ウェイクワードエンジンモジュールテスト
import pyaudio
import numpy as np
from openwakeword.model import Model
class SimpleWakeWordDetector:
def __init__(self, model_path="alexa_v0.1.onnx", threshold=0.5):
self.model_path = model_path
self.threshold = threshold
self.format = pyaudio.paInt16
self.channels = 1
self.rate = 16000
self.chunk = 1024
self.model_name = model_path # モデル名 = ファイル名
self.model = Model(
wakeword_models=[self.model_path],
inference_framework="onnx"
)
self.audio = pyaudio.PyAudio()
self.stream = self.audio.open(
format=self.format,
channels=self.channels,
rate=self.rate,
input=True,
frames_per_buffer=self.chunk
)
def listen_for_wakeword(self):
print(f"ウェイクワード待機中...（{self.model_path}）")
prev_detect = False
while True:
data = self.stream.read(self.chunk, exception_on_overflow=False)
audio = np.frombuffer(data, dtype=np.int16).astype(np.float32) / 32768.0
self.model.predict(audio)
score = self.model.get_last_prediction(self.model_name)
print(f"score: {score:.3f}", end='\r')
detect = score > self.threshold
if detect and not prev_detect:
print(f"\nWakeword Detected! (score: {score:.3f})")
return True
prev_detect = detect
を
touch simple_wakeword.py
で作成
touch wordtest.py
で
from simple_wakeword import SimpleWakeWordDetector
# モジュール初期化（Alexa用モデルとしきい値指定）
wakeword_detector = SimpleWakeWordDetector(
model_path="alexa_v0.1.onnx",
threshold=0.5
)
# 検知まで待機
detected = wakeword_detector.listen_for_wakeword()
if detected:
print("こんにちは")
しかし反応がない
とりあえずウェイクワードは保留とし
先にRAGと llama index を行う