From 2cfe010d7b0f17877a271cc401e2c2f8f8d4c42c Mon Sep 17 00:00:00 2001
From: speech_asr <wangjiaming.wjm@alibaba-inc.com>
Date: 星期三, 15 三月 2023 14:42:32 +0800
Subject: [PATCH] update

---
 funasr/bin/eend_ola_inference.py |    8 ++++++--
 1 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/funasr/bin/eend_ola_inference.py b/funasr/bin/eend_ola_inference.py
index d191877..96e7516 100755
--- a/funasr/bin/eend_ola_inference.py
+++ b/funasr/bin/eend_ola_inference.py
@@ -27,6 +27,8 @@
 from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
 
+from modelscope.utils.logger import get_logger
+logger = get_logger()
 
 class Speech2Diarization:
     """Speech2Diarlization class
@@ -121,7 +123,7 @@
                 Currently, the tags of espnet_model_zoo are supported.
 
         Returns:
-            Speech2Xvector: Speech2Xvector instance.
+            Speech2Diarization: Speech2Diarization instance.
 
         """
         if model_tag is not None:
@@ -179,7 +181,6 @@
         diar_model_file=diar_model_file,
         device=device,
         dtype=dtype,
-        streaming=streaming,
     )
     logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
     speech2diar = Speech2Diarization.from_pretrained(
@@ -210,6 +211,7 @@
             if isinstance(raw_inputs, torch.Tensor):
                 raw_inputs = raw_inputs.numpy()
             data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
+        logger.info(data_path_and_name_and_type)
         loader = EENDOLADiarTask.build_streaming_iterator(
             data_path_and_name_and_type,
             dtype=dtype,
@@ -229,6 +231,8 @@
             output_writer = open("{}/result.txt".format(output_path), "w")
         result_list = []
         for keys, batch in loader:
+            logger.info("keys: {}".format(keys))
+            logger.info("batch: {}".format(batch))
             assert isinstance(batch, dict), type(batch)
             assert all(isinstance(s, str) for s in keys), keys
             _bs = len(next(iter(batch.values())))

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