clearer implementation of remove_non_speech with respect to audio wit…

…hout speech

whisper_timestamped/transcribe.py

@@ -3,7 +3,7 @@
 __author__ = "Jérôme Louradour"
 __credits__ = ["Jérôme Louradour"]
 __license__ = "GPLv3"
-__version__ = "1.15.1"
+__version__ = "1.15.2"
 
 # Set some environment variables
 import os
@@ -277,9 +277,9 @@ def transcribe_timestamped(
         compression_ratio_threshold=compression_ratio_threshold,
     )
 
-    if vad:
+    if vad is not None:
         audio = get_audio_tensor(audio)
-        audio, vad_segments, convert_timestamps = remove_non_speech(audio, method=vad, sample_rate=SAMPLE_RATE, plot=plot_word_alignment)
+        audio, vad_segments, convert_timestamps = remove_non_speech(audio, method=vad, sample_rate=SAMPLE_RATE, plot=plot_word_alignment, avoid_empty_speech=True)
     else:
         vad_segments = None
 
@@ -1856,8 +1856,8 @@ def check_vad_method(method, with_version=False):
     """
     if method in [True, "True", "true"]:
         return check_vad_method("silero") # default method
-    elif method in [False, "False", "false"]:
-        return False
+    elif method in [None, False, "False", "false", "None", "none"]:
+        return None
     elif not isinstance(method, str) and hasattr(method, '__iter__'):
         # list of explicit timestamps
         checked_pairs = []
@@ -2063,6 +2063,7 @@ def remove_non_speech(audio,
     dilatation=0.5,
     sample_rate=SAMPLE_RATE,
     method="silero",
+    avoid_empty_speech=False,
     plot=False,
     ):
     """
@@ -2083,6 +2084,8 @@ def remove_non_speech(audio,
             how much (in sec) to enlarge each speech segment detected by the VAD
         method: str
             method to use to remove non-speech segments
+        avoid_empty_speech: bool
+            if True, avoid returning an empty speech segment (re)
         plot: bool or str
             if True, plot the result.
             If a string, save the plot to the given file
@@ -2100,7 +2103,10 @@ def remove_non_speech(audio,
 
     segments = [(seg["start"], seg["end"]) for seg in segments]
     if len(segments) == 0:
-        segments = [(0, audio.shape[-1])]
+        if avoid_empty_speech:
+            segments = [(0, audio.shape[-1])]
+        else:
+            return torch.Tensor([]), [], lambda t, t2 = None: do_convert_timestamps(segments, t, t2)
 
     audio_speech = torch.cat([audio[..., s:e] for s,e in segments], dim=-1)
 
@@ -2121,7 +2127,7 @@ def remove_non_speech(audio,
     if not use_sample:
         segments = [(float(s)/sample_rate, float(e)/sample_rate) for s,e in segments]
 
-    return audio_speech, segments, lambda t, t2 = None: do_convert_timestamps(segments, t, t2)
+    return audio_speech, segments, lambda t, t2 = None: t if t2 is None else [t, t2]
 
 def do_convert_timestamps(segments, t, t2 = None):
     """