update repo

.github/workflows/UnitTest.yml

@@ -8,6 +8,7 @@ on:
     branches:
       - dev_wjm
       - dev_jy
+      - dev_wjm_infer
 
 jobs:
   build:

funasr/bin/asr_inference_launch.py

@@ -31,6 +31,7 @@ from funasr.bin.asr_infer import Speech2TextUniASR
 from funasr.bin.punc_infer import Text2Punc
 from funasr.bin.tp_infer import Speech2Timestamp
 from funasr.bin.vad_infer import Speech2VadSegment
+from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
 from funasr.fileio.datadir_writer import DatadirWriter
 from funasr.modules.beam_search.beam_search import Hypothesis
 from funasr.modules.subsampling import TooShortUttError
@@ -142,18 +143,16 @@ def inference_asr(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        loader = ASRTask.build_streaming_iterator(
+        loader = build_streaming_iterator(
-            data_path_and_name_and_type,
+            task_name="asr",
+            preprocess_args=speech2text.asr_train_args,
+            data_path_and_name_and_type=data_path_and_name_and_type,
             dtype=dtype,
             fs=fs,
             mc=mc,
             batch_size=batch_size,
             key_file=key_file,
             num_workers=num_workers,
-            preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-            collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-            allow_variable_data_keys=allow_variable_data_keys,
-            inference=True,
         )
 
         finish_count = 0
@@ -329,17 +328,15 @@ def inference_paraformer(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        loader = ASRTask.build_streaming_iterator(
+        loader = build_streaming_iterator(
-            data_path_and_name_and_type,
+            task_name="asr",
+            preprocess_args=speech2text.asr_train_args,
+            data_path_and_name_and_type=data_path_and_name_and_type,
             dtype=dtype,
             fs=fs,
             batch_size=batch_size,
             key_file=key_file,
             num_workers=num_workers,
-            preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-            collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-            allow_variable_data_keys=allow_variable_data_keys,
-            inference=True,
         )
 
         if param_dict is not None:
@@ -580,17 +577,15 @@ def inference_paraformer_vad_punc(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        loader = ASRTask.build_streaming_iterator(
+        loader = build_streaming_iterator(
-            data_path_and_name_and_type,
+            task_name="asr",
+            preprocess_args=None,
+            data_path_and_name_and_type=data_path_and_name_and_type,
             dtype=dtype,
             fs=fs,
             batch_size=1,
             key_file=key_file,
             num_workers=num_workers,
-            preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
-            collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
-            allow_variable_data_keys=allow_variable_data_keys,
-            inference=True,
         )
 
         if param_dict is not None:
@@ -1027,17 +1022,15 @@ def inference_uniasr(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        loader = ASRTask.build_streaming_iterator(
+        loader = build_streaming_iterator(
-            data_path_and_name_and_type,
+            task_name="asr",
+            preprocess_args=speech2text.asr_train_args,
+            data_path_and_name_and_type=data_path_and_name_and_type,
             dtype=dtype,
             fs=fs,
             batch_size=batch_size,
             key_file=key_file,
             num_workers=num_workers,
-            preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-            collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-            allow_variable_data_keys=allow_variable_data_keys,
-            inference=True,
         )
 
         finish_count = 0
@@ -1182,18 +1175,16 @@ def inference_mfcca(
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
-        loader = ASRTask.build_streaming_iterator(
+        loader = build_streaming_iterator(
-            data_path_and_name_and_type,
+            task_name="asr",
+            preprocess_args=speech2text.asr_train_args,
+            data_path_and_name_and_type=data_path_and_name_and_type,
             dtype=dtype,
             batch_size=batch_size,
             fs=fs,
             mc=True,
             key_file=key_file,
             num_workers=num_workers,
-            preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-            collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-            allow_variable_data_keys=allow_variable_data_keys,
-            inference=True,
         )
 
         finish_count = 0

funasr/build_utils/build_streaming_iterator.py

@@ -24,7 +24,10 @@ def build_streaming_iterator(
     assert check_argument_types()
 
     # preprocess
-    preprocess_fn = build_preprocess(preprocess_args, train)
+    if preprocess_args is not None:
+        preprocess_fn = build_preprocess(preprocess_args, train)
+    else:
+        preprocess_fn = None
 
     # collate
     if task_name in ["punc", "lm"]: