🔨 refactor whisper_live according to flake8

run_server.py

@@ -4,15 +4,15 @@ from whisper_live.server import TranscriptionServer
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument('--port', '-p',
-                        type=int, 
+                        type=int,
                         default=9090,
                         help="Websocket port to run the server on.")
     parser.add_argument('--backend', '-b',
-                        type=str, 
+                        type=str,
-                        default='faster_whisper', 
+                        default='faster_whisper',
                         help='Backends from ["tensorrt", "faster_whisper"]')
     parser.add_argument('--faster_whisper_custom_model_path', '-fw',
-                        type=str, default=None, 
+                        type=str, default=None,
                         help="Custom Faster Whisper Model")
     parser.add_argument('--trt_model_path', '-trt',
                         type=str,
@@ -30,7 +30,7 @@ if __name__ == "__main__":
     server = TranscriptionServer()
     server.run(
         "0.0.0.0",
-        port=args.port, 
+        port=args.port,
         backend=args.backend,
         faster_whisper_custom_model_path=args.faster_whisper_custom_model_path,
         whisper_tensorrt_path=args.trt_model_path,

setup.py

@@ -10,36 +10,38 @@ HERE = pathlib.Path(__file__).parent
 README = (HERE / "README.md").read_text()
 
 # This call to setup() does all the work
-setup(name="whisper-live",
+setup(
-      version=__version__,
+    name="whisper-live",
-      description="A nearly-live implementation of OpenAI's Whisper.",
+    version=__version__,
-      long_description=README,
+    description="A nearly-live implementation of OpenAI's Whisper.",
-      long_description_content_type="text/markdown",
+    long_description=README,
-      include_package_data=True,
+    long_description_content_type="text/markdown",
-      url="https://github.com/collabora/WhisperLive",
+    include_package_data=True,
-      author="Collabora Ltd",
+    url="https://github.com/collabora/WhisperLive",
-      author_email="vineet.suryan@collabora.com",
+    author="Collabora Ltd",
-      license="MIT",
+    author_email="vineet.suryan@collabora.com",
-      classifiers=[
+    license="MIT",
-          "Development Status :: 4 - Beta",
+    classifiers=[
-          "Intended Audience :: Developers",
+        "Development Status :: 4 - Beta",
-          "Intended Audience :: Science/Research",
+        "Intended Audience :: Developers",
-          "License :: OSI Approved :: MIT License",
+        "Intended Audience :: Science/Research",
-          "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: MIT License",
-          "Programming Language :: Python :: 3 :: Only",
+        "Programming Language :: Python :: 3",
-          "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3 :: Only",
-          "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.8",
-          "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Programming Language :: Python :: 3.9",
-      ],
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
-      packages=find_packages(
+    ],
-        exclude=("examples",
+    packages=find_packages(
-                 "Audio-Transcription-Chrome",
+        exclude=(
-                 "Audio-Transcription-Firefox",
+            "examples",
-                 "requirements",
+            "Audio-Transcription-Chrome",
-                 "whisper-finetuning"
+            "Audio-Transcription-Firefox",
-                )
+            "requirements",
-      ),
+            "whisper-finetuning"
-      install_requires=[
+        )
+    ),
+    install_requires=[
         "PyAudio",
         "faster-whisper==0.10.0",
         "torch",
@@ -53,6 +55,6 @@ setup(name="whisper-live",
         "openai-whisper",
         "kaldialign",
         "soundfile",
-      ],
+    ],
-      python_requires=">=3.8"
+    python_requires=">=3.8"
 )

tests/test_client.py

@@ -4,7 +4,8 @@ import scipy
 import websocket
 import unittest
 from unittest.mock import patch, MagicMock
-from whisper_live.client import TranscriptionClient, resample
+from whisper_live.client import TranscriptionClient
+from whisper_live.utils import resample
 
 
 class BaseTestCase(unittest.TestCase):
@@ -68,7 +69,7 @@ class TestClientCallbacks(BaseTestCase):
             ]
         })
         self.client.on_message(self.mock_ws_app, message)
-    
+
         # Assert that the transcript was updated correctly
         self.assertEqual(len(self.client.transcript), 2)
         self.assertEqual(self.client.transcript[1]['text'], "Test transcript 2")
@@ -79,14 +80,14 @@ class TestClientCallbacks(BaseTestCase):
         self.client.on_close(self.mock_ws_app, close_status_code, close_msg)
 
         self.assertFalse(self.client.recording)
-        self.assertFalse(self.client.server_error)        
+        self.assertFalse(self.client.server_error)
         self.assertFalse(self.client.waiting)
-    
+
     def test_on_error(self):
         error_message = "Test Error"
         self.client.on_error(self.mock_ws_app, error_message)
 
-        self.assertTrue(self.client.server_error)        
+        self.assertTrue(self.client.server_error)
         self.assertEqual(self.client.error_message, error_message)
 
 
@@ -95,10 +96,10 @@ class TestAudioResampling(unittest.TestCase):
         original_audio = "assets/jfk.flac"
         expected_sr = 16000
         resampled_audio = resample(original_audio, expected_sr)
-        
+
         sr, _ = scipy.io.wavfile.read(resampled_audio)
         self.assertEqual(sr, expected_sr)
-        
+
         os.remove(resampled_audio)
 
 

tests/test_server.py

@@ -14,32 +14,31 @@ from whisper.normalizers import EnglishTextNormalizer
 class TestTranscriptionServerInitialization(unittest.TestCase):
     def test_initialization(self):
         server = TranscriptionServer()
-        self.assertEqual(server.max_clients, 4)
+        self.assertEqual(server.client_manager.max_clients, 4)
-        self.assertEqual(server.max_connection_time, 600)
+        self.assertEqual(server.client_manager.max_connection_time, 600)
-        self.assertDictEqual(server.clients, {})
+        self.assertDictEqual(server.client_manager.clients, {})
-        self.assertDictEqual(server.websockets, {})
+        self.assertDictEqual(server.client_manager.start_times, {})
-        self.assertDictEqual(server.clients_start_time, {})
 
 
 class TestGetWaitTime(unittest.TestCase):
     def setUp(self):
         self.server = TranscriptionServer()
-        self.server.clients_start_time = {
+        self.server.client_manager.start_times = {
             'client1': time.time() - 120,
             'client2': time.time() - 300
         }
-        self.server.max_connection_time = 600
+        self.server.client_manager.max_connection_time = 600
 
     def test_get_wait_time(self):
-        expected_wait_time = (600 - (time.time() - self.server.clients_start_time['client2'])) / 60
+        expected_wait_time = (600 - (time.time() - self.server.client_manager.start_times['client2'])) / 60
-        print(self.server.get_wait_time(), expected_wait_time)
+        print(self.server.client_manager.get_wait_time(), expected_wait_time)
-        self.assertAlmostEqual(self.server.get_wait_time(), expected_wait_time, places=2)
+        self.assertAlmostEqual(self.server.client_manager.get_wait_time(), expected_wait_time, places=2)
+
 
-    
 class TestServerConnection(unittest.TestCase):
     def setUp(self):
         self.server = TranscriptionServer()
-    
+
     @mock.patch('websockets.WebSocketCommonProtocol')
     def test_connection(self, mock_websocket):
         mock_websocket.recv.return_value = json.dumps({
@@ -50,7 +49,6 @@ class TestServerConnection(unittest.TestCase):
         })
         self.server.recv_audio(mock_websocket, "faster_whisper")
 
