FunASR/funasr/runtime/python/utils/test_rtf_gpu.py

import time
import sys
import librosa
from funasr.utils.types import str2bool

import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', type=str, required=True)
parser.add_argument('--backend', type=str, default='onnx', help='["onnx", "torch"]')
parser.add_argument('--wav_file', type=str, default=None, help='amp fallback number')
parser.add_argument('--quantize', type=str2bool, default=False, help='quantized model')
parser.add_argument('--intra_op_num_threads', type=int, default=1, help='intra_op_num_threads for onnx')
parser.add_argument('--batch_size', type=int, default=1, help='batch_size for onnx')
args = parser.parse_args()


from funasr.runtime.python.libtorch.funasr_torch import Paraformer
if args.backend == "onnx":
	from funasr.runtime.python.onnxruntime.funasr_onnx import Paraformer
	
if 'blade' in args.model_dir:
    import torch_blade

model = Paraformer(args.model_dir, batch_size=args.batch_size, quantize=args.quantize, intra_op_num_threads=args.intra_op_num_threads)

wav_file_f = open(args.wav_file, 'r')
wav_files = wav_file_f.readlines()

# warm-up
total = 0.0
num = 100
wav_path = []
for i in range(num * args.batch_size):
    wav_path_i = wav_files[i % len(wav_files)]
    wav_path_i = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
    wav_path += [wav_path_i]
beg_time = time.time()
result = model(wav_path)
end_time = time.time()
duration = end_time-beg_time
print(result)
print("num: {}, time, {}, avg: {}".format(len(wav_path), duration, total/(i+1)))

# infer time
wav_path = []
beg_time = time.time()
for i, wav_path_i in enumerate(wav_files):
	wav_path_i = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
	wav_path += [wav_path_i]
result = model(wav_path)
end_time = time.time()
duration = (end_time-beg_time)*1000
print("total_time_comput_ms: {}".format(int(duration)))

duration_time = 0.0
for i, wav_path_i in enumerate(wav_files):
	wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
	waveform, _ = librosa.load(wav_path, sr=16000)
	duration_time += len(waveform)/16.0
print("total_time_wav_ms: {}".format(int(duration_time)))

print("total_rtf: {:.5}".format(duration/duration_time))