app.py

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
import json
import io
import wave
from fastrtc import Stream, ReplyOnPause, AsyncStreamHandler, get_stt_model

# Simplified configuration parameters
SILENCE_THRESHS = [0, 0.4]
FINAL_TRANSCRIPTION_MODEL = "moonshine/base"  # Using FastRTC's moonshine model
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 DiarizationStreamHandler(AsyncStreamHandler):
    """FastRTC stream handler for real-time diarization"""
    def __init__(self, diarization_system):
        super().__init__(input_sample_rate=16000)
        self.diarization_system = diarization_system
        self.stt_model = get_stt_model(model=FINAL_TRANSCRIPTION_MODEL)
        self.current_text = ""
        self.current_audio_buffer = []
        self.transcript_queue = queue.Queue()
        
    def copy(self):
        return DiarizationStreamHandler(self.diarization_system)
    
    async def start_up(self):
        """Initialize the stream handler"""
        pass
    
    async def receive(self, frame):
        """Process incoming audio frame"""
        # Extract audio data
        sample_rate, audio_data = frame
        
        # Convert to numpy array if needed
        if isinstance(audio_data, torch.Tensor):
            audio_data = audio_data.numpy()
        
        # Add to buffer
        self.current_audio_buffer.append(audio_data)
        
        # If buffer is large enough, process it
        if len(self.current_audio_buffer) > 3:  # Process ~1.5 seconds of audio
            # Concatenate audio data
            combined_audio = np.concatenate(self.current_audio_buffer)
            
            # Run speech-to-text
            text = self.stt_model.stt((16000, combined_audio))
            
            if text and text.strip():
                # Save text and audio for processing
                self.transcript_queue.put((text, combined_audio))
                self.current_text = text
            
            # Reset buffer but keep some overlap
            if len(self.current_audio_buffer) > 5:
                self.current_audio_buffer = self.current_audio_buffer[-2:]
    
    async def emit(self):
        """Emit processed data"""
        # Return current text as dummy; actual processing is done in background
        return self.current_text


class RealtimeSpeakerDiarization:
    def __init__(self):
        self.encoder = None
        self.audio_processor = None
        self.speaker_detector = None
        self.stream = None
        self.stream_handler = 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
        
    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 start_stream(self, app):
        """Start the FastRTC stream"""
        if self.encoder is None:
            return "Please initialize models first!"
        
        try:
            # Create a FastRTC stream handler
            self.stream_handler = DiarizationStreamHandler(self)
            
            # Create FastRTC stream
            self.stream = Stream(
                handler=self.stream_handler,
                modality="audio",
                mode="send-receive"
            )
            
            # Mount the stream to the provided FastAPI app
            self.stream.mount(app)
            
            # 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 diarization processor thread
            self.diarization_thread = threading.Thread(target=self.process_transcript_queue, daemon=True)
            self.diarization_thread.start()
            
            return "Stream started successfully! Ready for audio input."
            
        except Exception as e:
            return f"Error starting stream: {e}"
    
    def process_transcript_queue(self):
        """Process transcripts from the stream handler"""
        while self.is_running:
            try:
                if self.stream_handler and not self.stream_handler.transcript_queue.empty():
                    text, audio_data = self.stream_handler.transcript_queue.get(timeout=1)
                    
                    # Add to sentence queue for diarization
                    self.pending_sentences.append(text)
                    self.sentence_queue.put((text, audio_data))
            except queue.Empty:
                time.sleep(0.1)  # Short sleep to prevent CPU hogging
            except Exception as e:
                print(f"Error processing transcript queue: {e}")
                time.sleep(0.5)  # Slightly longer sleep on error
    
    def process_sentence_queue(self):
        """Process sentences in the queue for speaker detection"""
        while self.is_running:
            try:
                text, audio_data = self.sentence_queue.get(timeout=1)
                
                # Convert audio data to int16
                if isinstance(audio_data, np.ndarray):
                    if audio_data.dtype != np.int16:
                        audio_int16 = (audio_data * 32767).astype(np.int16)
                    else:
                        audio_int16 = audio_data
                else:
                    audio_int16 = np.int16(audio_data * 32767)
                
                # 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)
                    
            except queue.Empty:
                continue
            except Exception as e:
                print(f"Error processing sentence: {e}")
    
    def stop_stream(self):
        """Stop the stream and processing"""
        self.is_running = False
        return "Stream 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 = ""
        
        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"
                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'<span style="color:{color};"><b>{speaker_name}:</b> {sentence_text}</span>')
            
            # Add pending sentences
            for pending_sentence in self.pending_sentences:
                sentences_with_style.append(
                    f'<span style="color:#60FFFF;"><b>Processing:</b> {pending_sentence}</span>')
            
            if sentences_with_style:
                return "<br><br>".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}"


# Global instance
diarization_system = RealtimeSpeakerDiarization()


# Create Gradio interface with FastAPI app integrated
def create_interface():
    app = gr.Blocks(title="Real-time Speaker Diarization", theme=gr.themes.Monochrome())
    
    with app:
        gr.Markdown("# 🎤 Real-time Speech Recognition with Speaker Diarization")
        gr.Markdown("This app performs real-time speech recognition with automatic speaker identification and color-coding using FastRTC.")
        
        with gr.Row():
            with gr.Column(scale=2):
                # Main conversation display
                conversation_output = gr.HTML(
                    value="<i>Click 'Initialize System' and then 'Start Stream' to begin...</i>",
                    label="Live Conversation"
                )
                
