Merge branch 'main' into add-merge-chunk-size-as-argument

README.md

@@ -127,6 +127,10 @@ To label the transcript with speaker ID's (set number of speakers if known e.g.
 
     whisperx examples/sample01.wav --model large-v2 --diarize --highlight_words True
 
+To run on CPU instead of GPU (and for running on Mac OS X):
+
+    whisperx examples/sample01.wav --compute_type int8
+
 ### Other languages
 
 The phoneme ASR alignment model is *language-specific*, for tested languages these models are [automatically picked from torchaudio pipelines or huggingface](https://github.com/m-bain/whisperX/blob/e909f2f766b23b2000f2d95df41f9b844ac53e49/whisperx/transcribe.py#L22).

whisperx/alignment.py

@@ -50,6 +50,7 @@ DEFAULT_ALIGN_MODELS_HF = {
     "ko": "kresnik/wav2vec2-large-xlsr-korean",
     "ur": "kingabzpro/wav2vec2-large-xls-r-300m-Urdu",
     "te": "anuragshas/wav2vec2-large-xlsr-53-telugu",
+    "hi": "theainerd/Wav2Vec2-large-xlsr-hindi"
 }
 
 
@@ -97,6 +98,9 @@ def align(
     device: str,
     interpolate_method: str = "nearest",
     return_char_alignments: bool = False,
+    print_progress: bool = False,
+    combined_progress: bool = False,
+    total_segments: int = 0
 ) -> AlignedTranscriptionResult:
     """
     Align phoneme recognition predictions to known transcription.
@@ -118,6 +122,11 @@ def align(
     # 1. Preprocess to keep only characters in dictionary
     for sdx, segment in enumerate(transcript):
         # strip spaces at beginning / end, but keep track of the amount.
+        if print_progress:
+            base_progress = ((sdx + 1) / total_segments) * 100
+            percent_complete = (50 + base_progress / 2) if combined_progress else base_progress
+            print(f"Progress: {percent_complete:.2f}%...")
+            
         num_leading = len(segment["text"]) - len(segment["text"].lstrip())
         num_trailing = len(segment["text"]) - len(segment["text"].rstrip())
         text = segment["text"]
@@ -161,9 +170,10 @@ def align(
         segment["sentence_spans"] = sentence_spans
     
     aligned_segments: List[SingleAlignedSegment] = []
-
+    
     # 2. Get prediction matrix from alignment model & align
     for sdx, segment in enumerate(transcript):
+        
         t1 = segment["start"]
         t2 = segment["end"]
         text = segment["text"]

whisperx/asr.py

@@ -247,7 +247,7 @@ class FasterWhisperPipeline(Pipeline):
         return final_iterator
 
     def transcribe(
-        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30
+        self, audio: Union[str, np.ndarray], batch_size=None, num_workers=0, language=None, task=None, chunk_size=30, print_progress = False, combined_progress=False
     ) -> TranscriptionResult:
         if isinstance(audio, str):
             audio = load_audio(audio)
@@ -285,7 +285,12 @@ class FasterWhisperPipeline(Pipeline):
 
         segments: List[SingleSegment] = []
         batch_size = batch_size or self._batch_size
+        total_segments = len(vad_segments)
         for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
+            if print_progress:
+                base_progress = ((idx + 1) / total_segments) * 100
+                percent_complete = base_progress / 2 if combined_progress else base_progress
+                print(f"Progress: {percent_complete:.2f}%...")
             text = out['text']
             if batch_size in [0, 1, None]:
                 text = text[0]

whisperx/transcribe.py

@@ -74,6 +74,8 @@ def cli():
     parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS")
 
     parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face Access Token to access PyAnnote gated models")
+
+    parser.add_argument("--print_progress", type=str2bool, default = False, help = "if True, progress will be printed in transcribe() and align() methods.")
     # fmt: on
 
     args = parser.parse_args().__dict__
@@ -107,6 +109,7 @@ def cli():
     diarize: bool = args.pop("diarize")
     min_speakers: int = args.pop("min_speakers")
     max_speakers: int = args.pop("max_speakers")
+    print_progress: bool = args.pop("print_progress")
 
 
     if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
@@ -159,7 +162,7 @@ def cli():
         audio = load_audio(audio_path)
         # >> VAD & ASR
         print(">>Performing transcription...")
-        result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size)
+        result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size, print_progress=print_progress)
         results.append((result, audio_path))
 
     # Unload Whisper and VAD
@@ -187,7 +190,7 @@ def cli():
                     print(f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language...")
                     align_model, align_metadata = load_align_model(result["language"], device)
                 print(">>Performing alignment...")
-                result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method, return_char_alignments=return_char_alignments)
+                result = align(result["segments"], align_model, align_metadata, input_audio, device, interpolate_method=interpolate_method, return_char_alignments=return_char_alignments, print_progress=print_progress)
 
             results.append((result, audio_path))
 

whisperx/utils.py

@@ -225,6 +225,9 @@ class SubtitlesWriter(ResultWriter):
         highlight_words: bool = options["highlight_words"]
         max_line_width = 1000 if raw_max_line_width is None else raw_max_line_width
         preserve_segments = max_line_count is None or raw_max_line_width is None
+        
+        if len(result["segments"]) == 0:
+            return
 
         if len(result["segments"]) == 0:
             return
@@ -296,7 +299,7 @@ class SubtitlesWriter(ResultWriter):
                             start = self.format_timestamp(this_word["start"])
                             end = self.format_timestamp(this_word["end"])
                             if last != start:
-                                yield last, start, subtitle_text
+                                yield last, start, prefix + subtitle_text
 
                             yield start, end, prefix + " ".join(
                                 [