From 2f7e99e1dd30ce8be0e69aa25efe88179cfeb77b Mon Sep 17 00:00:00 2001
From: speech_asr <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 23 二月 2023 10:41:20 +0800
Subject: [PATCH] update docs

---
 funasr/bin/asr_inference_uniasr.py |   17 +++++++++++++++--
 1 files changed, 15 insertions(+), 2 deletions(-)

diff --git a/funasr/bin/asr_inference_uniasr.py b/funasr/bin/asr_inference_uniasr.py
index cfec9a0..c50bf17 100644
--- a/funasr/bin/asr_inference_uniasr.py
+++ b/funasr/bin/asr_inference_uniasr.py
@@ -397,7 +397,7 @@
         device = "cuda"
     else:
         device = "cpu"
-
+    
     # 1. Set random-seed
     set_all_random_seed(seed)
 
@@ -433,12 +433,25 @@
                  output_dir_v2: Optional[str] = None,
                  fs: dict = None,
                  param_dict: dict = None,
+                 **kwargs,
                  ):
         # 3. Build data-iterator
         if data_path_and_name_and_type is None and raw_inputs is not None:
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
+        if param_dict is not None and "decoding_model" in param_dict:
+            if param_dict["decoding_model"] == "fast":
+                speech2text.decoding_ind = 0
+                speech2text.decoding_mode = "model1"
+            elif param_dict["decoding_model"] == "normal":
+                speech2text.decoding_ind = 0
+                speech2text.decoding_mode = "model2"
+            elif param_dict["decoding_model"] == "offline":
+                speech2text.decoding_ind = 1
+                speech2text.decoding_mode = "model2"
+            else:
+                raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
         loader = ASRTask.build_streaming_iterator(
             data_path_and_name_and_type,
             dtype=dtype,
@@ -492,7 +505,7 @@
                     ibest_writer["score"][key] = str(hyp.score)
     
                 if text is not None:
-                    text_postprocessed = postprocess_utils.sentence_postprocess(token)
+                    text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                     item = {'key': key, 'value': text_postprocessed}
                     asr_result_list.append(item)
                     finish_count += 1

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