update large dataset for sampling rate

funasr/datasets/large_datasets/build_dataloader.py

@@ -34,7 +34,7 @@ def load_seg_dict(seg_dict_file):
     return seg_dict
 
 class ArkDataLoader(AbsIterFactory):
-    def __init__(self, data_list, dict_file, dataset_conf, seg_dict_file=None, punc_dict_file=None, mode="train"):
+    def __init__(self, data_list, dict_file, dataset_conf, frontend_conf=None, seg_dict_file=None, punc_dict_file=None, mode="train"):
         symbol_table = read_symbol_table(dict_file) if dict_file is not None else None
         if seg_dict_file is not None:
             seg_dict = load_seg_dict(seg_dict_file)
@@ -45,10 +45,11 @@ class ArkDataLoader(AbsIterFactory):
         else:
             punc_dict = None
         self.dataset_conf = dataset_conf
+        self.frontend_conf = frontend_conf
         logging.info("dataloader config: {}".format(self.dataset_conf))
         batch_mode = self.dataset_conf.get("batch_mode", "padding")
         self.dataset = Dataset(data_list, symbol_table, seg_dict, punc_dict,
-                               self.dataset_conf, mode=mode, batch_mode=batch_mode)
+                               self.dataset_conf, self.frontend_conf, mode=mode, batch_mode=batch_mode)
 
     def build_iter(self, epoch, shuffle=True):
         self.dataset.set_epoch(epoch)

funasr/datasets/large_datasets/dataset.py

@@ -28,10 +28,11 @@ def read_lists(list_file):
 
 
 class AudioDataset(IterableDataset):
-    def __init__(self, scp_lists, data_names, data_types, shuffle=True, mode="train"):
+    def __init__(self, scp_lists, data_names, data_types, frontend_conf=None, shuffle=True, mode="train"):
         self.scp_lists = scp_lists
         self.data_names = data_names
         self.data_types = data_types
+        self.frontend_conf = frontend_conf
         self.shuffle = shuffle
         self.mode = mode
         self.epoch = -1
@@ -119,6 +120,11 @@ class AudioDataset(IterableDataset):
                     elif data_type == "sound":
                         key, path = item.strip().split()
                         waveform, sampling_rate = torchaudio.load(path)
+                        if self.frontend_conf is not None:
+                            if sampling_rate != self.frontend_conf["fs"]:
+                                waveform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
+                                                                          new_freq=self.frontend_conf["fs"])(waveform)
+                                sampling_rate = self.frontend_conf["fs"] 
                         waveform = waveform.numpy()
                         mat = waveform[0]
                         sample_dict[data_name] = mat
@@ -153,13 +159,14 @@ def Dataset(data_list_file,
             seg_dict,
             punc_dict,
             conf,
+            frontend_conf,
             mode="train",
             batch_mode="padding"):
     scp_lists = read_lists(data_list_file)
     shuffle = conf.get('shuffle', True)
     data_names = conf.get("data_names", "speech,text")
     data_types = conf.get("data_types", "kaldi_ark,text")
-    dataset = AudioDataset(scp_lists, data_names, data_types, shuffle=shuffle, mode=mode)
+    dataset = AudioDataset(scp_lists, data_names, data_types, frontend_conf=frontend_conf, shuffle=shuffle, mode=mode)
 
     filter_conf = conf.get('filter_conf', {})
     filter_fn = partial(filter, **filter_conf)

funasr/tasks/abs_task.py

@@ -1348,11 +1348,13 @@ class AbsTask(ABC):
             if args.dataset_type == "large":
                 from funasr.datasets.large_datasets.build_dataloader import ArkDataLoader
                 train_iter_factory = ArkDataLoader(args.train_data_file, args.token_list, args.dataset_conf,
+                                                   frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
                                                    seg_dict_file=args.seg_dict_file if hasattr(args,
                                                                                                "seg_dict_file") else None,
                                                    punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,
                                                    mode="train")
-                valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf,
+                valid_iter_factory = ArkDataLoader(args.valid_data_file, args.token_list, args.dataset_conf, 
+                                                   frontend_conf=args.frontend_conf if hasattr(args, "frontend_conf") else None,
                                                    seg_dict_file=args.seg_dict_file if hasattr(args,
                                                                                                "seg_dict_file") else None,
                                                    punc_dict_file=args.punc_list if hasattr(args, "punc_list") else None,