Upload app.py

app.py

@@ -1,28 +1,28 @@
-from transformers import pipeline
-
 import gradio as gr
-
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-classifier = pipeline("text-classification")
-
-def speech_to_text(speech):
-    text = asr(speech)["text"]  
-    return text
-
-def text_to_sentiment(text):
-    return classifier(text)[0]["label"]  
-
-demo = gr.Blocks()
-
-with demo:
-    audio_file = gr.Audio(type="filepath")
-    text = gr.Textbox()
-    label = gr.Label()
-
-    b1 = gr.Button("Recognize Speech")
-    b2 = gr.Button("Classify Sentiment")
-
-    b1.click(speech_to_text, inputs=audio_file, outputs=text)
-    b2.click(text_to_sentiment, inputs=text, outputs=label)
-
-demo.launch()
+import numpy as np
+import matplotlib.pyplot as plt
+
+# This function returns the waveform data to be displayed
+def record_audio(audio):
+    if audio is not None:
+        data = audio[0]  # Extract the audio data from the tuple
+        sample_rate = audio[1]  # Extract the sample rate
+
+        # Create a mm plot
+        plt.figure(figsize=(10, 4))
+        plt.plot(data)
+        plt.title("Real-Time Audio Waveform")
+        plt.xlabel("Sample Number")
+        plt.ylabel("Amplitude")
+        plt.grid(True)
+        return plt
+
+# Define the Gradio interface
+gr.Interface(
+    fn=record_audio,
+    inputs=gr.Audio(type="numpy"),  # Capture audio as numpy array
+    outputs="plot",  # Output the waveform plot
+    live=True,  # Enable real-time recording
+    title="Real-Time Audio Recording",
+    description="Record audio in real time and view the waveform."
+).launch()