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from pathlib import Path
import csv
import time
from funasr import AutoModel
from funasr_onnx import SeacoParaformer, CT_Transformer, Fsmn_vad
model_dir = Path("/Users/jeqin/work/code/Translator/moyoyo_asr_models")
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 export_onnx():
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'
model = AutoModel(model=asr_model_path)
output = model.export(type="onnx", quantize=True, disable_update=True)
print(output)
model = AutoModel(model=vad_model_path)
output = model.export(type="onnx", quantize=True, disable_update=True)
print(output)
model = AutoModel(model=punc_model_path)
output = model.export(type="onnx", quantize=True, disable_update=True)
print(output)
def run_funasr():
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("/Users/jeqin/work/code/TestTranslator/tests/test_data/test_audios")
rows = [["file_name", "inference_time", "inference_result"]]
for audio in sorted(audios.glob("Chinese-mayun-part2.mp3")):
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"run_funasr.csv", rows)
def run_onnx(quant=True):
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()
vad_model = Fsmn_vad(vad_model_path, quantize=quant)
asr_model = SeacoParaformer(asr_model_path, quantize=quant)
punc_model = CT_Transformer(punc_model_path, quantize=quant)
t1 = time.time()
print("load model: ", t1 - t0)
audios = Path("/Users/moyoyo/code/tests/audios")
rows = [["file_name", "inference_time", "inference_result"]]
for audio in sorted(audios.glob("*s/*.wav")):
t1 = time.time()
vad_res = vad_model(str(audio))
t2 = time.time()
# print("vad time:", t2 - t1)
asr_res = asr_model(str(audio), hotwords="")
asr_text = asr_res[0]["preds"]
t3 = time.time()
# print("asr time:", t3 - t2)
# print("asr text:", asr_text)
result = punc_model(asr_text)
text = result[0]
t4 = time.time()
# print("punc time:", t4 - t3)
# print("punc text:", text)
print(text)
t = t4 - t1
print("inference time:", t)
rows.append([f"{audio.parent.name}/{audio.name}", t, text])
file_name = "run_quant.csv" if quant else "run_onnx.csv"
save_csv(file_name, rows)
if __name__ == '__main__':
run_funasr() |