Merge branch 'main' into main

README.md

@@ -77,6 +77,9 @@ If you don't want this, set `--no_single_model`.
   - `use_vad`: Whether to use `Voice Activity Detection` on the server.
   - `save_output_recording`: Set to True to save the microphone input as a `.wav` file during live transcription. This option is helpful for recording sessions for later playback or analysis. Defaults to `False`. 
   - `output_recording_filename`: Specifies the `.wav` file path where the microphone input will be saved if `save_output_recording` is set to `True`.
+  - `max_clients`: Specifies the maximum number of clients the server should allow. Defaults to 4.
+  - `max_connection_time`: Maximum connection time for each client in seconds. Defaults to 600.
+
 ```python
 from whisper_live.client import TranscriptionClient
 client = TranscriptionClient(
@@ -87,10 +90,12 @@ client = TranscriptionClient(
   model="small",
   use_vad=False,
   save_output_recording=True,                         # Only used for microphone input, False by Default
-  output_recording_filename="./output_recording.wav"  # Only used for microphone input
+  output_recording_filename="./output_recording.wav", # Only used for microphone input
   options={
     'initial_prompt': None,                           #To add context replace None with any context for the model like this: 'Jane Doe context'
-  }
+  },
+  max_clients=4,
+  max_connection_time=600
 )
 ```
 It connects to the server running on localhost at port 9090. Using a multilingual model, language for the transcription will be automatically detected. You can also use the language option to specify the target language for the transcription, in this case, English ("en"). The translate option should be set to `True` if we want to translate from the source language to English and `False` if we want to transcribe in the source language.

requirements/server.txt

@@ -10,4 +10,4 @@ jiwer
 evaluate
 numpy<2
 tiktoken==0.3.3
-openai-whisper
+openai-whisper==20231117

tests/test_client.py

@@ -48,7 +48,9 @@ def test_on_open(self):
             "language": self.client.language,
             "task": self.client.task,
             "model": self.client.model,
-            "use_vad": True
+            "use_vad": True,
+            "max_clients": 4,
+            "max_connection_time": 600,
         })
         self.client.on_open(self.mock_ws_app)
         self.mock_ws_app.send.assert_called_with(expected_message)
@@ -66,15 +68,15 @@ def test_on_message(self):
         message = json.dumps({
             "uid": self.client.uid,
             "segments": [
-                {"start": 0, "end": 1, "text": "Test transcript"},
-                {"start": 1, "end": 2, "text": "Test transcript 2"},
-                {"start": 2, "end": 3, "text": "Test transcript 3"}
+                {"start": 0, "end": 1, "text": "Test transcript", "completed": True},
+                {"start": 1, "end": 2, "text": "Test transcript 2", "completed": True},
+                {"start": 2, "end": 3, "text": "Test transcript 3", "completed": True}
             ]
         })
         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(len(self.client.transcript), 3)
         self.assertEqual(self.client.transcript[1]['text'], "Test transcript 2")
 
     def test_on_close(self):

tests/test_server.py

@@ -5,17 +5,18 @@
 from unittest import mock
 
 import numpy as np
-import evaluate
+import jiwer
 
 from websockets.exceptions import ConnectionClosed
-from whisper_live.server import TranscriptionServer
+from whisper_live.server import TranscriptionServer, BackendType, ClientManager
 from whisper_live.client import Client, TranscriptionClient, TranscriptionTeeClient
 from whisper.normalizers import EnglishTextNormalizer
 
 
 class TestTranscriptionServerInitialization(unittest.TestCase):
     def test_initialization(self):
         server = TranscriptionServer()
+        server.client_manager = ClientManager(max_clients=4, max_connection_time=600)
         self.assertEqual(server.client_manager.max_clients, 4)
         self.assertEqual(server.client_manager.max_connection_time, 600)
         self.assertDictEqual(server.client_manager.clients, {})
@@ -25,6 +26,7 @@ def test_initialization(self):
 class TestGetWaitTime(unittest.TestCase):
     def setUp(self):
         self.server = TranscriptionServer()
+        self.server.client_manager = ClientManager(max_clients=4, max_connection_time=600)
         self.server.client_manager.start_times = {
             'client1': time.time() - 120,
             'client2': time.time() - 300
@@ -49,7 +51,7 @@ def test_connection(self, mock_websocket):
             'task': 'transcribe',
             'model': 'tiny.en'
         })
-        self.server.recv_audio(mock_websocket, "faster_whisper")
+        self.server.recv_audio(mock_websocket, BackendType("faster_whisper"))
 
