Create app.py

app.py

@@ -0,0 +1,27 @@
+import gradio as gr
+from transformers import pipeline
+import numpy as np
+from model import SAMPLING_RATE, clasificador, monitor
+# modelo = monitor
+modelo = clasificador
+pipe = pipeline("audio-classification", model=f"A-POR-LOS-8000/distilhubert-finetuned-cry-detector", device="cuda")
+
+def transcribe(audio):
+    _, y = audio
+    y = y.astype(np.float32) # con torch.float32 da error
+    y /= np.max(np.abs(y))
+    results = pipe({"sampling_rate": SAMPLING_RATE, "raw": y})
+    top_result = results[0]  # Get the top result (most likely classification)
+    label = top_result["label"]  # Extract the label from the top result
+    return label
+
+demo = gr.Interface(
+    transcribe,
+    gr.Audio(
+        min_length=1.0,
+        max_length=10.0,
+        format="wav",
+        ),
+    "text",
+)
+demo.launch()