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import argparse
import os
import sys
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
def transcribe_audio(file_path):
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "nyrahealth/CrisperWhisper" # You can change this to a different model if needed
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
chunk_length_s=30,
batch_size=16,
return_timestamps="word",
torch_dtype=torch_dtype,
device=device,
)
result = pipe(file_path)
return result
def main():
parser = argparse.ArgumentParser(description="Transcribe an audio file.")
parser.add_argument("--f", type=str, required=True, help="Path to the audio file")
args = parser.parse_args()
if not os.path.exists(args.f):
print(f"Error: The file '{args.f}' does not exist.")
sys.exit(1)
try:
transcription = transcribe_audio(args.f)
print("Transcription:")
print(transcription["text"])
except Exception as e:
print(f"An error occurred while transcribing the audio: {str(e)}")
sys.exit(1)
if __name__ == "__main__":
main()
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