| import streamlit as st | |
| import datetime | |
| from transformers import pipeline | |
| import gradio as gr | |
| asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") | |
| classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion") | |
| def transcribe(audio): | |
| text = asr(audio)["text"] | |
| return text | |
| def speech_to_text(speech): | |
| text = asr(speech)["text"] | |
| return text | |
| def text_to_sentiment(text): | |
| sentiment = classifier(text)[0]["label"] | |
| return sentiment | |
| demo = gr.Blocks() | |
| with demo: | |
| audio_file = gr.inputs.Audio(source="microphone", type="filepath") | |
| b1 = gr.Button("Recognize Speech") | |
| b1.click(speech_to_text, inputs=audio_file, outputs=text) | |
| text = gr.Textbox() | |
| b2 = gr.Button("Classify Sentiment") | |
| b2.click(text_to_sentiment, inputs=text, outputs=label) | |
| label = gr.Label() | |
| demo.launch(share=True) |