-    
     @mock.patch('websockets.WebSocketCommonProtocol')
     def test_recv_audio_exception_handling(self, mock_websocket):
         mock_websocket.recv.side_effect = [json.dumps({
@@ -58,12 +56,12 @@ class TestServerConnection(unittest.TestCase):
             'language': 'en',
             'task': 'transcribe',
             'model': 'tiny.en'
-        }),  np.array([1, 2, 3]).tobytes()]  
+        }),  np.array([1, 2, 3]).tobytes()]
-        
+
         with self.assertLogs(level="ERROR"):
             self.server.recv_audio(mock_websocket, "faster_whisper")
-        
+
-        self.assertNotIn(mock_websocket, self.server.clients)
+        self.assertNotIn(mock_websocket, self.server.client_manager.clients)
 
 
 class TestServerInferenceAccuracy(unittest.TestCase):
@@ -71,12 +69,12 @@ class TestServerInferenceAccuracy(unittest.TestCase):
     def setUpClass(cls):
         cls.server_process = subprocess.Popen(["python", "run_server.py"])  # Adjust the command as needed
         time.sleep(2)
-    
+
     @classmethod
     def tearDownClass(cls):
         cls.server_process.terminate()
         cls.server_process.wait()
-    
+
     @mock.patch('pyaudio.PyAudio')
     def setUp(self, mock_pyaudio):
         self.mock_pyaudio = mock_pyaudio.return_value
@@ -84,16 +82,16 @@ class TestServerInferenceAccuracy(unittest.TestCase):
         self.mock_pyaudio.open.return_value = self.mock_stream
         self.metric = evaluate.load("wer")
         self.normalizer = EnglishTextNormalizer()
-        self.client  = TranscriptionClient(
+        self.client = TranscriptionClient(
             "localhost", "9090", model="base.en", lang="en",
         )
-    
+
     def test_inference(self):
         gt = "And so my fellow Americans, ask not, what your country can do for you. Ask what you can do for your country!"
         self.client("assets/jfk.flac")
         with open("output.srt", "r") as f:
             lines = f.readlines()
-            prediction = " ".join([l.strip() for l in lines[2::4]])
+            prediction = " ".join([line.strip() for line in lines[2::4]])
         prediction_normalized = self.normalizer(prediction)
         gt_normalized = self.normalizer(gt)
 

tests/test_vad.py

@@ -1,7 +1,6 @@
 import unittest
 import numpy as np
 import torch
-import scipy.io as sio
 from whisper_live.tensorrt_utils import load_audio
 from whisper_live.vad import VoiceActivityDetection
 
@@ -25,4 +24,4 @@ class TestVoiceActivityDetection(unittest.TestCase):
     def test_vad_speech_detection(self):
         audio_tensor = torch.from_numpy(load_audio("assets/jfk.flac"))
         speech_prob = self.vad(audio_tensor, self.sample_rate).item()
-        self.assertGreater(speech_prob, 0.5, "VAD failed to identify speech segment.")
+        self.assertGreater(speech_prob, 0.5, "VAD failed to identify speech segment.")

whisper_live/__version__.py

@@ -1 +1 @@
-__version__="0.1.0"
+__version__ = "0.1.0"

whisper_live/client.py

@@ -2,68 +2,14 @@ import os
 import wave
 
 import numpy as np
-import scipy
-import ffmpeg
 import pyaudio
 import threading
-import textwrap
 import json
 import websocket
 import uuid
 import time
-
+import ffmpeg
-
+import whisper_live.utils as utils
-def format_time(s):
-    """Convert seconds (float) to SRT time format."""
-    hours = int(s // 3600)
-    minutes = int((s % 3600) // 60)
-    seconds = int(s % 60)
-    milliseconds = int((s - int(s)) * 1000)
-    return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
-
-def create_srt_file(segments, output_file):
-    with open(output_file, 'w', encoding='utf-8') as srt_file:
-        segment_number = 1
-        for segment in segments:
-            start_time = format_time(float(segment['start']))
-            end_time = format_time(float(segment['end']))
-            text = segment['text']
-            
-            srt_file.write(f"{segment_number}\n")
-            srt_file.write(f"{start_time} --> {end_time}\n")
-            srt_file.write(f"{text}\n\n")
-            
-            segment_number += 1
-
-
-def resample(file: str, sr: int = 16000):
-    """
-    # https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/audio.py#L22
-    Open an audio file and read as mono waveform, resampling as necessary,
-    save the resampled audio
-
-    Args:
-        file (str): The audio file to open
-        sr (int): The sample rate to resample the audio if necessary
-    
-    Returns:
-        resampled_file (str): The resampled audio file
-    """
-    try:
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        out, _ = (
-            ffmpeg.input(file, threads=0)
-            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
-            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
-        )
-    except ffmpeg.Error as e:
-        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
-    np_buffer = np.frombuffer(out, dtype=np.int16)
-
-    resampled_file = f"{file.split('.')[0]}_resampled.wav"
-    scipy.io.wavfile.write(resampled_file, sr, np_buffer.astype(np.int16))
-    return resampled_file
 
 
 class Client:
@@ -150,10 +96,40 @@ class Client:
         self.transcript = []
         print("[INFO]: * recording")
 
+    def handle_status_messages(self, message_data):
+        """Handles server status messages."""
+        status = message_data["status"]
+        if status == "WAIT":
+            self.waiting = True
+            print(f"[INFO]: Server is full. Estimated wait time {round(message_data['message'])} minutes.")
+        elif status == "ERROR":
+            print(f"Message from Server: {message_data['message']}")
+            self.server_error = True
+        elif status == "WARNING":
+            print(f"Message from Server: {message_data['message']}")
+
+    def process_segments(self, segments):
+        """Processes transcript segments."""
+        text = []
+        for i, seg in enumerate(segments):
+            if not text or text[-1] != seg["text"]:
+                text.append(seg["text"])
+                if i == len(segments) - 1:
+                    self.last_segment = seg
+                elif (self.server_backend == "faster_whisper" and
+                      (not self.transcript or
+                        float(seg['start']) >= float(self.transcript[-1]['end']))):
+                    self.transcript.append(seg)
+
+        # Truncate to last 3 entries for brevity.
+        text = text[-3:]
+        # utils.clear_screen()
+        utils.print_transcript(text)
+
     def on_message(self, ws, message):
         """
         Callback function called when a message is received from the server.
-        
+
         It updates various attributes of the client based on the received message, including
         recording status, language detection, and server messages. If a disconnect message
         is received, it sets the recording status to False.
@@ -171,14 +147,7 @@ class Client:
             return
 
         if "status" in message.keys():
-            if message["status"] == "WAIT":
+            self.handle_status_messages(message)
-                self.waiting = True
-                print(
-                    f"[INFO]:Server is full. Estimated wait time {round(message['message'])} minutes."
-                )
-            elif message["status"] == "ERROR":
-                print(f"Message from Server: {message['message']}")
-                self.server_error = True
             return
 
         if "message" in message.keys() and message["message"] == "DISCONNECT":
@@ -199,38 +168,8 @@ class Client:
             )
             return
 
-        if "segments" not in message.keys():
+        if "segments" in message.keys():
-            return
+            self.process_segments(message["segments"])
-
-        message = message["segments"]
-        text = []
-        n_segments = len(message)
-
-        if n_segments:
-            for i, seg in enumerate(message):
-                if text and text[-1] == seg["text"]:
-                    # already got it
-                    continue
-                text.append(seg["text"])
-
-                if i == n_segments-1: 
-                    self.last_segment = seg
-                elif self.server_backend == "faster_whisper":
-                    if not len(self.transcript) or float(seg['start']) >= float(self.transcript[-1]['end']):
-                        self.transcript.append(seg)
-
-        # keep only last 3
-        if len(text) > 3:
-            text = text[-3:]
-        wrapper = textwrap.TextWrapper(width=60)
-        word_list = wrapper.wrap(text="".join(text))
-        # Print each line.
-        if os.name == "nt":
-            os.system("cls")
-        else:
-            os.system("clear")
-        for element in word_list:
-            print(element)
 
     def on_error(self, ws, error):
         print(f"[ERROR] WebSocket Error: {error}")
@@ -246,7 +185,7 @@ class Client:
     def on_open(self, ws):
         """
         Callback function called when the WebSocket connection is successfully opened.
-        
+
         Sends an initial configuration message to the server, including client UID,
         language selection, and task type.
 
