deactivate beam search temporarily for speed

egs/librispeech/ASR/whisper/decode.py

@@ -3,6 +3,7 @@
 #                                            Fangjun Kuang,
 #                                            Wei Kang)
 #           2024 Yuekai Zhang
+#           2024 Xiaomi Corporation          Xiaoyu Yang
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -145,26 +146,6 @@ def remove_punctuation(text: str or List[str]):
         raise Exception(f"Not support type {type(text)}")
 
 
-def to_simple(text: str or List[str]):
-    """Convert traditional Chinese to simplified Chinese.
-    Args:
-        text: It can be a string or a list of strings.
-    Returns:
-        Return a string or a list of strings converted to simplified Chinese.
-    """
-    if isinstance(text, str):
-        text = convert(text, "zh-cn")
-        return text
-    elif isinstance(text, list):
-        result_text = []
-        for t in text:
-            t = convert(t, "zh-cn")
-            result_text.append(t)
-        return result_text
-    else:
-        raise Exception(f"Not support type{type(text)}")
-
-
 def get_parser():
     parser = argparse.ArgumentParser(
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -417,8 +398,8 @@ def main():
     options = whisper.DecodingOptions(
         task="transcribe",
         language="en",
-        # without_timestamps=True,
-        # beam_size=params.beam_size,
+        without_timestamps=True,
+        #beam_size=params.beam_size,
     )
     params.decoding_options = options
     params.cleaner = BasicTextNormalizer()
@@ -481,12 +462,17 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
+    def remove_short_and_long_utt(c):
+        if c.duration < 1.0 or c.duration > 30.0:
+            return False
+        return True
+
     # we need cut ids to display recognition results.
     args.return_cuts = True
     librispeech = LibriSpeechAsrDataModule(args)
 
-    test_clean_cuts = librispeech.test_clean_cuts().subset(first=200)
-    test_other_cuts = librispeech.test_other_cuts().subset(first=200)
+    test_clean_cuts = librispeech.test_clean_cuts().filter(remove_short_and_long_utt)
+    test_other_cuts = librispeech.test_other_cuts().filter(remove_short_and_long_utt)
 
     test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
     test_other_dl = librispeech.test_dataloaders(test_other_cuts)