     @mock.patch('websockets.WebSocketCommonProtocol')
     def test_recv_audio_exception_handling(self, mock_websocket):
@@ -61,7 +63,7 @@ def test_recv_audio_exception_handling(self, mock_websocket):
         }),  np.array([1, 2, 3]).tobytes()]
 
         with self.assertLogs(level="ERROR"):
-            self.server.recv_audio(mock_websocket, "faster_whisper")
+            self.server.recv_audio(mock_websocket, BackendType("faster_whisper"))
 
         self.assertNotIn(mock_websocket, self.server.client_manager.clients)
 
@@ -82,7 +84,6 @@ def tearDownClass(cls):
         cls.server_process.wait()
 
     def setUp(self):
-        self.metric = evaluate.load("wer")
         self.normalizer = EnglishTextNormalizer()
 
     def check_prediction(self, srt_path):
@@ -94,11 +95,8 @@ def check_prediction(self, srt_path):
         gt_normalized = self.normalizer(gt)
 
         # calculate WER
-        wer = self.metric.compute(
-            predictions=[prediction_normalized],
-            references=[gt_normalized]
-        )
-        self.assertLess(wer, 0.05)
+        wer_score = jiwer.wer(gt_normalized, prediction_normalized)
+        self.assertLess(wer_score, 0.05)
 
     def test_inference(self):
         client = TranscriptionClient(
@@ -124,26 +122,26 @@ def setUp(self):
 
     @mock.patch('websockets.WebSocketCommonProtocol')
     def test_connection_closed_exception(self, mock_websocket):
-        mock_websocket.recv.side_effect = ConnectionClosed(1001, "testing connection closed")
+        mock_websocket.recv.side_effect = ConnectionClosed(1001, "testing connection closed", rcvd_then_sent=mock.Mock())
 
         with self.assertLogs(level="INFO") as log:
-            self.server.recv_audio(mock_websocket, "faster_whisper")
+            self.server.recv_audio(mock_websocket, BackendType("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.server.recv_audio(mock_websocket, BackendType("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")
+            self.server.recv_audio(mock_websocket, BackendType("faster_whisper"))
             for message in log.output:
                 print(message)
             print()

whisper_live/client.py

@@ -32,6 +32,8 @@ def __init__(
         use_vad=True,
         log_transcription=True,
         options=None
+        max_clients=4,
+        max_connection_time=600,
     ):
         """
         Initializes a Client instance for audio recording and streaming to a server.
@@ -61,6 +63,9 @@ def __init__(
         self.last_received_segment = None
         self.log_transcription = log_transcription
         self.options = options
+        self.max_clients = max_clients
+        self.max_connection_time = max_connection_time
+
 
         if translate:
             self.task = "translate"
@@ -110,9 +115,9 @@ def process_segments(self, segments):
         for i, seg in enumerate(segments):
             if not text or text[-1] != seg["text"]:
                 text.append(seg["text"])
-                if i == len(segments) - 1:
+                if i == len(segments) - 1 and not seg["completed"]:
                     self.last_segment = seg
-                elif (self.server_backend == "faster_whisper" and
+                elif (self.server_backend == "faster_whisper" and seg["completed"] and
                       (not self.transcript or
                         float(seg['start']) >= float(self.transcript[-1]['end']))):
                     self.transcript.append(seg)
@@ -203,6 +208,8 @@ def on_open(self, ws):
                     "model": self.model,
                     "use_vad": self.use_vad,
                     "options": self.options
+                    "max_clients": self.max_clients,
+                    "max_connection_time": self.max_connection_time,
                 }
             )
         )
@@ -256,7 +263,7 @@ def write_srt_file(self, output_path="output.srt"):
 
