add diarization model list url

Vac update

LICENSE

@@ -1,10 +1,6 @@
 MIT License
 
 Copyright (c) 2025 Quentin Fuxa.  
-Based on:
-- The original work by ÚFAL. License: https://github.com/ufal/whisper_streaming/blob/main/LICENSE  
-- The work by Snakers4 (silero-vad). License: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE  
-- The work in Diart by juanmc2005. License: https://github.com/juanmc2005/diart/blob/main/LICENSE
 
 Permission is hereby granted, free of charge, to any person obtaining a copy  
 of this software and associated documentation files (the "Software"), to deal  
@@ -26,8 +22,7 @@ SOFTWARE.
 
 ---
 
-Third-party components included in this software:
+Based on:
-
+- **whisper_streaming** by ÚFAL – MIT License – https://github.com/ufal/whisper_streaming. The original work by ÚFAL. License: https://github.com/ufal/whisper_streaming/blob/main/LICENSE  
-- **whisper_streaming** by ÚFAL – MIT License – https://github.com/ufal/whisper_streaming  
+- **silero-vad** by Snakers4 – MIT License – https://github.com/snakers4/silero-vad. The work by Snakers4 (silero-vad). License: https://github.com/snakers4/silero-vad/blob/f6b1294cb27590fb2452899df98fb234dfef1134/LICENSE  
-- **silero-vad** by Snakers4 – MIT License – https://github.com/snakers4/silero-vad  
+- **Diart** by juanmc2005 – MIT License – https://github.com/juanmc2005/diart. The work in Diart by juanmc2005. License: https://github.com/juanmc2005/diart/blob/main/LICENSE
-- **Diart** by juanmc2005 – MIT License – https://github.com/juanmc2005/diart

README.md

@@ -9,8 +9,8 @@
 <p align="center">
   <a href="https://pypi.org/project/whisperlivekit/"><img alt="PyPI Version" src="https://img.shields.io/pypi/v/whisperlivekit?color=g"></a>
   <a href="https://pepy.tech/project/whisperlivekit"><img alt="PyPI Downloads" src="https://static.pepy.tech/personalized-badge/whisperlivekit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=downloads"></a>
-  <a href="https://pypi.org/project/whisperlivekit/"><img alt="Python Versions" src="https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%203.11%20%7C%203.12-dark_green"></a>
+  <a href="https://pypi.org/project/whisperlivekit/"><img alt="Python Versions" src="https://img.shields.io/badge/python-3.9--3.13-dark_green"></a>
-  <a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/QuentinFuxa/WhisperLiveKit?color=blue"></a>
+  <a href="https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-MIT-dark_green"></a>
 </p>
 
 ## 🚀 Overview
@@ -32,6 +32,7 @@ WhisperLiveKit consists of three main components:
 - **👥 Speaker Diarization** - Identify different speakers in real-time using [Diart](https://github.com/juanmc2005/diart)
 - **🔒 Fully Local** - All processing happens on your machine - no data sent to external servers
 - **📱 Multi-User Support** - Handle multiple users simultaneously with a single backend/server
+- **📝 Punctuation-Based Speaker Splitting [BETA] ** - Align speaker changes with natural sentence boundaries for more readable transcripts
  
 ### ⚙️ Core differences from [Whisper Streaming](https://github.com/ufal/whisper_streaming)
 
@@ -142,52 +143,79 @@ whisperlivekit-server --host 0.0.0.0 --port 8000 --model medium --diarization --
 ```
 
 ### Python API Integration (Backend)
+Check [basic_server.py](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/basic_server.py) for a complete example.
 
 ```python
-from whisperlivekit import WhisperLiveKit
+from whisperlivekit import TranscriptionEngine, AudioProcessor, get_web_interface_html, parse_args
-from whisperlivekit.audio_processor import AudioProcessor
+from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-from fastapi import FastAPI, WebSocket
-import asyncio
 from fastapi.responses import HTMLResponse
+from contextlib import asynccontextmanager
+import asyncio
 
-# Initialize components
+# Global variable for the transcription engine
-app = FastAPI()
+transcription_engine = None
-kit = WhisperLiveKit(model="medium", diarization=True)
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global transcription_engine
+    # Example: Initialize with specific parameters directly
+    # You can also load from command-line arguments using parse_args()
+    # args = parse_args()
+    # transcription_engine = TranscriptionEngine(**vars(args))
+    transcription_engine = TranscriptionEngine(model="medium", diarization=True, lan="en")
+    yield
+
+app = FastAPI(lifespan=lifespan)
 
 # Serve the web interface
 @app.get("/")
 async def get():
-    return HTMLResponse(kit.web_interface())  # Use the built-in web interface
+    return HTMLResponse(get_web_interface_html())
 
 # Process WebSocket connections
-async def handle_websocket_results(websocket, results_generator):
+async def handle_websocket_results(websocket: WebSocket, results_generator):
-    async for response in results_generator:
+    try:
-        await websocket.send_json(response)
+        async for response in results_generator:
+            await websocket.send_json(response)
+        await websocket.send_json({"type": "ready_to_stop"})
+    except WebSocketDisconnect:
+        print("WebSocket disconnected during results handling.")
 
 @app.websocket("/asr")
 async def websocket_endpoint(websocket: WebSocket):
-    audio_processor = AudioProcessor()
+    global transcription_engine
-    await websocket.accept()
-    results_generator = await audio_processor.create_tasks()
-    websocket_task = asyncio.create_task(
-        handle_websocket_results(websocket, results_generator)
-    )
 
+    # Create a new AudioProcessor for each connection, passing the shared engine
+    audio_processor = AudioProcessor(transcription_engine=transcription_engine)    
+    results_generator = await audio_processor.create_tasks()
+    send_results_to_client = handle_websocket_results(websocket, results_generator)
+    results_task = asyncio.create_task(send_results_to_client)
+    await websocket.accept()
     try:
         while True:
             message = await websocket.receive_bytes()
-            await audio_processor.process_audio(message)
+            await audio_processor.process_audio(message)        
+    except WebSocketDisconnect:
+        print(f"Client disconnected: {websocket.client}")
     except Exception as e:
-        print(f"WebSocket error: {e}")
+        await websocket.close(code=1011, reason=f"Server error: {e}")
-        websocket_task.cancel()
+    finally:
+        results_task.cancel()
+        try:
+            await results_task
+        except asyncio.CancelledError:
+            logger.info("Results task successfully cancelled.")
 ```
 
 ### Frontend Implementation
 
-The package includes a simple HTML/JavaScript implementation that you can adapt for your project. You can get in in [whisperlivekit/web/live_transcription.html](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/web/live_transcription.html), or using :
+The package includes a simple HTML/JavaScript implementation that you can adapt for your project. You can find it in `whisperlivekit/web/live_transcription.html`, or load its content using the `get_web_interface_html()` function from `whisperlivekit`:
 
 ```python
-kit.web_interface()
+from whisperlivekit import get_web_interface_html
+
+# ... later in your code where you need the HTML string ...
+html_content = get_web_interface_html()
 ```
 
 ## ⚙️ Configuration Reference
@@ -203,6 +231,7 @@ WhisperLiveKit offers extensive configuration options:
 | `--task` | `transcribe` or `translate` | `transcribe` |
 | `--backend` | Processing backend | `faster-whisper` |
 | `--diarization` | Enable speaker identification | `False` |
+| `--punctuation-split` | Use punctuation to improve speaker boundaries | `True` |
 | `--confidence-validation` | Use confidence scores for faster validation | `False` |
 | `--min-chunk-size` | Minimum audio chunk size (seconds) | `1.0` |
 | `--vac` | Use Voice Activity Controller | `False` |
@@ -211,6 +240,8 @@ WhisperLiveKit offers extensive configuration options:
 | `--warmup-file` | Audio file path for model warmup | `jfk.wav` |
 | `--ssl-certfile` | Path to the SSL certificate file (for HTTPS support) | `None` |
 | `--ssl-keyfile` | Path to the SSL private key file (for HTTPS support) | `None` |
+| `--segmentation-model` | Hugging Face model ID for pyannote.audio segmentation model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `pyannote/segmentation-3.0` |
+| `--embedding-model` | Hugging Face model ID for pyannote.audio embedding model. [Available models](https://github.com/juanmc2005/diart/tree/main?tab=readme-ov-file#pre-trained-models) | `speechbrain/spkrec-ecapa-voxceleb` |
 
 ## 🔧 How It Works
 

setup.py

@@ -1,7 +1,7 @@
 from setuptools import setup, find_packages
 setup(
     name="whisperlivekit",
-    version="0.1.6",
+    version="0.1.9",
     description="Real-time, Fully Local Whisper's Speech-to-Text and Speaker Diarization",
     long_description=open("README.md", "r", encoding="utf-8").read(),
     long_description_content_type="text/markdown",

whisperlivekit/__init__.py

@@ -1,4 +1,5 @@
-from .core import WhisperLiveKit, parse_args
+from .core import TranscriptionEngine
 from .audio_processor import AudioProcessor
-
+from .web.web_interface import get_web_interface_html
-__all__ = ['WhisperLiveKit', 'AudioProcessor', 'parse_args']
+from .parse_args import parse_args
+__all__ = ['TranscriptionEngine', 'AudioProcessor', 'get_web_interface_html', 'parse_args']

whisperlivekit/audio_processor.py

@@ -8,7 +8,7 @@ import traceback
 from datetime import timedelta
 from whisperlivekit.timed_objects import ASRToken
 from whisperlivekit.whisper_streaming_custom.whisper_online import online_factory
-from whisperlivekit.core import WhisperLiveKit
+from whisperlivekit.core import TranscriptionEngine
 
 # Set up logging once
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
@@ -27,10 +27,13 @@ class AudioProcessor:
     Handles audio processing, state management, and result formatting.
     """
     
-    def __init__(self):
+    def __init__(self, **kwargs):
         """Initialize the audio processor with configuration, models, and state."""
         
-        models = WhisperLiveKit()
+        if 'transcription_engine' in kwargs and isinstance(kwargs['transcription_engine'], TranscriptionEngine):
+            models = kwargs['transcription_engine']
+        else:
+            models = TranscriptionEngine(**kwargs)
         
         # Audio processing settings
         self.args = models.args
@@ -83,10 +86,33 @@ class AudioProcessor:
 
     def start_ffmpeg_decoder(self):
         """Start FFmpeg process for WebM to PCM conversion."""
-        return (ffmpeg.input("pipe:0", format="webm")
+        try:
-                .output("pipe:1", format="s16le", acodec="pcm_s16le", 
+            return (ffmpeg.input("pipe:0", format="webm")
-                        ac=self.channels, ar=str(self.sample_rate))
+                    .output("pipe:1", format="s16le", acodec="pcm_s16le", 
-                .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True))
+                            ac=self.channels, ar=str(self.sample_rate))
+                    .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True))
+        except FileNotFoundError:
+            error = """
+            FFmpeg is not installed or not found in your system's PATH.
+            Please install FFmpeg to enable audio processing.
+
+            Installation instructions:
+
+            # Ubuntu/Debian:
+            sudo apt update && sudo apt install ffmpeg
+
+            # macOS (using Homebrew):
+            brew install ffmpeg
+
+            # Windows:
+            # 1. Download the latest static build from https://ffmpeg.org/download.html
+            # 2. Extract the archive (e.g., to C:\\FFmpeg).
+            # 3. Add the 'bin' directory (e.g., C:\\FFmpeg\\bin) to your system's PATH environment variable.
+
+            After installation, please restart the application.
+            """
+            logger.error(error)
+            raise FileNotFoundError(error)
 
     async def restart_ffmpeg(self):
         """Restart the FFmpeg process after failure."""
@@ -269,6 +295,7 @@ class AudioProcessor:
         """Process audio chunks for transcription."""
         self.full_transcription = ""
         self.sep = self.online.asr.sep
+        cumulative_pcm_duration_stream_time = 0.0
         
         while True:
             try:
@@ -292,25 +319,38 @@ class AudioProcessor:
                 )
                 
                 # Process transcription
-                self.online.insert_audio_chunk(pcm_array)
+                duration_this_chunk = len(pcm_array) / self.sample_rate if isinstance(pcm_array, np.ndarray) else 0
-                new_tokens = self.online.process_iter()
+                cumulative_pcm_duration_stream_time += duration_this_chunk
+                stream_time_end_of_current_pcm = cumulative_pcm_duration_stream_time
+
+                self.online.insert_audio_chunk(pcm_array, stream_time_end_of_current_pcm)
+                new_tokens, current_audio_processed_upto = self.online.process_iter()
                 
                 if new_tokens:
                     self.full_transcription += self.sep.join([t.text for t in new_tokens])
                     
                 # Get buffer information
-                _buffer = self.online.get_buffer()
+                _buffer_transcript_obj = self.online.get_buffer()
-                buffer = _buffer.text
+                buffer_text = _buffer_transcript_obj.text
-                end_buffer = _buffer.end if _buffer.end else (
+                
-                    new_tokens[-1].end if new_tokens else 0
+                candidate_end_times = [self.end_buffer]
-                )
+
+                if new_tokens:
+                    candidate_end_times.append(new_tokens[-1].end)
+                
+                if _buffer_transcript_obj.end is not None:
+                    candidate_end_times.append(_buffer_transcript_obj.end)
+                
+                candidate_end_times.append(current_audio_processed_upto)
+                
+                new_end_buffer = max(candidate_end_times)
                 
                 # Avoid duplicating content
-                if buffer in self.full_transcription:
+                if buffer_text in self.full_transcription:
-                    buffer = ""
+                    buffer_text = ""
                     
                 await self.update_transcription(
-                    new_tokens, buffer, end_buffer, self.full_transcription, self.sep
+                    new_tokens, buffer_text, new_end_buffer, self.full_transcription, self.sep
                 )
                 self.transcription_queue.task_done()
                 
@@ -337,13 +377,16 @@ class AudioProcessor:
                 # Process diarization
                 await diarization_obj.diarize(pcm_array)
                 
-                # Get current state and update speakers
+                async with self.lock:
-                state = await self.get_current_state()
+                    new_end = diarization_obj.assign_speakers_to_tokens(
-                new_end = diarization_obj.assign_speakers_to_tokens(
+                        self.end_attributed_speaker, 
-                    state["end_attributed_speaker"], state["tokens"]
+                        self.tokens,
-                )
+                        use_punctuation_split=self.args.punctuation_split
+                    )
+                    self.end_attributed_speaker = new_end
+                    if buffer_diarization:
+                        self.buffer_diarization = buffer_diarization
                 
-                await self.update_diarization(new_end, buffer_diarization)
                 self.diarization_queue.task_done()
                 
             except Exception as e:
@@ -416,31 +459,38 @@ class AudioProcessor:
                     await self.update_diarization(end_attributed_speaker, combined)
                     buffer_diarization = combined
                 
-                # Create response object
+                response_status = "active_transcription"
-                if not lines:
+                final_lines_for_response = lines.copy()
-                    lines = [{
+
+                if not tokens and not buffer_transcription and not buffer_diarization:
+                    response_status = "no_audio_detected"
+                    final_lines_for_response = []
+                elif response_status == "active_transcription" and not final_lines_for_response:
+                    final_lines_for_response = [{
                         "speaker": 1,
                         "text": "",
-                        "beg": format_time(0),
+                        "beg": format_time(state.get("end_buffer", 0)),
-                        "end": format_time(tokens[-1].end if tokens else 0),
+                        "end": format_time(state.get("end_buffer", 0)),
                         "diff": 0
                     }]
                 
                 response = {
-                    "lines": lines, 
+                    "status": response_status,
+                    "lines": final_lines_for_response,
                     "buffer_transcription": buffer_transcription,
                     "buffer_diarization": buffer_diarization,
                     "remaining_time_transcription": state["remaining_time_transcription"],
                     "remaining_time_diarization": state["remaining_time_diarization"]
                 }
                 
-                # Only yield if content has changed
+                current_response_signature = f"{response_status} | " + \
-                response_content = ' '.join([f"{line['speaker']} {line['text']}" for line in lines]) + \
+                                           ' '.join([f"{line['speaker']} {line['text']}" for line in final_lines_for_response]) + \
-                                  f" | {buffer_transcription} | {buffer_diarization}"
+                                           f" | {buffer_transcription} | {buffer_diarization}"
                 
-                if response_content != self.last_response_content and (lines or buffer_transcription or buffer_diarization):
+                if current_response_signature != self.last_response_content and \
+                   (final_lines_for_response or buffer_transcription or buffer_diarization or response_status == "no_audio_detected"):
                     yield response
-                    self.last_response_content = response_content
+                    self.last_response_content = current_response_signature
                 
                 # Check for termination condition
                 if self.is_stopping:

whisperlivekit/basic_server.py

@@ -2,26 +2,24 @@ from contextlib import asynccontextmanager
 from fastapi import FastAPI, WebSocket, WebSocketDisconnect
 from fastapi.responses import HTMLResponse
 from fastapi.middleware.cors import CORSMiddleware
-
+from whisperlivekit import TranscriptionEngine, AudioProcessor, get_web_interface_html, parse_args
-from whisperlivekit import WhisperLiveKit, parse_args
-from whisperlivekit.audio_processor import AudioProcessor
-
 import asyncio
 import logging
-import os, sys
-import argparse
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logging.getLogger().setLevel(logging.WARNING)
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 
-kit = None
+args = parse_args()
+transcription_engine = None
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    global kit
+    global transcription_engine
-    kit = WhisperLiveKit()
+    transcription_engine = TranscriptionEngine(
+        **vars(args),
+    )
     yield
 
 app = FastAPI(lifespan=lifespan)
@@ -33,10 +31,9 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
-
 @app.get("/")
 async def get():
-    return HTMLResponse(kit.web_interface())
+    return HTMLResponse(get_web_interface_html())
 
 
 async def handle_websocket_results(websocket, results_generator):
@@ -55,8 +52,10 @@ async def handle_websocket_results(websocket, results_generator):
 
 @app.websocket("/asr")
 async def websocket_endpoint(websocket: WebSocket):
-    audio_processor = AudioProcessor()
+    global transcription_engine
-
+    audio_processor = AudioProcessor(
+        transcription_engine=transcription_engine,
+    )
     await websocket.accept()
     logger.info("WebSocket connection opened.")
             
@@ -94,8 +93,6 @@ def main():
     """Entry point for the CLI command."""
     import uvicorn
     
-    args = parse_args()
-    
     uvicorn_kwargs = {
         "app": "whisperlivekit.basic_server:app",
         "host":args.host, 
@@ -114,7 +111,6 @@ def main():
             "ssl_keyfile": args.ssl_keyfile
         }
 
-
     if ssl_kwargs:
         uvicorn_kwargs = {**uvicorn_kwargs, **ssl_kwargs}
 

whisperlivekit/core.py

@@ -2,148 +2,10 @@ try:
     from whisperlivekit.whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
 except ImportError:
     from .whisper_streaming_custom.whisper_online import backend_factory, warmup_asr
-from argparse import Namespace, ArgumentParser
