Update app.py

app.py

@@ -0,0 +1,68 @@
+import streamlit as st
+import os
+import soundfile as sf
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+# Assuming you have your .env file configured with necessary API keys or configurations
+# load_dotenv()
+
+# Initialize the model outside the main app function to load it only once
+
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "distil-whisper/distil-large-v2"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=15,
+    batch_size=16,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+
+
+def transcribe_audio(audio_file):
+    # Save the audio file to a temporary file
+    with open("temp_audio_file", "wb") as f:
+        f.write(audio_file.getbuffer())
+    
+    # Transcribe the audio file using the Whisper model
+    result = pipe("temp_audio_file")
+    return result["text"]
+
+# Streamlit app
+def main():
+    st.title('BETTER TRANSCRIBER')
+    
+    # Audio file uploader
+    uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a", "ogg", "flac"])
+
+    if uploaded_file is not None:
+        # Show a button to start the transcription process
+        if st.button('Transcribe'):
+            # Show a message while transcribing
+            with st.spinner('Transcribing...'):
+                text = transcribe_audio(uploaded_file)
+            
+            # Show the transcription
+            st.subheader('Transcription:')
+            st.write(text)
+        else:
+            st.write('Upload an audio file to get started.')
+
+if __name__ == "__main__":
+    main()