import time
import sys
import librosa
backend=sys.argv[1]
model_dir=sys.argv[2]
wav_file=sys.argv[3]

from torch_paraformer import Paraformer
if backend == "onnxruntime":
	from rapid_paraformer import Paraformer
	
model = Paraformer(model_dir, batch_size=1, device_id="-1")

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

# warm-up
total = 0.0
num = 100
wav_path = wav_files[0].split("\t")[1].strip() if "\t" in wav_files[0] else wav_files[0].split(" ")[1].strip()
for i in range(num):
	beg_time = time.time()
	result = model(wav_path)
	end_time = time.time()
	duration = end_time-beg_time
	total += duration
	print(result)
	print("num: {}, time, {}, avg: {}, rtf: {}".format(len(wav_path), duration, total/(i+1), (total/(i+1))/5.53))

# infer time
beg_time = time.time()
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()
	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))