Create Audio_Summarizer.py

Audio_Summarizer.py

@@ -0,0 +1,46 @@
+import whisper
+import re
+import openai
+import os
+
+def transcript_generator():
+    # Load Whisper model
+    model = whisper.load_model("base")
+
+    # Transcribe audio file
+    result = model.transcribe("audio.mp4")
+
+    # Send the transcript to the summarizer
+    provide_summarizer(result)
+
+
+def provide_summarizer(Text):
+    # Set up Groq OpenAI-compatible API credentials
+    openai.api_key = os.getenv("OPENAI_API_KEY", "your-api-key-here")  # Replace or set in environment
+    openai.api_base = "https://api.groq.com/openai/v1"
+
+    # Extract text from the Whisper result
+    text_to_summarize = Text["text"]
+
+    # Send the transcription to Groq for summarization
+    response = openai.ChatCompletion.create(
+        model="llama3-8b-8192",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant who summarizes long text into bullet points."},
+            {"role": "user", "content": f"Summarize the following:\n\n{text_to_summarize}"}
+        ]
+    )
+
+    # Split the response into sentences
+    summary = re.split(r'(?<=[.!?]) +', response["choices"][0]["message"]["content"])
+
+    # Save summary to file
+    with open("summary.txt", "w+", encoding="utf-8") as file:
+        for sentence in summary:
+            cleaned = sentence.strip()
+            if cleaned:
+                file.write("- " + cleaned + "\n")
+
+
+if __name__ == "__main__":
+    transcript_generator()