                # FastRTC microphone widget for visualization only (the real audio comes through FastRTC stream)
                audio_widget = gr.Audio(
                    label="🎙️ Microphone Input (Click Start Stream to enable)",
                    type="microphone"
                )
                
                # Control buttons
                with gr.Row():
                    init_btn = gr.Button("🔧 Initialize System", variant="secondary")
                    start_btn = gr.Button("🎙️ Start Stream", variant="primary", interactive=False)
                    stop_btn = gr.Button("⏹️ Stop Stream", variant="stop", interactive=False)
                    clear_btn = gr.Button("🗑️ Clear Conversation", interactive=False)
                
                # Status display
                status_output = gr.Textbox(
                    label="System Status",
                    value="System not initialized",
                    lines=8,
                    interactive=False
                )
            
            with gr.Column(scale=1):
                # Settings panel
                gr.Markdown("## ⚙️ Settings")
                
                threshold_slider = gr.Slider(
                    minimum=0.1,
                    maximum=0.95,
                    step=0.05,
                    value=DEFAULT_CHANGE_THRESHOLD,
                    label="Speaker Change Sensitivity",
                    info="Lower values = more sensitive to speaker changes"
                )
                
                max_speakers_slider = gr.Slider(
                    minimum=2,
                    maximum=ABSOLUTE_MAX_SPEAKERS,
                    step=1,
                    value=DEFAULT_MAX_SPEAKERS,
                    label="Maximum Number of Speakers"
                )
                
                update_settings_btn = gr.Button("Update Settings")
                
                # Instructions
                gr.Markdown("## 📝 Instructions")
                gr.Markdown("""
                1. Click **Initialize System** to load models
                2. Click **Start Stream** to begin processing
                3. Allow microphone access when prompted
                4. Speak into your microphone
                5. Watch real-time transcription with speaker labels
                6. Adjust settings as needed
                """)
                
                # QR code for mobile access
                gr.Markdown("## 📱 Mobile Access")
                gr.Markdown("Scan this QR code to access from mobile device:")
                qr_code = gr.HTML("""
                <div id="qrcode" style="text-align: center;"></div>
                <script src="https://cdn.jsdelivr.net/npm/[email protected]/qrcode.min.js"></script>
                <script>
                  setTimeout(function() {
                    var currentUrl = window.location.href;
                    var qr = qrcode(0, 'M');
                    qr.addData(currentUrl);
                    qr.make();
                    document.getElementById('qrcode').innerHTML = qr.createImgTag(5);
                  }, 1000);
                </script>
                """)
                
                # Speaker color legend
                gr.Markdown("## 🎨 Speaker Colors")
                color_info = []
                for i, (color, name) in enumerate(zip(SPEAKER_COLORS, SPEAKER_COLOR_NAMES)):
                    color_info.append(f'<span style="color:{color};">■</span> Speaker {i+1} ({name})')
                
                gr.HTML("<br>".join(color_info[:DEFAULT_MAX_SPEAKERS]))
        
        # Auto-refresh conversation and status
        def refresh_display():
            return get_formatted_conversation(), get_status()
        
        # Event handlers
        def on_initialize():
            result = initialize_system()
            if "successfully" in result:
                return (
                    result,
                    gr.update(interactive=True),   # start_btn
                    gr.update(interactive=True),   # clear_btn
                    get_formatted_conversation(),
                    get_status()
                )
            else:
                return (
                    result,
                    gr.update(interactive=False),  # start_btn
                    gr.update(interactive=False),  # clear_btn
                    get_formatted_conversation(),
                    get_status()
                )
        
        def on_start_stream():
            result = start_stream(app)
            return (
                result,
                gr.update(interactive=False),  # start_btn
                gr.update(interactive=True),   # stop_btn
            )
        
        def on_stop_stream():
            result = stop_stream()
            return (
                result,
                gr.update(interactive=True),   # start_btn
                gr.update(interactive=False),  # stop_btn
            )
        
        def initialize_system():
            """Initialize the diarization system"""
            success = diarization_system.initialize_models()
            if success:
                return "✅ System initialized successfully! Models loaded."
            else:
                return "❌ Failed to initialize system. Please check the logs."

        def start_stream(app):
            """Start the FastRTC stream"""
            return diarization_system.start_stream(app)

        def stop_stream():
            """Stop the FastRTC stream"""
            return diarization_system.stop_stream()

        def clear_conversation():
            """Clear the conversation"""
            return diarization_system.clear_conversation()

        def update_settings(threshold, max_speakers):
            """Update system settings"""
            return diarization_system.update_settings(threshold, max_speakers)

        def get_formatted_conversation():
            """Get the current conversation"""
            return diarization_system.get_formatted_conversation()

        def get_status():
            """Get system status"""
            return diarization_system.get_status_info()
        
        # Connect event handlers
        init_btn.click(
            on_initialize,
            outputs=[status_output, start_btn, clear_btn, conversation_output, status_output]
        )
        
        start_btn.click(
            on_start_stream,
            outputs=[status_output, start_btn, stop_btn]
        )
        
        stop_btn.click(
            on_stop_stream,
            outputs=[status_output, start_btn, stop_btn]
        )
        
        clear_btn.click(
            clear_conversation,
            outputs=[status_output]
        )
        
        update_settings_btn.click(
            update_settings,
            inputs=[threshold_slider, max_speakers_slider],
            outputs=[status_output]
        )
        
        # Auto-refresh every 2 seconds when streaming
        refresh_timer = gr.Timer(2.0)
        refresh_timer.tick(
            refresh_display,
            outputs=[conversation_output, status_output]
        )
    
    return app


if __name__ == "__main__":
    app = create_interface()
    app.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )