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import argparse
import torch
import os
from transcriber.model import load_model_and_processor
from transcriber.audio import preprocess_audio, transcribe_audio
from transcriber.utils import get_audio_files_from_directory
def transcribe_multiple_files(model, processor, audio_files, target_sr=16000):
"""
Transcribes multiple audio files.
Parameters:
model: The Whisper model.
processor: The processor used for preparing the input features.
audio_files (list): List of paths to audio files.
target_sr (int): The target sampling rate for the audio.
Returns:
results (dict): Dictionary mapping file names to their transcriptions.
"""
results = {}
for file_path in audio_files:
print(f"Processing file: {file_path}")
audio = preprocess_audio(file_path, target_sr=target_sr)
transcription = transcribe_audio(model, processor, audio, target_sr=target_sr)
results[file_path] = transcription
print(f"Transcription for {file_path}: {transcription}")
return results
def main():
# Argument parser to accept directory or audio files as input
parser = argparse.ArgumentParser(description="Transcribe audio files using Whisper.")
parser.add_argument('input_path', type=str, help="Path to the audio file or directory containing audio files.")
args = parser.parse_args()
# Load model and processor once
model, processor = load_model_and_processor()
# Check if input is a directory or a single file
input_path = args.input_path
audio_files = []
if os.path.isdir(input_path):
# Get all audio files from the directory
audio_files = get_audio_files_from_directory(input_path)
elif os.path.isfile(input_path):
# Single file path provided
audio_files = [input_path]
else:
print(f"Invalid input path: {input_path}")
return
# Transcribe all audio files
transcriptions = transcribe_multiple_files(model, processor, audio_files)
# Optionally, you can store the transcriptions in a file or print them out
for file, transcription in transcriptions.items():
print(f"File: {file}, Transcription: {transcription}")
if __name__ == "__main__":
main()
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