import gradio as gr import numpy as np import queue import torch import time import threading import os import urllib.request import torchaudio from scipy.spatial.distance import cosine from scipy.signal import resample from RealtimeSTT import AudioToTextRecorder from fastapi import FastAPI, APIRouter from fastrtc import Stream, AsyncStreamHandler import json import asyncio import uvicorn from queue import Queue import logging # Set up logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Simplified configuration parameters SILENCE_THRESHS = [0, 0.4] FINAL_TRANSCRIPTION_MODEL = "distil-large-v3" FINAL_BEAM_SIZE = 5 REALTIME_TRANSCRIPTION_MODEL = "distil-small.en" REALTIME_BEAM_SIZE = 5 TRANSCRIPTION_LANGUAGE = "en" SILERO_SENSITIVITY = 0.4 WEBRTC_SENSITIVITY = 3 MIN_LENGTH_OF_RECORDING = 0.7 PRE_RECORDING_BUFFER_DURATION = 0.35 # Speaker change detection parameters DEFAULT_CHANGE_THRESHOLD = 0.65 EMBEDDING_HISTORY_SIZE = 5 MIN_SEGMENT_DURATION = 1.5 DEFAULT_MAX_SPEAKERS = 4 ABSOLUTE_MAX_SPEAKERS = 8 # Global variables SAMPLE_RATE = 16000 BUFFER_SIZE = 1024 CHANNELS = 1 # Speaker colors - more distinguishable colors SPEAKER_COLORS = [ "#FF6B6B", # Red "#4ECDC4", # Teal "#45B7D1", # Blue "#96CEB4", # Green "#FFEAA7", # Yellow "#DDA0DD", # Plum "#98D8C8", # Mint "#F7DC6F", # Gold ] SPEAKER_COLOR_NAMES = [ "Red", "Teal", "Blue", "Green", "Yellow", "Plum", "Mint", "Gold" ] class SpeechBrainEncoder: """ECAPA-TDNN encoder from SpeechBrain for speaker embeddings""" def __init__(self, device="cpu"): self.device = device self.model = None self.embedding_dim = 192 self.model_loaded = False self.cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "speechbrain") os.makedirs(self.cache_dir, exist_ok=True) def load_model(self): """Load the ECAPA-TDNN model""" try: from speechbrain.pretrained import EncoderClassifier self.model = EncoderClassifier.from_hparams( source="speechbrain/spkrec-ecapa-voxceleb", savedir=self.cache_dir, run_opts={"device": self.device} ) self.model_loaded = True logger.info("ECAPA-TDNN model loaded successfully!") return True except Exception as e: logger.error(f"Error loading ECAPA-TDNN model: {e}") return False def embed_utterance(self, audio, sr=16000): """Extract speaker embedding from audio""" if not self.model_loaded: raise ValueError("Model not loaded. Call load_model() first.") try: if isinstance(audio, np.ndarray): # Ensure audio is float32 and properly normalized audio = audio.astype(np.float32) if np.max(np.abs(audio)) > 1.0: audio = audio / np.max(np.abs(audio)) waveform = torch.tensor(audio).unsqueeze(0) else: waveform = audio.unsqueeze(0) # Resample if necessary if sr != 16000: waveform = torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000) with torch.no_grad(): embedding = self.model.encode_batch(waveform) return embedding.squeeze().cpu().numpy() except Exception as e: logger.error(f"Error extracting embedding: {e}") return np.zeros(self.embedding_dim) class AudioProcessor: """Processes audio data to extract speaker embeddings""" def __init__(self, encoder): self.encoder = encoder self.audio_buffer = [] self.min_audio_length = int(SAMPLE_RATE * 1.0) # Minimum 1 second of audio def add_audio_chunk(self, audio_chunk): """Add audio chunk to buffer""" self.audio_buffer.extend(audio_chunk) # Keep buffer from getting too large max_buffer_size = int(SAMPLE_RATE * 10) # 10 seconds max if len(self.audio_buffer) > max_buffer_size: self.audio_buffer = self.audio_buffer[-max_buffer_size:] def extract_embedding_from_buffer(self): """Extract embedding from current audio buffer""" if len(self.audio_buffer) < self.min_audio_length: return None try: # Use the last portion of the buffer for embedding audio_segment = np.array(self.audio_buffer[-self.min_audio_length:], dtype=np.float32) # Normalize audio if np.max(np.abs(audio_segment)) > 0: audio_segment = audio_segment / np.max(np.abs(audio_segment)) else: return None embedding = self.encoder.embed_utterance(audio_segment) return embedding except Exception as e: logger.error(f"Embedding extraction error: {e}") return None class SpeakerChangeDetector: """Improved speaker change detector""" def __init__(self, embedding_dim=192, change_threshold=DEFAULT_CHANGE_THRESHOLD, max_speakers=DEFAULT_MAX_SPEAKERS): self.embedding_dim = embedding_dim self.change_threshold = change_threshold self.max_speakers = min(max_speakers, ABSOLUTE_MAX_SPEAKERS) self.current_speaker = 0 self.speaker_embeddings = [[] for _ in range(self.max_speakers)] self.speaker_centroids = [None] * self.max_speakers self.last_change_time = time.time() self.last_similarity = 1.0 self.active_speakers = set([0]) self.segment_counter = 0 def set_max_speakers(self, max_speakers): """Update the maximum number of speakers""" new_max = min(max_speakers, ABSOLUTE_MAX_SPEAKERS) if new_max < self.max_speakers: # Remove speakers beyond the new limit for speaker_id in list(self.active_speakers): if speaker_id >= new_max: self.active_speakers.discard(speaker_id) if self.current_speaker >= new_max: self.current_speaker = 0 # Resize arrays if new_max > self.max_speakers: self.speaker_embeddings.extend([[] for _ in range(new_max - self.max_speakers)]) self.speaker_centroids.extend([None] * (new_max - self.max_speakers)) else: self.speaker_embeddings = self.speaker_embeddings[:new_max] self.speaker_centroids = self.speaker_centroids[:new_max] self.max_speakers = new_max def set_change_threshold(self, threshold): """Update the threshold for detecting speaker changes""" self.change_threshold = max(0.1, min(threshold, 0.95)) def add_embedding(self, embedding, timestamp=None): """Add a new embedding and detect speaker changes""" current_time = timestamp or time.time() self.segment_counter += 1 # Initialize first speaker if not self.speaker_embeddings[0]: self.speaker_embeddings[0].append(embedding) self.speaker_centroids[0] = embedding.copy() self.active_speakers.add(0) return 0, 1.0 # Calculate similarity with current speaker current_centroid = self.speaker_centroids[self.current_speaker] if current_centroid is not None: similarity = 1.0 - cosine(embedding, current_centroid) else: similarity = 0.5 self.last_similarity = similarity # Check for speaker change time_since_last_change = current_time - self.last_change_time speaker_changed = False if time_since_last_change >= MIN_SEGMENT_DURATION and similarity < self.change_threshold: # Find best matching speaker best_speaker = self.current_speaker best_similarity = similarity for speaker_id in self.active_speakers: if speaker_id == self.current_speaker: continue centroid = self.speaker_centroids[speaker_id] if centroid is not None: speaker_similarity = 1.0 - cosine(embedding, centroid) if speaker_similarity > best_similarity and speaker_similarity > self.change_threshold: best_similarity = speaker_similarity best_speaker = speaker_id # If no good match found and we can add a new speaker if best_speaker == self.current_speaker and len(self.active_speakers) < self.max_speakers: for new_id in range(self.max_speakers): if new_id not in self.active_speakers: best_speaker = new_id self.active_speakers.add(new_id) break if best_speaker != self.current_speaker: self.current_speaker = best_speaker self.last_change_time = current_time speaker_changed = True # Update speaker embeddings and centroids self.speaker_embeddings[self.current_speaker].append(embedding) # Keep only recent embeddings (sliding window) max_embeddings = 20 if len(self.speaker_embeddings[self.current_speaker]) > max_embeddings: self.speaker_embeddings[self.current_speaker] = self.speaker_embeddings[self.current_speaker][-max_embeddings:] # Update centroid if self.speaker_embeddings[self.current_speaker]: self.speaker_centroids[self.current_speaker] = np.mean( self.speaker_embeddings[self.current_speaker], axis=0 ) return self.current_speaker, similarity def get_color_for_speaker(self, speaker_id): """Return color for speaker ID""" if 0 <= speaker_id < len(SPEAKER_COLORS): return SPEAKER_COLORS[speaker_id] return "#FFFFFF" def get_status_info(self): """Return status information""" speaker_counts = [len(self.speaker_embeddings[i]) for i in range(self.max_speakers)] return { "current_speaker": self.current_speaker, "speaker_counts": speaker_counts, "active_speakers": len(self.active_speakers), "max_speakers": self.max_speakers, "last_similarity": self.last_similarity, "threshold": self.change_threshold, "segment_counter": self.segment_counter } class RealtimeSpeakerDiarization: def __init__(self): self.encoder = None self.audio_processor = None self.speaker_detector = None self.recorder = None self.sentence_queue = queue.Queue() self.full_sentences = [] self.sentence_speakers = [] self.pending_sentences = [] self.current_conversation = "" self.is_running = False self.change_threshold = DEFAULT_CHANGE_THRESHOLD self.max_speakers = DEFAULT_MAX_SPEAKERS self.last_transcription = "" self.transcription_lock = threading.Lock() def initialize_models(self): """Initialize the speaker encoder model""" try: device_str = "cuda" if torch.cuda.is_available() else "cpu" logger.info(f"Using device: {device_str}") self.encoder = SpeechBrainEncoder(device=device_str) success = self.encoder.load_model() if success: self.audio_processor = AudioProcessor(self.encoder) self.speaker_detector = SpeakerChangeDetector( embedding_dim=self.encoder.embedding_dim, change_threshold=self.change_threshold, max_speakers=self.max_speakers ) logger.info("Models initialized successfully!") return True else: logger.error("Failed to load models") return False except Exception as e: logger.error(f"Model initialization error: {e}") return False def live_text_detected(self, text): """Callback for real-time transcription updates""" with self.transcription_lock: self.last_transcription = text.strip() def process_final_text(self, text): """Process final transcribed text with speaker embedding""" text = text.strip() if text: try: # Get audio data for this transcription audio_bytes = getattr(self.recorder, 'last_transcription_bytes', None) if audio_bytes: self.sentence_queue.put((text, audio_bytes)) else: # If no audio bytes, use current speaker self.sentence_queue.put((text, None)) except Exception as e: logger.error(f"Error processing final text: {e}") def process_sentence_queue(self): """Process sentences in the queue for speaker detection""" while self.is_running: try: text, audio_bytes = self.sentence_queue.get(timeout=1) current_speaker = self.speaker_detector.current_speaker if audio_bytes: # Convert audio data and extract embedding audio_int16 = np.frombuffer(audio_bytes, dtype=np.int16) audio_float = audio_int16.astype(np.float32) / 32768.0 # Extract embedding embedding = self.audio_processor.encoder.embed_utterance(audio_float) if embedding is not None: current_speaker, similarity = self.speaker_detector.add_embedding(embedding) # Store sentence with speaker with self.transcription_lock: self.full_sentences.append((text, current_speaker)) self.update_conversation_display() except queue.Empty: continue except Exception as e: logger.error(f"Error processing sentence: {e}") def update_conversation_display(self): """Update the conversation display""" try: sentences_with_style = [] for sentence_text, speaker_id in self.full_sentences: color = self.speaker_detector.get_color_for_speaker(speaker_id) speaker_name = f"Speaker {speaker_id + 1}" sentences_with_style.append( f'{speaker_name}: ' f'{sentence_text}' ) # Add current transcription if available if self.last_transcription: current_color = self.speaker_detector.get_color_for_speaker(self.speaker_detector.current_speaker) current_speaker = f"Speaker {self.speaker_detector.current_speaker + 1}" sentences_with_style.append( f'{current_speaker}: ' f'{self.last_transcription}...' ) if sentences_with_style: self.current_conversation = "

".join(sentences_with_style) else: self.current_conversation = "Waiting for speech input..." except Exception as e: logger.error(f"Error updating conversation display: {e}") self.current_conversation = f"Error: {str(e)}" def start_recording(self): """Start the recording and transcription process""" if self.encoder is None: return "Please initialize models first!" try: # Setup recorder configuration recorder_config = { 'spinner': False, 'use_microphone': False, # Using FastRTC for audio input 'model': FINAL_TRANSCRIPTION_MODEL, 'language': TRANSCRIPTION_LANGUAGE, 'silero_sensitivity': SILERO_SENSITIVITY, 'webrtc_sensitivity': WEBRTC_SENSITIVITY, 'post_speech_silence_duration': SILENCE_THRESHS[1], 'min_length_of_recording': MIN_LENGTH_OF_RECORDING, 'pre_recording_buffer_duration': PRE_RECORDING_BUFFER_DURATION, 'min_gap_between_recordings': 0, 'enable_realtime_transcription': True, 'realtime_processing_pause': 0.1, 'realtime_model_type': REALTIME_TRANSCRIPTION_MODEL, 'on_realtime_transcription_update': self.live_text_detected, 'beam_size': FINAL_BEAM_SIZE, 'beam_size_realtime': REALTIME_BEAM_SIZE, 'sample_rate': SAMPLE_RATE, } self.recorder = AudioToTextRecorder(**recorder_config) # Start processing threads self.is_running = True self.sentence_thread = threading.Thread(target=self.process_sentence_queue, daemon=True) self.sentence_thread.start() self.transcription_thread = threading.Thread(target=self.run_transcription, daemon=True) self.transcription_thread.start() return "Recording started successfully!" except Exception as e: logger.error(f"Error starting recording: {e}") return f"Error starting recording: {e}" def run_transcription(self): """Run the transcription loop""" try: logger.info("Starting transcription thread") while self.is_running: # Just check for final text from recorder, audio is fed externally via FastRTC text = self.recorder.text(self.process_final_text) time.sleep(0.01) # Small sleep to prevent CPU hogging except Exception as e: logger.error(f"Transcription error: {e}") def stop_recording(self): """Stop the recording process""" self.is_running = False if self.recorder: self.recorder.stop() return "Recording stopped!" def clear_conversation(self): """Clear all conversation data""" with self.transcription_lock: self.full_sentences = [] self.last_transcription = "" self.current_conversation = "Conversation cleared!" if self.speaker_detector: self.speaker_detector = SpeakerChangeDetector( embedding_dim=self.encoder.embedding_dim, change_threshold=self.change_threshold, max_speakers=self.max_speakers ) return "Conversation cleared!" def update_settings(self, threshold, max_speakers): """Update speaker detection settings""" self.change_threshold = threshold self.max_speakers = max_speakers if self.speaker_detector: self.speaker_detector.set_change_threshold(threshold) self.speaker_detector.set_max_speakers(max_speakers) return f"Settings updated: Threshold={threshold:.2f}, Max Speakers={max_speakers}" def get_formatted_conversation(self): """Get the formatted conversation""" return self.current_conversation def get_status_info(self): """Get current status information""" if not self.speaker_detector: return "Speaker detector not initialized" try: status = self.speaker_detector.get_status_info() status_lines = [ f"**Current Speaker:** {status['current_speaker'] + 1}", f"**Active Speakers:** {status['active_speakers']} of {status['max_speakers']}", f"**Last Similarity:** {status['last_similarity']:.3f}", f"**Change Threshold:** {status['threshold']:.2f}", f"**Total Sentences:** {len(self.full_sentences)}", f"**Segments Processed:** {status['segment_counter']}", "", "**Speaker Activity:**" ] for i in range(status['max_speakers']): color_name = SPEAKER_COLOR_NAMES[i] if i < len(SPEAKER_COLOR_NAMES) else f"Speaker {i+1}" count = status['speaker_counts'][i] active = "đŸŸĸ" if count > 0 else "âšĢ" status_lines.append(f"{active} Speaker {i+1} ({color_name}): {count} segments") return "\n".join(status_lines) except Exception as e: return f"Error getting status: {e}" def process_audio_chunk(self, audio_data, sample_rate=16000): """Process audio chunk from FastRTC input""" if not self.is_running or self.audio_processor is None: return try: # Ensure audio is float32 if isinstance(audio_data, np.ndarray): if audio_data.dtype != np.float32: audio_data = audio_data.astype(np.float32) else: audio_data = np.array(audio_data, dtype=np.float32) # Ensure mono if len(audio_data.shape) > 1: audio_data = np.mean(audio_data, axis=1) if audio_data.shape[1] > 1 else audio_data.flatten() # Normalize if needed if np.max(np.abs(audio_data)) > 1.0: audio_data = audio_data / np.max(np.abs(audio_data)) # Add to audio processor buffer for speaker detection self.audio_processor.add_audio_chunk(audio_data) # Periodically extract embeddings for speaker detection if len(self.audio_processor.audio_buffer) % (SAMPLE_RATE // 2) == 0: # Every 0.5 seconds embedding = self.audio_processor.extract_embedding_from_buffer() if embedding is not None: self.speaker_detector.add_embedding(embedding) # Feed audio to RealtimeSTT recorder if self.recorder and self.is_running: # Convert float32 [-1.0, 1.0] to int16 for RealtimeSTT int16_data = (audio_data * 32768.0).astype(np.int16).tobytes() if sample_rate != 16000: int16_data = self.resample_audio(int16_data, sample_rate, 16000) self.recorder.feed_audio(int16_data) except Exception as e: logger.error(f"Error processing audio chunk: {e}") def resample_audio(self, audio_bytes, from_rate, to_rate): """Resample audio to target sample rate""" try: audio_np = np.frombuffer(audio_bytes, dtype=np.int16) num_samples = len(audio_np) num_target_samples = int(num_samples * to_rate / from_rate) resampled = resample(audio_np, num_target_samples) return resampled.astype(np.int16).tobytes() except Exception as e: logger.error(f"Error resampling audio: {e}") return audio_bytes # FastRTC Audio Handler class DiarizationHandler(AsyncStreamHandler): def __init__(self, diarization_system): super().__init__() self.diarization_system = diarization_system self.audio_buffer = [] self.buffer_size = BUFFER_SIZE def copy(self): """Return a fresh handler for each new stream connection""" return DiarizationHandler(self.diarization_system) async def emit(self): """Not used - we only receive audio""" return None async def receive(self, frame): """Receive audio data from FastRTC""" try: if not self.diarization_system.is_running: return # Extract audio data audio_data = getattr(frame, 'data', frame) # Convert to numpy array if isinstance(audio_data, bytes): audio_array = np.frombuffer(audio_data, dtype=np.int16).astype(np.float32) / 32768.0 elif isinstance(audio_data, (list, tuple)): sample_rate, audio_array = audio_data if isinstance(audio_array, (list, tuple)): audio_array = np.array(audio_array, dtype=np.float32) else: audio_array = np.array(audio_data, dtype=np.float32) # Ensure 1D if len(audio_array.shape) > 1: audio_array = audio_array.flatten() # Buffer audio chunks self.audio_buffer.extend(audio_array) # Process in chunks while len(self.audio_buffer) >= self.buffer_size: chunk = np.array(self.audio_buffer[:self.buffer_size]) self.audio_buffer = self.audio_buffer[self.buffer_size:] # Process asynchronously await self.process_audio_async(chunk) except Exception as e: logger.error(f"Error in FastRTC receive: {e}") async def process_audio_async(self, audio_data): """Process audio data asynchronously""" try: loop = asyncio.get_event_loop() await loop.run_in_executor( None, self.diarization_system.process_audio_chunk, audio_data, SAMPLE_RATE ) except Exception as e: logger.error(f"Error in async audio processing: {e}") # Global instances diarization_system = RealtimeSpeakerDiarization() audio_handler = None def initialize_system(): """Initialize the diarization system""" global audio_handler try: success = diarization_system.initialize_models() if success: audio_handler = DiarizationHandler(diarization_system) return "✅ System initialized successfully!" else: return "❌ Failed to initialize system. Check logs for details." except Exception as e: logger.error(f"Initialization error: {e}") return f"❌ Initialization error: {str(e)}" def start_recording(): """Start recording and transcription""" try: result = diarization_system.start_recording() return f"🎙ī¸ {result}" except Exception as e: return f"❌ Failed to start recording: {str(e)}" def stop_recording(): """Stop recording and transcription""" try: result = diarization_system.stop_recording() return f"⏚ī¸ {result}" except Exception as e: return f"❌ Failed to stop recording: {str(e)}" def clear_conversation(): """Clear the conversation""" try: result = diarization_system.clear_conversation() return f"🗑ī¸ {result}" except Exception as e: return f"❌ Failed to clear conversation: {str(e)}" def update_settings(threshold, max_speakers): """Update system settings""" try: result = diarization_system.update_settings(threshold, max_speakers) return f"⚙ī¸ {result}" except Exception as e: return f"❌ Failed to update settings: {str(e)}" def get_conversation(): """Get the current conversation""" try: return diarization_system.get_formatted_conversation() except Exception as e: return f"Error getting conversation: {str(e)}" def get_status(): """Get system status""" try: return diarization_system.get_status_info() except Exception as e: return f"Error getting status: {str(e)}" # Create Gradio interface def create_interface(): with gr.Blocks(title="Real-time Speaker Diarization", theme=gr.themes.Soft()) as interface: gr.Markdown("# 🎤 Real-time Speech Recognition with Speaker Diarization") gr.Markdown("Live transcription with automatic speaker identification using FastRTC audio streaming.") with gr.Row(): with gr.Column(scale=2): # Conversation display conversation_output = gr.HTML( value="
Click 'Initialize System' to start...
", label="Live Conversation" ) # Control buttons with gr.Row(): init_btn = gr.Button("🔧 Initialize System", variant="secondary", size="lg") start_btn = gr.Button("🎙ī¸ Start", variant="primary", size="lg", interactive=False) stop_btn = gr.Button("⏚ī¸ Stop", variant="stop", size="lg", interactive=False) clear_btn = gr.Button("🗑ī¸ Clear", variant="secondary", size="lg", interactive=False) # Status display status_output = gr.Textbox( label="System Status", value="Ready to initialize...", lines=8, interactive=False ) with gr.Column(scale=1): # Settings gr.Markdown("## ⚙ī¸ Settings") threshold_slider = gr.Slider( minimum=0.3, maximum=0.9, step=0.05, value=DEFAULT_CHANGE_THRESHOLD, label="Speaker Change Sensitivity", info="Lower = more sensitive" ) max_speakers_slider = gr.Slider( minimum=2, maximum=ABSOLUTE_MAX_SPEAKERS, step=1, value=DEFAULT_MAX_SPEAKERS, label="Maximum Speakers" ) update_btn = gr.Button("Update Settings", variant="secondary") # Instructions gr.Markdown(""" ## 📋 Instructions 1. **Initialize** the system (loads AI models) 2. **Start** recording 3. **Speak** - system will transcribe and identify speakers 4. **Monitor** real-time results below ## 🎨 Speaker Colors - 🔴 Speaker 1 (Red) - đŸŸĸ Speaker 2 (Teal) - đŸ”ĩ Speaker 3 (Blue) - 🟡 Speaker 4 (Green) - đŸŸŖ Speaker 5 (Yellow) - 🟤 Speaker 6 (Plum) - đŸŸĢ Speaker 7 (Mint) - 🟨 Speaker 8 (Gold) """) # Event handlers def on_initialize(): result = initialize_system() if "✅" in result: return result, gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True) else: return