@@ -270,8 +209,8 @@ class Client:
     def bytes_to_float_array(audio_bytes):
         """
         Convert audio data from bytes to a NumPy float array.
-        
+
-        It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to 
+        It assumes that the audio data is in 16-bit PCM format. The audio data is normalized to
         have values between -1 and 1.
 
         Args:
@@ -299,10 +238,10 @@ class Client:
     def play_file(self, filename):
         """
         Play an audio file and send it to the server for processing.
-        
+
         Reads an audio file, plays it through the audio output, and simultaneously sends
-        the audio data to the server for processing. It uses PyAudio to create an audio 
+        the audio data to the server for processing. It uses PyAudio to create an audio
-        stream for playback. The audio data is read from the file in chunks, converted to 
+        stream for playback. The audio data is read from the file in chunks, converted to
         floating-point format, and sent to the server using WebSocket communication.
         This method is typically used when you want to process pre-recorded audio and send it
         to the server in real-time.
@@ -310,7 +249,7 @@ class Client:
         Args:
             filename (str): The path to the audio file to be played and sent to the server.
         """
-        
+
         # read audio and create pyaudio stream
         with wave.open(filename, "rb") as wavfile:
             self.stream = self.p.open(
@@ -356,7 +295,7 @@ class Client:
         """
         Close the WebSocket connection and join the WebSocket thread.
 
-        First attempts to close the WebSocket connection using `self.client_socket.close()`. After 
+        First attempts to close the WebSocket connection using `self.client_socket.close()`. After
         closing the connection, it joins the WebSocket thread to ensure proper termination.
 
         """
@@ -383,7 +322,7 @@ class Client:
         """
         Write audio frames to a WAV file.
 
-        The WAV file is created or overwritten with the specified name. The audio frames should be 
+        The WAV file is created or overwritten with the specified name. The audio frames should be
         in the correct format and match the specified channel, sample width, and sample rate.
 
         Args:
@@ -433,7 +372,6 @@ class Client:
 
         print("[INFO]: HLS stream processing finished.")
 
-
     def record(self, out_file="output_recording.wav"):
         """
         Record audio data from the input stream and save it to a WAV file.
@@ -444,11 +382,12 @@ class Client:
 
         Audio data is saved in chunks to the "chunks" directory. Each chunk is saved as a separate WAV file.
         The recording will continue until the specified duration is reached or until the `RECORDING` flag is set to `False`.
-        The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording, 
+        The recording process can be interrupted by sending a KeyboardInterrupt (e.g., pressing Ctrl+C). After recording,
         the method combines all the saved audio chunks into the specified `out_file`.
 
         Args:
-            out_file (str, optional): The name of the output WAV file to save the entire recording. Default is "output_recording.wav".
+            out_file (str, optional): The name of the output WAV file to save the entire recording.
+                                      Default is "output_recording.wav".
 
         """
         n_audio_file = 0
@@ -458,7 +397,7 @@ class Client:
             for _ in range(0, int(self.rate / self.chunk * self.record_seconds)):
                 if not self.recording:
                     break
-                data = self.stream.read(self.chunk, exception_on_overflow = False)
+                data = self.stream.read(self.chunk, exception_on_overflow=False)
                 self.frames += data
 
                 audio_array = Client.bytes_to_float_array(data)
@@ -498,8 +437,8 @@ class Client:
     def write_output_recording(self, n_audio_file, out_file):
         """
         Combine and save recorded audio chunks into a single WAV file.
-        
+
-        The individual audio chunk files are expected to be located in the "chunks" directory. Reads each chunk 
+        The individual audio chunk files are expected to be located in the "chunks" directory. Reads each chunk
         file, appends its audio data to the final recording, and then deletes the chunk file. After combining
         and saving, the final recording is stored in the specified `out_file`.
 
@@ -532,7 +471,7 @@ class Client:
 
     def write_srt_file(self, output_path="output.srt"):
         self.transcript.append(self.last_segment)
-        create_srt_file(self.transcript, output_path)
+        utils.create_srt_file(self.transcript, output_path)
 
 
 class TranscriptionClient:
@@ -558,13 +497,7 @@ class TranscriptionClient:
         transcription_client()
         ```
     """
-    def __init__(self,
+    def __init__(self, host, port, lang=None, translate=False, model="small"):
-        host,
-        port,
-        lang=None,
-        translate=False,
-        model="small",
-    ):
         self.client = Client(host, port, lang, translate, model)
 
     def __call__(self, audio=None, hls_url=None):
@@ -572,12 +505,12 @@ class TranscriptionClient:
         Start the transcription process.
 
         Initiates the transcription process by connecting to the server via a WebSocket. It waits for the server
-        to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it 
+        to be ready to receive audio data and then sends audio for transcription. If an audio file is provided, it
         will be played and streamed to the server; otherwise, it will perform live recording.
 
         Args:
             audio (str, optional): Path to an audio file for transcription. Default is None, which triggers live recording.
-                   
+
         """
         print("[INFO]: Waiting for server ready ...")
         while not self.client.recording:
@@ -589,7 +522,7 @@ class TranscriptionClient:
         if hls_url is not None:
             self.client.process_hls_stream(hls_url)
         elif audio is not None:
-            resampled_file = resample(audio)
+            resampled_file = utils.resample(audio)
             self.client.play_file(resampled_file)
         else:
-            self.client.record()
+            self.client.record()

whisper_live/server.py

@@ -3,25 +3,80 @@ import time
 import threading
 import json
 import textwrap
-
+import functools
 import logging
-logging.basicConfig(level = logging.INFO)
-
-from websockets.sync.server import serve
-
 import torch
 import numpy as np
-
+from websockets.sync.server import serve
-from whisper_live.vad import VoiceActivityDetection
-import functools
 
 from whisper_live.vad import VoiceActivityDetection
 from whisper_live.transcriber import WhisperModel
 try:
     from whisper_live.transcriber_tensorrt import WhisperTRTLLM
-except Exception as e:
+except Exception:
     pass
 
