feat: add advanced transcription options for VAD, word-level timestamps, and translation

app.py

@@ -219,6 +219,21 @@ class App:
             ts_frame, text="Include timestamps in transcription",
             variable=self.timestamps_var, font=font_b)
         self.timestamps_switch.pack(side=tk.LEFT, padx=5)
+        # Advanced options frame
+        adv_frame = customtkinter.CTkFrame(master)
+        adv_frame.pack(fill=tk.BOTH, padx=10, pady=10)
+        self.vad_var = tk.BooleanVar(value=False)
+        customtkinter.CTkSwitch(
+            adv_frame, text="VAD filter (remove silence)",
+            variable=self.vad_var, font=font_b).pack(side=tk.LEFT, padx=5)
+        self.word_ts_var = tk.BooleanVar(value=False)
+        customtkinter.CTkSwitch(
+            adv_frame, text="Word-level timestamps",
+            variable=self.word_ts_var, font=font_b).pack(side=tk.LEFT, padx=5)
+        self.translate_var = tk.BooleanVar(value=False)
+        customtkinter.CTkSwitch(
+            adv_frame, text="Translate to English",
+            variable=self.translate_var, font=font_b).pack(side=tk.LEFT, padx=5)
         # Progress Bar
         self.progress_bar = ttk.Progressbar(master, length=200, mode='indeterminate')
         # Worker process handle (replaces thread+stop_event for true immediate cancellation)
@@ -320,6 +335,9 @@ class App:
         lang_label = self.language_combobox.get()
         language = WHISPER_LANGUAGES.get(lang_label, lang_label) if lang_label else None
         timestamps = self.timestamps_var.get()
+        vad_filter = self.vad_var.get()
+        word_timestamps = self.word_ts_var.get()
+        translate = self.translate_var.get()
         glob_file = get_path(path)
         self.progress_bar.pack(fill=tk.X, padx=5, pady=5)
         self.progress_bar.start()
@@ -327,6 +345,7 @@ class App:
         self._proc = mp.Process(
             target=_transcribe_worker_process,
             args=(self._child_conn, path, glob_file, model, language, True, timestamps),
+            kwargs={"vad_filter": vad_filter, "word_timestamps": word_timestamps, "translate": translate},
             daemon=True,
         )
         self._proc.start()

src/_LocalTranscribe.py

@@ -152,7 +152,7 @@ def _decode_audio_pyav(file_path):
     return np.concatenate(chunks, axis=0), duration
 
 
-def _transcribe_mlx_file(file, mlx_model_id, language, timestamps, verbose):
+def _transcribe_mlx_file(file, mlx_model_id, language, timestamps, verbose, vad_filter=False, word_timestamps=False, translate=False):
     """Transcribe a single file with mlx-whisper (Apple GPU/NPU).
 
     Decodes audio via PyAV (no system ffmpeg needed), then runs MLX inference.
@@ -166,6 +166,10 @@ def _transcribe_mlx_file(file, mlx_model_id, language, timestamps, verbose):
     decode_opts = {}
     if language:
         decode_opts["language"] = language
+    if translate:
+        decode_opts["task"] = "translate"
+    if word_timestamps:
+        decode_opts["word_timestamps"] = True
 
     result = mlx_whisper.transcribe(
         audio_array,
@@ -200,7 +204,7 @@ def get_path(path):
     return sorted(media_files)
 
 # Main function
-def transcribe(path, glob_file, model=None, language=None, verbose=False, timestamps=True, stop_event=None):
+def transcribe(path, glob_file, model=None, language=None, verbose=False, timestamps=True, stop_event=None, vad_filter=False, word_timestamps=False, translate=False):
     """
     Transcribes audio files in a specified folder using faster-whisper (CTranslate2).
 
@@ -274,7 +278,9 @@ def transcribe(path, glob_file, model=None, language=None, verbose=False, timest
                 try:
                     t_start = time.time()
                     segments, audio_duration = _transcribe_mlx_file(
-                        file, mlx_model_id, language, timestamps, verbose
+                        file, mlx_model_id, language, timestamps, verbose,
+                        vad_filter=vad_filter, word_timestamps=word_timestamps,
+                        translate=translate
                     )
                     os.makedirs('{}/transcriptions'.format(path), exist_ok=True)
                     segment_list = []
@@ -373,7 +379,10 @@ def transcribe(path, glob_file, model=None, language=None, verbose=False, timest
             segments, info = whisper_model.transcribe(
                 file,
                 language=language,
-                beam_size=5
+                beam_size=5,
+                task="translate" if translate else "transcribe",
+                vad_filter=vad_filter,
+                word_timestamps=word_timestamps,
             )
             audio_duration = info.duration  # seconds
             # Make folder if missing
@@ -396,7 +405,17 @@ def transcribe(path, glob_file, model=None, language=None, verbose=False, timest
                         f.write('\n[{} --> {}] {}'.format(start_ts, end_ts, text))
                     else:
                         f.write('\n{}'.format(text))
-                    srt_f.write(f'{idx}\n{_srt_timestamp(seg.start)} --> {_srt_timestamp(seg.end)}\n{text}\n\n')
+                    # Use word-level timestamps for SRT if available
+                    if word_timestamps and hasattr(seg, 'words') and seg.words:
+                        for w_idx, word in enumerate(seg.words, start=1):
+                            w_text = word.word.strip()
+                            if not w_text:
+                                continue
+                            w_start = _srt_timestamp(word.start)
+                            w_end = _srt_timestamp(word.end)
+                            srt_f.write(f'{idx}.{w_idx}\n{w_start} --> {w_end}\n{w_text}\n\n')
+                    else:
+                        srt_f.write(f'{idx}\n{_srt_timestamp(seg.start)} --> {_srt_timestamp(seg.end)}\n{text}\n\n')
                     f.flush()
                     srt_f.flush()
                     if verbose:
@@ -426,7 +445,7 @@ def transcribe(path, glob_file, model=None, language=None, verbose=False, timest
     return output_text
 
 
-def _transcribe_worker_process(conn, path, glob_file, model, language, verbose, timestamps):
+def _transcribe_worker_process(conn, path, glob_file, model, language, verbose, timestamps, vad_filter=False, word_timestamps=False, translate=False):
     """Child-process entry point for the UI's multiprocessing backend.
 
     Redirects stdout/stderr → pipe connection so the main process can display
@@ -455,7 +474,9 @@ def _transcribe_worker_process(conn, path, glob_file, model, language, verbose,
 
     result = '⚠  No output produced.'
     try:
-        result = transcribe(path, glob_file, model, language, verbose, timestamps)
+        result = transcribe(path, glob_file, model, language, verbose, timestamps,
+                           vad_filter=vad_filter, word_timestamps=word_timestamps,
+                           translate=translate)
     except Exception as exc:
         result = f'⚠  Unexpected error: {exc}'
     finally: