refactor: include additional parameters

Refactor ServeClientBase and its subclasses to include additional parameters for segment handling and audio clipping.

whisper_live/backend/base.py

@@ -10,22 +10,46 @@ class ServeClientBase(object):
     SERVER_READY = "SERVER_READY"
     DISCONNECT = "DISCONNECT"
 
-    def __init__(self, client_uid, websocket):
+    client_uid: str
+    """A unique identifier for the client."""
+    websocket: object
+    """The WebSocket connection for the client."""
+    send_last_n_segments: int
+    """Number of most recent segments to send to the client."""
+    no_speech_thresh: float
+    """Segments with no speech probability above this threshold will be discarded."""
+    clip_audio: bool
+    """Whether to clip audio with no valid segments."""
+    same_output_threshold: int
+    """Number of repeated outputs before considering it as a valid segment."""
+
+    def __init__(
+        self,
+        client_uid,
+        websocket,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+    ):
         self.client_uid = client_uid
         self.websocket = websocket
+        self.send_last_n_segments = send_last_n_segments
+        self.no_speech_thresh = no_speech_thresh
+        self.clip_audio = clip_audio
+        self.same_output_threshold = same_output_threshold
+
         self.frames = b""
         self.timestamp_offset = 0.0
         self.frames_np = None
         self.frames_offset = 0.0
         self.text = []
-        self.current_out = ''
-        self.prev_out = ''
+        self.current_out = ""
+        self.prev_out = ""
         self.exit = False
         self.same_output_count = 0
         self.transcript = []
-        self.send_last_n_segments = 10
-        self.no_speech_thresh = 0.45
-        self.clip_audio = False
+        self.end_time_for_same_output = None
 
         # threading
         self.lock = threading.Lock()

whisper_live/backend/faster_whisper_backend.py

@@ -9,12 +9,26 @@ from whisper_live.backend.base import ServeClientBase
 
 
 class ServeClientFasterWhisper(ServeClientBase):
-
     SINGLE_MODEL = None
     SINGLE_MODEL_LOCK = threading.Lock()
 
-    def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
-                 initial_prompt=None, vad_parameters=None, use_vad=True, single_model=False):
+    def __init__(
+        self,
+        websocket,
+        task="transcribe",
+        device=None,
+        language=None,
+        client_uid=None,
+        model="small.en",
+        initial_prompt=None,
+        vad_parameters=None,
+        use_vad=True,
+        single_model=False,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+    ):
         """
         Initialize a ServeClient instance.
         The Whisper model is initialized based on the client's language and device availability.
@@ -23,15 +37,27 @@ class ServeClientFasterWhisper(ServeClientBase):
 
         Args:
             websocket (WebSocket): The WebSocket connection for the client.
-            task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
+            task (str, optional): The task type, e.g., "transcribe". Defaults to "transcribe".
             device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
             language (str, optional): The language for transcription. Defaults to None.
             client_uid (str, optional): A unique identifier for the client. Defaults to None.
             model (str, optional): The whisper model size. Defaults to 'small.en'
             initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
             single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to 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.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
+
         """
-        super().__init__(client_uid, websocket)
+        super().__init__(
+            client_uid,
+            websocket,
+            send_last_n_segments,
+            no_speech_thresh,
+            clip_audio,
+            same_output_threshold,
+        )
         self.model_sizes = [
             "tiny", "tiny.en", "base", "base.en", "small", "small.en",
             "medium", "medium.en", "large-v2", "large-v3", "distil-small.en",
@@ -45,9 +71,6 @@ class ServeClientFasterWhisper(ServeClientBase):
         self.initial_prompt = initial_prompt
         self.vad_parameters = vad_parameters or {"onset": 0.5}
 
-        self.same_output_threshold = 10
-        self.end_time_for_same_output = None
-
         device = "cuda" if torch.cuda.is_available() else "cpu"
         if device == "cuda":
             major, _ = torch.cuda.get_device_capability(device)

whisper_live/backend/openvino_backend.py

@@ -12,8 +12,23 @@ class ServeClientOpenVINO(ServeClientBase):
     SINGLE_MODEL = None
     SINGLE_MODEL_LOCK = threading.Lock()
 
-    def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
-                 initial_prompt=None, vad_parameters=None, use_vad=True, single_model=False):
+    def __init__(
+        self,
+        websocket,
+        task="transcribe",
+        device=None,
+        language=None,
+        client_uid=None,
+        model="small.en",
+        initial_prompt=None,
+        vad_parameters=None,
+        use_vad=True,
+        single_model=False,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+    ):
         """
         Initialize a ServeClient instance.
         The Whisper model is initialized based on the client's language and device availability.
@@ -29,15 +44,25 @@ class ServeClientOpenVINO(ServeClientBase):
             model (str, optional): Huggingface model_id for a valid OpenVINO model.
             initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
             single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to 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.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
         """
-        super().__init__(client_uid, websocket)
+        super().__init__(
+            client_uid,
+            websocket,
+            send_last_n_segments,
+            no_speech_thresh,
+            clip_audio,
+            same_output_threshold,
+        )
         self.language = "en" if language is None else language
         if not self.language.startswith("<|"):
             self.language = f"<|{self.language}|>"
 
         self.task = "transcribe" if task is None else task
-        self.same_output_threshold = 10
-        self.end_time_for_same_output = None
+
         self.clip_audio = True
 
         core = Core()

whisper_live/backend/trt_backend.py

@@ -11,7 +11,20 @@ class ServeClientTensorRT(ServeClientBase):
     SINGLE_MODEL = None
     SINGLE_MODEL_LOCK = threading.Lock()
 
-    def __init__(self, websocket, task="transcribe", multilingual=False, language=None, client_uid=None, model=None, single_model=False):
+    def __init__(
+        self,
+        websocket,
+        task="transcribe",
+        multilingual=False,
+        language=None,
+        client_uid=None,
+        model=None,
+        single_model=False,
+        send_last_n_segments=10,
+        no_speech_thresh=0.45,
+        clip_audio=False,
+        same_output_threshold=10,
+    ):
         """
         Initialize a ServeClient instance.
         The Whisper model is initialized based on the client's language and device availability.
@@ -26,9 +39,19 @@ class ServeClientTensorRT(ServeClientBase):
             language (str, optional): The language for transcription. Defaults to None.
             client_uid (str, optional): A unique identifier for the client. Defaults to None.
             single_model (bool, optional): Whether to instantiate a new model for each client connection. Defaults to 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.
+            clip_audio (bool, optional): Whether to clip audio with no valid segments. Defaults to False.
+            same_output_threshold (int, optional): Number of repeated outputs before considering it as a valid segment. Defaults to 10.
         """
-        super().__init__(client_uid, websocket)
+        super().__init__(
+            client_uid,
+            websocket,
+            send_last_n_segments,
+            no_speech_thresh,
+            clip_audio,
+            same_output_threshold,
+        )
         self.language = language if multilingual else "en"
         self.task = task
         self.eos = False