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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
from transformers import pipeline
import sys
import os
MODEL_NAME = "openai/whisper-large-v3-turbo"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
def transcribe(audio_file_path, task="transcribe"):
if not os.path.exists(audio_file_path):
print(f"Error: The file '{audio_file_path}' does not exist.")
return
try:
text = pipe(audio_file_path, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
return text
except Exception as e:
print(f"Error during transcription: {str(e)}")
return None
if __name__ == "__main__":
if len(sys.argv) < 2:
print("Usage: python script.py <audio_file_path> [task]")
print("task can be 'transcribe' or 'translate' (default is 'transcribe')")
sys.exit(1)
audio_file_path = sys.argv[1]
task = sys.argv[2] if len(sys.argv) > 2 else "transcribe"
if task not in ["transcribe", "translate"]:
print("Error: task must be either 'transcribe' or 'translate'")
sys.exit(1)
result = transcribe(audio_file_path, task)
if result:
print("Transcription result:")
print(result)
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