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Change max_clients max_connection_time from server only

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Signed-off-by: makaveli10 <vineet.suryan@collabora.com>
This commit is contained in:
makaveli10
2025-07-21 22:52:24 +05:30
parent 8d785e5681
commit 8d6ddd4f7b
5 changed files with 93 additions and 75 deletions
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README.md
+14 -9
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@@ -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
+64 -47
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@@ -3,55 +3,72 @@ import sys
from whisper_live.client import TranscriptionClient
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--port', '-p',
type=int,
default=9090,
help="Websocket port to run the server on.")
parser.add_argument('--server', '-s',
type=str,
default='localhost',
help='hostname or ip address of server')
parser.add_argument('--files', '-f',
type=str,
nargs='+',
help='hostname or ip address of server')
parser.add_argument('--output_file', '-o',
type=str,
default='./output_recording.wav',
help='hostname or ip address of server')
args = parser.parse_args()
parser = argparse.ArgumentParser()
parser.add_argument('--port', '-p',
type=int,
default=9090,
help="Websocket port to run the server on.")
parser.add_argument('--server', '-s',
type=str,
default='localhost',
help='hostname or ip address of server')
parser.add_argument('--files', '-f',
type=str,
nargs='+',
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='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
valid_files = []
for file_path in args.files:
path = Path(file_path)
if path.exists() and path.is_file():
valid_files.append(str(path))
else:
print(f"Warning: File not found: {file_path}")
# Validate audio files
valid_files = []
for file_path in args.files:
path = Path(file_path)
if path.exists() and path.is_file():
valid_files.append(str(path))
else:
print(f"Warning: File not found: {file_path}")
if not valid_files:
print("Error: No valid audio files found!")
sys.exit(1)
if not valid_files:
print("Error: No valid audio files found!")
sys.exit(1)
print(f"Found {len(valid_files)} audio file(s) to stream:")
for file_path in valid_files:
print(f" - {file_path}")
print(f"Found {len(valid_files)} audio file(s) to stream:")
for file_path in valid_files:
print(f" - {file_path}")
for f in valid_files:
client = TranscriptionClient(
args.server,
args.port,
lang="en",
translate=False,
model="large-v3", # also support hf_model => `Systran/faster-whisper-small`
use_vad=False,
save_output_recording=False, # Only used for microphone input, False by Default
output_recording_filename=args.output_file, # Only used for microphone input
max_clients=4,
max_connection_time=600,
mute_audio_playback=True, # Only used for file input, False by Default
)
client(f)
for f in valid_files:
client = TranscriptionClient(
args.server,
args.port,
lang=args.lang,
translate=args.translate,
model=args.model, # also support hf_model => `Systran/faster-whisper-small`
use_vad=True,
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
mute_audio_playback=args.mute_audio_playback, # Only used for file input, False by Default
)
client(f)
run_server.py
+10
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@@ -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
)
whisper_live/client.py
-14
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@@ -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
+5 -5
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@@ -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: