Merge pull request #387 from makaveli10/modify_max_client_time_server_only

Change max_clients max_connection_time from server only

README.md

@@ -45,12 +45,16 @@ The server supports 3 backends `faster_whisper`, `tensorrt` and `openvino`. If r
 - [Faster Whisper](https://github.com/SYSTRAN/faster-whisper) backend
 ```bash
 python3 run_server.py --port 9090 \
-                      --backend faster_whisper
+                      --backend faster_whisper \
+                      --max_clients 4 \
+                      --max_connection_time 600
   
 # running with custom model and cache_dir to save auto-converted ctranslate2 models
 python3 run_server.py --port 9090 \
                       --backend faster_whisper \
-                      -fw "/path/to/custom/faster/whisper/model"
+                      --max_clients 4 \
+                      --max_connection_time 600 \
+                      -fw "/path/to/custom/faster/whisper/model" \
                       -c ~/.cache/whisper-live/
 ```
 
@@ -59,15 +63,20 @@ python3 run_server.py --port 9090 \
 # Run English only model
 python3 run_server.py -p 9090 \
                       -b tensorrt \
-                      -trt /home/TensorRT-LLM/examples/whisper/whisper_small_en
+                      -trt /home/TensorRT-LLM/examples/whisper/whisper_small_en \
+                      --max_clients 4 \
+                      --max_connection_time 600
 
 # Run Multilingual model
 python3 run_server.py -p 9090 \
                       -b tensorrt \
                       -trt /home/TensorRT-LLM/examples/whisper/whisper_small \
-                      -m
+                      -m \
+                      --max_clients 4 \
+                      --max_connection_time 600
 ```
-
+- Use `--max_clients` option to restrict the number of clients the server should allow. Defaults to 4.
+- Use `--max_connection_time` options to limit connection time for a client in seconds. Defaults to 600.
 - WhisperLive now supports the [OpenVINO](https://github.com/openvinotoolkit/openvino) backend for efficient inference on Intel CPUs, iGPU and dGPUs. Currently, we tested the models uploaded to [huggingface by OpenVINO](https://huggingface.co/OpenVINO?search_models=whisper).
   - > **Docker Recommended:** Running WhisperLive with OpenVINO inside Docker automatically enables GPU support (iGPU/dGPU) without requiring additional host setup.
   - > **Native (non-Docker) Use:** If you prefer running outside Docker, ensure the Intel drivers and OpenVINO runtime are installed and properly configured on your system. Refer to the documentation for [installing OpenVINO](https://docs.openvino.ai/2025/get-started/install-openvino.html?PACKAGE=OPENVINO_BASE&VERSION=v_2025_0_0&OP_SYSTEM=LINUX&DISTRIBUTION=PIP#).
@@ -101,8 +110,6 @@ If you don't want this, set `--no_single_model`.
   - `use_vad`: Whether to use `Voice Activity Detection` on the server.
   - `save_output_recording`: Set to True to save the microphone input as a `.wav` file during live transcription. This option is helpful for recording sessions for later playback or analysis. Defaults to `False`. 
   - `output_recording_filename`: Specifies the `.wav` file path where the microphone input will be saved if `save_output_recording` is set to `True`.
-  - `max_clients`: Specifies the maximum number of clients the server should allow. Defaults to 4.
-  - `max_connection_time`: Maximum connection time for each client in seconds. Defaults to 600.
   - `mute_audio_playback`: Whether to mute audio playback when transcribing an audio file. Defaults to False.
 
