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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
# 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'<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}"
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
# FastRTC Audio Handler for Real-time Diarization
import asyncio
import numpy as np
from fastrtc import FastRTCClient, AudioFrame
from fastapi import FastAPI, APIRouter
import gradio as gr
import time
import os
import threading
from queue import Queue
import json
class DiarizationHandler:
def __init__(self, diarization_system):
self.diarization_system = diarization_system
self.audio_queue = Queue()
self.is_processing = False
def copy(self):
# Return a fresh handler for each new stream connection
return DiarizationHandler(self.diarization_system)
async def on_audio_frame(self, frame: AudioFrame):
"""Handle incoming audio frames from FastRTC"""
try:
if self.diarization_system.is_running and frame.data is not None:
# Convert audio frame to numpy array
if isinstance(frame.data, bytes):
# Convert bytes to numpy array (assuming 16-bit PCM)
audio_data = np.frombuffer(frame.data, dtype=np.int16)
elif hasattr(frame, 'to_ndarray'):
audio_data = frame.to_ndarray()
else:
audio_data = np.array(frame.data, dtype=np.float32)
# Ensure audio is in the right format (mono, float32, -1 to 1 range)
if audio_data.dtype == np.int16:
audio_data = audio_data.astype(np.float32) / 32768.0
# If stereo, convert to mono
if len(audio_data.shape) > 1:
audio_data = np.mean(audio_data, axis=1)
# Feed to diarization system
await self.process_audio_async(audio_data, frame.sample_rate)
except Exception as e:
print(f"Error processing audio frame: {e}")
async def process_audio_async(self, audio_data, sample_rate=16000):
"""Process audio data asynchronously"""
try:
# Run in thread pool to avoid blocking
loop = asyncio.get_event_loop()
await loop.run_in_executor(
None,
self.diarization_system.feed_audio_data,
audio_data,
sample_rate
)
except Exception as e:
print(f"Error in async audio processing: {e}")
# Global instance
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! Models loaded and FastRTC handler ready."
else:
return "❌ Failed to initialize system. Please check the logs."
except Exception as e:
return f"❌ Initialization error: {str(e)}"
def start_recording():
"""Start recording and transcription"""
try:
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:
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:
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:
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:
return diarization_system.get_formatted_conversation()
except Exception as e:
return f"<i>Error getting conversation: {str(e)}</i>"
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("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="<div style='padding: 20px; background: #f5f5f5; border-radius: 10px;'><i>Click 'Initialize System' to start...</i></div>",
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)
# Audio connection status
with gr.Row():
connection_status = gr.HTML(
value="<div style='padding: 10px; background: #fff3cd; border-radius: 5px;'>🔌 FastRTC: Not connected</div>",
label="Connection Status"
)
# 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 Settings")
gr.Markdown("""
**Recommended settings:**
- Use a good quality microphone
- Ensure stable internet connection
- Speak clearly and avoid background noise
- Position microphone 6-12 inches from mouth
""")
# Instructions
gr.Markdown("## 📝 How to Use")
gr.Markdown("""
1. **Initialize**: Click "Initialize System" to load AI models
2. **Connect**: Allow microphone access when prompted
3. **Start**: Click "Start Recording" to begin processing
4. **Speak**: Talk into your microphone naturally
5. **Monitor**: Watch real-time transcription with speaker labels
6. **Adjust**: Fine-tune settings as needed
""")
# 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'<div style="display: inline-block; margin: 5px;"><span style="color:{color}; font-size: 20px;">●</span> Speaker {i+1} ({name})</div><br>'
gr.HTML(color_html)
# Auto-refresh conversation and status
def refresh_display():
try:
conversation = get_conversation()
status = get_status()
# Update connection status based on system state
if diarization_system.is_running:
conn_status = "<div style='padding: 10px; background: #d4edda; border-radius: 5px;'>🟢 FastRTC: Connected & Recording</div>"
elif hasattr(diarization_system, 'encoder') and diarization_system.encoder is not None:
conn_status = "<div style='padding: 10px; background: #d1ecf1; border-radius: 5px;'>🔵 FastRTC: Ready to connect</div>"
else:
conn_status = "<div style='padding: 10px; background: #f8d7da; border-radius: 5px;'>🔴 FastRTC: System not initialized</div>"
return conversation, status, conn_status
except Exception as e:
error_msg = f"Error refreshing display: {str(e)}"
return f"<i>{error_msg}</i>", error_msg, "<div style='padding: 10px; background: #f8d7da; border-radius: 5px;'>❌ FastRTC: Error</div>"
# Event handlers
def on_initialize():
try:
result = initialize_system()
success = "successfully" in result.lower()
conversation, status, conn_status = refresh_display()
return (
result, # status_output
gr.update(interactive=success), # start_btn
gr.update(interactive=success), # clear_btn
conversation, # conversation_output
conn_status # connection_status
)
except Exception as e:
error_msg = f"❌ Initialization failed: {str(e)}"
return (
error_msg,
gr.update(interactive=False),
gr.update(interactive=False),
"<i>System not ready</i>",
"<div style='padding: 10px; background: #f8d7da; border-radius: 5px;'>❌ FastRTC: Initialization failed</div>"
)
def on_start():
try:
result = start_recording()
conversation, status, conn_status = refresh_display()
return (
result, # status_output
gr.update(interactive=False), # start_btn
gr.update(interactive=True), # stop_btn
conn_status # connection_status
)
except Exception as e:
error_msg = f"❌ Failed to start: {str(e)}"
return (
error_msg,
gr.update(interactive=True),
gr.update(interactive=False),
"<div style='padding: 10px; background: #f8d7da; border-radius: 5px;'>❌ FastRTC: Start failed</div>"
)
def on_stop():
try:
result = stop_recording()
conversation, status, conn_status = refresh_display()
return (
result, # status_output
gr.update(interactive=True), # start_btn
gr.update(interactive=False), # stop_btn
conn_status # connection_status
)
except Exception as e:
error_msg = f"❌ Failed to stop: {str(e)}"
return (
error_msg,
gr.update(interactive=False),
gr.update(interactive=True),
"<div style='padding: 10px; background: #f8d7da; border-radius: 5px;'>❌ FastRTC: Stop failed</div>"
)
def on_clear():
try:
result = clear_conversation()
conversation, status, conn_status = refresh_display()
return result, conversation
except Exception as e:
error_msg = f"❌ Failed to clear: {str(e)}"
return error_msg, "<i>Error clearing conversation</i>"
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, connection_status]
)
start_btn.click(
on_start,
outputs=[status_output, start_btn, stop_btn, connection_status]
)
stop_btn.click(
on_stop,
outputs=[status_output, start_btn, stop_btn, connection_status]
)
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, connection_status]
)
return interface
# FastAPI setup for HuggingFace Spaces
def create_fastapi_app():
"""Create FastAPI app with proper FastRTC integration"""
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": hasattr(diarization_system, 'encoder') and diarization_system.encoder is not None,
"recording_active": diarization_system.is_running if 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 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 hasattr(diarization_system, 'is_running') else False,
"timestamp": time.time()
}
except Exception as e:
return {"error": str(e), "timestamp": time.time()}
# FastRTC WebSocket endpoint for audio streaming
@router.websocket("/ws/audio")
async def websocket_audio_endpoint(websocket):
"""WebSocket endpoint for FastRTC audio streaming"""
await websocket.accept()
try:
while True:
# Receive audio data from FastRTC client
data = await websocket.receive_bytes()
if audio_handler and diarization_system.is_running:
# Create audio frame and process
frame = AudioFrame(data=data, sample_rate=16000)
await audio_handler.on_audio_frame(frame)
except Exception as e:
print(f"WebSocket error: {e}")
finally:
await websocket.close()
app.include_router(router)
return app
# Main application entry point
def create_app():
"""Create the complete application for HuggingFace Spaces"""
# 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="/")
return app, gradio_interface
# Entry point for HuggingFace Spaces
if __name__ == "__main__":
try:
# Create the application
app, interface = create_app()
# Launch for HuggingFace Spaces
interface.launch(
server_name="0.0.0.0",
server_port=int(os.environ.get("PORT", 7860)),
share=False,
show_error=True,
quiet=False
)
except Exception as e:
print(f"Failed to launch application: {e}")
# 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}")