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| import streamlit as st | |
| import requests | |
| from transformers import pipeline | |
| # Function to send the audio file to the Hugging Face Whisper API | |
| def query(file_data, my_key): | |
| API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo" | |
| headers = {"Authorization": f"Bearer {my_key}"} | |
| try: | |
| response = requests.post(API_URL, headers=headers, files={"file": file_data}) | |
| if response.status_code == 200: | |
| return response.json() | |
| else: | |
| return {"error": f"API returned status code {response.status_code}: {response.text}"} | |
| except requests.exceptions.RequestException as e: | |
| return {"error": str(e)} | |
| # Streamlit UI elements | |
| st.title("Whisper Transcription App") | |
| st.write("Upload one or more .wav, .mp3, or .flac audio files, and get the transcription.") | |
| # Get the user's Hugging Face API key | |
| my_key = st.text_input("Enter your Hugging Face API Key", type="password") | |
| # File uploader for audio files | |
| uploaded_files = st.file_uploader("Choose audio file(s)", type=["mp3", "wav", "flac"], accept_multiple_files=True) | |
| if my_key and uploaded_files: # Proceed only if the API key is provided and files are uploaded | |
| st.write("Processing your files...") | |
| results = {} | |
| for uploaded_file in uploaded_files: | |
| try: | |
| # Read the file data | |
| file_data = uploaded_file.read() | |
| # Query the API | |
| st.write(f"Transcribing: {uploaded_file.name}") | |
| result = query(file_data, my_key) | |
| # Store the result | |
| results[uploaded_file.name] = result | |
| except Exception as e: | |
| st.write(f"Error processing {uploaded_file.name}: {str(e)}") | |
| # Display results | |
| for file, result in results.items(): | |
| st.write(f"**Results for {file}:**") | |
| if "error" in result: | |
| st.error(result["error"]) | |
| else: | |
| st.json(result) | |