 ```python
@@ -116,8 +123,6 @@ client = TranscriptionClient(
   use_vad=False,
   save_output_recording=True,                         # Only used for microphone input, False by Default
   output_recording_filename="./output_recording.wav", # Only used for microphone input
-  max_clients=4,
-  max_connection_time=600,
   mute_audio_playback=False,                          # Only used for file input, False by Default
 )
 ```

run_client.py

@@ -3,6 +3,7 @@ import sys
 from whisper_live.client import TranscriptionClient
 import argparse
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--port', '-p',
@@ -16,11 +17,29 @@ if __name__ == '__main__':
     parser.add_argument('--files', '-f',
                           type=str,
                           nargs='+',
-                        help='hostname or ip address of server')
+                          help='Files to transcribe, separated by spaces. '
+                              'If not provided, will use microphone input.')
     parser.add_argument('--output_file', '-o',
                           type=str,
                           default='./output_recording.wav',
-                        help='hostname or ip address of server')
+                          help='output recording filename, only used for microphone input.')
+    parser.add_argument('--model', '-m',
+                          type=str,
+                          default='small',
+                          help='Model to use for transcription, e.g., "tiny, small.en, large-v3".')
+    parser.add_argument('--lang', '-l',
+                          type=str,
+                          default='en',
+                          help='Language code for transcription, e.g., "en" for English.')
+    parser.add_argument('--translate', '-t',
+                          action='store_true',
+                          help='Enable translation of the transcription output.')
+    parser.add_argument('--mute_audio_playback', '-a',
+                          action='store_true',
+                          help='Mute audio playback during transcription.') 
+    parser.add_argument('--save_output_recording', '-r',
+                          action='store_true',
+                          help='Save the output recording, only used for microphone input.')                  
     args = parser.parse_args()
 
     # Validate audio files
@@ -44,14 +63,12 @@ if __name__ == '__main__':
         client = TranscriptionClient(
             args.server,
             args.port,
-      lang="en",
+            lang=args.lang,
-      translate=False,
+            translate=args.translate,
-      model="large-v3",                                   # also support hf_model => `Systran/faster-whisper-small`
+            model=args.model,                                  # also support hf_model => `Systran/faster-whisper-small`
-      use_vad=False,
+            use_vad=True,
-      save_output_recording=False,                         # Only used for microphone input, False by Default
+            save_output_recording=args.save_output_recording,  # Only used for microphone input, False by Default
             output_recording_filename=args.output_file,        # Only used for microphone input
-      max_clients=4,
+            mute_audio_playback=args.mute_audio_playback,      # Only used for file input, False by Default
-      max_connection_time=600,
-      mute_audio_playback=True,                          # Only used for file input, False by Default
         )
         client(f)

run_server.py

@@ -31,6 +31,14 @@ if __name__ == "__main__":
     parser.add_argument('--no_single_model', '-nsm',
                         action='store_true',
                         help='Set this if every connection should instantiate its own model. Only relevant for custom model, passed using -trt or -fw.')
+    parser.add_argument('--max_clients',
+                        type=int,
+                        default=4,
+                        help='Maximum clients supported by the server.')
+    parser.add_argument('--max_connection_time',
+                        type=int,
+                        default=300,
+                        help='Path to cache the converted ctranslate2 models.')
     parser.add_argument('--cache_path', '-c',
                         type=str,
                         default="~/.cache/whisper-live/",
@@ -55,5 +63,7 @@ if __name__ == "__main__":
         trt_multilingual=args.trt_multilingual,
         trt_py_session=args.trt_py_session,
         single_model=not args.no_single_model,
+        max_clients=args.max_clients,
+        max_connection_time=args.max_connection_time,
         cache_path=args.cache_path
     )

tests/test_client.py

@@ -49,8 +49,6 @@ class TestClientCallbacks(BaseTestCase):
             "task": self.client.task,
             "model": self.client.model,
             "use_vad": True,
-            "max_clients": 4,
-            "max_connection_time": 600,
             "send_last_n_segments": 10,
             "no_speech_thresh": 0.45,
             "clip_audio": False,

tests/test_server.py

@@ -42,6 +42,8 @@ class TestGetWaitTime(unittest.TestCase):
 class TestServerConnection(unittest.TestCase):
     def setUp(self):
         self.server = TranscriptionServer()
+        self.server.client_manager = ClientManager(max_clients=4, max_connection_time=600)
+        self.server.cache_path = "~/.cache/whisper-live/"
 