+logging.basicConfig(level=logging.INFO)
+
+
+class VoiceActivityDetector:
+    def __init__(self, threshold=0.5):
+        self.model = VoiceActivityDetection()
+        self.threshold = threshold
+
+    def __call__(self, audio_frame):
+        speech_prob = self.model(torch.from_numpy(audio_frame), TranscriptionServer.RATE).item()
+        return speech_prob > self.threshold
+
+
+class ClientManager:
+    def __init__(self, max_clients=4, max_connection_time=600):
+        self.clients = {}
+        self.start_times = {}
+        self.max_clients = max_clients
+        self.max_connection_time = max_connection_time
+
+    def add_client(self, websocket, client):
+        self.clients[websocket] = client
+        self.start_times[websocket] = time.time()
+
+    def get_client(self, websocket):
+        if websocket in self.clients:
+            return self.clients[websocket]
+        return False
+
+    def remove_client(self, websocket):
+        client = self.clients.pop(websocket, None)
+        if client:
+            client.cleanup()
+        self.start_times.pop(websocket, None)
+
+    def get_wait_time(self):
+        """Calculate and return the estimated wait time for clients."""
+        wait_time = None
+        for start_time in self.start_times.values():
+            current_client_time_remaining = self.max_connection_time - (time.time() - start_time)
+            if wait_time is None or current_client_time_remaining < wait_time:
+                wait_time = current_client_time_remaining
+        return wait_time / 60 if wait_time is not None else 0
+
+    def is_server_full(self, websocket, options):
+        """Check if the server is full and send wait message if necessary."""
+        if len(self.clients) >= self.max_clients:
+            wait_time = self.get_wait_time()
+            response = {"uid": options["uid"], "status": "WAIT", "message": wait_time}
+            websocket.send(json.dumps(response))
+            return True
+        return False
+
+    def is_client_timeout(self, websocket):
+        elapsed_time = time.time() - self.start_times[websocket]
+        if elapsed_time >= self.max_connection_time:
+            self.clients[websocket].disconnect()
+            logging.warning(f"Client with uid '{self.clients[websocket].client_uid}' disconnected due to overtime.")
+            return True
+        return False
+
 
 class TranscriptionServer:
     """
@@ -42,12 +97,8 @@ class TranscriptionServer:
 
     def __init__(self):
         # voice activity detection model
-        
+        self.client_manager = ClientManager()
-        self.clients = {}
+        self.no_voice_activity_chunks = 0
-        self.websockets = {}
-        self.clients_start_time = {}
-        self.max_clients = 4
-        self.max_connection_time = 600
 
     def get_wait_time(self):
         """
@@ -58,7 +109,7 @@ class TranscriptionServer:
         """
         wait_time = None
 
-        for k, v in self.clients_start_time.items():
+        for _, v in self.clients_start_time.items():
             current_client_time_remaining = self.max_connection_time - (time.time() - v)
 
             if wait_time is None or current_client_time_remaining < wait_time:
@@ -66,6 +117,64 @@ class TranscriptionServer:
 
         return wait_time / 60
 
+    def is_server_full(self, websocket, options):
+        if len(self.clients) >= self.max_clients:
+            wait_time = self.get_wait_time()
+            response = {"uid": options["uid"], "status": "WAIT", "message": wait_time}
+            websocket.send(json.dumps(response))
+            websocket.close()
+            return True
+        return False
+
+    def initialize_client(
+        self, websocket, options, faster_whisper_custom_model_path,
+        whisper_tensorrt_path, trt_multilingual
+    ):
+        if self.backend == "tensorrt":
+            try:
+                client = ServeClientTensorRT(
+                    websocket,
+                    multilingual=trt_multilingual,
+                    language=options["language"],
+                    task=options["task"],
+                    client_uid=options["uid"],
+                    model=whisper_tensorrt_path
+                )
+                logging.info("Running TensorRT backend.")
+            except Exception as e:
+                logging.error(f"TensorRT-LLM not supported: {e}")
+                self.client_uid = options["uid"]
+                websocket.send(json.dumps({
+                    "uid": self.client_uid,
+                    "status": "WARNING",
+                    "message": "TensorRT-LLM not supported on Server yet. "
+                               "Reverting to available backend: 'faster_whisper'"
+                }))
+                self.backend = "faster_whisper"
+
+        if self.backend == "faster_whisper":
+            if faster_whisper_custom_model_path is not None and os.path.exists(faster_whisper_custom_model_path):
+                logging.info(f"Using custom model {faster_whisper_custom_model_path}")
+                options["model"] = faster_whisper_custom_model_path
+            client = ServeClientFasterWhisper(
+                websocket,
+                language=options["language"],
+                task=options["task"],
+                client_uid=options["uid"],
+                model=options["model"],
+                initial_prompt=options.get("initial_prompt"),
+                vad_parameters=options.get("vad_parameters")
+            )
+            logging.info("Running faster_whisper backend.")
+
+        # self.clients[websocket] = client
+        # self.clients_start_time[websocket] = time.time()
+        self.client_manager.add_client(websocket, client)
+
+    def get_audio_from_websocket(self, websocket):
+        frame_data = websocket.recv()
+        return np.frombuffer(frame_data, dtype=np.float32)
+
     def recv_audio(self,
                    websocket,
                    backend="faster_whisper",
@@ -74,7 +183,7 @@ class TranscriptionServer:
                    trt_multilingual=False):
         """
         Receive audio chunks from a client in an infinite loop.
-        
+
         Continuously receives audio frames from a connected client
         over a WebSocket connection. It processes the audio frames using a
         voice activity detection (VAD) model to determine if they contain speech
@@ -96,127 +205,53 @@ class TranscriptionServer:
         Raises:
             Exception: If there is an error during the audio frame processing.
         """
-        self.backend = backend
-        if self.backend == "tensorrt":
-            self.vad_model = VoiceActivityDetection()
-            self.vad_threshold = 0.5
-
         logging.info("New client connected")
         options = websocket.recv()
         options = json.loads(options)
 
-        if len(self.clients) >= self.max_clients:
+        if self.client_manager.is_server_full(websocket, options):
-            logging.warning("Client Queue Full. Asking client to wait ...")
-            wait_time = self.get_wait_time()
-            response = {
-                "uid": options["uid"],
-                "status": "WAIT",
-                "message": wait_time,
-            }
-            websocket.send(json.dumps(response))
             websocket.close()
-            del websocket
             return
 
+        self.backend = backend
         if self.backend == "tensorrt":
-            try:
+            self.vad_detector = VoiceActivityDetector()
-                import tensorrt as trt
-                import tensorrt_llm
-                self.backend = "tensorrt"
-                client = ServeClientTensorRT(
-                    websocket,
-                    multilingual=trt_multilingual,
-                    language=options["language"],
-                    task=options["task"],
-                    client_uid=options["uid"],
-                    model=whisper_tensorrt_path
-                )
-                logging.info(f"Running TensorRT backend.")
-            except Exception as e:
-                self.client_uid = options["uid"]
-                websocket.send(
-                    json.dumps(
-                        {
-                            "uid": self.client_uid,
-                            "status": "ERROR",
-                            "message": f"TensorRT-LLM not supported on Server yet. Reverting to available backend: 'faster_whisper'"
-                        }
-                    )
-                )
-                self.backend = "faster_whisper"
 
-        if self.backend == "faster_whisper":
+        self.initialize_client(
-            # validate custom model
+            websocket, options, faster_whisper_custom_model_path, whisper_tensorrt_path, trt_multilingual)
-            if faster_whisper_custom_model_path is not None and os.path.exists(faster_whisper_custom_model_path):
-                logging.info(f"Using custom model {faster_whisper_custom_model_path}")
-                options["model"] = faster_whisper_custom_model_path
-            client = ServeClientFasterWhisper(
-                websocket,
-                language=options["language"],
-                task=options["task"],
-                client_uid=options["uid"],
-                model=options["model"],
-                initial_prompt=options.get("initial_prompt"),
-                vad_parameters=options.get("vad_parameters")
-            )
-            logging.info(f"Running faster_whisper backend.")
-        
-        self.clients[websocket] = client
-        self.clients_start_time[websocket] = time.time()
-        no_voice_activity_chunks = 0
 
