Merge pull request #146 from makaveli10/code_formatting

Code formatting

.github/workflows/ci.yml

@@ -11,12 +11,11 @@ on:
     types: [opened, synchronize, reopened]
 
 jobs:
-  test:
+  run-tests:
-    runs-on: ubuntu-latest
+    runs-on: ubuntu-22.04
-    timeout-minutes: 60
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10', '3.11']
+        python-version: [3.8, 3.9, '3.10', 3.11]
     steps:
       - uses: actions/checkout@v2
       
@@ -48,10 +47,36 @@ jobs:
         run: |
           echo "Running tests with Python ${{ matrix.python-version }}"
           python -m unittest discover -s tests
+  
+  check-code-format:
+    runs-on: ubuntu-22.04
+    strategy:
+      matrix:
+        python-version: [3.8, 3.9, '3.10', 3.11]
 
-  build-and-push:
+    steps:
-    needs: test
+      - uses: actions/checkout@v2
-    runs-on: ubuntu-latest
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install flake8
+
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+  publish-to-pypi:
+    needs: [run-tests, check-code-format]
+    runs-on: ubuntu-22.04
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
     steps:
       - uses: actions/checkout@v2

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

@@ -6,6 +6,8 @@ from unittest import mock
 
 import numpy as np
 import evaluate
+
+from websockets.exceptions import ConnectionClosed
 from whisper_live.server import TranscriptionServer
 from whisper_live.client import TranscriptionClient
 from whisper.normalizers import EnglishTextNormalizer
@@ -14,32 +16,31 @@ 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 +51,6 @@ 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 +58,12 @@ 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 +71,12 @@ 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 +84,16 @@ 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)
 
@@ -103,3 +103,35 @@ class TestServerInferenceAccuracy(unittest.TestCase):
             references=[gt_normalized]
         )
         self.assertLess(wer, 0.05)
+
+
+class TestExceptionHandling(unittest.TestCase):
+    def setUp(self):
+        self.server = TranscriptionServer()
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_connection_closed_exception(self, mock_websocket):
+        mock_websocket.recv.side_effect = ConnectionClosed(1001, "testing connection closed")
+
+        with self.assertLogs(level="INFO") as log:
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+            self.assertTrue(any("Connection closed by client" in message for message in log.output))
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_json_decode_exception(self, mock_websocket):
+        mock_websocket.recv.return_value = "invalid json"
+
+        with self.assertLogs(level="ERROR") as log:
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+            self.assertTrue(any("Failed to decode JSON from client" in message for message in log.output))
+
+    @mock.patch('websockets.WebSocketCommonProtocol')
+    def test_unexpected_exception_handling(self, mock_websocket):
+        mock_websocket.recv.side_effect = RuntimeError("Unexpected error")
+
+        with self.assertLogs(level="ERROR") as log:
+            self.server.recv_audio(mock_websocket, "faster_whisper")
+            for message in log.output:
+                print(message)
+            print()
+            self.assertTrue(any("Unexpected error: Unexpected error" in message for message in log.output))

tests/test_vad.py

@@ -1,14 +1,12 @@
 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
+from whisper_live.vad import VoiceActivityDetector
 
 
 class TestVoiceActivityDetection(unittest.TestCase):
     def setUp(self):
-        self.vad = VoiceActivityDetection()
+        self.vad = VoiceActivityDetector()
         self.sample_rate = 16000
 
     def generate_silence(self, duration_seconds):
@@ -19,10 +17,10 @@ class TestVoiceActivityDetection(unittest.TestCase):
 
     def test_vad_silence_detection(self):
         silence = self.generate_silence(3)
-        speech_prob = self.vad(torch.from_numpy(silence.copy()), self.sample_rate).item()
+        is_speech_present = self.vad(silence.copy())
-        self.assertLess(speech_prob, 0.5, "VAD incorrectly identified silence as speech.")
+        self.assertFalse(is_speech_present, "VAD incorrectly identified silence as speech.")
 
     def test_vad_speech_detection(self):
-        audio_tensor = torch.from_numpy(load_audio("assets/jfk.flac"))
+        audio_tensor = load_audio("assets/jfk.flac")
-        speech_prob = self.vad(audio_tensor, self.sample_rate).item()
+        is_speech_present = self.vad(audio_tensor)
-        self.assertGreater(speech_prob, 0.5, "VAD failed to identify speech segment.")
+        self.assertTrue(is_speech_present, "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.
@@ -171,18 +147,11 @@ 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":
-            print("[INFO]: Server overtime disconnected.")
+            print("[INFO]: Server disconnected due to overtime.")
             self.recording = False
 
         if "message" in message.keys() and message["message"] == "SERVER_READY":
@@ -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/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 +110,33 @@ 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
+
+
+class VoiceActivityDetector:
+    def __init__(self, threshold=0.5, frame_rate=16000):
+        """
+        Initializes the VoiceActivityDetector with a voice activity detection model and a threshold.
+
+        Args:
+            threshold (float, optional): The probability threshold for detecting voice activity. Defaults to 0.5.
+        """
+        self.model = VoiceActivityDetection()
+        self.threshold = threshold
+        self.frame_rate = frame_rate
+
+    def __call__(self, audio_frame):
+        """
+        Determines if the given audio frame contains speech by comparing the detected speech probability against
+        the threshold.
+
+        Args:
+            audio_frame (np.ndarray): The audio frame to be analyzed for voice activity. It is expected to be a
+                                      NumPy array of audio samples.
+
+        Returns:
+            bool: True if the speech probability exceeds the threshold, indicating the presence of voice activity;
+                  False otherwise.
+        """
+        speech_prob = self.model(torch.from_numpy(audio_frame), self.frame_rate).item()
+        return speech_prob > self.threshold