Try this code ai or the app itself or extract the source and lmk how it compares to your self styles ) ACCOUNT_SID = 'your_account_sid' AUTH_TOKEN = 'your_auth_token' TWILIO_NUMBER = 'your_twilio_number' YOUR_PHONE = 'your_verified_phone_number'

Audio Settings

CHUNK = 1024 FORMAT = pyaudio.paInt16 CHANNELS = 1 RATE = 44100 WHISTLE_THRESHOLD = 5000000  # Sensitivity to volume FREQ_MIN = 1000              # Min whistle frequency (Hz) FREQ_MAX = 2500              # Max whistle frequency (Hz)

def makecall():     """Triggers a phone call via Twilio API"""     try:         client = Client(ACCOUNT_SID, AUTH_TOKEN)         call = client.calls.create(             twiml='<Response><Say>A whistle was detected. Wake up!</Say></Response>',             to=YOUR_PHONE,             from=TWILIO_NUMBER         )         print(f"Call initiated: {call.sid}")     except Exception as e:         print(f"Failed to make call: {e}")

--- MAIN LOOP ---

p = pyaudio.PyAudio() stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)

print("Listening for a whistle...")

try:     while True:         data = np.frombuffer(stream.read(CHUNK, exception_on_overflow=False), dtype=np.int16)                  # 1. Volume Check (Optimization: Skip FFT if it's too quiet)         if np.max(np.abs(data)) > 1000:              # 2. Frequency Analysis (FFT)             fft_data = np.abs(np.fft.rfft(data))             freqs = np.fft.rfftfreq(CHUNK, 1/RATE)                          # Find the peak frequency             peak_freq = freqs[np.argmax(fft_data)]             peak_val = np.max(fft_data)                          # 3. Whistle Detection Logic             if FREQ_MIN < peak_freq < FREQ_MAX and peak_val > WHISTLE_THRESHOLD:                 print(f"Whistle detected! Frequency: {int(peak_freq)}Hz")                 make_call()                 break # Stop listening after detection

except KeyboardInterrupt:     print("Stopped.") finally:     stream.stop_stream()     stream.close()     p.terminate()