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 RealtimeSTT import AudioToTextRecorder from fastapi import FastAPI, APIRouter from fastrtc import Stream, AsyncStreamHandler, ReplyOnPause, get_cloudflare_turn_credentials_async, get_cloudflare_turn_credentials import json import io import wave import asyncio import uvicorn import socket from queue import Queue import time # 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 = 0.7 EMBEDDING_HISTORY_SIZE = 5 MIN_SEGMENT_DURATION = 1.0 DEFAULT_MAX_SPEAKERS = 4 ABSOLUTE_MAX_SPEAKERS = 10 # Global variables FAST_SENTENCE_END = True SAMPLE_RATE = 16000 BUFFER_SIZE = 512 CHANNELS = 1 # Speaker colors SPEAKER_COLORS = [ "#FFFF00", # Yellow "#FF0000", # Red "#00FF00", # Green "#00FFFF", # Cyan "#FF00FF", # Magenta "#0000FF", # Blue "#FF8000", # Orange "#00FF80", # Spring Green "#8000FF", # Purple "#FFFFFF", # White ] SPEAKER_COLOR_NAMES = [ "Yellow", "Red", "Green", "Cyan", "Magenta", "Blue", "Orange", "Spring Green", "Purple", "White" ] 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 _download_model(self): """Download pre-trained SpeechBrain ECAPA-TDNN model if not present""" model_url = "https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb/resolve/main/embedding_model.ckpt" model_path = os.path.join(self.cache_dir, "embedding_model.ckpt") if not os.path.exists(model_path): print(f"Downloading ECAPA-TDNN model to {model_path}...") urllib.request.urlretrieve(model_url, model_path) return model_path def load_model(self): """Load the ECAPA-TDNN model""" try: from speechbrain.pretrained import EncoderClassifier model_path = self._download_model() self.model = EncoderClassifier.from_hparams( source="speechbrain/spkrec-ecapa-voxceleb", savedir=self.cache_dir, run_opts={"device": self.device} ) self.model_loaded = True return True except Exception as e: print(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): waveform = torch.tensor(audio, dtype=torch.float32).unsqueeze(0) else: waveform = audio.unsqueeze(0) 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: print(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 def extract_embedding(self, audio_int16): try: float_audio = audio_int16.astype(np.float32) / 32768.0 if np.abs(float_audio).max() > 1.0: float_audio = float_audio / np.abs(float_audio).max() embedding = self.encoder.embed_utterance(float_audio) return embedding except Exception as e: print(f"Embedding extraction error: {e}") return np.zeros(self.encoder.embedding_dim) class SpeakerChangeDetector: """Speaker change detector that supports a configurable number of speakers""" 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.previous_embeddings = [] self.last_change_time = time.time() self.mean_embeddings = [None] * self.max_speakers self.speaker_embeddings = [[] for _ in range(self.max_speakers)] self.last_similarity = 0.0 self.active_speakers = set([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: 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 if new_max > self.max_speakers: self.mean_embeddings.extend([None] * (new_max - self.max_speakers)) self.speaker_embeddings.extend([[] for _ in range(new_max - self.max_speakers)]) else: self.mean_embeddings = self.mean_embeddings[:new_max] self.speaker_embeddings = self.speaker_embeddings[: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, 0.99)) def add_embedding(self, embedding, timestamp=None): """Add a new embedding and check if there's a speaker change""" current_time = timestamp or time.time() if not self.previous_embeddings: self.previous_embeddings.append(embedding) self.speaker_embeddings[self.current_speaker].append(embedding) if self.mean_embeddings[self.current_speaker] is None: self.mean_embeddings[self.current_speaker] = embedding.copy() return self.current_speaker, 1.0 current_mean = self.mean_embeddings[self.current_speaker] if current_mean is not None: similarity = 1.0 - cosine(embedding, current_mean) else: similarity = 1.0 - cosine(embedding, self.previous_embeddings[-1]) self.last_similarity = similarity time_since_last_change = current_time - self.last_change_time is_speaker_change = False if time_since_last_change >= MIN_SEGMENT_DURATION: if similarity < self.change_threshold: best_speaker = self.current_speaker best_similarity = similarity for speaker_id in range(self.max_speakers): if speaker_id == self.current_speaker: continue speaker_mean = self.mean_embeddings[speaker_id] if speaker_mean is not None: speaker_similarity = 1.0 - cosine(embedding, speaker_mean) if speaker_similarity > best_similarity: best_similarity = speaker_similarity best_speaker = speaker_id if best_speaker != self.current_speaker: is_speaker_change = True self.current_speaker = best_speaker elif len(self.active_speakers) < self.max_speakers: for new_id in range(self.max_speakers): if new_id not in self.active_speakers: is_speaker_change = True self.current_speaker = new_id self.active_speakers.add(new_id) break if is_speaker_change: self.last_change_time = current_time self.previous_embeddings.append(embedding) if len(self.previous_embeddings) > EMBEDDING_HISTORY_SIZE: self.previous_embeddings.pop(0) self.speaker_embeddings[self.current_speaker].append(embedding) self.active_speakers.add(self.current_speaker) if len(self.speaker_embeddings[self.current_speaker]) > 30: self.speaker_embeddings[self.current_speaker] = self.speaker_embeddings[self.current_speaker][-30:] if self.speaker_embeddings[self.current_speaker]: self.mean_embeddings[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 about the speaker change detector""" 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 } 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.displayed_text = "" self.last_realtime_text = "" self.is_running = False self.change_threshold = DEFAULT_CHANGE_THRESHOLD self.max_speakers = DEFAULT_MAX_SPEAKERS self.current_conversation = "" self.audio_buffer = [] def initialize_models(self): """Initialize the speaker encoder model""" try: device_str = "cuda" if torch.cuda.is_available() else "cpu" print(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 ) print("ECAPA-TDNN model loaded successfully!") return True else: print("Failed to load ECAPA-TDNN model") return False except Exception as e: print(f"Model initialization error: {e}") return False def live_text_detected(self, text): """Callback for real-time transcription updates""" text = text.strip() if text: sentence_delimiters = '.?!。' prob_sentence_end = ( len(self.last_realtime_text) > 0 and text[-1] in sentence_delimiters and self.last_realtime_text[-1] in sentence_delimiters ) self.last_realtime_text = text if prob_sentence_end and FAST_SENTENCE_END: self.recorder.stop() elif prob_sentence_end: self.recorder.post_speech_silence_duration = SILENCE_THRESHS[0] else: self.recorder.post_speech_silence_duration = SILENCE_THRESHS[1] def process_final_text(self, text): """Process final transcribed text with speaker embedding""" text = text.strip() if text: try: bytes_data = self.recorder.last_transcription_bytes self.sentence_queue.put((text, bytes_data)) self.pending_sentences.append(text) except Exception as e: print(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, bytes_data = self.sentence_queue.get(timeout=1) # Convert audio data to int16 audio_int16 = np.frombuffer(bytes_data, dtype=np.int16) # Extract speaker embedding speaker_embedding = self.audio_processor.extract_embedding(audio_int16) # Store sentence and embedding self.full_sentences.append((text, speaker_embedding)) # Fill in missing speaker assignments while len(self.sentence_speakers) < len(self.full_sentences) - 1: self.sentence_speakers.append(0) # Detect speaker changes speaker_id, similarity = self.speaker_detector.add_embedding(speaker_embedding) self.sentence_speakers.append(speaker_id) # Remove from pending if text in self.pending_sentences: self.pending_sentences.remove(text) # Update conversation display self.current_conversation = self.get_formatted_conversation() except queue.Empty: continue except Exception as e: print(f"Error processing sentence: {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 for manual audio input recorder_config = { 'spinner': False, 'use_microphone': False, # We'll feed audio manually '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, '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, 'buffer_size': BUFFER_SIZE, 'sample_rate': SAMPLE_RATE, } self.recorder = AudioToTextRecorder(**recorder_config) # Start sentence processing thread self.is_running = True self.sentence_thread = threading.Thread(target=self.process_sentence_queue, daemon=True) self.sentence_thread.start() # Start transcription thread self.transcription_thread = threading.Thread(target=self.run_transcription, daemon=True) self.transcription_thread.start() return "Recording started successfully! FastRTC audio input ready." except Exception as e: return f"Error starting recording: {e}" def run_transcription(self): """Run the transcription loop""" try: while self.is_running: self.recorder.text(self.process_final_text) except Exception as e: print(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""" self.full_sentences = [] self.sentence_speakers = [] self.pending_sentences = [] self.displayed_text = "" self.last_realtime_text = "" 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 with speaker colors""" try: sentences_with_style = [] # Process completed sentences for i, sentence in enumerate(self.full_sentences): sentence_text, _ = sentence if i >= len(self.sentence_speakers): color = "#FFFFFF" speaker_name = "Unknown" else: speaker_id = self.sentence_speakers[i] color = self.speaker_detector.get_color_for_speaker(speaker_id) speaker_name = f"Speaker {speaker_id + 1}" sentences_with_style.append( f'{speaker_name}: {sentence_text}') # Add pending sentences for pending_sentence in self.pending_sentences: sentences_with_style.append( f'Processing: {pending_sentence}') if sentences_with_style: return "

".join(sentences_with_style) else: return "Waiting for speech input..." except Exception as e: return f"Error formatting conversation: {e}" 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)}", "", "**Speaker Segment Counts:**" ] for i in range(status['max_speakers']): color_name = SPEAKER_COLOR_NAMES[i] if i < len(SPEAKER_COLOR_NAMES) else f"Speaker {i+1}" status_lines.append(f"Speaker {i+1} ({color_name}): {status['speaker_counts'][i]}") return "\n".join(status_lines) except Exception as e: return f"Error getting status: {e}" def feed_audio_data(self, audio_data): """Feed audio data to the recorder""" if not self.is_running or not self.recorder: return try: # Ensure audio is in the correct format (16-bit PCM) if isinstance(audio_data, np.ndarray): if audio_data.dtype != np.int16: # Convert float to int16 if audio_data.dtype == np.float32 or audio_data.dtype == np.float64: audio_data = (audio_data * 32767).astype(np.int16) else: audio_data = audio_data.astype(np.int16) # Convert to bytes audio_bytes = audio_data.tobytes() else: audio_bytes = audio_data # Feed to recorder self.recorder.feed_audio(audio_bytes) except Exception as e: print(f"Error feeding audio data: {e}") # FastRTC Audio Handler for Real-time Diarization class DiarizationHandler(AsyncStreamHandler): def __init__(self, diarization_system): super().__init__() self.diarization_system = diarization_system self.audio_queue = Queue() self.is_processing = False self.sample_rate = 16000 # Default sample rate def copy(self): """Return a fresh handler for each new stream connection""" return DiarizationHandler(self.diarization_system) async def emit(self): """Not used in this implementation - we only receive audio""" return None async def receive(self, frame): """Receive audio data from FastRTC and process it""" try: if not self.diarization_system.is_running: return # Extract audio data from frame if hasattr(frame, 'data') and frame.data is not None: audio_data = frame.data elif hasattr(frame, 'audio') and frame.audio is not None: audio_data = frame.audio else: audio_data = frame # Convert to numpy array if needed if isinstance(audio_data, bytes): # Convert bytes to numpy array (assuming 16-bit PCM) audio_array = np.frombuffer(audio_data, dtype=np.int16) # Normalize to float32 range [-1, 1] audio_array = audio_array.astype(np.float32) / 32768.0 elif isinstance(audio_data, (list, tuple)): audio_array = np.array(audio_data, dtype=np.float32) elif isinstance(audio_data, np.ndarray): audio_array = audio_data.astype(np.float32) else: print(f"Unknown audio data type: {type(audio_data)}") return # Ensure mono audio if len(audio_array.shape) > 1 and audio_array.shape[1] > 1: audio_array = np.mean(audio_array, axis=1) # Ensure 1D array if len(audio_array.shape) > 1: audio_array = audio_array.flatten() # Get sample rate from frame if available sample_rate = getattr(frame, 'sample_rate', self.sample_rate) # Process audio asynchronously to avoid blocking await self.process_audio_async(audio_array, sample_rate) except Exception as e: print(f"Error in FastRTC audio receive: {e}") import traceback traceback.print_exc() async def process_audio_async(self, audio_data, sample_rate=16000): """Process audio data asynchronously""" try: # Run the audio processing in a thread pool to avoid blocking 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: print(f"Error in async audio processing: {e}") # Global instances diarization_system = None # Will be initialized when RealtimeSpeakerDiarization is available audio_handler = None def initialize_system(): """Initialize the diarization system""" global audio_handler, diarization_system try: if diarization_system is None: print("Error: RealtimeSpeakerDiarization not initialized") return "❌ Diarization system not available. Please ensure RealtimeSpeakerDiarization is properly imported." success = diarization_system.initialize_models() if success: audio_handler = DiarizationHandler(diarization_system) return "✅ System initialized successfully! Models loaded and FastRTC handler ready." else: return "❌ Failed to initialize system. Please check the logs." except Exception as e: print(f"Initialization error: {e}") return f"❌ Initialization error: {str(e)}" def start_recording(): """Start recording and transcription""" try: if diarization_system is None: return "❌ System not initialized" result = diarization_system.start_recording() return f"🎙️ {result} - FastRTC audio streaming is active." except Exception as e: return f"❌ Failed to start recording: {str(e)}" def stop_recording(): """Stop recording and transcription""" try: if diarization_system is None: return "❌ System not initialized" result = diarization_system.stop_recording() return f"⏹️ {result}" except Exception as e: return f"❌ Failed to stop recording: {str(e)}" def clear_conversation(): """Clear the conversation""" try: if diarization_system is None: return "❌ System not initialized" result = diarization_system.clear_conversation() return f"🗑️ {result}" except Exception as e: return f"❌ Failed to clear conversation: {str(e)}" def update_settings(threshold, max_speakers): """Update system settings""" try: if diarization_system is None: return "❌ System not initialized" result = diarization_system.update_settings(threshold, max_speakers) return f"⚙️ {result}" except Exception as e: return f"❌ Failed to update settings: {str(e)}" def get_conversation(): """Get the current conversation""" try: if diarization_system is None: return "System not initialized" return diarization_system.get_formatted_conversation() except Exception as e: return f"Error getting conversation: {str(e)}" def get_status(): """Get system status""" try: if diarization_system is None: return "System not initialized" 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("This app performs real-time speech recognition with automatic speaker identification using FastRTC for low-latency audio streaming.") with gr.Row(): with gr.Column(scale=2): # Main conversation display conversation_output = gr.HTML( value="
Click 'Initialize System' to start...
", label="Live Conversation", elem_id="conversation_display" ) # Control buttons with gr.Row(): init_btn = gr.Button("🔧 Initialize System", variant="secondary", size="lg") start_btn = gr.Button("🎙️ Start Recording", variant="primary", size="lg", interactive=False) stop_btn = gr.Button("⏹️ Stop Recording", variant="stop", size="lg", interactive=False) clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="lg", interactive=False) # FastRTC Stream Interface with gr.Row(): gr.HTML("""

🎵 Audio Stream

FastRTC audio stream will appear here when recording starts.

Status: Waiting for initialization...
""") # Status display status_output = gr.Textbox( label="System Status", value="System not initialized. Please click 'Initialize System' to begin.", lines=6, interactive=False, show_copy_button=True ) with gr.Column(scale=1): # Settings panel gr.Markdown("## ⚙️ Settings") threshold_slider = gr.Slider( minimum=0.1, maximum=0.95, step=0.05, value=0.5, # DEFAULT_CHANGE_THRESHOLD label="Speaker Change Sensitivity", info="Lower = more sensitive to speaker changes" ) max_speakers_slider = gr.Slider( minimum=2, maximum=10, # ABSOLUTE_MAX_SPEAKERS step=1, value=4, # DEFAULT_MAX_SPEAKERS label="Maximum Number of Speakers" ) update_settings_btn = gr.Button("Update Settings", variant="secondary") # Audio settings gr.Markdown("## 🔊 Audio Configuration") with gr.Accordion("Advanced Audio Settings", open=False): gr.Markdown(""" **Current Configuration:** - Sample Rate: 16kHz - Audio Format: 16-bit PCM → Float32 (via AudioProcessor) - Channels: Mono (stereo converted automatically) - Buffer Size: 1024 samples for real-time processing - Processing: Uses existing AudioProcessor.extract_embedding() """) # Instructions gr.Markdown("## 📝 How to Use") gr.Markdown(""" 1. **Initialize**: Click "Initialize System" to load AI models 2. **Start**: Click "Start Recording" to begin processing 3. **Connect**: The FastRTC stream will activate automatically 4. **Allow Access**: Grant microphone permissions when prompted 5. **Speak**: Talk naturally into your microphone 6. **Monitor**: Watch real-time transcription with speaker colors """) # Performance tips with gr.Accordion("💡 Performance Tips", open=False): gr.Markdown(""" - Use Chrome/Edge for best FastRTC performance - Ensure stable internet connection - Use headphones to prevent echo - Position microphone 6-12 inches away - Minimize background noise - Allow browser microphone access """) # Speaker color legend gr.Markdown("## 🎨 Speaker Colors") speaker_colors = [ ("#FF6B6B", "Red"), ("#4ECDC4", "Teal"), ("#45B7D1", "Blue"), ("#96CEB4", "Green"), ("#FFEAA7", "Yellow"), ("#DDA0DD", "Plum"), ("#98D8C8", "Mint"), ("#F7DC6F", "Gold") ] color_html = "" for i, (color, name) in enumerate(speaker_colors[:4]): color_html += f'
● Speaker {i+1} ({name})
' gr.HTML(f"
{color_html}
") # Auto-refresh conversation and status def refresh_display(): try: conversation = get_conversation() status = get_status() return conversation, status except Exception as e: error_msg = f"Error refreshing display: {str(e)}" return f"{error_msg}", error_msg # Event handlers def on_initialize(): try: result = initialize_system() success = "successfully" in result.lower() conversation, status = refresh_display() return ( result, # status_output gr.update(interactive=success), # start_btn gr.update(interactive=success), # clear_btn conversation, # conversation_output ) except Exception as e: error_msg = f"❌ Initialization failed: {str(e)}" return ( error_msg, gr.update(interactive=False), gr.update(interactive=False), "System not ready", ) def on_start(): try: result = start_recording() return ( result, # status_output gr.update(interactive=False), # start_btn gr.update(interactive=True), # stop_btn ) except Exception as e: error_msg = f"❌ Failed to start: {str(e)}" return ( error_msg, gr.update(interactive=True), gr.update(interactive=False), ) def on_stop(): try: result = stop_recording() return ( result, # status_output gr.update(interactive=True), # start_btn gr.update(interactive=False), # stop_btn ) except Exception as e: error_msg = f"❌ Failed to stop: {str(e)}" return ( error_msg, gr.update(interactive=False), gr.update(interactive=True), ) def on_clear(): try: result = clear_conversation() conversation, status = refresh_display() return result, conversation except Exception as e: error_msg = f"❌ Failed to clear: {str(e)}" return error_msg, "Error clearing conversation" def on_update_settings(threshold, max_speakers): try: result = update_settings(threshold, max_speakers) return result except Exception as e: return f"❌ Failed to update settings: {str(e)}" # Connect event handlers init_btn.click( on_initialize, outputs=[status_output, start_btn, clear_btn, conversation_output] ) start_btn.click( on_start, outputs=[status_output, start_btn, stop_btn] ) stop_btn.click( on_stop, outputs=[status_output, start_btn, stop_btn] ) clear_btn.click( on_clear, outputs=[status_output, conversation_output] ) update_settings_btn.click( on_update_settings, inputs=[threshold_slider, max_speakers_slider], outputs=[status_output] ) # Auto-refresh