         """
         if self.server_backend == "faster_whisper":
-            if (self.last_segment):
+            if (self.last_segment) and self.transcript[-1]["text"] != self.last_segment["text"]:
                 self.transcript.append(self.last_segment)
             utils.create_srt_file(self.transcript, output_path)
 
@@ -685,8 +692,15 @@ def __init__(
         output_transcription_path="./output.srt",
         log_transcription=True,
         options=None,
+        max_clients=4,
+        max_connection_time=600,
     ):
-        self.client = Client(host, port, lang, translate, model, srt_file_path=output_transcription_path, use_vad=use_vad, log_transcription=log_transcription, options=options)
+        self.client = Client(
+            host, port, lang, translate, model, srt_file_path=output_transcription_path,
+            use_vad=use_vad, log_transcription=log_transcription, options=options, max_clients=max_clients,
+            max_connection_time=max_connection_time
+        )
+
         if save_output_recording and not output_recording_filename.endswith(".wav"):
             raise ValueError(f"Please provide a valid `output_recording_filename`: {output_recording_filename}")
         if not output_transcription_path.endswith(".srt"):

whisper_live/server.py

@@ -147,7 +147,7 @@ class TranscriptionServer:
     RATE = 16000
 
     def __init__(self):
-        self.client_manager = ClientManager()
+        self.client_manager = None
         self.no_voice_activity_chunks = 0
         self.use_vad = True
         self.single_model = False
@@ -224,6 +224,12 @@ def handle_new_connection(self, websocket, faster_whisper_custom_model_path,
             logging.info("New client connected")
             options = websocket.recv()
             options = json.loads(options)
+
+            if self.client_manager is None:
+                max_clients = options.get('max_clients', 4)
+                max_connection_time = options.get('max_connection_time', 600)
+                self.client_manager = ClientManager(max_clients, max_connection_time)
+
             self.use_vad = options.get('use_vad')
             if self.client_manager.is_server_full(websocket, options):
                 websocket.close()
@@ -995,7 +1001,7 @@ def speech_to_text(self):
                 logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}")
                 time.sleep(0.01)
 
-    def format_segment(self, start, end, text):
+    def format_segment(self, start, end, text, completed=False):
         """
         Formats a transcription segment with precise start and end times alongside the transcribed text.
 
@@ -1012,7 +1018,8 @@ def format_segment(self, start, end, text):
         return {
             'start': "{:.3f}".format(start),
             'end': "{:.3f}".format(end),
-            'text': text
+            'text': text,
+            'completed': completed
         }
 
     def update_segments(self, segments, duration):
@@ -1052,7 +1059,7 @@ def update_segments(self, segments, duration):
                 if s.no_speech_prob > self.no_speech_thresh:
                     continue
 
-                self.transcript.append(self.format_segment(start, end, text_))
+                self.transcript.append(self.format_segment(start, end, text_, completed=True))
                 offset = min(duration, s.end)
 
         # only process the segments if it satisfies the no_speech_thresh
@@ -1061,7 +1068,8 @@ def update_segments(self, segments, duration):
             last_segment = self.format_segment(
                 self.timestamp_offset + segments[-1].start,
                 self.timestamp_offset + min(duration, segments[-1].end),
-                self.current_out
+                self.current_out,
+                completed=False
             )
 
         # if same incomplete segment is seen multiple times then update the offset
@@ -1077,7 +1085,8 @@ def update_segments(self, segments, duration):
                 self.transcript.append(self.format_segment(
                     self.timestamp_offset,
                     self.timestamp_offset + duration,
-                    self.current_out
+                    self.current_out,
+                    completed=True
                 ))
             self.current_out = ''
             offset = duration