-        while True:
+        while not self.client_manager.is_client_timeout(websocket):
             try:
-                frame_data = websocket.recv()
+                frame_np = self.get_audio_from_websocket(websocket)
-                frame_np = np.frombuffer(frame_data, dtype=np.float32)
+                client = self.client_manager.get_client(websocket)
 
                 # VAD, for faster_whisper VAD model is already integrated
                 if self.backend == "tensorrt":
-                    try:
+                    if not self.voice_activity(websocket, frame_np):
-                        speech_prob = self.vad_model(torch.from_numpy(frame_np.copy()), self.RATE).item()
+                        continue
-                        if speech_prob < self.vad_threshold:
+                    self.no_voice_activity_chunks = 0
-                            no_voice_activity_chunks += 1
+                    client.set_eos(False)
-                            if no_voice_activity_chunks > 3:
-                                if not self.clients[websocket].eos:
-                                    self.clients[websocket].set_eos(True)
-                                time.sleep(0.1)    # Sleep 100m; wait some voice activity.
-                            continue
-                        no_voice_activity_chunks = 0
-                        self.clients[websocket].set_eos(False)
 
-                    except Exception as e:
+                client.add_frames(frame_np)
-                        logging.error(e)
-                        return
-                
-                self.clients[websocket].add_frames(frame_np)
-
-                elapsed_time = time.time() - self.clients_start_time[websocket]
-                if elapsed_time >= self.max_connection_time:
-                    self.clients[websocket].disconnect()
-                    logging.warning(f"Client with uid '{self.clients[websocket].client_uid}' disconnected due to overtime.")
-                    self.clients[websocket].cleanup()
-                    self.clients.pop(websocket)
-                    self.clients_start_time.pop(websocket)
-                    websocket.close()
-                    del websocket
-                    break
 
             except Exception as e:
                 logging.error(e)
-                self.clients[websocket].cleanup()
+                self.cleanup(websocket)
-                self.clients.pop(websocket)
+                websocket.close()
-                self.clients_start_time.pop(websocket)
-                del websocket
                 break
 
-    def run(self, 
+        if self.client_manager.get_client(websocket):
-            host, 
+            self.cleanup(websocket)
-            port=9090, 
+            websocket.close()
-            backend="tensorrt", 
+        del websocket
+
+    def run(self,
+            host,
+            port=9090,
+            backend="tensorrt",
             faster_whisper_custom_model_path=None,
-            whisper_tensorrt_path=None, 
+            whisper_tensorrt_path=None,
-            trt_multilingual=False
+            trt_multilingual=False):
-        ):
         """
         Run the transcription server.
 
@@ -237,6 +272,21 @@ class TranscriptionServer:
         ) as server:
             server.serve_forever()
 
+    def voice_activity(self, websocket, frame_np):
+        if not self.vad_detector(frame_np):
+            self.no_voice_activity_chunks += 1
+            if self.no_voice_activity_chunks > 3:
+                client = self.client_manager.get_client(websocket)
+                if not client.eos:
+                    client.set_eos(True)
+                time.sleep(0.1)    # Sleep 100m; wait some voice activity.
+            return False
+        return True
+
+    def cleanup(self, websocket):
+        if self.client_manager.get_client(websocket):
+            self.client_manager.remove_client(websocket)
+
 
 class ServeClientBase(object):
     RATE = 16000
@@ -254,7 +304,7 @@ class ServeClientBase(object):
         self.text = []
         self.current_out = ''
         self.prev_out = ''
-        self.t_start=None
+        self.t_start = None
         self.exit = False
         self.same_output_threshold = 0
         self.show_prev_out_thresh = 5   # if pause(no output from whisper) show previous output for 5 seconds
@@ -268,7 +318,16 @@ class ServeClientBase(object):
 
         # threading
         self.lock = threading.Lock()
-    
+
+    def speech_to_text(self):
+        raise NotImplementedError
+
+    def transcribe_audio(self):
+        raise NotImplementedError
+
+    def handle_transcription_output(self):
+        raise NotImplementedError
+
     def add_frames(self, frame_np):
         """
         Add audio frames to the ongoing audio stream buffer.
@@ -295,9 +354,50 @@ class ServeClientBase(object):
             self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
         self.lock.release()
 
-    def speech_to_text(self):
+    def clip_audio_if_no_valid_segment(self):
-        raise NotImplementedError("Please implement in child Class.")
+        """
-    
+        Update the timestamp offset based on audio buffer status.
+        Clip audio if the current chunk exceeds 30 seconds, this basically implies that
+        no valid segment for the last 30 seconds from whisper
+        """
+        if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
+            duration = self.frames_np.shape[0] / self.RATE
+            self.timestamp_offset = self.frames_offset + duration - 5
+
+    def get_audio_chunk_for_processing(self):
+        """Retrieve the next chunk of audio data for processing."""
+        samples_take = max(0, (self.timestamp_offset - self.frames_offset) * self.RATE)
+        input_bytes = self.frames_np[int(samples_take):].copy()
+        duration = input_bytes.shape[0] / self.RATE
+        return input_bytes, duration
+
+    def prepare_segments(self, last_segment=None):
+        """Prepare the segments to be sent to the client."""
+        segments = []
+        if len(self.transcript) >= self.send_last_n_segments:
+            segments = self.transcript[-self.send_last_n_segments:].copy()
+        else:
+            segments = self.transcript.copy()
+        if last_segment is not None:
+            segments = segments + [last_segment]
+        return segments
+
+    def get_audio_chunk_duration(self, input_bytes):
+        """Calculate the duration of the current audio chunk."""
+        return input_bytes.shape[0] / self.RATE
+
+    def send_transcription_to_client(self, segments):
+        """Send the transcription segments to the client."""
+        try:
+            self.websocket.send(
+                json.dumps({
+                    "uid": self.client_uid,
+                    "segments": segments,
+                })
+            )
+        except Exception as e:
+            logging.error(f"[ERROR]: Sending data to client: {e}")
+
     def disconnect(self):
         """
         Notify the client of disconnection and send a disconnect message.
@@ -306,15 +406,11 @@ class ServeClientBase(object):
         that the transcription service is disconnecting gracefully.
 
         """
-        self.websocket.send(
+        self.websocket.send(json.dumps({
-            json.dumps(
+            "uid": self.client_uid,
-                {
+            "message": self.DISCONNECT
-                    "uid": self.client_uid,
+        }))
-                    "message": self.DISCONNECT
+
-                }
-            )
-        )
-    
     def cleanup(self):
         """
         Perform cleanup tasks before exiting the transcription service.
@@ -357,16 +453,7 @@ class ServeClientTensorRT(ServeClientBase):
         pick_previous_segments (int): Number of previous segments to include in the output.
         websocket: The WebSocket connection for the client.
     """
-    def __init__(
+    def __init__(self, websocket, task="transcribe", multilingual=False, language=None, client_uid=None, model=None):
-        self,
-        websocket,
-        task="transcribe",
-        device=None,
-        multilingual=False,
-        language=None, 
-        client_uid=None,
-        model=None
-        ):
         """
         Initialize a ServeClient instance.
         The Whisper model is initialized based on the client's language and device availability.
@@ -387,8 +474,8 @@ class ServeClientTensorRT(ServeClientBase):
         self.task = task
         self.eos = False
         self.transcriber = WhisperTRTLLM(
-            model, 
+            model,
-            assets_dir="assets", 
+            assets_dir="assets",
             device="cuda",
             is_multilingual=multilingual,
             language=self.language,
@@ -400,52 +487,44 @@ class ServeClientTensorRT(ServeClientBase):
         self.trans_thread = threading.Thread(target=self.speech_to_text)
         self.trans_thread.start()
 
-        self.websocket.send(
+        self.websocket.send(json.dumps({
-            json.dumps(
+            "uid": self.client_uid,
-                {
+            "message": self.SERVER_READY,
-                    "uid": self.client_uid,
+            "backend": "tensorrt"
-                    "message": self.SERVER_READY,
+        }))
-                    "backend": "tensorrt"
-                }
-            )
-        )
 
     def warmup(self, warmup_steps=10):
         logging.info("[INFO:] Warming up TensorRT engine..")
         mel, _ = self.transcriber.log_mel_spectrogram("tests/jfk.flac")
         for i in range(warmup_steps):
             self.transcriber.transcribe(mel)
-    
+
     def set_eos(self, eos):
         self.lock.acquire()
         self.eos = eos
         self.lock.release()
-    
-    def add_frames(self, frame_np):
-        """
-        Add audio frames to the ongoing audio stream buffer.
 
-        This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
+    def handle_transcription_output(self, last_segment, duration):
-        of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
+        """Handle the transcription output, updating the transcript and sending data to the client."""
-        to prevent excessive memory usage.
+        segments = self.prepare_segments({"text": last_segment})
+        self.send_transcription_to_client(segments)
+        if self.eos:
+            self.update_timestamp_offset(last_segment, duration)
 
-        If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
+    def transcribe_audio(self, input_bytes):
-        of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
+        """Transcribe the audio chunk and send the results to the client."""
-        audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
+        logging.info(f"[WhisperTensorRT:] Processing audio with duration: {input_bytes.shape[0] / self.RATE}")
+        mel, duration = self.transcriber.log_mel_spectrogram(input_bytes)
+        last_segment = self.transcriber.transcribe(mel)
+        if last_segment:
+            self.handle_transcription_output(last_segment, duration)
 
-        Args:
+    def update_timestamp_offset(self, last_segment, duration):
-            frame_np (numpy.ndarray): The audio frame data as a NumPy array.
+        if not len(self.transcript):
-
+            self.transcript.append({"text": last_segment + " "})
-        """
+        elif self.transcript[-1]["text"].strip() != last_segment:
-        self.lock.acquire()
+            self.transcript.append({"text": last_segment + " "})
-        if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
+        self.timestamp_offset += duration
-            self.frames_offset += 30.0
-            self.frames_np = self.frames_np[int(30*self.RATE):]
-        if self.frames_np is None:
-            self.frames_np = frame_np.copy()
-        else:
-            self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
-        self.lock.release()
 
     def speech_to_text(self):
         """
@@ -456,8 +535,8 @@ class ServeClientTensorRT(ServeClientBase):
 
         If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
         It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
-        are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech 
+        are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
-        (no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if 
+        (no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
         there is no speech for a specified duration to indicate a pause.
 
         Raises:
@@ -468,54 +547,21 @@ class ServeClientTensorRT(ServeClientBase):
             if self.exit:
                 logging.info("Exiting speech to text thread")
                 break
-            
+
             if self.frames_np is None:
                 time.sleep(0.02)    # wait for any audio to arrive
                 continue
 
-            # clip audio if the current chunk exceeds 30 seconds, this basically implies that
+            self.clip_audio_if_no_valid_segment()
-            # no valid segment for the last 30 seconds from whisper
+
-            if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
+            input_bytes, duration = self.get_audio_chunk_for_processing()
-                duration = self.frames_np.shape[0] / self.RATE
+            if duration < 0.4:
-                self.timestamp_offset = self.frames_offset + duration - 5
-    
-            samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE)
-            input_bytes = self.frames_np[int(samples_take):].copy()
-            duration = input_bytes.shape[0] / self.RATE
-            if duration<0.4:
                 continue
 
             try:
                 input_sample = input_bytes.copy()
                 logging.info(f"[WhisperTensorRT:] Processing audio with duration: {duration}")
-                mel, duration = self.transcriber.log_mel_spectrogram(input_sample)
+                self.transcribe_audio(input_sample)
-                last_segment = self.transcriber.transcribe(mel)
-                segments = []
-                if len(last_segment):
-                    if len(self.transcript) < self.send_last_n_segments:
-                        segments = self.transcript[:].copy()
-                    else:
-                        segments = self.transcript[-self.send_last_n_segments:].copy()
-                    if last_segment is not None:
-                        segments.append({"text": last_segment})
-                    try:
-                        self.websocket.send(
-                            json.dumps({
-                                "uid": self.client_uid,
-                                "segments": segments,
-                            })
-                        )
-
-                        if self.eos:
-                            if not len(self.transcript):
-                                self.transcript.append({"text": last_segment + " "})
-                            elif self.transcript[-1]["text"].strip() != last_segment:
-                                self.transcript.append({"text": last_segment + " "})
-                            self.timestamp_offset += duration
-
-                            
-                    except Exception as e:
-                        logging.error(f"[ERROR]: {e}")
 
             except Exception as e:
                 logging.error(f"[ERROR]: {e}")
@@ -550,17 +596,8 @@ class ServeClientFasterWhisper(ServeClientBase):
         pick_previous_segments (int): Number of previous segments to include in the output.
         websocket: The WebSocket connection for the client.
     """
-    def __init__(
+    def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en",
-        self,
+                 initial_prompt=None, vad_parameters=None):
-        websocket,
-        task="transcribe",
-        device=None,
-        language=None,
-        client_uid=None,
-        model="small.en",
-        initial_prompt=None,
-        vad_parameters=None,
-        ):
         """
         Initialize a ServeClient instance.
         The Whisper model is initialized based on the client's language and device availability.
@@ -589,16 +626,16 @@ class ServeClientFasterWhisper(ServeClientBase):
         self.initial_prompt = initial_prompt
         self.vad_parameters = vad_parameters or {"threshold": 0.5}
         self.no_speech_thresh = 0.45
-        
+
         device = "cuda" if torch.cuda.is_available() else "cpu"
-        
+
-        if self.model_size_or_path == None:
+        if self.model_size_or_path is None:
             return
 
         self.transcriber = WhisperModel(
-            self.model_size_or_path, 
+            self.model_size_or_path,
             device=device,
-            compute_type="int8" if device=="cpu" else "float16", 
+            compute_type="int8" if device == "cpu" else "float16",
             local_files_only=False,
         )
 
@@ -614,7 +651,7 @@ class ServeClientFasterWhisper(ServeClientBase):
                 }
             )
         )
-    
+
     def check_valid_model(self, model_size):
         """
         Check if it's a valid whisper model size.
@@ -637,7 +674,39 @@ class ServeClientFasterWhisper(ServeClientBase):
             )
             return None
         return model_size
-    
+
+    def set_language(self, info):
+        if info.language_probability > 0.5:
+            self.language = info.language
+            logging.info(f"Detected language {self.language} with probability {info.language_probability}")
+            self.websocket.send(json.dumps(
+                {"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
+
+    def transcribe_audio(self, input_sample):
+        result, info = self.transcriber.transcribe(
+            input_sample,
+            initial_prompt=self.initial_prompt,
+            language=self.language,
+            task=self.task,
+            vad_filter=True,
+            vad_parameters=self.vad_parameters)
+        if self.language is None:
+            self.set_language(info)
+        return result
+
+    def get_previous_output(self):
+        segments = []
+        if self.t_start is None:
+            self.t_start = time.time()
+        if time.time() - self.t_start < self.show_prev_out_thresh:
+            segments = self.prepare_segments()
+
+        # add a blank if there is no speech for 3 seconds
+        if len(self.text) and self.text[-1] != '':
+            if time.time() - self.t_start > self.add_pause_thresh:
+                self.text.append('')
+        return segments
+
     def speech_to_text(self):
         """
         Process an audio stream in an infinite loop, continuously transcribing the speech.
@@ -647,8 +716,8 @@ class ServeClientFasterWhisper(ServeClientBase):
 
         If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
         It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
-        are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech 
+        are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
-        (no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if 
+        (no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
         there is no speech for a specified duration to indicate a pause.
 