     @mock.patch('websockets.WebSocketCommonProtocol')
     def test_connection(self, mock_websocket):

whisper_live/client.py

@@ -32,8 +32,6 @@ class Client:
         use_vad=True,
         use_wss=False,
         log_transcription=True,
-        max_clients=4,
-        max_connection_time=600,
         send_last_n_segments=10,
         no_speech_thresh=0.45,
         clip_audio=False,
@@ -56,8 +54,6 @@ class Client:
             srt_file_path (str, optional): The file path to save the output SRT file. Default is "output.srt".
             use_vad (bool, optional): Whether to enable voice activity detection. Default is True.
             log_transcription (bool, optional): Whether to log transcription output to the console. Default is True.
-            max_clients (int, optional): Maximum number of client connections allowed. Default is 4.
-            max_connection_time (int, optional): Maximum allowed connection time in seconds. Default is 600.
             send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
             no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
             clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
@@ -79,8 +75,6 @@ class Client:
         self.last_segment = None
         self.last_received_segment = None
         self.log_transcription = log_transcription
-        self.max_clients = max_clients
-        self.max_connection_time = max_connection_time
         self.send_last_n_segments = send_last_n_segments
         self.no_speech_thresh = no_speech_thresh
         self.clip_audio = clip_audio
@@ -236,8 +230,6 @@ class Client:
                     "task": self.task,
                     "model": self.model,
                     "use_vad": self.use_vad,
-                    "max_clients": self.max_clients,
-                    "max_connection_time": self.max_connection_time,
                     "send_last_n_segments": self.send_last_n_segments,
                     "no_speech_thresh": self.no_speech_thresh,
                     "clip_audio": self.clip_audio,
@@ -714,8 +706,6 @@ class TranscriptionClient(TranscriptionTeeClient):
         output_recording_filename (str, optional): Path to save the output recording WAV file. Default is "./output_recording.wav".
         output_transcription_path (str, optional): File path to save the output transcription (SRT file). Default is "./output.srt".
         log_transcription (bool, optional): Whether to log transcription output to the console. Default is True.
-        max_clients (int, optional): Maximum number of client connections allowed. Default is 4.
-        max_connection_time (int, optional): Maximum allowed connection time in seconds. Default is 600.
         mute_audio_playback (bool, optional): If True, mutes audio playback during file playback. Default is False.
         send_last_n_segments (int, optional): Number of most recent segments to send to the client. Defaults to 10.
         no_speech_thresh (float, optional): Segments with no speech probability above this threshold will be discarded. Defaults to 0.45.
@@ -746,8 +736,6 @@ class TranscriptionClient(TranscriptionTeeClient):
         output_recording_filename="./output_recording.wav",
         output_transcription_path="./output.srt",
         log_transcription=True,
-        max_clients=4,
-        max_connection_time=600,
         mute_audio_playback=False,
         send_last_n_segments=10,
         no_speech_thresh=0.45,
@@ -765,8 +753,6 @@ class TranscriptionClient(TranscriptionTeeClient):
             use_vad=use_vad,
             use_wss=use_wss,
             log_transcription=log_transcription,
-            max_clients=max_clients,
-            max_connection_time=max_connection_time,
             send_last_n_segments=send_last_n_segments,
             no_speech_thresh=no_speech_thresh,
             clip_audio=clip_audio,

whisper_live/server.py

@@ -269,11 +269,6 @@ class TranscriptionServer:
             options = websocket.recv()
             options = json.loads(options)
 
-            if self.client_manager is None:
-                max_clients = options.get('max_clients', 4)
-                max_connection_time = options.get('max_connection_time', 600)
-                self.client_manager = ClientManager(max_clients, max_connection_time)
-
             self.use_vad = options.get('use_vad')
             if self.client_manager.is_server_full(websocket, options):
                 websocket.close()
@@ -372,6 +367,8 @@ class TranscriptionServer:
             trt_multilingual=False,
             trt_py_session=False,
             single_model=False,
+            max_clients=4,
+            max_connection_time=600,
             cache_path="~/.cache/whisper-live/"):
         """
         Run the transcription server.
@@ -381,6 +378,9 @@ class TranscriptionServer:
             port (int): The port number to bind the server.
         """
         self.cache_path = cache_path
+        self.client_manager = ClientManager(max_clients, max_connection_time)
+        if faster_whisper_custom_model_path is not None and not os.path.exists(faster_whisper_custom_model_path):
+            raise ValueError(f"Custom faster_whisper model '{faster_whisper_custom_model_path}' is not a valid path.")
         if whisper_tensorrt_path is not None and not os.path.exists(whisper_tensorrt_path):
             raise ValueError(f"TensorRT model '{whisper_tensorrt_path}' is not a valid path.")
         if single_model: