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import argparse
import editdistance
import re
import subprocess
import sys
import time
from whisper.normalizers import EnglishTextNormalizer
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("reference_path", type=str, help="Path to reference transcript")
parser.add_argument("audio_path", type=str, help="Path to audio file to transcribe")
parser.add_argument("model_path", type=str, help="Path to Whisper model to use")
parser.add_argument("decode_method", type=str, help="Decoding method. Either 'greedy' or 'beam'")
args = parser.parse_args()
cmd = "./WhisperCLI.exe"
cmd_args = [
"--audio_path", args.audio_path,
"--model_path", args.model_path,
"--decode_method", args.decode_method,
]
t0 = time.time()
result = subprocess.run([cmd] + cmd_args, stdout=subprocess.PIPE)
t1 = time.time()
if result.returncode != 0:
print(f"Failed to transcribe: cmd returned {result.returncode}",
file=sys.stderr)
test_transcript = result.stdout.decode("utf-8")
with open(args.reference_path, "r") as f:
ref_transcript = f.read()
# Normalize transcripts before computing edit distance (as described in
# whisper paper).
normalize = EnglishTextNormalizer()
test_transcript = normalize(test_transcript)
ref_transcript = normalize(ref_transcript)
dist = editdistance.eval(ref_transcript, test_transcript)
print(f"Duration: {t1 - t0}")
print(f"Levenshtein distance: {dist}")
print(f"Control: {ref_transcript}")
print(f"Experiment: {test_transcript}")
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