full?

app.py

@@ -6,7 +6,7 @@ from simple_diarizer.diarizer import Diarizer
 import streamlit as st
 
 def speech_to_text(uploaded):
-    model = whisper.load_model('tiny')
+    model = whisper.load_model('base')
     result = model.transcribe(uploaded,verbose=True)
     return f'You said: {result["text"]}'
 
@@ -26,7 +26,7 @@ def segment(nu_speakers):
 
 def audio_to_df(uploaded):
     monotize(uploaded)
-    model = whisper.load_model('tiny')
+    model = whisper.load_model('base')
     result = model.transcribe('mono.wav',verbose=True,
                           without_timestamps=False)
     tdf = pd.DataFrame(result['segments'])
@@ -75,6 +75,17 @@ def transcribe(uploaded, nu_speakers):
     return '\n'.join(lines)
 
 
+descript = ("This web app creates transcripts using OpenAI's [Whisper](https://github.com/openai/whisper) to transcribe "
+            "audio files combined with [Chau](https://github.com/cvqluu)'s [Simple Diarizer](https://github.com/cvqluu/simple_diarizer) "
+            "to partition the text by speaker.\n"
+            "* You can upload a audio or video file of up to 200MBs.\n"
+            "* Creating the transcript takes some time. "
+            "Using the default base transcription model, the process takes approximately 20% of the length of the audio file.\n "
+            "* After uploading the file, **be sure to select the number of speakers**." )
+
+st.title("Automated Transcription")
+st.markdown(descript)
+
 form = st.form(key='my_form')
 uploaded = form.file_uploader("Choose a file")
 nu_speakers = form.slider('Number of speakers in audio file:', min_value=1, max_value=6, value=2, step=1)