remove ctc

main.py

@@ -18,10 +18,6 @@
     @hydra.main(config_path=args.cp, config_name=args.cn)
     def main(cfg: DictConfig):
         text_process = TextProcess(**cfg.text_process)
-        if cfg.decoder.type == "beamsearch":
-            ctc_decoder = CTCDecoder(text_process=text_process, **cfg.ctcdecoder)
-        else:
-            ctc_decoder = None
 
         trainset = VivosDataset(**cfg.dataset, subset="train")
         testset = VivosDataset(**cfg.dataset, subset="test")
@@ -32,7 +28,6 @@ def main(cfg: DictConfig):
         model = DeepSpeechModule(
             n_class=n_class,
             text_process=text_process,
-            ctc_decoder=ctc_decoder,
             cfg_optim=cfg.optimizer,
             **cfg.model
         )

model.py

@@ -16,7 +16,6 @@ def __init__(
         n_class: int,
         lr: float,
         text_process: TextProcess,
-        ctc_decoder: CTCDecoder,
         cfg_optim: dict,
     ):
         super().__init__()
@@ -25,7 +24,6 @@ def __init__(
         )
         self.lr = lr
         self.text_process = text_process
-        self.ctc_decoder = ctc_decoder
         self.cal_wer = torchmetrics.WordErrorRate()
         self.cfg_optim = cfg_optim
         self.criterion = nn.CTCLoss(zero_infinity=True)
@@ -63,12 +61,8 @@ def validation_step(self, batch, batch_idx):
             outputs.permute(1, 0, 2), targets, input_lengths, target_lengths
         )
 
-        if self.ctc_decoder:
-            # unsqueeze for batchsize 1
-            predicts = [self.ctc_decoder(sent.unsqueeze(0)) for sent in outputs]
-        else:
-            decode = outputs.argmax(dim=-1)
-            predicts = [self.text_process.decode(sent) for sent in decode]
+        decode = outputs.argmax(dim=-1)
+        predicts = [self.text_process.decode(sent) for sent in decode]
 
         targets = [self.text_process.int2text(sent) for sent in targets]
 
@@ -92,12 +86,8 @@ def test_step(self, batch, batch_idx):
             outputs.permute(1, 0, 2), targets, input_lengths, target_lengths
         )
 
-        if self.ctc_decoder:
-            # unsqueeze for batchsize 1
-            predicts = [self.ctc_decoder(sent.unsqueeze(0)) for sent in outputs]
-        else:
-            decode = outputs.argmax(dim=-1)
-            predicts = [self.text_process.decode(sent) for sent in decode]
+        decode = outputs.argmax(dim=-1)
+        predicts = [self.text_process.decode(sent) for sent in decode]
 
         targets = [self.text_process.int2text(sent) for sent in targets]
 

utils.py

@@ -1,5 +1,4 @@
 import torch
-import ctcdecode
 
 
 class TextProcess:
@@ -45,32 +44,32 @@ def int2text(self, s: torch.Tensor) -> str:
         return "".join([self.list_vocab[i] for i in s if i > 2])
 
 
-class CTCDecoder:
-    def __init__(
-        self,
-        alpha: float = 0.5,
-        beta: float = 0.96,
-        beam_size: int = 100,
-        kenlm_path: str = None,
-        text_process: TextProcess = None,
-    ):
-        self.text_process = text_process
-        labels = text_process.list_vocab
-        blank_id = labels.index("<p>")
+# class CTCDecoder:
+#     def __init__(
+#         self,
+#         alpha: float = 0.5,
+#         beta: float = 0.96,
+#         beam_size: int = 100,
+#         kenlm_path: str = None,
+#         text_process: TextProcess = None,
+#     ):
+#         self.text_process = text_process
+#         labels = text_process.list_vocab
+#         blank_id = labels.index("<p>")
 
-        print("loading beam search with lm...")
-        self.decoder = ctcdecode.CTCBeamDecoder(
-            labels,
-            alpha=alpha,
-            beta=beta,
-            beam_width=beam_size,
-            blank_id=blank_id,
-            model_path=kenlm_path,
-        )
-        print("finished loading beam search")
+#         print("loading beam search with lm...")
+#         self.decoder = ctcdecode.CTCBeamDecoder(
+#             labels,
+#             alpha=alpha,
+#             beta=beta,
+#             beam_width=beam_size,
+#             blank_id=blank_id,
+#             model_path=kenlm_path,
+#         )
+#         print("finished loading beam search")
 
-    def __call__(self, output: torch.Tensor) -> str:
-        beam_result, beam_scores, timesteps, out_seq_len = self.decoder.decode(output)
-        tokens = beam_result[0][0]
-        seq_len = out_seq_len[0][0]
-        return self.text_process.int2text(tokens[:seq_len])
+#     def __call__(self, output: torch.Tensor) -> str:
+#         beam_result, beam_scores, timesteps, out_seq_len = self.decoder.decode(output)
+#         tokens = beam_result[0][0]
+#         seq_len = out_seq_len[0][0]
+#         return self.text_process.int2text(tokens[:seq_len])