Support whisper params from cli

diarize.py

@@ -89,12 +89,27 @@
     help="if you have a GPU use 'cuda', otherwise 'cpu'",
 )
 
+parser.add_argument("--temperature", type=float, default=0.1, help="Temperature for sampling")
+parser.add_argument("--initial-prompt", type=str, default="", help="Initial prompt for context")
+parser.add_argument("--hotwords", type=str, default="", help="Hotwords as a single string")
+parser.add_argument("--repetition-penalty", type=float, default=1.1, help="Penalty for repeated tokens")
+parser.add_argument("--best-of", type=int, default=10, help="Number of candidates when sampling")
+parser.add_argument("--beam-size", type=int, default=10, help="Beam size for beam search")
+parser.add_argument("--patience", type=float, default=1.2, help="Beam search patience")
+parser.add_argument("--no-repeat-ngram-size", type=int, default=3, help="Prevent repeating ngrams of this size")
+parser.add_argument("--chunk-length", type=int, default=30, help="Length of audio chunks in seconds")
+parser.add_argument("--length-penalty", type=float, default=1.0, help="Penalty for shorter/longer sequences")
+parser.add_argument("--condition-on-previous-text", action="store_true", help="Condition decoding on previous text")
+parser.add_argument("--multilingual", action="store_true", help="Enable multilingual mode")
+parser.add_argument("--vad-filter", action="store_true", help="Enable voice activity detection filter")
+parser.add_argument("--without-timestamps", action="store_true", help="Do not include timestamps in output")
+
 args = parser.parse_args()
 language = process_language_arg(args.language, args.model_name)
 
 if args.stemming:
     # Isolate vocals from the rest of the audio
-
+    
     return_code = os.system(
         f'python -m demucs.separate -n htdemucs --two-stems=vocals "{args.audio}" -o temp_outputs --device "{args.device}"'
     )
@@ -115,7 +130,6 @@
 else:
     vocal_target = args.audio
 
-
 # Transcribe the audio file
 
 whisper_model = faster_whisper.WhisperModel(
@@ -129,20 +143,32 @@
     else [-1]
 )
 
+transcribe_kwargs = dict(
+    audio=audio_waveform,
+    language=language,
+    suppress_tokens=suppress_tokens,
+    log_progress=True,
+    multilingual=args.multilingual,
+    temperature=args.temperature,
+    initial_prompt=args.initial_prompt,
+    repetition_penalty=args.repetition_penalty,
+    best_of=args.best_of,
+    vad_filter=args.vad_filter,
+    without_timestamps=args.without_timestamps,
+    beam_size=args.beam_size,
+    patience=args.patience,
+    no_repeat_ngram_size=args.no_repeat_ngram_size,
+    chunk_length=args.chunk_length,
+    length_penalty=args.length_penalty,
+    condition_on_previous_text=args.condition_on_previous_text,
+    hotwords=args.hotwords,
+)
+
 if args.batch_size > 0:
-    transcript_segments, info = whisper_pipeline.transcribe(
-        audio_waveform,
-        language,
-        suppress_tokens=suppress_tokens,
-        batch_size=args.batch_size,
-    )
+    transcribe_kwargs["batch_size"] = args.batch_size
+    transcript_segments, info = whisper_pipeline.transcribe(**transcribe_kwargs)
 else:
-    transcript_segments, info = whisper_model.transcribe(
-        audio_waveform,
-        language,
-        suppress_tokens=suppress_tokens,
-        vad_filter=True,
-    )
+    transcript_segments, info = whisper_model.transcribe(**transcribe_kwargs)
 
 full_transcript = "".join(segment.text for segment in transcript_segments)