full demo.

app.py

@@ -4,38 +4,68 @@ import tempfile
 import torch
 import gradio as gr
 from transformers import pipeline
+from huggingface_hub import InferenceClient
 
 
-MODEL_NAME = "openai/whisper-large-v3"
-BATCH_SIZE = 8
-
 device = 0 if torch.cuda.is_available() else "cpu"
 
+AUDIO_MODEL_NAME = "distil-whisper/distil-large-v3" # faster and very close in performance to the full-size "openai/whisper-large-v3"
+TEXT_MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
+BATCH_SIZE = 8
+
 pipe = pipeline(
     task="automatic-speech-recognition",
-    model=MODEL_NAME,
+    model=AUDIO_MODEL_NAME,
     chunk_length_s=30,
     device=device,
 )
 
 
-def transcribe(inputs, task="transcribe"):
-    if inputs is None:
+def transcribe(audio_input):
+    """Function to convert audio to text."""
+    if audio_input is None:
         raise gr.Error("No audio file submitted!")
 
     output = pipe(
-        inputs, 
-        batch_size=BATCH_SIZE, 
-        generate_kwargs={"task": task}, 
+        audio_input,
+        batch_size=BATCH_SIZE,
+        generate_kwargs={"task": "transcribe"},
         return_timestamps=True
     )
     return output["text"]
 
-demo = gr.Interface(
-    fn=transcribe,
-    inputs=["audio"],
-    outputs="text",
-    title="Transcribe Audio to Text", # Give our demo a title
-)
 
-demo.launch()
+def organize_text(meeting_transcript):
+    messages = build_messages(meeting_transcript)
+    response = client.chat_completion(
+        messages, model=TEXT_MODEL_NAME, max_tokens=250, seed=430
+    )
+    return response.choices[0].message.content
+
+
+def build_messages(meeting_transcript) -> list:
+    system_input = "You are an assitant that organizes meeting minutes."
+    user_input = """Take this raw meeting transcript and return an organized version.
+    Here is the transcript:
+    {meeting_transcript}
+    """.format(meeting_transcript=meeting_transcript)
+
+    messages = [
+        {"role": "system", "content": system_input},
+        {"role": "user", "content": user_input},
+    ]
+    return messages
+
+def meeting_transcript_tool(audio_input):
+    meeting_text = transcribe(audio_input)
+    organized_text = organize_text(meeting_text)
+    return organized_text
+
+
+full_demo = gr.Interface(
+    fn=meeting_transcript_tool,
+    inputs=gr.Audio(type="filepath"),
+    outputs=gr.Textbox(show_copy_button=True),
+    title="The Complete Meeting Transcript Tool",
+)
+full_demo.launch()