75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
import requests
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import argparse
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import psycopg2
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import json
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import os
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from dotenv import load_dotenv
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load_dotenv()
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db_host = os.getenv('DB_HOST')
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db_name = os.getenv('DB_NAME')
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db_user = os.getenv('DB_USER')
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db_password = os.getenv('DB_PASSWORD')
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openai_api_key = os.getenv('OPENAI_API_KEY')
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# Set up the database connection
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conn = psycopg2.connect(
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host= db_host
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,database= db_name
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,user= db_user
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,password= db_password
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,connect_timeout = 120
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)
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# Define the API endpoint and headers
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url = 'https://api.openai.com/v1/audio/translations'
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headers = {
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'Authorization': f'Bearer {openai_api_key}'
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}
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params = {
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'model': 'whisper-1',
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'response_format': 'vtt'
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}
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data = {
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'model': 'whisper-1'
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}
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# Parse command-line arguments
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parser = argparse.ArgumentParser()
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parser.add_argument('dir_path', help='path to directory containing audio files to transcribe')
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args = parser.parse_args()
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# Define the audio file to be transcribed
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for file_name in os.listdir(args.dir_path):
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if file_name.endswith('.wav'):
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file_path = os.path.join(args.dir_path, file_name)
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file_name = os.path.basename(file_path)
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file_date = file_name[:10]
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# Check if there is a row in the database with a matching filename
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cur = conn.cursor()
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cur.execute("SELECT COUNT(*) FROM rlarp.thirtysec WHERE filename = %s", (file_name,))
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count = cur.fetchone()[0]
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cur.close()
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if count > 0:
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print(f"Skipping {file_name} (already processed)")
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continue
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# Send the transcription request and retrieve the results
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print(f"to be processed {file_path}")
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audio_file = open(file_path, 'rb')
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response = requests.post(url, headers=headers, params=params, data=data, files={'file': audio_file})
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transcript = response.text
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print(response.text)
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# Insert the JSON summary into the database
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cur = conn.cursor()
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cur.execute("INSERT INTO rlarp.thirtysec (filename, mdate, message) VALUES (%s, %s, %s);", (file_name, file_date, response.text))
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conn.commit()
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cur.close()
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#close db connection
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conn.close()
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