From c542eacb0aadcbc49c63db40429fca4e08f807a4 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 七月 2023 10:27:35 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add

---
 funasr/bin/asr_inference_launch.py |   12 +++++-------
 1 files changed, 5 insertions(+), 7 deletions(-)

diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index fd0eecd..edb4ad6 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -565,6 +565,8 @@
         if 'hotword' in kwargs:
             hotword_list_or_file = kwargs['hotword']
 
+        speech2vadsegment.vad_model.vad_opts.max_single_segment_time = kwargs.get("max_single_segment_time", 60000)
+        batch_size_token_threshold_ms = kwargs.get("batch_size_token_threshold_ms", int(speech2vadsegment.vad_model.vad_opts.max_single_segment_time*0.67))
         batch_size_token = kwargs.get("batch_size_token", 6000)
         print("batch_size_token: ", batch_size_token)
 
@@ -647,8 +649,7 @@
             beg_idx = 0
             for j, _ in enumerate(range(0, n)):
                 batch_size_token_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
-                if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][
-                    0]) < batch_size_token_ms:
+                if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_ms and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_threshold_ms:
                     continue
                 batch_size_token_ms_cum = 0
                 end_idx = j + 1
@@ -1340,7 +1341,7 @@
         format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
     )
 
-    if ngpu >= 1:
+    if ngpu >= 1 and torch.cuda.is_available():
         device = "cuda"
     else:
         device = "cpu"
@@ -1371,10 +1372,7 @@
         left_context=left_context,
         right_context=right_context,
     )
-    speech2text = Speech2TextTransducer.from_pretrained(
-        model_tag=model_tag,
-        **speech2text_kwargs,
-    )
+    speech2text = Speech2TextTransducer(**speech2text_kwargs)
 
     def _forward(data_path_and_name_and_type,
                  raw_inputs: Union[np.ndarray, torch.Tensor] = None,

--
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