feat: added the first version of the script

.python-version

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+3.12.3

init.py

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+import sys
+import subprocess
+import os
+
+from openai import OpenAI
+
+client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+
+AUDIO_FILE_PATH = None
+LANGUAGE = "English"  # Default language
+
+
+def print_help():
+    help_message = """
+Usage: python script.py <path_to_audio_file> [options]
+
+Options:
+  -h, --help         Show this help message and exit
+  -l, --language     Specify the language for the summary (default is English)
+
+Description:
+  This script converts an audio file to FLAC format, transcribes the audio using OpenAI's Whisper model,
+  and summarizes the transcription using OpenAI's GPT-4 model. The converted FLAC file is deleted after processing.
+
+Arguments:
+  <path_to_audio_file>  Path to the audio file you want to transcribe and summarize.
+"""
+    print(help_message)
+
+
+# Ensure the audio file path is provided or help is requested
+if len(sys.argv) < 2 or "-h" in sys.argv or "--help" in sys.argv:
+    print_help()
+    sys.exit(0)
+
+
+for i, arg in enumerate(sys.argv):
+    if arg not in ("-l", "--language"):
+        if AUDIO_FILE_PATH is None and arg not in (sys.argv[0], "-h", "--help"):
+            AUDIO_FILE_PATH = arg
+    if arg in ("-l", "--language") and i + 1 < len(sys.argv):
+        LANGUAGE = sys.argv[i + 1]
+
+if AUDIO_FILE_PATH is None:
+    print("Error: Path to audio file is required.")
+    print_help()
+    sys.exit(1)
+
+
+def convert_audio_file(file_path):
+    converted_file = "converted_audio.flac"
+    subprocess.run(
+        ["ffmpeg", "-i", file_path, "-ar", "16000", "-ac", "1", converted_file],
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.DEVNULL,
+        check=True,
+    )
+    return converted_file
+
+
+def transcribe_audio(file_path):
+    with open(file_path, "rb") as audio_file:
+        response = client.audio.transcriptions.create(
+            model="whisper-1",
+            file=audio_file,
+        )
+    return response.text
+
+
+def summarize_transcription(transcription_text):
+    response = client.chat.completions.create(
+        model="gpt-4",
+        messages=[
+            {"role": "system", "content": f"Please summarize this text in {LANGUAGE}"},
+            {"role": "user", "content": transcription_text},
+        ],
+    )
+    return response.choices[0].message.content
+
+
+def main():
+    try:
+        converted_file_path = convert_audio_file(AUDIO_FILE_PATH)
+        transcription = transcribe_audio(converted_file_path)
+        summary = summarize_transcription(transcription)
+
+        print("Transcription:")
+        print(transcription, "\n")
+        print("Summary:")
+        print(summary, "\n")
+    finally:
+        if os.path.exists(converted_file_path):
+            os.remove(converted_file_path)
+
+
+if __name__ == "__main__":
+    main()