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import whisper |
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import os |
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from pytube import YouTube |
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import pandas as pd |
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import plotly_express as px |
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import nltk |
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import plotly.graph_objects as go |
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from optimum.onnxruntime import ORTModelForSequenceClassification |
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification |
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from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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from sentence_transformers import SentenceTransformer, CrossEncoder, util |
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import streamlit as st |
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import en_core_web_lg |
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from functions import * |
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nltk.download('punkt') |
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from nltk import sent_tokenize |
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st.sidebar.header("Home") |
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asr_model_options = ['base.en','small.en'] |
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asr_model_name = st.sidebar.selectbox("Whisper Model Options", options=asr_model_options, key='sbox') |
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st.markdown("## Earnings Call Analysis Whisperer") |
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st.markdown( |
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""" |
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This app assists finance analysts with transcribing and analysis Earnings Calls by carrying out the following tasks: |
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- Transcribing earnings calls using Open AI's [Whisper](https://github.com/openai/whisper). |
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- Analysing the sentiment of transcribed text using the quantized version of [FinBert-Tone](https://huggingface.co/nickmuchi/quantized-optimum-finbert-tone). |
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- Summarization of the call with [FaceBook-Bart-Large-CNN](https://huggingface.co/facebook/bart-large-cnn) model with entity extraction |
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- Semantic search engine with [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) and reranking results with a Cross-Encoder. |
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**π Enter a YouTube Earnings Call URL below and navigate to the sidebar tabs** |
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""" |
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) |
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if 'sbox' not in st.session_state: |
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st.session_state.sbox = '' |
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if "url" not in st.session_state: |
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st.session_state.url = "https://www.youtube.com/watch?v=iWobmXvCM0c" |
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if "earnings_passages" not in st.session_state: |
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st.session_state["earnings_passages"] = '' |
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if "sen_df" not in st.session_state: |
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st.session_state['sen_df'] = '' |
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url_input = st.text_input( |
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label="Enter YouTube URL, example below is FaceBiik Earnings Call 2022", key="url") |
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st.markdown( |
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"<h3 style='text-align: center; color: red;'>OR</h3>", |
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unsafe_allow_html=True |
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) |
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upload_wav = st.file_uploader("Upload a .wav sound file ",key="upload") |
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auth_token = os.environ.get("auth_token") |
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st.markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-earnings-call-whisperer)") |
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