From 0f3a4ca84d17d282f5791e9eee383948d67dfe1b Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期三, 14 六月 2023 17:29:57 +0800
Subject: [PATCH] update repo

---
 funasr/bin/asr_inference_launch.py |   77 ++++++++++++++------------------------
 1 files changed, 29 insertions(+), 48 deletions(-)

diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index a56552d..367b9a8 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -31,11 +31,10 @@
 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
-from funasr.tasks.asr import ASRTask
-from funasr.tasks.vad import VADTask
 from funasr.torch_utils.device_funcs import to_device
 from funasr.torch_utils.set_all_random_seed import set_all_random_seed
 from funasr.utils import asr_utils, postprocess_utils
@@ -142,18 +141,16 @@
             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(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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 +326,15 @@
             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(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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 +575,15 @@
             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(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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 +1020,15 @@
             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(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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 +1173,16 @@
             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(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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
@@ -1369,20 +1358,14 @@
                  **kwargs,
                  ):
         # 3. Build data-iterator
-        loader = ASRTask.build_streaming_iterator(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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,
             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,
         )
 
         # 4 .Start for-loop
@@ -1529,18 +1512,16 @@
             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(
-            data_path_and_name_and_type,
+        loader = build_streaming_iterator(
+            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

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