| import streamlit as st | |
| import requests | |
| import os | |
| API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo" | |
| headers = {"Authorization": f"Bearer {st.secrets['HF_API_KEY']}"} | |
| def query(file): | |
| data = file.read() | |
| response = requests.post(API_URL, headers=headers, data=data) | |
| return response.json() | |
| st.title("Speech Recognition with Whisper") | |
| uploaded_file = st.file_uploader("Choose an audio file", type=['wav', 'mp3', 'flac']) | |
| if uploaded_file is not None: | |
| st.audio(uploaded_file, format='audio/wav') | |
| if st.button('Transcribe'): | |
| with st.spinner('Transcribing...'): | |
| result = query(uploaded_file) | |
| if 'text' in result: | |
| st.success("Transcription completed!") | |
| st.write("Transcribed text:") | |
| st.write(result['text']) | |
| else: | |
| st.error("An error occurred during transcription.") | |
| st.write("Error details:") | |
| st.write(result) | |
| st.markdown("---") | |
| st.write("Note: This app uses the Whisper API from Hugging Face.") |