+from argparse import Namespace
-
-def parse_args():
-    parser = ArgumentParser(description="Whisper FastAPI Online Server")
-    parser.add_argument(
-        "--host",
-        type=str,
-        default="localhost",
-        help="The host address to bind the server to.",
-    )
-    parser.add_argument(
-        "--port", type=int, default=8000, help="The port number to bind the server to."
-    )
-    parser.add_argument(
-        "--warmup-file",
-        type=str,
-        default=None,
-        dest="warmup_file",
-        help="""
-        The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast.
-        If not set, uses https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav.
-        If False, no warmup is performed.
-        """,
-    )
-
-    parser.add_argument(
-        "--confidence-validation",
-        action="store_true",
-        help="Accelerates validation of tokens using confidence scores. Transcription will be faster but punctuation might be less accurate.",
-    )
-
-    parser.add_argument(
-        "--diarization",
-        action="store_true",
-        default=False,
-        help="Enable speaker diarization.",
-    )
-
-    parser.add_argument(
-        "--no-transcription",
-        action="store_true",
-        help="Disable transcription to only see live diarization results.",
-    )
-    
-    parser.add_argument(
-        "--min-chunk-size",
-        type=float,
-        default=0.5,
-        help="Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.",
-    )
-    
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="tiny",
-        help="Name size of the Whisper model to use (default: tiny). Suggested values: tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo. The model is automatically downloaded from the model hub if not present in model cache dir.",
-    )
-    
-    parser.add_argument(
-        "--model_cache_dir",
-        type=str,
-        default=None,
-        help="Overriding the default model cache dir where models downloaded from the hub are saved",
-    )
-    parser.add_argument(
-        "--model_dir",
-        type=str,
-        default=None,
-        help="Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.",
-    )
-    parser.add_argument(
-        "--lan",
-        "--language",
-        type=str,
-        default="auto",
-        help="Source language code, e.g. en,de,cs, or 'auto' for language detection.",
-    )
-    parser.add_argument(
-        "--task",
-        type=str,
-        default="transcribe",
-        choices=["transcribe", "translate"],
-        help="Transcribe or translate.",
-    )
-    parser.add_argument(
-        "--backend",
-        type=str,
-        default="faster-whisper",
-        choices=["faster-whisper", "whisper_timestamped", "mlx-whisper", "openai-api"],
-        help="Load only this backend for Whisper processing.",
-    )
-    parser.add_argument(
-        "--vac",
-        action="store_true",
-        default=False,
-        help="Use VAC = voice activity controller. Recommended. Requires torch.",
-    )
-    parser.add_argument(
-        "--vac-chunk-size", type=float, default=0.04, help="VAC sample size in seconds."
-    )
-
-    parser.add_argument(
-        "--no-vad",
-        action="store_true",
-        help="Disable VAD (voice activity detection).",
-    )
-    
-    parser.add_argument(
-        "--buffer_trimming",
-        type=str,
-        default="segment",
-        choices=["sentence", "segment"],
-        help='Buffer trimming strategy -- trim completed sentences marked with punctuation mark and detected by sentence segmenter, or the completed segments returned by Whisper. Sentence segmenter must be installed for "sentence" option.',
-    )
-    parser.add_argument(
-        "--buffer_trimming_sec",
-        type=float,
-        default=15,
-        help="Buffer trimming length threshold in seconds. If buffer length is longer, trimming sentence/segment is triggered.",
-    )
-    parser.add_argument(
-        "-l",
-        "--log-level",
-        dest="log_level",
-        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
-        help="Set the log level",
-        default="DEBUG",
-    )
-    parser.add_argument("--ssl-certfile", type=str, help="Path to the SSL certificate file.", default=None)
-    parser.add_argument("--ssl-keyfile", type=str, help="Path to the SSL private key file.", default=None)
 
 
-    args = parser.parse_args()
+class TranscriptionEngine:
-    
-    args.transcription = not args.no_transcription
-    args.vad = not args.no_vad    
-    delattr(args, 'no_transcription')
-    delattr(args, 'no_vad')
-    
-    return args
-
-class WhisperLiveKit:
     _instance = None
     _initialized = False
     
@@ -153,14 +15,51 @@ class WhisperLiveKit:
         return cls._instance
     
     def __init__(self, **kwargs):
-        if WhisperLiveKit._initialized:
+        if TranscriptionEngine._initialized:
             return
-            
+
-        default_args = vars(parse_args())
+        defaults = {
+            "host": "localhost",
+            "port": 8000,
+            "warmup_file": None,
+            "confidence_validation": False,
+            "diarization": False,
+            "punctuation_split": False,
+            "min_chunk_size": 0.5,
+            "model": "tiny",
+            "model_cache_dir": None,
+            "model_dir": None,
+            "lan": "auto",
+            "task": "transcribe",
+            "backend": "faster-whisper",
+            "vac": False,
+            "vac_chunk_size": 0.04,
+            "buffer_trimming": "segment",
+            "buffer_trimming_sec": 15,
+            "log_level": "DEBUG",
+            "ssl_certfile": None,
+            "ssl_keyfile": None,
+            "transcription": True,
+            "vad": True,
+            "segmentation_model": "pyannote/segmentation-3.0",
+            "embedding_model": "pyannote/embedding",
+        }
+
+        config_dict = {**defaults, **kwargs}
+
+        if 'no_transcription' in kwargs:
+            config_dict['transcription'] = not kwargs['no_transcription']
+        if 'no_vad' in kwargs:
+            config_dict['vad'] = not kwargs['no_vad']
         
-        merged_args = {**default_args, **kwargs}
+        config_dict.pop('no_transcription', None)
-        
+        config_dict.pop('no_vad', None)
-        self.args = Namespace(**merged_args)
+
+        if 'language' in kwargs:
+            config_dict['lan'] = kwargs['language']
+        config_dict.pop('language', None) 
+
+        self.args = Namespace(**config_dict)
         
         self.asr = None
         self.tokenizer = None
@@ -172,13 +71,10 @@ class WhisperLiveKit:
 
         if self.args.diarization:
             from whisperlivekit.diarization.diarization_online import DiartDiarization
-            self.diarization = DiartDiarization()
+            self.diarization = DiartDiarization(
+                block_duration=self.args.min_chunk_size,
