update with multilingual option

run_server.py

@@ -3,11 +3,15 @@ from whisper_live.server import TranscriptionServer
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--backend', type=str, default='tensorrt', help='Backends from ["tensorrt", "faster_whisper"]')
+    parser.add_argument('--port', type=int, default=9090, help="Websocket port to run the server on.")
+    parser.add_argument('--backend', type=str, default='faster_whisper', help='Backends from ["tensorrt", "faster_whisper"]')
     parser.add_argument('--whisper_tensorrt_path',
                         type=str,
-                        default="/root/TensorRT-LLM/examples/whisper/whisper_small_en",
+                        default=None,
                         help='Whisper TensorRT model path')
+    parser.add_argument('--trt_multilingual',
+                        action="store_true",
+                        help='Boolean only for TensorRT model. True if multilingual.')
     args = parser.parse_args()
 
     if args.backend == "tensorrt":
@@ -15,4 +19,10 @@ if __name__ == "__main__":
             raise ValueError("Please Provide a valid tensorrt model path")
 
     server = TranscriptionServer()
-    server.run("0.0.0.0", port=6006, backend=args.backend, whisper_tensorrt_path=args.whisper_tensorrt_path)
+    server.run(
+        "0.0.0.0",
+        port=6006, 
+        backend=args.backend, 
+        whisper_tensorrt_path=args.whisper_tensorrt_path,
+        multilingual=args.trt_multilingual
+    )

whisper_live/server.py

@@ -68,7 +68,7 @@ class TranscriptionServer:
 
         return wait_time / 60
 
-    def recv_audio(self, websocket, backend="tensorrt", whisper_tensorrt_path=None):
+    def recv_audio(self, websocket, backend="tensorrt", whisper_tensorrt_path=None, multilingual=False):
         """
         Receive audio chunks from a client in an infinite loop.
         
@@ -118,7 +118,7 @@ class TranscriptionServer:
                 self.backend = "tensorrt"
                 client = ServeClientTensorRT(
                     websocket,
-                    multilingual=options["multilingual"],
+                    multilingual=multilingual,
                     language=options["language"],
                     task=options["task"],
                     client_uid=options["uid"],
@@ -200,7 +200,7 @@ class TranscriptionServer:
                 del websocket
                 break
 
-    def run(self, host, port=9090, backend="tensorrt", whisper_tensorrt_path=None):
+    def run(self, host, port=9090, backend="tensorrt", whisper_tensorrt_path=None, multilingual=False):
         """
         Run the transcription server.
 
@@ -212,7 +212,8 @@ class TranscriptionServer:
             functools.partial(
                 self.recv_audio,
                 backend=backend,
-                whisper_tensorrt_path=whisper_tensorrt_path
+                whisper_tensorrt_path=whisper_tensorrt_path,
+                multilingual=multilingual
             ),
             host,
             port
@@ -369,7 +370,14 @@ class ServeClientTensorRT(ServeClientBase):
         self.language = language if multilingual else "en"
         self.task = task
         self.eos = False
-        self.transcriber = WhisperTRTLLM(model_path, False, "assets", device="cuda")
+        self.transcriber = WhisperTRTLLM(
+            model_path, 
+            assets_dir="assets", 
+            device="cuda",
+            is_multilingual=multilingual,
+            language=self.language,
+            task=self.task
+        )
 
         # threading
         self.trans_thread = threading.Thread(target=self.speech_to_text)

whisper_live/transcriber_tensorrt.py

@@ -181,7 +181,10 @@ class WhisperTRTLLM(object):
         engine_dir,
         debug_mode=False,
         assets_dir=None,
-        device=None
+        device=None,
+        is_multilingual=False,
+        language="en",
+        task="transcribe"
         ):
         world_size = 1
         runtime_rank = tensorrt_llm.mpi_rank()
@@ -198,10 +201,10 @@ class WhisperTRTLLM(object):
         #                                tokenizer_dir=assets_dir)
         self.device = device
         self.tokenizer = get_tokenizer(
-            False,
+            is_multilingual,
             num_languages=self.encoder.num_languages,
-            language="en",
+            language=language,
-            task="transcribe",
+            task=task,
         )
         self.filters = mel_filters(self.device, self.encoder.n_mels, assets_dir)