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import streamlit as st
from models import BagOfModels, SoundToText, TextToSummary
from settings import MODEL_PARSER
args = MODEL_PARSER
st.set_page_config(
page_title="TTS Applications | Incore Solutions",
layout="wide",
menu_items={
"About": """This is a simple GUI for OpenAI's Whisper.""",
},
)
def open_instructions():
with open("instructions.md", "r") as f:
st.write(f.read())
# Render input type selection on the sidebar & the form
input_type = st.sidebar.selectbox("Input Type", ["YouTube", "File"])
with st.sidebar.form("input_form"):
if input_type == "YouTube":
youtube_url = st.text_input("Youtube URL")
elif input_type == "File":
input_file = st.file_uploader("File", type=["mp3", "wav"])
whisper_model = st.selectbox("Whisper model", options = [whisper for whisper in BagOfModels.get_model_names() if "whisper" in whisper] , index=1)
summary = st.checkbox("summarize")
if summary:
min_sum = st.number_input("Minimum words in the summary", min_value=1, step=1)
max_sum = min(min_sum,st.number_input("Maximum words in the summary", min_value=2, step=1))
st.form_submit_button(label="Save settings")
with st.sidebar.form("save settings"):
transcribe = st.form_submit_button(label="Transcribe!")
if transcribe:
if input_type == "YouTube":
if youtube_url and youtube_url.startswith("http"):
model = BagOfModels.load_model(whisper_model,**vars(args))
st.session_state.transcription = model.predict_stt(source=youtube_url,source_type=input_type,model_task="stt")
else:
st.error("Please enter a valid YouTube URL")
open_instructions()
elif input_type == "File":
if input_file:
model = BagOfModels.load_model(whisper_model,**vars(args))
st.session_state.transcription = model.predict_stt(source=input_file,source_type=input_type,model_task="stt")
else:
st.error("Please upload a file")
if "transcription" in st.session_state:
st.session_state.transcription.whisper()
# create two columns to separate page and youtube video
transcription_col, media_col = st.columns(2, gap="large")
transcription_col.markdown("#### Audio")
with open(st.session_state.transcription.audio_path, "rb") as f:
transcription_col.audio(f.read())
transcription_col.markdown("---")
transcription_col.markdown(f"#### Transcription (whisper model - `{whisper_model}`)")
transcription_col.markdown(f"##### Language: `{st.session_state.transcription.language}`")
# Trim raw transcribed output off tokens to simplify
raw_output = transcription_col.expander("Raw output")
raw_output.markdown(st.session_state.transcription.raw_output["text"])
if summary:
summarized_output = transcription_col.expander("summarized output")
# CURRENTLY ONLY SUPPORTS 1024 WORD TOKENS -> TODO: FIND METHOD TO INCREASE SUMMARY FOR LONGER VIDS -> 1024 * 4 = aprox 800 words within 1024 range
text_summary = TextToSummary(str(st.session_state.transcription.text[:1024*4]),min_sum,max_sum).get_summary()
summarized_output.markdown(text_summary[0]["summary_text"])
# Show transcription in format with timers added to text
time_annotated_output = transcription_col.expander("time_annotated_output")
for segment in st.session_state.transcription.segments:
time_annotated_output.markdown(
f"""[{round(segment["start"], 1)} - {round(segment["end"], 1)}] - {segment["text"]}"""
)
# Show input youtube video
if input_type == "YouTube":
media_col.markdown("---")
media_col.markdown("#### Original YouTube Video")
media_col.video(st.session_state.transcription.source)
else:
pass
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