Improve the command line interface and add script to store transcriptions in individual folders under 'Results'

run_and_save.py

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+import argparse
+import os
+from datetime import datetime
+from whisper import load_model, transcribe  # Import necessary functions from Whisper
+from colorama import Fore, Style
+
+
+def create_output_folder(audio_file):
+    # Base folder where all test folders will be created
+    base_folder = "Results"
+
+    # Use the audio file name (without extension) and a timestamp to create a unique folder name
+    folder_name = os.path.splitext(os.path.basename(audio_file))[0]
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_folder = os.path.join(base_folder, f"{folder_name}_{timestamp}")
+
+    # Create the Tests folder and the output folder if they don’t exist
+    os.makedirs(output_folder, exist_ok=True)
+    return output_folder
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Transcribe audio files and save results to a unique folder.")
+    parser.add_argument("file", type=str, help="Path to the audio file.")
+    parser.add_argument("--model", choices=["tiny", "base", "small", "medium", "large"], default="tiny",
+                        help="Choose the model size for transcription (default is 'tiny').")
+
+    args = parser.parse_args()
+
+    # Check if the audio file exists
+    if not os.path.isfile(args.file):
+        print(Fore.RED + f"Error: The file '{args.file}' does not exist." + Style.RESET_ALL)
+        return
+
+    # Load the Whisper model
+    print(Fore.CYAN + f"Loading model '{args.model}'..." + Style.RESET_ALL)
+    model = load_model(args.model)
+
+    # Create a unique folder under "Tests" for this run
+    output_folder = create_output_folder(args.file)
+    print(Fore.CYAN + f"Created folder for results: {output_folder}" + Style.RESET_ALL)
+
+    # Run the transcription
+    try:
+        print(Fore.CYAN + f"Transcribing '{args.file}' using the '{args.model}' model..." + Style.RESET_ALL)
+        result = transcribe(model, args.file)
+
+        # Save transcription to a text file in the output folder
+        output_file = os.path.join(output_folder, "transcription.txt")
+        with open(output_file, "w") as f:
+            f.write(result["text"])
+        print(Fore.GREEN + f"Transcription completed successfully! Saved to {output_file}" + Style.RESET_ALL)
+
+    except Exception as e:
+        print(Fore.RED + f"An error occurred: {e}" + Style.RESET_ALL)
+
+
+if __name__ == "__main__":
+    main()

whisper_cli.py

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+import argparse
+import os
+from whisper import load_model  # For loading the Whisper model
+from whisper.transcribe import transcribe  # Import the transcribe function
+from colorama import Fore, Style
+
+def main():
+    parser = argparse.ArgumentParser(description="Transcribe audio files using OpenAI's Whisper model.")
+    parser.add_argument("file", type=str, help="Path to the audio file.")
+    parser.add_argument("--model", choices=["tiny", "base", "small", "medium", "large"], default="tiny",
+                        help="Choose the model size for transcription (default is 'tiny').")
+
+    args = parser.parse_args()
+
+    # Check if the audio file exists
+    if not os.path.isfile(args.file):
+        print(Fore.RED + f"Error: The file '{args.file}' does not exist." + Style.RESET_ALL)
+        return
+
+    print(Fore.CYAN + f"Transcribing '{args.file}' using the '{args.model}' model..." + Style.RESET_ALL)
+
+    try:
+        # Load the model
+        model = load_model(args.model)
+
+        # Transcribe the audio file
+        result = transcribe(model, args.file)
+
+        # Print the transcription result
+        print(Fore.GREEN + "Transcription completed successfully!" + Style.RESET_ALL)
+        print(result)
+
+    except Exception as e:
+        print(Fore.RED + f"An error occurred: {e}" + Style.RESET_ALL)
+
+if __name__ == "__main__":
+    main()