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import os
from smolagents import tool # Assuming smolagents.tool is the correct decorator
@tool
def transcribe_mp3_audio_file(mp3_file_path: str) -> str:
"""
Transcribes an MP3 audio file using OpenAI's Whisper 'base' model.
Args:
mp3_file_path (str): The absolute local path to the MP3 audio file.
This path should be obtained from the 'File Information' section
if the file was downloaded by the agent.
Returns:
str: The transcribed text from the audio file, or an error message if transcription fails.
"""
try:
import whisper # Attempt to import whisper only when the tool is called
except ImportError:
return ("Error: The 'openai-whisper' library is required but not installed. "
"Please install it using 'pip install openai-whisper' and ensure ffmpeg is also installed.")
if not os.path.exists(mp3_file_path):
return f"Error: Audio file not found at the specified path: '{mp3_file_path}'. Please verify the path."
if not mp3_file_path.lower().endswith(".mp3"):
return f"Error: The provided file path '{mp3_file_path}' does not appear to be an MP3 file. This tool currently only supports .mp3 files."
try:
print(
f"AudioTool: Loading Whisper 'base' model to transcribe '{mp3_file_path}'...")
# You can choose different model sizes: "tiny", "base", "small", "medium", "large"
# "base" is a good balance of speed and accuracy for many use cases.
# Larger models are more accurate but slower and require more resources.
model = whisper.load_model("base")
print(f"AudioTool: Transcribing audio from '{mp3_file_path}'...")
# fp16=False can improve compatibility on CPU
result = model.transcribe(mp3_file_path, fp16=False)
transcribed_text = result.get("text", "")
if transcribed_text:
print(
f"AudioTool: Transcription successful for '{mp3_file_path}'.")
return transcribed_text
else:
# This case might occur if the audio is silent or whisper couldn't detect speech
return f"Notice: Transcription resulted in empty text for '{mp3_file_path}'. The audio might be silent or contain no clear speech."
except FileNotFoundError: # Should be caught by os.path.exists, but as a fallback for whisper's internal handling
return f"Error: Whisper could not find the audio file at path: '{mp3_file_path}' (even if it was initially detected)."
except Exception as e:
# Check if the error is due to ffmpeg not being found
if "ffmpeg" in str(e).lower() and ("not found" in str(e).lower() or "not installed" in str(e).lower()):
return ("Error during transcription: ffmpeg not found. "
"OpenAI Whisper requires ffmpeg to be installed and in your system's PATH. "
f"Details: {type(e).__name__} - {str(e)}")
return f"Error during audio transcription for '{mp3_file_path}': {type(e).__name__} - {str(e)}"
if __name__ == '__main__':
# This is a placeholder for testing.
# To test, you would need an actual MP3 file.
# For example:
# test_mp3_path = "path/to/your/test_audio.mp3"
# if os.path.exists(test_mp3_path):
# print(f"--- Testing with MP3 file: {test_mp3_path} ---")
# transcript = transcribe_mp3_audio_file(test_mp3_path)
# print("Transcription Result:")
# print(transcript)
# else:
# print(f"Test MP3 file not found at: {test_mp3_path}. Cannot run local test.")
print("Audio transcription tool defined. To test, provide a path to an MP3 file in the example block.")
# Test with a non-existent file
print("\n--- Testing with non-existent file ---")
print(transcribe_mp3_audio_file("non_existent_file.mp3"))
# Test with a non-mp3 file
print("\n--- Testing with non-MP3 file extension ---")
# Create a dummy file for this test
dummy_file = "dummy.txt"
with open(dummy_file, "w") as f:
f.write("this is not an mp3")
print(transcribe_mp3_audio_file(dummy_file))
os.remove(dummy_file)
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