every 2 seconds when active refresh_timer = gr.Timer(2.0) refresh_timer.tick( refresh_display, outputs=[conversation_output, status_output] ) return interface # FastAPI setup for API endpoints def create_fastapi_app(): """Create FastAPI app with API endpoints""" app = FastAPI( title="Real-time Speaker Diarization", description="Real-time speech recognition with speaker diarization using FastRTC", version="1.0.0" ) # API Routes router = APIRouter() @router.get("/health") async def health_check(): """Health check endpoint""" return { "status": "healthy", "timestamp": time.time(), "system_initialized": diarization_system is not None and hasattr(diarization_system, 'encoder') and diarization_system.encoder is not None, "recording_active": diarization_system.is_running if diarization_system and hasattr(diarization_system, 'is_running') else False } @router.get("/api/conversation") async def get_conversation_api(): """Get current conversation""" try: return { "conversation": get_conversation(), "status": get_status(), "is_recording": diarization_system.is_running if diarization_system and hasattr(diarization_system, 'is_running') else False, "timestamp": time.time() } except Exception as e: return {"error": str(e), "timestamp": time.time()} @router.post("/api/control/{action}") async def control_recording(action: str): """Control recording actions""" try: if action == "start": result = start_recording() elif action == "stop": result = stop_recording() elif action == "clear": result = clear_conversation() elif action == "initialize": result = initialize_system() else: return {"error": "Invalid action. Use: start, stop, clear, or initialize"} return { "result": result, "is_recording": diarization_system.is_running if diarization_system and hasattr(diarization_system, 'is_running') else False, "timestamp": time.time() } except Exception as e: return {"error": str(e), "timestamp": time.time()} app.include_router(router) return app # Function to setup FastRTC stream def setup_fastrtc_stream(app): """Setup FastRTC stream with proper configuration""" try: if audio_handler is None: print("Warning: Audio handler not initialized. Initialize system first.") return None # Get HuggingFace token for TURN server (optional) hf_token = os.environ.get("HF_TOKEN") # Configure RTC settings rtc_config = { "iceServers": [ {"urls": "stun:stun.l.google.com:19302"}, {"urls": "stun:stun1.l.google.com:19302"} ] } # Create FastRTC stream stream = Stream( handler=audio_handler, rtc_configuration=rtc_config, modality="audio", mode="receive" # We only receive audio, don't send ) # Mount the stream app.mount("/stream", stream) print("✅ FastRTC stream configured successfully!") return stream except Exception as e: print(f"⚠️ Warning: Failed to setup FastRTC stream: {e}") print("Audio streaming may not work properly.") return None # Main application setup def create_app(diarization_sys=None): """Create the complete application""" global diarization_system # Set the diarization system if diarization_sys is not None: diarization_system = diarization_sys # Create FastAPI app fastapi_app = create_fastapi_app() # Create Gradio interface gradio_interface = create_interface() # Mount Gradio on FastAPI app = gr.mount_gradio_app(fastapi_app, gradio_interface, path="/") # Setup FastRTC stream (will be called after initialization) # Note: The stream setup happens when the system is initialized return app, gradio_interface # Entry point for HuggingFace Spaces if __name__ == "__main__": try: # Import your diarization system here # from your_module import RealtimeSpeakerDiarization # diarization_system = RealtimeSpeakerDiarization() # Create the application app, interface = create_app() # Launch for HuggingFace Spaces interface.launch( server_name="0.0.0.0", server_port=7860, share=True, show_error=True, quiet=False ) except Exception as e: print(f"Failed to launch application: {e}") import traceback traceback.print_exc() # Fallback - launch just Gradio interface try: interface = create_interface() interface.launch( server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)), share=False ) except Exception as fallback_error: print(f"Fallback launch also failed: {fallback_error}") # Helper function to initialize with your diarization system def initialize_with_diarization_system(diarization_sys): """Initialize the application with your diarization system""" global diarization_system diarization_system = diarization_sys return create_app(diarization_sys)