)rr#embr$rrr r!szWavTokenizer.encodecCs4|j|dd}|jj|tjd|jdd}|S)aDecodes audio from tokens Arguments --------- tokens : torch.Tensor A (Batch x NQ x Length) tensor of audio tokens Returns ------- audio : torch.Tensor the reconstructed audio r1r)devicer/)rcodes_to_featuresr3r"torchtensorr5)rr$featssigrrr r"s zWavTokenizer.decode)NrrrT) __name__ __module__ __qualname____doc__r r'r7no_gradrr!r" __classcell__rrrr rs0 r) r>r r7torch.nnnnhuggingface_hubrModulerrrrr s