Upload 2 files

app.py

@@ -0,0 +1,110 @@
+import os
+from huggingface_hub import hf_hub_download
+import gradio as gr
+import json
+import pandas as pd
+import collections
+import scipy.signal
+import numpy as np
+from functools import partial
+from openwakeword.model import Model
+
+from openwakeword.utils import download_models
+download_models()
+
+# 用 Secret token 從 HF Model Hub 下載私有模型
+hf_token = os.environ.get("HF_TOKEN")
+model_path = hf_hub_download(
+    repo_id="JTBTechnology/kmu_wakeword",
+    filename="hi_kmu_0721.onnx",   # 改成你模型內的正確檔名
+    token=hf_token,
+    repo_type="model"
+)
+
+# 直接用下載的模型路徑載入
+model = Model(wakeword_models=[model_path], inference_framework="onnx")
+
+# Define function to process audio
+# def process_audio(audio, state=collections.defaultdict(partial(collections.deque, maxlen=60))):
+def process_audio(audio, state=None):
+    if state is None:
+        state = collections.defaultdict(partial(collections.deque, maxlen=60))
+    # Resample audio to 16khz if needed
+    if audio[0] != 16000:
+        data = scipy.signal.resample(audio[1], int(float(audio[1].shape[0])/audio[0]*16000))    
+    
+    # Get predictions
+    for i in range(0, data.shape[0], 1280):
+        if len(data.shape) == 2 or data.shape[-1] == 2:
+            chunk = data[i:i+1280][:, 0]  # just get one channel of audio
+        else:
+            chunk = data[i:i+1280]
+
+        if chunk.shape[0] == 1280:
+            prediction = model.predict(chunk)
+            for key in prediction:
+                #Fill deque with zeros if it's empty
+                if len(state[key]) == 0:
+                    state[key].extend(np.zeros(60))
+                    
+                # Add prediction
+                state[key].append(prediction[key])
+    
+    # Make line plot
+    dfs = []
+    for key in state.keys():
+        df = pd.DataFrame({"x": np.arange(len(state[key])), "y": state[key], "Model": key})
+        dfs.append(df)
+    
+    df = pd.concat(dfs)
+
+    plot = gr.LinePlot(
+        value=df,
+        x='x',
+        y='y',
+        color="Model",
+        y_lim=(0,1),
+        tooltip="Model",
+        width=600,
+        height=300,
+        x_title="Time (frames)",
+        y_title="Model Score",
+        color_legend_position="bottom"
+    )
+    # 1. 將 state 轉成可 JSON 序列化格式（dict of lists）
+    serializable_state = {k: [float(x) for x in v] for k, v in state.items()}
+
+    # 2. 回傳 serializable_state 給 Gradio
+    return plot, serializable_state
+
+# Create Gradio interface and launch
+
+desc = """
+這是 [openWakeWord](https://github.com/dscripka/openWakeWord) 最新版本預設模型的小工具示範。
+請點一下下面的「開始錄音」按鈕，就能直接用麥克風測試。
+系統會即時把每個模型的分數用折線圖秀出來，你也可以把滑鼠移到線上看是哪一個模型。
+
+每一個模型都有自己專屬的喚醒詞或指令句（更多可以參考 [模型說明](https://github.com/dscripka/openWakeWord/tree/main/docs/models)）。
+如果偵測到你講了對的關鍵詞，圖上對應模型的分數會突然變高。你可以試著講下面的範例語句試試看：
+
+| 模型名稱          | 建議語句   |
+| ------------- | ------ |
+| hi\_kmu\_0721 | 「嗨，高醫」 |
+"""
+
+gr_int = gr.Interface(
+    title = "語音喚醒展示",
+    description = desc,
+    css = ".flex {flex-direction: column} .gr-panel {width: 100%}",
+    fn=process_audio,
+    inputs=[
+        gr.Audio(sources=["microphone"], type="numpy", streaming=True, show_label=False), 
+        "state"
+    ],
+    outputs=[
+        gr.LinePlot(show_label=False),
+        "state"
+    ],
+    live=True)
+
+gr_int.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio
+pandas
+numpy
+scipy
+huggingface_hub
+openwakeword