+                segmentation_model_name=self.args.segmentation_model,
+                embedding_model_name=self.args.embedding_model
+            )
             
-        WhisperLiveKit._initialized = True
+        TranscriptionEngine._initialized = True
-
-    def web_interface(self):
-        import pkg_resources
-        html_path = pkg_resources.resource_filename('whisperlivekit', 'web/live_transcription.html')
-        with open(html_path, "r", encoding="utf-8") as f:
-            html = f.read()
-        return html

whisperlivekit/diarization/diarization_online.py

@@ -3,7 +3,8 @@ import re
 import threading
 import numpy as np
 import logging
-
+import time
+from queue import SimpleQueue, Empty
 
 from diart import SpeakerDiarization, SpeakerDiarizationConfig
 from diart.inference import StreamingInference
@@ -13,6 +14,7 @@ from diart.sources import MicrophoneAudioSource
 from rx.core import Observer
 from typing import Tuple, Any, List
 from pyannote.core import Annotation
+import diart.models as m
 
 logger = logging.getLogger(__name__)
 
@@ -78,40 +80,114 @@ class DiarizationObserver(Observer):
 
 class WebSocketAudioSource(AudioSource):
     """
-    Custom AudioSource that blocks in read() until close() is called.
+    Buffers incoming audio and releases it in fixed-size chunks at regular intervals.
-    Use push_audio() to inject PCM chunks.
     """
-    def __init__(self, uri: str = "websocket", sample_rate: int = 16000):
+    def __init__(self, uri: str = "websocket", sample_rate: int = 16000, block_duration: float = 0.5):
         super().__init__(uri, sample_rate)
+        self.block_duration = block_duration
+        self.block_size = int(np.rint(block_duration * sample_rate))
+        self._queue = SimpleQueue()
+        self._buffer = np.array([], dtype=np.float32)
+        self._buffer_lock = threading.Lock()
         self._closed = False
         self._close_event = threading.Event()
+        self._processing_thread = None
+        self._last_chunk_time = time.time()
 
     def read(self):
+        """Start processing buffered audio and emit fixed-size chunks."""
+        self._processing_thread = threading.Thread(target=self._process_chunks)
+        self._processing_thread.daemon = True
+        self._processing_thread.start()
+        
         self._close_event.wait()
+        if self._processing_thread:
+            self._processing_thread.join(timeout=2.0)
+
+    def _process_chunks(self):
+        """Process audio from queue and emit fixed-size chunks at regular intervals."""
+        while not self._closed:
+            try:
+                audio_chunk = self._queue.get(timeout=0.1)
+                
+                with self._buffer_lock:
+                    self._buffer = np.concatenate([self._buffer, audio_chunk])
+                    
+                    while len(self._buffer) >= self.block_size:
+                        chunk = self._buffer[:self.block_size]
+                        self._buffer = self._buffer[self.block_size:]
+                        
+                        current_time = time.time()
+                        time_since_last = current_time - self._last_chunk_time
+                        if time_since_last < self.block_duration:
+                            time.sleep(self.block_duration - time_since_last)
+                        
+                        chunk_reshaped = chunk.reshape(1, -1)
+                        self.stream.on_next(chunk_reshaped)
+                        self._last_chunk_time = time.time()
+                        
+            except Empty:
+                with self._buffer_lock:
+                    if len(self._buffer) > 0 and time.time() - self._last_chunk_time > self.block_duration:
+                        padded_chunk = np.zeros(self.block_size, dtype=np.float32)
+                        padded_chunk[:len(self._buffer)] = self._buffer
+                        self._buffer = np.array([], dtype=np.float32)
+                        
+                        chunk_reshaped = padded_chunk.reshape(1, -1)
+                        self.stream.on_next(chunk_reshaped)
+                        self._last_chunk_time = time.time()
+            except Exception as e:
+                logger.error(f"Error in audio processing thread: {e}")
+                self.stream.on_error(e)
+                break
+        
+        with self._buffer_lock:
+            if len(self._buffer) > 0:
+                padded_chunk = np.zeros(self.block_size, dtype=np.float32)
+                padded_chunk[:len(self._buffer)] = self._buffer
+                chunk_reshaped = padded_chunk.reshape(1, -1)
+                self.stream.on_next(chunk_reshaped)
+        
+        self.stream.on_completed()
 
     def close(self):
         if not self._closed:
             self._closed = True
-            self.stream.on_completed()
             self._close_event.set()
 
     def push_audio(self, chunk: np.ndarray):
+        """Add audio chunk to the processing queue."""
         if not self._closed:
-            new_audio = np.expand_dims(chunk, axis=0)
+            if chunk.ndim > 1:
-            logger.debug('Add new chunk with shape:', new_audio.shape)
+                chunk = chunk.flatten()
-            self.stream.on_next(new_audio)
+            self._queue.put(chunk)
+            logger.debug(f'Added chunk to queue with {len(chunk)} samples')
 
 
 class DiartDiarization:
-    def __init__(self, sample_rate: int = 16000, config : SpeakerDiarizationConfig = None, use_microphone: bool = False):
+    def __init__(self, sample_rate: int = 16000, config : SpeakerDiarizationConfig = None, use_microphone: bool = False, block_duration: float = 0.5, segmentation_model_name: str = "pyannote/segmentation-3.0", embedding_model_name: str = "speechbrain/spkrec-ecapa-voxceleb"):
+        segmentation_model = m.SegmentationModel.from_pretrained(segmentation_model_name)
+        embedding_model = m.EmbeddingModel.from_pretrained(embedding_model_name)
+        
+        if config is None:
+            config = SpeakerDiarizationConfig(
+                segmentation=segmentation_model,
+                embedding=embedding_model,
+            )
+        
         self.pipeline = SpeakerDiarization(config=config)        
         self.observer = DiarizationObserver()
+        self.lag_diart = None
         
         if use_microphone:
-            self.source = MicrophoneAudioSource()
+            self.source = MicrophoneAudioSource(block_duration=block_duration)
             self.custom_source = None
         else:
-            self.custom_source = WebSocketAudioSource(uri="websocket_source", sample_rate=sample_rate)
+            self.custom_source = WebSocketAudioSource(
+                uri="websocket_source", 
+                sample_rate=sample_rate,
+                block_duration=block_duration
+            )
             self.source = self.custom_source
             
         self.inference = StreamingInference(
@@ -138,16 +214,102 @@ class DiartDiarization:
