test_rtf.py 2.0 KB

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  1. import time
  2. import sys
  3. import librosa
  4. from funasr.utils.types import str2bool
  5. import argparse
  6. parser = argparse.ArgumentParser()
  7. parser.add_argument('--model_dir', type=str, required=True)
  8. parser.add_argument('--backend', type=str, default='onnx', help='["onnx", "torch"]')
  9. parser.add_argument('--wav_file', type=str, default=None, help='amp fallback number')
  10. parser.add_argument('--quantize', type=str2bool, default=False, help='quantized model')
  11. parser.add_argument('--intra_op_num_threads', type=int, default=1, help='intra_op_num_threads for onnx')
  12. args = parser.parse_args()
  13. from funasr.runtime.python.libtorch.funasr_torch import Paraformer
  14. if args.backend == "onnx":
  15. from funasr.runtime.python.onnxruntime.funasr_onnx import Paraformer
  16. model = Paraformer(args.model_dir, batch_size=1, quantize=args.quantize, intra_op_num_threads=args.intra_op_num_threads)
  17. wav_file_f = open(args.wav_file, 'r')
  18. wav_files = wav_file_f.readlines()
  19. # warm-up
  20. total = 0.0
  21. num = 30
  22. wav_path = wav_files[0].split("\t")[1].strip() if "\t" in wav_files[0] else wav_files[0].split(" ")[1].strip()
  23. for i in range(num):
  24. beg_time = time.time()
  25. result = model(wav_path)
  26. end_time = time.time()
  27. duration = end_time-beg_time
  28. total += duration
  29. print(result)
  30. print("num: {}, time, {}, avg: {}, rtf: {}".format(len(wav_path), duration, total/(i+1), (total/(i+1))/5.53))
  31. # infer time
  32. beg_time = time.time()
  33. for i, wav_path_i in enumerate(wav_files):
  34. wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
  35. result = model(wav_path)
  36. end_time = time.time()
  37. duration = (end_time-beg_time)*1000
  38. print("total_time_comput_ms: {}".format(int(duration)))
  39. duration_time = 0.0
  40. for i, wav_path_i in enumerate(wav_files):
  41. wav_path = wav_path_i.split("\t")[1].strip() if "\t" in wav_path_i else wav_path_i.split(" ")[1].strip()
  42. waveform, _ = librosa.load(wav_path, sr=16000)
  43. duration_time += len(waveform)/16.0
  44. print("total_time_wav_ms: {}".format(int(duration_time)))
  45. print("total_rtf: {:.5}".format(duration/duration_time))