result, gr.update(interactive=False), gr.update(interactive=False), gr.update(interactive=False) def on_start(): result = start_recording() return result, gr.update(interactive=False), gr.update(interactive=True) def on_stop(): result = stop_recording() return result, gr.update(interactive=True), gr.update(interactive=False) def on_clear(): result = clear_conversation() return result def on_update_settings(threshold, max_speakers): result = update_settings(threshold, int(max_speakers)) return result def refresh_conversation(): return get_conversation() def refresh_status(): return get_status() # Button click handlers init_btn.click( fn=on_initialize, outputs=[status_output, start_btn, stop_btn, clear_btn] ) start_btn.click( fn=on_start, outputs=[status_output, start_btn, stop_btn] ) stop_btn.click( fn=on_stop, outputs=[status_output, start_btn, stop_btn] ) clear_btn.click( fn=on_clear, outputs=[status_output] ) update_btn.click( fn=on_update_settings, inputs=[threshold_slider, max_speakers_slider], outputs=[status_output] ) # Auto-refresh conversation display every 1 second conversation_timer = gr.Timer(1) conversation_timer.tick(refresh_conversation, outputs=[conversation_output]) # Auto-refresh status every 2 seconds status_timer = gr.Timer(2) status_timer.tick(refresh_status, outputs=[status_output]) return interface # FastAPI setup for FastRTC integration app = FastAPI() @app.get("/") async def root(): return {"message": "Real-time Speaker Diarization API"} @app.get("/health") async def health_check(): return {"status": "healthy", "system_running": diarization_system.is_running} @app.post("/initialize") async def api_initialize(): result = initialize_system() return {"result": result, "success": "✅" in result} @app.post("/start") async def api_start(): result = start_recording() return {"result": result, "success": "🎙ī¸" in result} @app.post("/stop") async def api_stop(): result = stop_recording() return {"result": result, "success": "⏚ī¸" in result} @app.post("/clear") async def api_clear(): result = clear_conversation() return {"result": result} @app.get("/conversation") async def api_get_conversation(): return {"conversation": get_conversation()} @app.get("/status") async def api_get_status(): return {"status": get_status()} @app.post("/settings") async def api_update_settings(threshold: float, max_speakers: int): result = update_settings(threshold, max_speakers) return {"result": result} # FastRTC Stream setup if audio_handler: stream = Stream(handler=audio_handler) app.include_router(stream.router, prefix="/stream") # Main execution if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Real-time Speaker Diarization System") parser.add_argument("--mode", choices=["gradio", "api", "both"], default="gradio", help="Run mode: gradio interface, API only, or both") parser.add_argument("--host", default="0.0.0.0", help="Host to bind to") parser.add_argument("--port", type=int, default=7860, help="Port to bind to") parser.add_argument("--api-port", type=int, default=8000, help="API port (when running both)") args = parser.parse_args() if args.mode == "gradio": # Run Gradio interface only interface = create_interface() interface.launch( server_name=args.host, server_port=args.port, share=True, show_error=True ) elif args.mode == "api": # Run FastAPI only uvicorn.run( app, host=args.host, port=args.port, log_level="info" ) elif args.mode == "both": # Run both Gradio and FastAPI import multiprocessing import threading def run_gradio(): interface = create_interface() interface.launch( server_name=args.host, server_port=args.port, share=True, show_error=True ) def run_fastapi(): uvicorn.run( app, host=args.host, port=args.api_port, log_level="info" ) # Start FastAPI in a separate thread api_thread = threading.Thread(target=run_fastapi, daemon=True) api_thread.start() # Start Gradio in main thread run_gradio()