Experimental client to test Whisper on GroqCloud

groq-whisper.py

import os
import argparse
import requests
from datetime import datetime

# Define the parser for command line arguments
parser = argparse.ArgumentParser(description='Groq Whisper transcribe')
parser.add_argument('file', type=str, help='Audio file to transcribe')
parser.add_argument('--output_file', type=str, help='File to write output', default="transcription.txt")
args = parser.parse_args()

# Set the API endpoint for Groq's OpenAI-compatible Whisper service
whisper_api_endpoint = "https://api.groq.com/openai/v1/whisper"

# Read the API key from an environment variable
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
    raise ValueError("Please set the GROQ_API_KEY environment variable.")

# Read the audio file
with open(args.file, "rb") as audio_file:
    audio_data = audio_file.read()

# Prepare the headers for the HTTP request
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/octet-stream"
}

# Make the request to the Whisper API
before = datetime.now()
response = requests.post(whisper_api_endpoint, headers=headers, data=audio_data)
after = datetime.now()

# Check if the request was successful
if response.status_code != 200:
    raise Exception(f"Whisper API request failed with status code {response.status_code}: {response.text}")

# Parse the response
transcription = response.json().get('transcription')
if not transcription:
    raise Exception("No transcription found in the response.")

# Write the transcription to the output file
with open(args.output_file, "w") as output_file:
    output_file.write(transcription)

# Calculate the time taken for the transcription
difference = after - before
print(f"Wrote {args.output_file} in {difference.total_seconds()} seconds")