         if self.custom_source:
             self.custom_source.close()
 
-    def assign_speakers_to_tokens(self, end_attributed_speaker, tokens: list) -> float:
+    def assign_speakers_to_tokens(self, end_attributed_speaker, tokens: list, use_punctuation_split: bool = False) -> float:
         """
         Assign speakers to tokens based on timing overlap with speaker segments.
         Uses the segments collected by the observer.
+        
+        If use_punctuation_split is True, uses punctuation marks to refine speaker boundaries.
         """
         segments = self.observer.get_segments()
         
+        # Debug logging
+        logger.debug(f"assign_speakers_to_tokens called with {len(tokens)} tokens")
+        logger.debug(f"Available segments: {len(segments)}")
+        for i, seg in enumerate(segments[:5]):  # Show first 5 segments
+            logger.debug(f"  Segment {i}: {seg.speaker} [{seg.start:.2f}-{seg.end:.2f}]")
+        
+        if not self.lag_diart and segments and tokens:
+            self.lag_diart = segments[0].start - tokens[0].start
         for token in tokens:
             for segment in segments:
-                if not (segment.end <= token.start or segment.start >= token.end):
+                if not (segment.end <= token.start + self.lag_diart or segment.start >= token.end + self.lag_diart):
                     token.speaker = extract_number(segment.speaker) + 1
                     end_attributed_speaker = max(token.end, end_attributed_speaker)
-        return end_attributed_speaker
+        
+        if use_punctuation_split and len(tokens) > 1:
+            punctuation_marks = {'.', '!', '?'}
+            
+            print("Here are the tokens:", 
+                  [(t.text, t.start, t.end, t.speaker) for t in tokens[:10]])
+            
+            segment_map = []
+            for segment in segments:
+                speaker_num = extract_number(segment.speaker) + 1
+                segment_map.append((segment.start, segment.end, speaker_num))
+            segment_map.sort(key=lambda x: x[0])
+            
+            i = 0
+            while i < len(tokens):
+                current_token = tokens[i]
+                
+                is_sentence_end = False
+                if current_token.text and current_token.text.strip():
+                    text = current_token.text.strip()
+                    if text[-1] in punctuation_marks:
+                        is_sentence_end = True
+                        logger.debug(f"Token {i} ends sentence: '{current_token.text}' at {current_token.end:.2f}s")
+                
+                if is_sentence_end and current_token.speaker != -1:
+                    punctuation_time = current_token.end
+                    current_speaker = current_token.speaker
+                    
+                    j = i + 1
+                    next_sentence_tokens = []                    
+                    while j < len(tokens):
+                        next_token = tokens[j]
+                        next_sentence_tokens.append(j)
+                        
+                        # Check if this token ends the next sentence
+                        if next_token.text and next_token.text.strip():
+                            if next_token.text.strip()[-1] in punctuation_marks:
+                                break
+                        j += 1
+                    
+                    if next_sentence_tokens:
+                        speaker_times = {}
+                        
+                        for idx in next_sentence_tokens:
+                            token = tokens[idx]
+                            # Find which segments overlap with this token
+                            for seg_start, seg_end, seg_speaker in segment_map:
+                                if not (seg_end <= token.start or seg_start >= token.end):
+                                    # Calculate overlap duration
+                                    overlap_start = max(seg_start, token.start)
+                                    overlap_end = min(seg_end, token.end)
+                                    overlap_duration = overlap_end - overlap_start
+                                    
+                                    if seg_speaker not in speaker_times:
+                                        speaker_times[seg_speaker] = 0
+                                    speaker_times[seg_speaker] += overlap_duration
+                        
+                        if speaker_times:
+                            dominant_speaker = max(speaker_times.items(), key=lambda x: x[1])[0]
+                            
+                            if dominant_speaker != current_speaker:
+                                logger.debug(f"  Speaker change after punctuation: {current_speaker} → {dominant_speaker}")
+                                
+                                for idx in next_sentence_tokens:
+                                    if tokens[idx].speaker != dominant_speaker:
+                                        logger.debug(f"    Reassigning token {idx} ('{tokens[idx].text}') to Speaker {dominant_speaker}")
+                                        tokens[idx].speaker = dominant_speaker
+                                        end_attributed_speaker = max(tokens[idx].end, end_attributed_speaker)
+                            else:
+                                for idx in next_sentence_tokens:
+                                    if tokens[idx].speaker == -1:
+                                        tokens[idx].speaker = current_speaker
+                                        end_attributed_speaker = max(tokens[idx].end, end_attributed_speaker)
+                
+                i += 1         
+        
+        return end_attributed_speaker

whisperlivekit/parse_args.py

@@ -0,0 +1,162 @@
+
+from argparse import ArgumentParser
+
+def parse_args():
+    parser = ArgumentParser(description="Whisper FastAPI Online Server")
+    parser.add_argument(
+        "--host",
+        type=str,
+        default="localhost",
+        help="The host address to bind the server to.",
+    )
+    parser.add_argument(
+        "--port", type=int, default=8000, help="The port number to bind the server to."
+    )
+    parser.add_argument(
+        "--warmup-file",
+        type=str,
+        default=None,
+        dest="warmup_file",
+        help="""
+        The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast.
+        If not set, uses https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav.
+        If False, no warmup is performed.
+        """,
+    )
+
+    parser.add_argument(
+        "--confidence-validation",
+        action="store_true",
+        help="Accelerates validation of tokens using confidence scores. Transcription will be faster but punctuation might be less accurate.",
+    )
+
+    parser.add_argument(
+        "--diarization",
+        action="store_true",
+        default=False,
+        help="Enable speaker diarization.",
