o ei@s.dZddlZddlmZ    d dd ZdS) z This file contains the loss functions for k2 training. Currently, we only support CTC loss. Authors: * Pierre Champion 2023 * Zeyu Zhao 2023 * Georgios Karakasidis 2023 N)k2mean Tcs|jd|jd}tjfddt|Ddtjd} |j||d\} } t || } tj | |j | | |j |||d} | j |ksKJ| S) a CTC loss implemented with k2. Make sure that k2 has been installed properly. Note that the blank index must be 0 in this implementation. Arguments --------- log_probs: torch.Tensor Log-probs of shape (batch, time, num_classes). input_lens : torch.Tensor Length of each utterance. graph_compiler : k2.Fsa Decoding graph. texts : List[str] List of texts. reduction : str What reduction to apply to the output. 'mean', 'sum', 'none'. See k2.ctc_loss for 'mean', 'sum', 'none'. beam_size : int Beam size. use_double_scores : bool If true, use double precision for scores. is_training : bool If true, the returned loss requires gradient. Returns ------- loss: torch.Tensor CTC loss. Example ------- >>> import torch >>> from speechbrain.k2_integration.losses import ctc_k2 >>> from speechbrain.k2_integration.graph_compiler import CtcGraphCompiler >>> from speechbrain.k2_integration.lexicon import Lexicon >>> from speechbrain.k2_integration.prepare_lang import prepare_lang >>> # Create a random batch of log-probs >>> batch_size = 4 >>> log_probs = torch.randn(batch_size, 100, 30) >>> log_probs.requires_grad = True >>> # Assume all utterances have the same length so no padding was needed. >>> input_lens = torch.ones(batch_size) >>> # Create a small lexicon containing only two words and write it to a file. >>> lang_tmpdir = getfixture('tmpdir') >>> lexicon_sample = "hello h e l l o\nworld w o r l d\n " >>> lexicon_file = lang_tmpdir.join("lexicon.txt") >>> lexicon_file.write(lexicon_sample) >>> # Create a lang directory with the lexicon and L.pt, L_inv.pt, L_disambig.pt >>> prepare_lang(lang_tmpdir) >>> # Create a lexicon object >>> lexicon = Lexicon(lang_tmpdir) >>> # Create a random decoding graph >>> graph = CtcGraphCompiler( ... lexicon, ... log_probs.device, ... ) >>> # Create a random batch of texts >>> texts = ["hello world", "world hello", "hello", "world"] >>> # Compute the loss >>> loss = ctc_k2( ... log_probs=log_probs, ... input_lens=input_lens, ... graph_compiler=graph, ... texts=texts, ... reduction="mean", ... beam_size=10, ... use_double_scores=True, ... is_training=True, ... ) rrcsg|] }|d|gqS)r).0i input_lensr_/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/speechbrain/k2_integration/losses.py fszctc_k2..cpu)devicedtype) is_training)decoding_graph dense_fsa_vectarget_lengths output_beam reductionuse_double_scores)shaperoundinttorchtensorrangeint32compiler DenseFsaVecctc_losstor requires_grad) log_probsr graph_compilertextsr beam_sizerr batch_sizesupervision_segmentsr target_lensrlossrr r ctc_k2s*R     r+)rrTT)__doc__rrr+rrrr s