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benchmark_whisper_s2t.py
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import argparse
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--repo_path', default="", type=str)
parser.add_argument('--backend', default="CTranslate2", type=str)
parser.add_argument('--batch_size', default=16, type=int)
parser.add_argument('--flash_attention', default="no", type=str)
parser.add_argument('--better_transformer', default="no", type=str)
parser.add_argument('--eval_mp3', default="no", type=str)
parser.add_argument('--eval_multilingual', default="yes", type=str)
args = parser.parse_args()
return args
def run(repo_path, backend, flash_attention=False, better_transformer=False, batch_size=16, eval_mp3=False, eval_multilingual=True):
import sys, time, os
if len(repo_path):
sys.path.append(repo_path)
import whisper_s2t
import pandas as pd
if backend.lower() in ["huggingface", "hf"]:
asr_options = {
"use_flash_attention": flash_attention,
"use_better_transformer": better_transformer
}
if flash_attention:
results_dir = f"{repo_path}/results/WhisperS2T-{backend}-bs_{batch_size}-fa"
elif better_transformer:
results_dir = f"{repo_path}/results/WhisperS2T-{backend}-bs_{batch_size}-bt"
else:
results_dir = f"{repo_path}/results/WhisperS2T-{backend}-bs_{batch_size}"
else:
asr_options = {}
results_dir = f"{repo_path}/results/WhisperS2T-{backend}-bs_{batch_size}"
os.makedirs(results_dir, exist_ok=True)
model = whisper_s2t.load_model("large-v2", backend=backend, asr_options=asr_options)
# KINCAID46 WAV >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
data = pd.read_csv(f'{repo_path}/data/KINCAID46/manifest_wav.tsv', sep="\t")
files = [f"{repo_path}/{fn}" for fn in data['audio_path']]
lang_codes = len(files)*['en']
tasks = len(files)*['transcribe']
initial_prompts = len(files)*[None]
_ = model.transcribe_with_vad(files,
lang_codes=lang_codes,
tasks=tasks,
initial_prompts=initial_prompts,
batch_size=batch_size)
st = time.time()
out = model.transcribe_with_vad(files,
lang_codes=lang_codes,
tasks=tasks,
initial_prompts=initial_prompts,
batch_size=batch_size)
time_kincaid46_wav = time.time()-st
data['pred_text'] = [" ".join([_['text'] for _ in _transcript]).strip() for _transcript in out]
data.to_csv(f"{results_dir}/KINCAID46_WAV.tsv", sep="\t", index=False)
# KINCAID46 MP3 >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
if eval_mp3:
data = pd.read_csv(f'{repo_path}/data/KINCAID46/manifest_mp3.tsv', sep="\t")
files = [f"{repo_path}/{fn}" for fn in data['audio_path']]
lang_codes = len(files)*['en']
tasks = len(files)*['transcribe']
initial_prompts = len(files)*[None]
st = time.time()
out = model.transcribe_with_vad(files,
lang_codes=lang_codes,
tasks=tasks,
initial_prompts=initial_prompts,
batch_size=batch_size)
time_kincaid46_mp3 = time.time()-st
data['pred_text'] = [" ".join([_['text'] for _ in _transcript]).strip() for _transcript in out]
data.to_csv(f"{results_dir}/KINCAID46_MP3.tsv", sep="\t", index=False)
else:
time_kincaid46_mp3 = 0.0
# MultiLingualLongform
if eval_multilingual:
data = pd.read_csv(f'{repo_path}/data/MultiLingualLongform/manifest.tsv', sep="\t")
files = [f"{repo_path}/{fn}" for fn in data['audio_path']]
lang_codes = data['lang_code'].to_list()
tasks = len(files)*['transcribe']
initial_prompts = len(files)*[None]
st = time.time()
out = model.transcribe_with_vad(files,
lang_codes=lang_codes,
tasks=tasks,
initial_prompts=initial_prompts,
batch_size=batch_size)
time_multilingual = time.time()-st
data['pred_text'] = [" ".join([_['text'] for _ in _transcript]).strip() for _transcript in out]
data.to_csv(f"{results_dir}/MultiLingualLongform.tsv", sep="\t", index=False)
else:
time_multilingual = 0.0
infer_time = [
["Dataset", "Time"],
["KINCAID46 WAV", time_kincaid46_wav],
["KINCAID46 MP3", time_kincaid46_mp3],
["MultiLingualLongform", time_multilingual]
]
infer_time = pd.DataFrame(infer_time[1:], columns=infer_time[0])
infer_time.to_csv(f"{results_dir}/infer_time.tsv", sep="\t", index=False)
if __name__ == '__main__':
args = parse_arguments()
eval_mp3 = True if args.eval_mp3 == "yes" else False
eval_multilingual = True if args.eval_multilingual == "yes" else False
flash_attention = True if args.flash_attention == "yes" else False
better_transformer = True if args.better_transformer == "yes" else False
run(args.repo_path, args.backend, flash_attention=flash_attention, better_transformer=better_transformer, batch_size=args.batch_size, eval_mp3=eval_mp3, eval_multilingual=eval_multilingual)