60f18f8e71
- Implement SpeechToText class with Docker-based transcription capabilities - Add wake word detection using OpenWakeWord and fast-whisper models - Create Dockerfile for speech processing container - Develop comprehensive test suite for speech recognition functionality - Include audio processing and event-driven transcription features
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
import os
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import json
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import queue
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import threading
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import numpy as np
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import sounddevice as sd
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from openwakeword import Model
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from datetime import datetime
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import wave
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# Configuration
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SAMPLE_RATE = 16000
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CHANNELS = 1
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CHUNK_SIZE = 1024
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BUFFER_DURATION = 30 # seconds to keep in buffer
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DETECTION_THRESHOLD = 0.5
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class AudioProcessor:
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def __init__(self):
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self.wake_word_model = Model(
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wakeword_models=["hey_jarvis", "ok_google", "alexa"],
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model_path=os.environ.get('WAKEWORD_MODEL_PATH', '/models/wake_word')
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)
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self.audio_buffer = queue.Queue()
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self.recording = False
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self.buffer = np.zeros(SAMPLE_RATE * BUFFER_DURATION)
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self.buffer_lock = threading.Lock()
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def audio_callback(self, indata, frames, time, status):
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"""Callback for audio input"""
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if status:
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print(f"Audio callback status: {status}")
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# Convert to mono if necessary
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if CHANNELS > 1:
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audio_data = np.mean(indata, axis=1)
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else:
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audio_data = indata.flatten()
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# Update circular buffer
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with self.buffer_lock:
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self.buffer = np.roll(self.buffer, -len(audio_data))
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self.buffer[-len(audio_data):] = audio_data
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# Process for wake word detection
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prediction = self.wake_word_model.predict(audio_data)
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# Check if wake word detected
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for wake_word, score in prediction.items():
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if score > DETECTION_THRESHOLD:
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print(f"Wake word detected: {wake_word} (confidence: {score:.2f})")
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self.save_audio_segment()
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break
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def save_audio_segment(self):
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"""Save the audio buffer when wake word is detected"""
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"/audio/wake_word_{timestamp}.wav"
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# Save the audio buffer to a WAV file
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with wave.open(filename, 'wb') as wf:
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wf.setnchannels(CHANNELS)
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wf.setsampwidth(2) # 16-bit audio
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wf.setframerate(SAMPLE_RATE)
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# Convert float32 to int16
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audio_data = (self.buffer * 32767).astype(np.int16)
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wf.writeframes(audio_data.tobytes())
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print(f"Saved audio segment to {filename}")
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# Write metadata
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metadata = {
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"timestamp": timestamp,
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"sample_rate": SAMPLE_RATE,
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"channels": CHANNELS,
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"duration": BUFFER_DURATION
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}
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with open(f"{filename}.json", 'w') as f:
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json.dump(metadata, f, indent=2)
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def start(self):
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"""Start audio processing"""
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try:
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with sd.InputStream(
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channels=CHANNELS,
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samplerate=SAMPLE_RATE,
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blocksize=CHUNK_SIZE,
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callback=self.audio_callback
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):
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print("Wake word detection started. Listening...")
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while True:
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sd.sleep(1000) # Sleep for 1 second
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except KeyboardInterrupt:
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print("\nStopping wake word detection...")
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except Exception as e:
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print(f"Error in audio processing: {e}")
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if __name__ == "__main__":
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print("Initializing wake word detection...")
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processor = AudioProcessor()
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processor.start() |