+    )
+
+    parser.add_argument(
+        "--punctuation-split",
+        action="store_true",
+        default=False,
+        help="Use punctuation marks from transcription to improve speaker boundary detection. Requires both transcription and diarization to be enabled.",
+    )
+
+    parser.add_argument(
+        "--segmentation-model",
+        type=str,
+        default="pyannote/segmentation-3.0",
+        help="Hugging Face model ID for pyannote.audio segmentation model.",
+    )
+
+    parser.add_argument(
+        "--embedding-model",
+        type=str,
+        default="pyannote/embedding",
+        help="Hugging Face model ID for pyannote.audio embedding model.",
+    )
+
+    parser.add_argument(
+        "--no-transcription",
+        action="store_true",
+        help="Disable transcription to only see live diarization results.",
+    )
+    
+    parser.add_argument(
+        "--min-chunk-size",
+        type=float,
+        default=0.5,
+        help="Minimum audio chunk size in seconds. It waits up to this time to do processing. If the processing takes shorter time, it waits, otherwise it processes the whole segment that was received by this time.",
+    )
+    
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="tiny",
+        help="Name size of the Whisper model to use (default: tiny). Suggested values: tiny.en,tiny,base.en,base,small.en,small,medium.en,medium,large-v1,large-v2,large-v3,large,large-v3-turbo. The model is automatically downloaded from the model hub if not present in model cache dir.",
+    )
+    
+    parser.add_argument(
+        "--model_cache_dir",
+        type=str,
+        default=None,
+        help="Overriding the default model cache dir where models downloaded from the hub are saved",
+    )
+    parser.add_argument(
+        "--model_dir",
+        type=str,
+        default=None,
+        help="Dir where Whisper model.bin and other files are saved. This option overrides --model and --model_cache_dir parameter.",
+    )
+    parser.add_argument(
+        "--lan",
+        "--language",
+        type=str,
+        default="auto",
+        help="Source language code, e.g. en,de,cs, or 'auto' for language detection.",
+    )
+    parser.add_argument(
+        "--task",
+        type=str,
+        default="transcribe",
+        choices=["transcribe", "translate"],
+        help="Transcribe or translate.",
+    )
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="faster-whisper",
+        choices=["faster-whisper", "whisper_timestamped", "mlx-whisper", "openai-api"],
+        help="Load only this backend for Whisper processing.",
+    )
+    parser.add_argument(
+        "--vac",
+        action="store_true",
+        default=False,
+        help="Use VAC = voice activity controller. Recommended. Requires torch.",
+    )
+    parser.add_argument(
+        "--vac-chunk-size", type=float, default=0.04, help="VAC sample size in seconds."
+    )
+
+    parser.add_argument(
+        "--no-vad",
+        action="store_true",
+        help="Disable VAD (voice activity detection).",
+    )
+    
+    parser.add_argument(
+        "--buffer_trimming",
+        type=str,
+        default="segment",
+        choices=["sentence", "segment"],
+        help='Buffer trimming strategy -- trim completed sentences marked with punctuation mark and detected by sentence segmenter, or the completed segments returned by Whisper. Sentence segmenter must be installed for "sentence" option.',
+    )
+    parser.add_argument(
+        "--buffer_trimming_sec",
+        type=float,
+        default=15,
+        help="Buffer trimming length threshold in seconds. If buffer length is longer, trimming sentence/segment is triggered.",
+    )
+    parser.add_argument(
+        "-l",
+        "--log-level",
+        dest="log_level",
+        choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+        help="Set the log level",
+        default="DEBUG",
+    )
+    parser.add_argument("--ssl-certfile", type=str, help="Path to the SSL certificate file.", default=None)
+    parser.add_argument("--ssl-keyfile", type=str, help="Path to the SSL private key file.", default=None)
+
+
+    args = parser.parse_args()
+    
+    args.transcription = not args.no_transcription
+    args.vad = not args.no_vad    
+    delattr(args, 'no_transcription')
+    delattr(args, 'no_vad')
+    
+    return args

whisperlivekit/timed_objects.py

@@ -26,4 +26,7 @@ class Transcript(TimedText):
 
 @dataclass
 class SpeakerSegment(TimedText):
+    """Represents a segment of audio attributed to a specific speaker.
+    No text nor probability is associated with this segment.
+    """
     pass

whisperlivekit/web/live_transcription.html

@@ -427,7 +427,8 @@
                         buffer_transcription = "", 
                         buffer_diarization = "",
                         remaining_time_transcription = 0,
-                        remaining_time_diarization = 0
+                        remaining_time_diarization = 0,
+                        status = "active_transcription"
                     } = data;
                     
                     renderLinesWithBuffer(
@@ -436,13 +437,19 @@
                         buffer_transcription, 
                         remaining_time_diarization,
                         remaining_time_transcription,
-                        false // isFinalizing = false for normal updates
+                        false,
+                        status
                     );
                 };
             });
         }
 
-        function renderLinesWithBuffer(lines, buffer_diarization, buffer_transcription, remaining_time_diarization, remaining_time_transcription, isFinalizing = false) {
+        function renderLinesWithBuffer(lines, buffer_diarization, buffer_transcription, remaining_time_diarization, remaining_time_transcription, isFinalizing = false, current_status = "active_transcription") {
+            if (current_status === "no_audio_detected") {
+                linesTranscriptDiv.innerHTML = "<p style='text-align: center; color: #666; margin-top: 20px;'><em>No audio detected...</em></p>";
+                return; 
+            }
+
             const linesHtml = lines.map((item, idx) => {
                 let timeInfo = "";
                 if (item.beg !== undefined && item.end !== undefined) {

whisperlivekit/web/web_interface.py

@@ -0,0 +1,13 @@
+import logging
+import importlib.resources as resources
+
+logger = logging.getLogger(__name__)
+
+def get_web_interface_html():
+    """Loads the HTML for the web interface using importlib.resources."""
+    try:
+        with resources.files('whisperlivekit.web').joinpath('live_transcription.html').open('r', encoding='utf-8') as f:
+            return f.read()
+    except Exception as e:
+        logger.error(f"Error loading web interface HTML: {e}")
+        return "<html><body><h1>Error loading interface</h1></body></html>"

whisperlivekit/whisper_streaming_custom/online_asr.py

