Create app.py

App without streamlit_audio_recorder

app.py

@@ -0,0 +1,45 @@
+import streamlit as st
+import librosa
+from transformers import WhisperProcessor, WhisperForConditionalGeneration, WhisperConfig, AutoConfig
+import requests
+import io
+
+# Initialize the processor and model outside the function
+processor = WhisperProcessor.from_pretrained("openai/whisper-small")
+config_url = "https://raw.githubusercontent.com/yash-412/Yash-Projects/main/marathi-enhanced/config.json"  # Specify the path to your config.json file
+config_response = requests.get(config_url)
+config_dict = config_response.json() if config_response.status_code == 200 else None
+
+config = WhisperConfig.from_dict(config_dict) if config_dict else None
+model = WhisperForConditionalGeneration.from_pretrained("yash-412/whisper-marathi", config=config)
+model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="mr", task="transcribe")
+
+def get_transcription(speech):
+    # Process audio using the Whisper processor
+    input_features = processor(speech, sampling_rate=16000, return_tensors="pt").input_features
+
+    # Generate transcription using the Whisper model
+    predicted_ids = model.generate(input_features)
+
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+    return transcription
+
+def main():
+    st.title("Marathi Enhanced-Whisper Transcription")
+    st.write("Upload an audio file")
+    
+    uploaded_file = st.file_uploader("Choose an audio file", type=["mp3", "wav"])
+
+    if uploaded_file:
+        st.audio(uploaded_file, format='audio/wav', start_time=0)
+
+        audio_bytes = uploaded_file.read()
+        speech, _ = librosa.load(io.BytesIO(audio_bytes), sr=16000)
+
+        if st.button("Transcribe"):
+            transcription = get_transcription(speech)
+            st.subheader("Transcription:")
+            st.write(transcription[0])
+
+if __name__ == "__main__":
+    main()