Update app.py

app.py

@@ -1,10 +1,53 @@
+#from transformers import pipeline
+#import streamlit as st
+
+#pipe = pipeline('sentiment-analysis')
+#text = st.text_area('Enter some text here!')
+
+#if text:
+#    out = pipe(text)
+#    st.json(out)
+
+
 from transformers import pipeline
-import streamlit as st
+import torch
+
+classifier = pipeline(
+    "audio-classification", model="MIT/ast-finetuned-speech-commands-v2", device=device
+)
+
+from transformers.pipelines.audio_utils import ffmpeg_microphone_live
+
+
+def launch_fn(
+    wake_word="marvin",
+    prob_threshold=0.5,
+    chunk_length_s=2.0,
+    stream_chunk_s=1,
+    debug=False,
+):
+    if wake_word not in classifier.model.config.label2id.keys():
+        raise ValueError(
+            f"Wake word {wake_word} not in set of valid class labels, pick a wake word in the set {classifier.model.config.label2id.keys()}."
+        )
+
+    sampling_rate = classifier.feature_extractor.sampling_rate
+
+    mic = ffmpeg_microphone_live(
+        sampling_rate=sampling_rate,
+        chunk_length_s=chunk_length_s,
+        stream_chunk_s=stream_chunk_s,
+    )
 
-pipe = pipeline('sentiment-analysis')
-text = st.text_area('Enter some text here!')
+    print("Listening for wake word...")
+    mic_results = classifier(mic)
+    for prediction in mic_results:
+        prediction = prediction[0]
+        if debug:
+            print(prediction)
+        if prediction["label"] == wake_word:
+            if prediction["score"] > prob_threshold:
+                return True
 
-if text:
-    out = pipe(text)
-    st.json(out)
+launch_fn(debug=True)