         Raises:
@@ -659,83 +728,38 @@ class ServeClientFasterWhisper(ServeClientBase):
             if self.exit:
                 logging.info("Exiting speech to text thread")
                 break
-            
+
-            if self.frames_np is None: 
+            if self.frames_np is None:
                 continue
 
-            # clip audio if the current chunk exceeds 30 seconds, this basically implies that
+            self.clip_audio_if_no_valid_segment()
-            # no valid segment for the last 30 seconds from whisper
+
-            if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
+            input_bytes, duration = self.get_audio_chunk_for_processing()
-                duration = self.frames_np.shape[0] / self.RATE
+            if duration < 1.0:
-                self.timestamp_offset = self.frames_offset + duration - 5
-    
-            samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE)
-            input_bytes = self.frames_np[int(samples_take):].copy()
-            duration = input_bytes.shape[0] / self.RATE
-            if duration<1.0:
                 continue
             try:
                 input_sample = input_bytes.copy()
-                
+                result = self.transcribe_audio(input_sample)
-                # whisper transcribe with prompt
-                result, info = self.transcriber.transcribe(
-                    input_sample, 
-                    initial_prompt=self.initial_prompt,
-                    language=self.language,
-                    task=self.task,
-                    vad_filter=True,
-                    vad_parameters=self.vad_parameters
-                )
 
                 if self.language is None:
-                    if info.language_probability > 0.5:
+                    continue
-                        self.language = info.language
-                        logging.info(f"Detected language {self.language} with probability {info.language_probability}")
-                        self.websocket.send(json.dumps(
-                            {"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
-                    else:
-                        # detect language again
-                        continue
 
                 if len(result):
                     self.t_start = None
                     last_segment = self.update_segments(result, duration)
-                    if len(self.transcript) < self.send_last_n_segments:
+                    segments = self.prepare_segments(last_segment)
-                        segments = self.transcript
-                    else:
-                        segments = self.transcript[-self.send_last_n_segments:]
-                    if last_segment is not None:
-                        segments = segments + [last_segment]                    
                 else:
                     # show previous output if there is pause i.e. no output from whisper
-                    segments = []
+                    segments = self.get_previous_output()
-                    if self.t_start is None: self.t_start = time.time()
-                    if time.time() - self.t_start < self.show_prev_out_thresh:
-                        if len(self.transcript) < self.send_last_n_segments:
-                            segments = self.transcript
-                        else:
-                            segments = self.transcript[-self.send_last_n_segments:]
-                    
-                    # add a blank if there is no speech for 3 seconds
-                    if len(self.text) and self.text[-1] != '':
-                        if time.time() - self.t_start > self.add_pause_thresh:
-                            self.text.append('')
 
-                if not len(segments): continue
+                if not len(segments):
-                try:
+                    continue
-                    self.websocket.send(
+                self.send_transcription_to_client(segments)
-                        json.dumps({
-                            "uid": self.client_uid,
-                            "segments": segments
-                        })
-                    )
-                except Exception as e:
-                    logging.error(f"[ERROR]: Failed to send message to client: {e}")
 
             except Exception as e:
                 logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}")
                 time.sleep(0.01)
-    
+
     def format_segment(self, start, end, text):
         """Helper function to format a segment with string timestamps."""
         return {
@@ -750,17 +774,17 @@ class ServeClientFasterWhisper(ServeClientBase):
         except for the last segment assuming that it is incomplete.
 
         Updates the ongoing transcript with transcribed segments, including their start and end times.
-        Complete segments are appended to the transcript in chronological order. Incomplete segments 
+        Complete segments are appended to the transcript in chronological order. Incomplete segments
-        (assumed to be the last one) are processed to identify repeated content. If the same incomplete 
+        (assumed to be the last one) are processed to identify repeated content. If the same incomplete
         segment is seen multiple times, it updates the offset and appends the segment to the transcript.
         A threshold is used to detect repeated content and ensure it is only included once in the transcript.
-        The timestamp offset is updated based on the duration of processed segments. The method returns the 
+        The timestamp offset is updated based on the duration of processed segments. The method returns the
         last processed segment, allowing it to be sent to the client for real-time updates.
 
         Args:
             segments(dict) : dictionary of segments as returned by whisper
             duration(float): duration of the current chunk
-        
+
         Returns:
             dict or None: The last processed segment with its start time, end time, and transcribed text.
                      Returns None if there are no valid segments to process.
@@ -775,11 +799,12 @@ class ServeClientFasterWhisper(ServeClientBase):
                 self.text.append(text_)
                 start, end = self.timestamp_offset + s.start, self.timestamp_offset + min(duration, s.end)
 
-                if start >= end: continue
+                if start >= end:
-                if s.no_speech_prob > self.no_speech_thresh: continue
+                    continue
+                if s.no_speech_prob > self.no_speech_thresh:
+                    continue
 
                 self.transcript.append(self.format_segment(start, end, text_))
-                
                 offset = min(duration, s.end)
 
         self.current_out += segments[-1].text
@@ -788,16 +813,16 @@ class ServeClientFasterWhisper(ServeClientBase):
             self.timestamp_offset + min(duration, segments[-1].end),
             self.current_out
         )
-        
+
         # if same incomplete segment is seen multiple times then update the offset
         # and append the segment to the list
-        if self.current_out.strip() == self.prev_out.strip() and self.current_out != '': 
+        if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
             self.same_output_threshold += 1
-        else: 
+        else:
             self.same_output_threshold = 0
-        
+
         if self.same_output_threshold > 5:
-            if not len(self.text) or self.text[-1].strip().lower()!=self.current_out.strip().lower():          
+            if not len(self.text) or self.text[-1].strip().lower() != self.current_out.strip().lower():
                 self.text.append(self.current_out)
                 self.transcript.append(self.format_segment(
                     self.timestamp_offset,
@@ -810,7 +835,7 @@ class ServeClientFasterWhisper(ServeClientBase):
             last_segment = None
         else:
             self.prev_out = self.current_out
-        
+
         # update offset
         if offset is not None:
             self.timestamp_offset += offset

whisper_live/tensorrt_utils.py

@@ -214,7 +214,7 @@ def store_transcripts(filename: Pathlike, texts: Iterable[Tuple[str, str,
             print(f"{cut_id}:\thyp={hyp}", file=f)
 
 
-def write_error_stats(
+def write_error_stats(                                              # noqa: C901
     f: TextIO,
     test_set_name: str,
     results: List[Tuple[str, str]],
@@ -362,4 +362,4 @@ def write_error_stats(
         hyp_count = corr + hyp_sub + ins
 
         print(f"{word}   {corr} {tot_errs} {ref_count} {hyp_count}", file=f)
-    return float(tot_err_rate)
+    return float(tot_err_rate)

whisper_live/transcriber.py

@@ -400,7 +400,7 @@ class WhisperModel:
 
         return segments, info
 
-    def generate_segments(
+    def generate_segments(                                                  # noqa: C901
         self,
         features: np.ndarray,
         tokenizer: Tokenizer,
@@ -425,7 +425,7 @@ class WhisperModel:
         all_segments = []
         while seek < content_frames:
             time_offset = seek * self.feature_extractor.time_per_frame
-            segment = features[:, seek : seek + self.feature_extractor.nb_max_frames]
+            segment = features[:, seek:seek + self.feature_extractor.nb_max_frames]
             segment_size = min(
                 self.feature_extractor.nb_max_frames, content_frames - seek
             )
@@ -749,7 +749,7 @@ class WhisperModel:
 
         if previous_tokens:
             prompt.append(tokenizer.sot_prev)
-            prompt.extend(previous_tokens[-(self.max_length // 2 - 1) :])
+            prompt.extend(previous_tokens[-(self.max_length // 2 - 1):])
 
         prompt.extend(tokenizer.sot_sequence)
 
