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import os
os.system("pip install gradio==3.0.18")
os.system("pip install git+https://github.com/openai/whisper.git")
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
import gradio as gr
import whisper
import spacy
nlp = spacy.load('en_core_web_sm')
nlp.add_pipe('sentencizer')
model = whisper.load_model("small")
def inference(audio):
audio = whisper.load_audio(audio)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(model.device)
_, probs = model.detect_language(mel)
options = whisper.DecodingOptions(fp16 = False)
result = whisper.decode(model, mel, options)
return result["text"]
def inference_full(audio):
result = model.transcribe(audio)
return result["text"]
def split_in_sentences(text):
doc = nlp(text)
return [str(sent).strip() for sent in doc.sents]
def make_spans(text,results):
results_list = []
for i in range(len(results)):
results_list.append(results[i]['label'])
facts_spans = []
facts_spans = list(zip(split_in_sentences(text),results_list))
return facts_spans
auth_token = os.environ.get("HF_Token")
##Speech Recognition
asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
def transcribe(audio):
text = asr(audio)["text"]
return text
def speech_to_text(speech):
text = asr(speech)["text"]
return text
##Summarization
summarizer = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM")
def summarize_text(text):
resp = summarizer(text)
stext = resp[0]['summary_text']
return stext
summarizer1 = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
def summarize_text1(text):
resp = summarizer1(text)
stext = resp[0]['summary_text']
return stext
summarizer2 = pipeline("summarization", model="knkarthick/MEETING-SUMMARY-BART-LARGE-XSUM-SAMSUM-DIALOGSUM-AMI")
def summarize_text2(text):
resp = summarizer2(text)
stext = resp[0]['summary_text']
return stext
##Fiscal Tone Analysis
sen_model= pipeline("sentiment-analysis", model='knkarthick/Sentiment-Analysis', tokenizer='knkarthick/Sentiment-Analysis')
def text_to_sentiment(text):
sentiment = sen_model(text)[0]["label"]
return sentiment
##Fiscal Sentiment by Sentence
def sen_ext(text):
results = sen_model(split_in_sentences(text))
return make_spans(text,results)
demo = gr.Blocks()
with demo:
gr.Markdown("## Meeting Transcript AI Use Cases")
gr.Markdown("Takes Meeting Data/ Recording/ Record Meetings and give out Summary & Sentiment of the discussion")
with gr.Row():
with gr.Column():
audio_file = gr.inputs.Audio(source="microphone", type="filepath")
with gr.Row():
b1 = gr.Button("Recognize Speech")
with gr.Row():
text = gr.Textbox(label="FB Model", value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
b1.click(speech_to_text, inputs=audio_file, outputs=text)
with gr.Row():
text = gr.Textbox(label="Whisper", value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
b1.click(inference, inputs=audio_file, outputs=text)
with gr.Row():
text = gr.Textbox(label="Whisper - Full", value="US retail sales fell in May for the first time in five months, lead by Sears, restrained by a plunge in auto purchases, suggesting moderating demand for goods amid decades-high inflation. The value of overall retail purchases decreased 0.3%, after a downwardly revised 0.7% gain in April, Commerce Department figures showed Wednesday. Excluding Tesla vehicles, sales rose 0.5% last month. The department expects inflation to continue to rise.")
b1.click(inference_full, inputs=audio_file, outputs=text)
with gr.Row():
b2 = gr.Button("Overall Sentiment Analysis of Dialogues")
fin_spans = gr.HighlightedText()
b2.click(sen_ext, inputs=text, outputs=fin_spans)
with gr.Row():
b3 = gr.Button("Summary Text Outputs")
with gr.Column():
with gr.Row():
stext = gr.Textbox(label="Model-I")
b3.click(summarize_text, inputs=text, outputs=stext)
with gr.Column():
with gr.Row():
stext1 = gr.Textbox(label="Model-II")
b3.click(summarize_text1, inputs=text, outputs=stext1)
with gr.Column():
with gr.Row():
stext2 = gr.Textbox(label="Model-III")
b3.click(summarize_text2, inputs=text, outputs=stext2)
with gr.Row():
b4 = gr.Button("Sentiment Analysis")
with gr.Column():
with gr.Row():
label = gr.Label(label="Sentiment Of Summary-I")
b4.click(text_to_sentiment, inputs=stext, outputs=label)
with gr.Column():
with gr.Row():
label1 = gr.Label(label="Sentiment Of Summary-II")
b4.click(text_to_sentiment, inputs=stext1, outputs=label1)
with gr.Column():
with gr.Row():
label2 = gr.Label(label="Sentiment Of Summary-III")
b4.click(text_to_sentiment, inputs=stext2, outputs=label2)
with gr.Row():
b5 = gr.Button("Dialogue Sentiment Analysis")
with gr.Column():
with gr.Row():
fin_spans = gr.HighlightedText(label="Sentiment Of Summary-I Dialogues")
b5.click(sen_ext, inputs=stext, outputs=fin_spans)
with gr.Column():
with gr.Row():
fin_spans1 = gr.HighlightedText(label="Sentiment Of Summary-II Dialogues")
b5.click(sen_ext, inputs=stext1, outputs=fin_spans1)
with gr.Column():
with gr.Row():
fin_spans2 = gr.HighlightedText(label="Sentiment Of Summary-III Dialogues")
b5.click(sen_ext, inputs=stext2, outputs=fin_spans2)
demo.launch()