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from funasr import AutoModel
from pathlib import Path
import time
import csv
def save_csv(file_path, rows):
with open(file_path, "w", encoding="utf-8") as f:
writer = csv.writer(f)
writer.writerows(rows)
print(f"write csv to {file_path}")
def main():
model_dir = Path("/Users/jeqin/work/code/Translator/python_server/moyoyo_asr_models")
asr_model_path = model_dir / 'speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
vad_model_path = model_dir / 'speech_fsmn_vad_zh-cn-16k-common-pytorch'
punc_model_path = model_dir / 'punc_ct-transformer_cn-en-common-vocab471067-large'
t0 = time.time()
model = AutoModel(
model=asr_model_path.as_posix(),
vad_model=vad_model_path.as_posix(),
punc_model=punc_model_path.as_posix(),
log_level="ERROR",
disable_update=True
)
t1 = time.time()
print("load model: ", t1 - t0)
audios = Path("/test_data/audio_clips/")
rows = [["file_name", "inference_time", "inference_result"]]
for audio in sorted(audios.glob("*mix/*")):
print(audio)
t1 = time.time()
try:
result = model.generate(input=str(audio), disable_pbar=True,
hotword="")
except Exception as e:
print(audio)
print(e)
t2 = time.time()
t = t2-t1
print("inference time:", t)
text = result[0]["text"]
print("inference result", text)
rows.append([f"{audio.parent.name}/{audio.name}", t, text])
save_csv(f"csv/funasr.csv", rows)
if __name__ == '__main__':
main() |