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import streamlit as st | |
import requests | |
from pydub import AudioSegment | |
from io import BytesIO | |
# Access the Hugging Face API key from the secrets | |
CTP_DATASCIENCE = st.secrets.get("CTP_DATASCIENCE") | |
# Set up the headers for the Hugging Face API request using the API key | |
headers = {"Authorization": f"Bearer {CTP_DATASCIENCE}"} | |
# Define the Hugging Face API URL for Whisper model | |
API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3-turbo" | |
# Function to make the API request with the given file URL | |
def query(file_url): | |
# Download the audio file from the Hugging Face Space URL | |
response = requests.get(file_url) | |
if response.status_code == 200: | |
# Process the audio file into a compatible format (16 kHz, mono) | |
audio_file = BytesIO(response.content) | |
# Load and process the audio file using pydub | |
audio = AudioSegment.from_file(audio_file) | |
audio = audio.set_channels(1) # Ensure mono audio | |
audio = audio.set_frame_rate(16000) # Ensure 16 kHz sample rate | |
# Save the processed audio to a buffer in WAV format | |
audio_buffer = BytesIO() | |
audio.export(audio_buffer, format="wav") # Export as WAV to ensure compatibility | |
audio_buffer.seek(0) # Reset pointer to the start of the buffer | |
# Send the audio data to the Hugging Face API | |
response_api = requests.post(API_URL, headers=headers, files={"file": audio_buffer}) | |
if response_api.status_code == 200: | |
return response_api.json() # Return the transcription result | |
else: | |
return {"error": "Failed to process the audio with Hugging Face API."} | |
else: | |
return {"error": "Failed to download the file from the provided URL."} | |
# URL to your audio file on Hugging Face Space | |
file_url = "https://huggingface.co/spaces/AndrewLam489/PillID_Transcribe/resolve/main/ce7581cd-534c-4f4b-b6a7-35be3e52fbc2.webm" | |
# Call the function with the audio file URL | |
output = query(file_url) | |
# Display the output of the API request | |
st.write(output) | |