@@ -144,7 +144,11 @@ class OnlineASRProcessor:
         self.transcript_buffer.last_committed_time = self.buffer_time_offset
         self.committed: List[ASRToken] = []
 
-    def insert_audio_chunk(self, audio: np.ndarray):
+    def get_audio_buffer_end_time(self) -> float:
+        """Returns the absolute end time of the current audio_buffer."""
+        return self.buffer_time_offset + (len(self.audio_buffer) / self.SAMPLING_RATE)
+
+    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: Optional[float] = None):
         """Append an audio chunk (a numpy array) to the current audio buffer."""
         self.audio_buffer = np.append(self.audio_buffer, audio)
 
@@ -179,18 +183,19 @@ class OnlineASRProcessor:
         return self.concatenate_tokens(self.transcript_buffer.buffer)
         
 
-    def process_iter(self) -> Transcript:
+    def process_iter(self) -> Tuple[List[ASRToken], float]:
         """
         Processes the current audio buffer.
 
-        Returns a Transcript object representing the committed transcript.
+        Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
         """
+        current_audio_processed_upto = self.get_audio_buffer_end_time()
         prompt_text, _ = self.prompt()
         logger.debug(
             f"Transcribing {len(self.audio_buffer)/self.SAMPLING_RATE:.2f} seconds from {self.buffer_time_offset:.2f}"
         )
         res = self.asr.transcribe(self.audio_buffer, init_prompt=prompt_text)
-        tokens = self.asr.ts_words(res)  # Expecting List[ASRToken]
+        tokens = self.asr.ts_words(res)
         self.transcript_buffer.insert(tokens, self.buffer_time_offset)
         committed_tokens = self.transcript_buffer.flush()
         self.committed.extend(committed_tokens)
@@ -210,7 +215,7 @@ class OnlineASRProcessor:
         logger.debug(
             f"Length of audio buffer now: {len(self.audio_buffer)/self.SAMPLING_RATE:.2f} seconds"
         )
-        return committed_tokens
+        return committed_tokens, current_audio_processed_upto
 
     def chunk_completed_sentence(self):
         """
@@ -343,15 +348,17 @@ class OnlineASRProcessor:
                 )
                 sentences.append(sentence)
         return sentences
-    def finish(self) -> Transcript:
+    
+    def finish(self) -> Tuple[List[ASRToken], float]:
         """
         Flush the remaining transcript when processing ends.
+        Returns a tuple: (list of remaining ASRToken objects, float representing the final audio processed up to time).
         """
         remaining_tokens = self.transcript_buffer.buffer
-        final_transcript = self.concatenate_tokens(remaining_tokens)
+        logger.debug(f"Final non-committed tokens: {remaining_tokens}")
-        logger.debug(f"Final non-committed transcript: {final_transcript}")
+        final_processed_upto = self.buffer_time_offset + (len(self.audio_buffer) / self.SAMPLING_RATE)
-        self.buffer_time_offset += len(self.audio_buffer) / self.SAMPLING_RATE
+        self.buffer_time_offset = final_processed_upto
-        return final_transcript
+        return remaining_tokens, final_processed_upto
 
     def concatenate_tokens(
         self,
@@ -384,7 +391,8 @@ class VACOnlineASRProcessor:
     def __init__(self, online_chunk_size: float, *args, **kwargs):
         self.online_chunk_size = online_chunk_size
         self.online = OnlineASRProcessor(*args, **kwargs)
-
+        self.asr = self.online.asr
+        
         # Load a VAD model (e.g. Silero VAD)
         import torch
         model, _ = torch.hub.load(repo_or_dir="snakers4/silero-vad", model="silero_vad")
@@ -392,28 +400,35 @@ class VACOnlineASRProcessor:
 
         self.vac = FixedVADIterator(model)
         self.logfile = self.online.logfile
+        self.last_input_audio_stream_end_time: float = 0.0
         self.init()
 
     def init(self):
         self.online.init()
         self.vac.reset_states()
         self.current_online_chunk_buffer_size = 0
+        self.last_input_audio_stream_end_time = self.online.buffer_time_offset
         self.is_currently_final = False
         self.status: Optional[str] = None  # "voice" or "nonvoice"
         self.audio_buffer = np.array([], dtype=np.float32)
         self.buffer_offset = 0  # in frames
 
+    def get_audio_buffer_end_time(self) -> float:
+        """Returns the absolute end time of the audio processed by the underlying OnlineASRProcessor."""
+        return self.online.get_audio_buffer_end_time()
+
     def clear_buffer(self):
         self.buffer_offset += len(self.audio_buffer)
         self.audio_buffer = np.array([], dtype=np.float32)
 
-    def insert_audio_chunk(self, audio: np.ndarray):
+    def insert_audio_chunk(self, audio: np.ndarray, audio_stream_end_time: float):
         """
         Process an incoming small audio chunk:
           - run VAD on the chunk,
           - decide whether to send the audio to the online ASR processor immediately,
           - and/or to mark the current utterance as finished.
         """
+        self.last_input_audio_stream_end_time = audio_stream_end_time
         res = self.vac(audio)
         self.audio_buffer = np.append(self.audio_buffer, audio)
 
@@ -455,10 +470,11 @@ class VACOnlineASRProcessor:
                 self.buffer_offset += max(0, len(self.audio_buffer) - self.SAMPLING_RATE)
                 self.audio_buffer = self.audio_buffer[-self.SAMPLING_RATE:]
 
-    def process_iter(self) -> Transcript:
+    def process_iter(self) -> Tuple[List[ASRToken], float]:
         """
         Depending on the VAD status and the amount of accumulated audio,
         process the current audio chunk.
+        Returns a tuple: (list of committed ASRToken objects, float representing the audio processed up to time).
         """
         if self.is_currently_final:
             return self.finish()
@@ -467,17 +483,20 @@ class VACOnlineASRProcessor:
             return self.online.process_iter()
         else:
             logger.debug("No online update, only VAD")
-            return Transcript(None, None, "")
+            return [], self.last_input_audio_stream_end_time
 
-    def finish(self) -> Transcript:
+    def finish(self) -> Tuple[List[ASRToken], float]:
-        """Finish processing by flushing any remaining text."""
+        """
-        result = self.online.finish()
+        Finish processing by flushing any remaining text.
+        Returns a tuple: (list of remaining ASRToken objects, float representing the final audio processed up to time).
+        """
+        result_tokens, processed_upto = self.online.finish()
         self.current_online_chunk_buffer_size = 0
         self.is_currently_final = False
-        return result
+        return result_tokens, processed_upto
     
     def get_buffer(self):
         """
         Get the unvalidated buffer in string format.
         """
-        return self.online.concatenate_tokens(self.online.transcript_buffer.buffer).text
+        return self.online.concatenate_tokens(self.online.transcript_buffer.buffer)