Merge pull request #205 from prashanthellina/v3-fix-diarization

whisperx/alignment.py

@@ -450,8 +450,8 @@ def align(
                 "end": srow["end"],
                 "text": text,
                 "words": word_list,
-                # "word-segments": wseg,
-                # "char-segments": cseg
+                "word-segments": wseg,
+                "char-segments": cseg
             }
         )
     

whisperx/asr.py

@@ -207,7 +207,7 @@ class FasterWhisperPipeline(Pipeline):
         return final_iterator
 
     def transcribe(
-        self, audio: Union[str, np.ndarray], batch_size=None
+        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0
     ):
         if isinstance(audio, str):
             audio = load_audio(audio)
@@ -232,7 +232,7 @@ class FasterWhisperPipeline(Pipeline):
 
         segments = []
         batch_size = batch_size or self._batch_size
-        for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size)):
+        for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
             text = out['text']
             if batch_size in [0, 1, None]:
                 text = text[0]
@@ -251,7 +251,10 @@ class FasterWhisperPipeline(Pipeline):
 
 
     def detect_language(self, audio: np.ndarray):
-        segment = log_mel_spectrogram(audio[: N_SAMPLES], padding=0)
+        if audio.shape[0] < N_SAMPLES:
+            print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
+        segment = log_mel_spectrogram(audio[: N_SAMPLES],
+                                      padding=0 if audio.shape[0] >= N_SAMPLES else N_SAMPLES - audio.shape[0])
         encoder_output = self.model.encode(segment)
         results = self.model.model.detect_language(encoder_output)
         language_token, language_probability = results[0][0]

whisperx/transcribe.py

@@ -203,6 +203,12 @@ def cli():
 
     # >> Write
     for result, audio_path in results:
+        # Remove pandas dataframes from result so that
+        # we can serialize the result with json
+        for seg in result["segments"]:
+            seg.pop("word-segments", None)
+            seg.pop("char-segments", None)
+
         writer(result, audio_path, writer_args)
 
 if __name__ == "__main__":