housekeeping

ouijaGPT.py

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+import sounddevice as sd
+import numpy as np
+from openai import OpenAI
+import queue
+import threading
+import time
+import wave
+import os
+
+# Initialize the OpenAI client
+client = OpenAI()
+
+# Parameters
+sample_rate = 16000  # Whisper API uses 16 kHz audio
+chunk_duration = 0.5  # Analyze audio in 0.5-second chunks
+silence_threshold = 10  # Initial silence threshold
+silence_duration = 1  # Number of seconds to consider as the end of a phrase
+output_file = "output_phrase.wav"  # Local file to save the captured audio
+margin = 5  # Margin above baseline noise level to set as threshold
+
+# Adaptive threshold parameters
+baseline_noise = 0
+adaptive_threshold = silence_threshold
+
+# Queue to hold audio chunks
+audio_queue = queue.Queue()
+silence_counter = 0
+recording = False
+audio_buffer = []
+
+# Function to capture audio in real-time and detect phrases
+def audio_callback(indata, frames, time, status):
+    global silence_counter, recording, audio_buffer, baseline_noise, adaptive_threshold
+    if status:
+        print(status, flush=True)
+
+    # Calculate the volume of the current audio chunk
+    volume_norm = np.linalg.norm(indata) * 10
+
+    # Update baseline noise when not recording
+    if not recording:
+        baseline_noise = (baseline_noise * 0.9) + (volume_norm * 0.1)  # Moving average
+        adaptive_threshold = baseline_noise + margin
+
+    if volume_norm > adaptive_threshold:
+        if not recording:
+            recording = True
+        silence_counter = 0
+        audio_buffer.append(indata.copy())
+    else:
+        if recording:
+            silence_counter += chunk_duration
+            if silence_counter >= silence_duration:
+                audio_queue.put(np.concatenate(audio_buffer))
+                audio_buffer = []
+                recording = False
+                silence_counter = 0
+
+# Function to transcribe audio and get ChatGPT response
+def transcribe_and_respond():
+    while True:
+        audio_data = audio_queue.get()
+
+        # Normalize audio data to 16-bit PCM
+        audio_data = np.int16(audio_data / np.max(np.abs(audio_data)) * 32767)
+
+        # Save the audio data to a local WAV file
+        with wave.open(output_file, "wb") as wav_file:
+            wav_file.setnchannels(1)  # Mono
+            wav_file.setsampwidth(2)  # 16-bit audio
+            wav_file.setframerate(sample_rate)
+            wav_file.writeframes(audio_data.tobytes())
+
+        # Reopen the saved file for reading to send to the API
+        with open(output_file, "rb") as wav_buffer:
+            transcript = client.audio.transcriptions.create(model="whisper-1", file=wav_buffer)
+            print("You:", transcript.text)
+
+            # Get a response from ChatGPT
+            response = client.chat.completions.create(
+                model="gpt-4",  # You can use 'gpt-3.5-turbo' for a cheaper option
+                messages=[
+                    {"role": "system", "content": (
+                        "You are a ghost communicating to a person during a séance. Your responses should be short direct. "
+                        "Often, they should evoke vivid, haunting imagery. Use as few words as possible to convey your meaning, one or two, THREE MAXIMUM. "
+                        "Try as often as possible to answer 'YES' or 'NO' when given the opportunity."
+                        "For example, if asked where you are from, you might say, 'BEYOND THE VEIL' "
+                        "If asked about a favorite color, a response could be, 'TWILIGHT MIST' "
+                        "RESPOND IN ALL CAPITAL LETTERS WITH NO PUNCTUATION"
+                    )},
+                    {"role": "user", "content": transcript.text},
+                ]
+            )
+
+            # Print the response
+            print("Ghost:", response.choices[0].message.content)
+
+        # Clean up the file after processing
+        os.remove(output_file)
+
+# Start the transcription and response thread
+response_thread = threading.Thread(target=transcribe_and_respond)
+response_thread.daemon = True
+response_thread.start()
+
+# Start recording and analyzing audio in chunks
+with sd.InputStream(callback=audio_callback, channels=1, samplerate=sample_rate, dtype='float32', blocksize=int(sample_rate * chunk_duration)):
+    print("Listening for phrases... Press Ctrl+C to stop.")
+    while True:
+        time.sleep(1)