| | |
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| | | parser = argparse.ArgumentParser() |
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| | | parser.add_argument('--model_dir', type=str, required=True) |
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| | | parser.add_argument('--backend', type=str, default='onnx', help='["onnx", "torch"]') |
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| | | parser.add_argument('--wav_file', type=int, default=0, help='amp fallback number') |
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| | | parser.add_argument('--wav_file', type=str, default=None, help='amp fallback number') |
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| | | parser.add_argument('--quantize', type=bool, default=False, help='quantized model') |
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| | | parser.add_argument('--intra_op_num_threads', type=int, default=1, help='intra_op_num_threads for onnx') |
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| | | args = parser.parse_args() |
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| | | |
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| | | |
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| | | from torch_paraformer import Paraformer |
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| | | if args.backend == "onnxruntime": |
|---|
| | | from rapid_paraformer import Paraformer |
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| | | from funasr.runtime.python.libtorch.torch_paraformer import Paraformer |
|---|
| | | if args.backend == "onnx": |
|---|
| | | from funasr.runtime.python.onnxruntime.rapid_paraformer import Paraformer |
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| | | |
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| | | model = Paraformer(args.model_dir, batch_size=1, quantize=args.quantize) |
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| | | model = Paraformer(args.model_dir, batch_size=1, quantize=args.quantize, intra_op_num_threads=args.intra_op_num_threads) |
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| | | |
|---|
| | | wav_file_f = open(args.wav_file, 'r') |
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| | | wav_files = wav_file_f.readlines() |
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| | | |
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| | | # warm-up |
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| | | total = 0.0 |
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| | | num = 100 |
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| | | num = 30 |
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| | | wav_path = wav_files[0].split("\t")[1].strip() if "\t" in wav_files[0] else wav_files[0].split(" ")[1].strip() |
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| | | for i in range(num): |
|---|
| | | beg_time = time.time() |
|---|
