Create app.py

app.py

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+import gradio as gr
+import whisper
+from transformers import pipeline
+
+# Load Whisper model
+whisper_model = whisper.load_model("base", device="cpu")
+
+# Load the text correction model
+correction_pipeline = pipeline("text2text-generation", model="tiiuae/falcon-7b-instruct", device=-1)
+
+# Function to preprocess audio and transcribe it using Whisper
+def transcribe_audio(audio_file):
+    transcription = whisper_model.transcribe(audio_file)
+    return transcription["text"]
+
+# Function to correct grammar in text
+def correct_text(raw_text):
+    corrected = correction_pipeline(raw_text, max_length=200, num_return_sequences=1)[0]["generated_text"]
+    return corrected
+
+# Function to process the pipeline
+def process_pipeline(audio_file):
+    raw_transcription = transcribe_audio(audio_file)
+    corrected_transcription = correct_text(raw_transcription)
+    return raw_transcription, corrected_transcription
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=process_pipeline,
+    inputs=gr.Audio(type="filepath", label="Upload Audio"),
+    outputs=[
+        gr.Textbox(label="Raw Transcription"),
+        gr.Textbox(label="Corrected Transcription"),
+    ],
+    title="Speech Correction Demo",
+    description="Upload an audio file to see raw transcription and grammar-corrected output.",
+)
+
+# Launch the app
+interface.launch()