@@ -766,7 +766,7 @@ class WhisperModel:
 
         return prompt
 
-    def add_word_timestamps(
+    def add_word_timestamps(                                                    # noqa: C901
         self,
         segments: List[dict],
         tokenizer: Tokenizer,

whisper_live/transcriber_tensorrt.py

@@ -1,17 +1,14 @@
-import argparse
 import json
 import re
-import time
 from collections import OrderedDict
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, TextIO, Tuple, Union
+from typing import Union
 
 import torch
 import numpy as np
+import torch.nn.functional as F
 from whisper.tokenizer import get_tokenizer
-from whisper_live.tensorrt_utils import (mel_filters, store_transcripts,
+from whisper_live.tensorrt_utils import (mel_filters, load_audio_wav_format, pad_or_trim, load_audio)
-                           write_error_stats, load_audio_wav_format,
-                           pad_or_trim, load_audio)
 
 import tensorrt_llm
 import tensorrt_llm.logger as logger
@@ -38,8 +35,6 @@ class WhisperEncoding:
         with open(config_path, 'r') as f:
             config = json.load(f)
 
-        use_gpt_attention_plugin = config['plugin_config'][
-            'gpt_attention_plugin']
         dtype = config['builder_config']['precision']
         n_mels = config['builder_config']['n_mels']
         num_languages = config['builder_config']['num_languages']
@@ -176,16 +171,8 @@ class WhisperDecoding:
 
 class WhisperTRTLLM(object):
 
-    def __init__(
+    def __init__(self, engine_dir, assets_dir=None, device=None, is_multilingual=False,
-        self,
+                 language="en", task="transcribe"):
-        engine_dir,
-        debug_mode=False,
-        assets_dir=None,
-        device=None,
-        is_multilingual=False,
-        language="en",
-        task="transcribe"
-        ):
         world_size = 1
         runtime_rank = tensorrt_llm.mpi_rank()
         runtime_mapping = tensorrt_llm.Mapping(world_size, runtime_rank)
@@ -212,7 +199,7 @@ class WhisperTRTLLM(object):
         self,
         audio: Union[str, np.ndarray, torch.Tensor],
         padding: int = 0,
-        return_duration = True
+        return_duration=True
     ):
         """
         Compute the log-Mel spectrogram of
@@ -242,8 +229,7 @@ class WhisperTRTLLM(object):
                     audio, _ = load_audio_wav_format(audio)
                 else:
                     audio = load_audio(audio)
-            assert isinstance(audio,
+            assert isinstance(audio, np.ndarray), f"Unsupported audio type: {type(audio)}"
-                            np.ndarray), f"Unsupported audio type: {type(audio)}"
             duration = audio.shape[-1] / SAMPLE_RATE
             audio = pad_or_trim(audio, N_SAMPLES)
             audio = audio.astype(np.float32)
@@ -254,14 +240,9 @@ class WhisperTRTLLM(object):
         if padding > 0:
             audio = F.pad(audio, (0, padding))
         window = torch.hann_window(N_FFT).to(audio.device)
-        stft = torch.stft(audio,
+        stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)
-                        N_FFT,
-                        HOP_LENGTH,
-                        window=window,
-                        return_complex=True)
         magnitudes = stft[..., :-1].abs()**2
 
-        
         mel_spec = self.filters @ magnitudes
 
         log_spec = torch.clamp(mel_spec, min=1e-10).log10()
@@ -272,7 +253,6 @@ class WhisperTRTLLM(object):
         else:
             return log_spec
 
-
     def process_batch(
             self,
             mel,
@@ -296,7 +276,7 @@ class WhisperTRTLLM(object):
             text = self.tokenizer.decode(output_ids[i][0]).strip()
             texts.append(text)
         return texts
-    
+
     def transcribe(
             self,
             mel,
@@ -336,5 +316,5 @@ def decode_wav_file(
     prediction = re.sub(r'<\|.*?\|>', '', prediction)
     if normalizer:
         prediction = normalizer(prediction)
-    
+
     return prediction.strip()

whisper_live/utils.py

@@ -0,0 +1,71 @@
+import os
+import textwrap
+import scipy
+import ffmpeg
+import numpy as np
+
+
+def clear_screen():
+    """Clears the console screen."""
+    os.system("cls" if os.name == "nt" else "clear")
+
+
+def print_transcript(text):
+    """Prints formatted transcript text."""
+    wrapper = textwrap.TextWrapper(width=60)
+    for line in wrapper.wrap(text="".join(text)):
+        print(line)
+
+
+def format_time(s):
+    """Convert seconds (float) to SRT time format."""
+    hours = int(s // 3600)
+    minutes = int((s % 3600) // 60)
+    seconds = int(s % 60)
+    milliseconds = int((s - int(s)) * 1000)
+    return f"{hours:02}:{minutes:02}:{seconds:02},{milliseconds:03}"
+
+
+def create_srt_file(segments, output_file):
+    with open(output_file, 'w', encoding='utf-8') as srt_file:
+        segment_number = 1
+        for segment in segments:
+            start_time = format_time(float(segment['start']))
+            end_time = format_time(float(segment['end']))
+            text = segment['text']
+
+            srt_file.write(f"{segment_number}\n")
+            srt_file.write(f"{start_time} --> {end_time}\n")
+            srt_file.write(f"{text}\n\n")
+
+            segment_number += 1
+
+
+def resample(file: str, sr: int = 16000):
+    """
+    # https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/audio.py#L22
+    Open an audio file and read as mono waveform, resampling as necessary,
+    save the resampled audio
+
+    Args:
+        file (str): The audio file to open
+        sr (int): The sample rate to resample the audio if necessary
+
+    Returns:
+        resampled_file (str): The resampled audio file
+    """
+    try:
+        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
+        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
+        out, _ = (
+            ffmpeg.input(file, threads=0)
+            .output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=sr)
+            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
+        )
+    except ffmpeg.Error as e:
+        raise RuntimeError(f"Failed to load audio: {e.stderr.decode()}") from e
+    np_buffer = np.frombuffer(out, dtype=np.int16)
+
+    resampled_file = f"{file.split('.')[0]}_resampled.wav"
+    scipy.io.wavfile.write(resampled_file, sr, np_buffer.astype(np.int16))
+    return resampled_file

whisper_live/vad.py

@@ -34,7 +34,7 @@ class VoiceActivityDetection():
 
         if sr != 16000 and (sr % 16000 == 0):
             step = sr // 16000
-            x = x[:,::step]
+            x = x[:, ::step]
             sr = 16000
 
         if sr not in self.sample_rates:
@@ -110,4 +110,4 @@ class VoiceActivityDetection():
                 subprocess.run(["wget", "-O", model_filename, model_url], check=True)
             except subprocess.CalledProcessError:
                 print("Failed to download the model using wget.")
-        return model_filename
+        return model_filename