import whisper import os from pytube import YouTube 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 from functions import * nltk.download('punkt') from nltk import sent_tokenize st.sidebar.header("Home") asr_model_options = ['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") 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](https://github.com/openai/whisper). - 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 [FaceBook-Bart-Large-CNN](https://huggingface.co/facebook/bart-large-cnn) model with entity extraction - Question Answering 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 "url" not in st.session_state: st.session_state.url = "https://www.youtube.com/watch?v=iWobmXvCM0c" 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 FaceBook Earnings Call 2022", key="url") st.markdown( "

OR

", unsafe_allow_html=True ) upload_wav = st.file_uploader("Upload a .wav sound file ",key="upload") auth_token = os.environ.get("auth_token") st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-earnings-call-whisperer)")