Update README.md

README.md

@@ -11,3 +11,183 @@ Output: Score matrix per class [num_frames, num_classes]
 
 Classes: See mapping in yamnet_class_map.csv
 
+Example for uses
+
+import sounddevice as sd
+import numpy as np
+import onnxruntime
+import scipy.signal
+import csv
+import threading
+import time
+from collections import deque, Counter
+
+# Path to the ONNX model file
+MODEL_PATH = "./yamnet.onnx"
+# Create an inference session with ONNX runtime using CPU provider
+session = onnxruntime.InferenceSession(MODEL_PATH, providers=['CPUExecutionProvider'])
+# Get the name of the model's input node
+input_name = session.get_inputs()[0].name
+
+def load_class_map(csv_path="yamnet_class_map.csv"):
+    """Load class names from a CSV file into a dictionary mapping class IDs to names."""
+    class_map = {}
+    with open(csv_path, newline='') as csvfile:
+        reader = csv.reader(csvfile)
+        next(reader)  # Skip header row
+        for row in reader:
+            class_id = int(row[0])
+            class_name = row[2]  # Class name is in the third column
+            class_map[class_id] = class_name
+    return class_map
+
+# Load the class mapping from CSV
+class_map = load_class_map()
+
+# Audio buffer settings (1.5 seconds at 16kHz sample rate)
+BUFFER_SIZE = int(16000 * 1.5) 
+# Circular buffer to store audio samples
+audio_buffer = deque(maxlen=BUFFER_SIZE)
+
+# Queues for storing and consolidating detection results
+detections_queue = deque(maxlen=3)  # Stores recent detections
+consolidated_results = deque(maxlen=3)  # Stores consolidated results
+last_printed_result = None  # Last result printed to console
+last_inference_time = 0  # Timestamp of last inference
+
+# Configuration for sleep mode (temporarily pausing inference)
+sleep_triggers = {"music", "speech", "silence"}  # Classes that trigger sleep mode
+sleep_duration = 3.0  # How long to sleep after detecting trigger classes
+same_class_count = 0  # Counter for consecutive same-class detections
+last_detected_class = None  # Last class detected
+is_sleeping_until = 0  # Timestamp until which we're in sleep mode
+
+def resample_if_needed(audio, original_sr, target_sr=16000):
+    """Resample audio to target sample rate if needed."""
+    if original_sr != target_sr:
+        audio = scipy.signal.resample_poly(audio, target_sr, int(original_sr))
+    return audio
+
+def run_inference(audio_chunk):
+    """Run the ONNX model inference on an audio chunk and process results."""
+    global detections_queue, consolidated_results, last_printed_result
+    global is_sleeping_until, same_class_count, last_detected_class
+
+    try:
+        # Run the ONNX model
+        outputs = session.run(None, {input_name: audio_chunk.astype(np.float32)})
+        scores = outputs[0]
+        mean_scores = np.mean(scores, axis=0)  # Average scores across frames
+
+        # Get the class with highest score
+        class_id = np.argmax(mean_scores)
+        confidence = mean_scores[class_id]
+        class_name = class_map.get(class_id, f"Class {class_id}")
+
+        # Track consecutive detections of the same class
+        if class_name == last_detected_class:
+            same_class_count += 1
+        else:
+            same_class_count = 1
+            last_detected_class = class_name
+
+        # Enter sleep mode if we detect trigger classes consecutively
+        if same_class_count >= 3 and class_name in sleep_triggers:
+            is_sleeping_until = time.time() + sleep_duration
+            print(f" Pausing inferences for {sleep_duration} seconds (detected: {class_name})")
+            same_class_count = 0
+
+        # Add detection to queue
+        detections_queue.append((class_name, confidence))
+
+        # When queue is full, consolidate results
+        if len(detections_queue) == 1:
+            most_common_name, _ = Counter([d[0] for d in detections_queue]).most_common(1)[0]
+            relevant_conf = [conf for n, conf in detections_queue if n == most_common_name]
+            avg_conf = np.mean(relevant_conf) * 100
+            if avg_conf > 20:  # Only consider results with >20% confidence
+                consolidated_results.append((most_common_name, avg_conf))
+            detections_queue.clear()
+
+        # When we have enough consolidated results, print the final result
+        if len(consolidated_results) == 3:
+            names = [r[0] for r in consolidated_results]
+            confidences = [r[1] for r in consolidated_results]
+            most_common_name, _ = Counter(names).most_common(1)[0]
+            avg_conf = np.mean([c for n, c in consolidated_results if n == most_common_name])
+            msg = f" Consolidated result: {most_common_name} with average confidence {avg_conf:.2f}%"
+            if msg != last_printed_result:
+                print(msg)
+                last_printed_result = msg
+
+    except Exception as e:
+        print(f" ONNX Error: {e}")
+
+def audio_callback(indata, frames, time_info, status):
+    """Callback function for audio input stream."""
+    global last_inference_time, is_sleeping_until
+
+    if status:
+        print(f" Status: {status}")
+
+    # Get mono audio and resample if needed
+    audio = indata[:, 0]
+    original_sr = sd.query_devices(sd.default.device[0], 'input')['default_samplerate']
+    audio = resample_if_needed(audio, original_sr)
+
+    # Normalize audio
+    max_val = np.max(np.abs(audio))
+    if max_val > 0:
+        audio = audio / max_val
+
+    # Add audio to buffer
+    audio_buffer.extend(audio)
+
+    # Run inference if buffer is full and not in sleep mode
+    if len(audio_buffer) >= BUFFER_SIZE:
+        now = time.time()
+
+        if now < is_sleeping_until:
+            return
+
+        # Throttle inference to once per second
+        if now - last_inference_time > 1.0:
+            last_inference_time = now
+            audio_chunk = np.array(audio_buffer)
+            # Run inference in a separate thread
+            threading.Thread(target=run_inference, args=(audio_chunk,), daemon=True).start()
+
+def main():
+    """Main function to start audio streaming and processing."""
+    print(" Starting real-time listening (Ctrl+C to stop)...")
+
+    # List available input devices
+    devices = sd.query_devices()
+    input_devices = [i for i, d in enumerate(devices) if d['max_input_channels'] > 0]
+
+    print("\nAvailable input devices:")
+    for i in input_devices:
+        print(f"{i}: {devices[i]['name']}")
+
+    # Let user select input device
+    device_id = int(input("Select input device ID: "))
+    sd.default.device = (device_id, None)
+
+    # Configure audio settings
+    samplerate = int(sd.query_devices(device_id, 'input')['default_samplerate'])
+    sd.default.samplerate = samplerate
+    sd.default.channels = 1  # Mono audio
+
+    print("🎧 Listening...")
+
+    # Start audio stream with callback
+    with sd.InputStream(callback=audio_callback, channels=1, samplerate=samplerate):
+        try:
+            while True:
+                time.sleep(0.1)  # Keep main thread alive
+        except KeyboardInterrupt:
+            print("\n Stopping listener...")
+
+if __name__ == "__main__":
+    main()
+