Create app.py

app.py

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+# Import the Gradio library for creating web interfaces
+import gradio as gr
+# Import the pipeline module from transformers for using pre-trained models
+from transformers import pipeline
+# Import numpy for numerical operations
+import numpy as np
+
+# Initialize the automatic speech recognition pipeline using the Whisper base English model
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+
+# Define the transcription function that takes audio input and returns transcribed text
+def transcribe(stream,new_chunk):
+    # Unpack the audio tuple into sample rate (sr) and audio data (y)
+    sr, y = new_chunk
+    # Convert the audio data to 32-bit float
+    y = y.astype(np.float32)
+    # Normalize the audio data to be between -1 and 1
+    y /= np.max(np.abs(y))
+
+    if stream is not None:
+        stream = np.concatenate([stream, y])
+    else:
+        stream = y 
+
+    # Use the transcriber to convert audio to text and return the result
+    return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"]
+
+
+# Create a Gradio interface for the transcribe function
+demo = gr.Interface(
+    # Specify the function to run
+    transcribe,
+    # Define the input component as an audio recorder with microphone source
+    ["state", gr.Audio(sources=["microphone"], streaming=True)],
+    # Specify the output component as text
+    ["state", "text"],
+    live = True 
+)
+
+demo.launch()