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app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the speech recognition pipeline
+pipe = pipeline("automatic-speech-recognition", model="AqeelShafy7/AudioSangraha-Audio_to_Text")
+
+# Streamlit app layout
+st.title("Speech to Text Transcription")
+
+# Sidebar layout for uploading audio and processing it
+st.sidebar.title("Upload Audio for Transcription")
+
+# File uploader widget for the audio file in the sidebar
+audio_file = st.sidebar.file_uploader("Upload Audio File (MP3 format)", type=["mp3"])
+
+# Button to process the audio file
+if st.sidebar.button("Process Audio"):
+    if audio_file is not None:
+        # Define a path for the uploaded file (within the app's directory)
+        upload_path = "uploaded_audio.mp3"
+
+        # Save the uploaded file to the defined path
+        with open(upload_path, "wb") as f:
+            f.write(audio_file.getbuffer())
+
+        # Provide the file path to the pipeline
+        result = pipe(upload_path)
+
+        # Display the transcription result in the main area
+        transcribed_text = result['text']
+        st.text_area("Transcribed Text", transcribed_text, height=300)
+    else:
+        st.error("Please upload an audio file to process.")

requirements.txt

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+streamlit==1.17.0
+altair==4.0.0
+transformers==4.38.0
+torch==2.2.0
+numpy==1.24.3
+ffmpeg
+