import whisper import os import pandas as pd import plotly_express as px import nltk import plotly.graph_objects as go from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification from sentence_transformers import SentenceTransformer, CrossEncoder, util import streamlit as st import en_core_web_lg nltk.download('punkt') from nltk import sent_tokenize auth_token = os.environ.get("auth_token") st.sidebar.header("Home") asr_model_options = ['tiny.en','base.en','small.en'] asr_model_name = st.sidebar.selectbox("Whisper Model Options", options=asr_model_options, key="sbox") st.markdown("## Earnings Call Analysis Whisperer") twitter_link = """ [![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi) """ st.markdown(twitter_link) st.markdown( """ This app assists finance analysts with transcribing and analysis Earnings Calls by carrying out the following tasks: - Transcribing earnings calls using Open AI's Whisper API, takes approx 3mins to transcribe a 1hr call less than 25mb in size. - Analysing the sentiment of transcribed text using the quantized version of [FinBert-Tone](https://huggingface.co/nickmuchi/quantized-optimum-finbert-tone). - Summarization of the call with [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) model with entity extraction - Question Answering Search engine powered by Langchain and [Sentence Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). - Knowledge Graph generation using [Babelscape/rebel-large](https://huggingface.co/Babelscape/rebel-large) model. **👇 Enter a YouTube Earnings Call URL below and navigate to the sidebar tabs** """ ) if 'sbox' not in st.session_state: st.session_state.sbox = asr_model_name if "earnings_passages" not in st.session_state: st.session_state["earnings_passages"] = '' if "sen_df" not in st.session_state: st.session_state['sen_df'] = '' url_input = st.text_input( label="Enter YouTube URL, example below is McDonalds Earnings Call Q1 2023", value="https://www.youtube.com/watch?v=4p6o5kkZYyA") if 'url' not in st.session_state: st.session_state['url'] = "" st.session_state['url'] = url_input st.markdown( "

OR

", unsafe_allow_html=True ) upload_wav = st.file_uploader("Upload a .wav/.mp3/.mp4 audio file ",key="upload",type=['.wav','.mp3','.mp4']) st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-earnings-call-whisperer)")