fix task.py with no dest_sample_rate task; fix bug in train and infer

funasr/bin/punc_train_vadrealtime.py

@@ -1,44 +0,0 @@
-#!/usr/bin/env python3
-import os
-from funasr.tasks.punctuation import PunctuationTask
-
-
-def parse_args():
-    parser = PunctuationTask.get_parser()
-    parser.add_argument(
-        "--gpu_id",
-        type=int,
-        default=0,
-        help="local gpu id.",
-    )
-    parser.add_argument(
-        "--punc_list",
-        type=str,
-        default=None,
-        help="Punctuation list",
-    )
-    args = parser.parse_args()
-    return args
-
-
-def main(args=None, cmd=None):
-    """
-    punc training.
-    """
-    PunctuationTask.main(args=args, cmd=cmd)
-
-
-if __name__ == "__main__":
-    args = parse_args()
-
-    # setup local gpu_id
-    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
-
-    # DDP settings
-    if args.ngpu > 1:
-        args.distributed = True
-    else:
-        args.distributed = False
-    assert args.num_worker_count == 1
-
-    main(args=args)

funasr/bin/punctuation_infer_vadrealtime.py

@@ -90,7 +90,7 @@ class Text2Punc:
             data = {
                 "text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
                 "text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
-                "vad_indexes": torch.from_numpy(np.array([len(cache)-1], dtype='int32')),
+                "vad_indexes": torch.from_numpy(np.array([len(cache)], dtype='int32')),
             }
             data = to_device(data, self.device)
             y, _ = self.wrapped_model(**data)

funasr/datasets/large_datasets/utils/tokenize.py

@@ -47,8 +47,8 @@ def tokenize(data,
     length = len(text)
     for i in range(length):
         x = text[i]
-        if i == length-1 and "punc" in data and text[i].startswith("vad:"):
-            vad = x[-1][4:]
+        if i == length-1 and "punc" in data and x.startswith("vad:"):
+            vad = x[4:]
             if len(vad) == 0:
                 vad = -1
             else:

funasr/datasets/preprocessor.py

@@ -786,6 +786,7 @@ class PuncTrainTokenizerCommonPreprocessor(CommonPreprocessor):
     ) -> Dict[str, np.ndarray]:
         for i in range(self.num_tokenizer):
             text_name = self.text_name[i]
+            #import pdb; pdb.set_trace()
             if text_name in data and self.tokenizer[i] is not None:
                 text = data[text_name]
                 text = self.text_cleaner(text)
@@ -800,7 +801,7 @@ class PuncTrainTokenizerCommonPreprocessor(CommonPreprocessor):
                     data[self.vad_name] = np.array([vad], dtype=np.int64)
                 text_ints = self.token_id_converter[i].tokens2ids(tokens)
                 data[text_name] = np.array(text_ints, dtype=np.int64)
-
+        return data
 
 def split_to_mini_sentence(words: list, word_limit: int = 20):
     assert word_limit > 1
@@ -813,4 +814,4 @@ def split_to_mini_sentence(words: list, word_limit: int = 20):
         sentences.append(words[i * word_limit:(i + 1) * word_limit])
     if length % word_limit > 0:
         sentences.append(words[sentence_len * word_limit:])
-    return sentences
+    return sentences

funasr/runtime/python/onnxruntime/funasr_onnx/punc_bin.py

@@ -159,7 +159,7 @@ class CT_Transformer_VadRealtime(CT_Transformer):
             data = {
                 "input": mini_sentence_id[None,:],
                 "text_lengths": np.array([text_length], dtype='int32'),
-                "vad_mask": self.vad_mask(text_length, len(cache) - 1)[None, None, :, :].astype(np.float32),
+                "vad_mask": self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32),
                 "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32)
             }
             try:

funasr/tasks/abs_task.py

@@ -1587,6 +1587,8 @@ class AbsTask(ABC):
                 dest_sample_rate = args.frontend_conf["fs"]
             else:
                 dest_sample_rate = 16000
+        else:
+            dest_sample_rate = 16000
 
         dataset = ESPnetDataset(
             iter_options.data_path_and_name_and_type,