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@ -975,12 +975,14 @@ def run(rank, world_size, args):
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logging.info(params)
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logging.info("About to create model")
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model = get_transducer_model(params)
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transducer_model = get_transducer_model(params)
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pre_trained_model = torch.load('/workspace/icefall/egs/librispeech/ASR/incremental_transf/conformer_12layers.pt')
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pre_trained_model = pre_trained_model['model']
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model.load_state_dict(pre_trained_model, strict=True)
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print(model)
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transducer_model.load_state_dict(pre_trained_model, strict=True)
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model = get_interformer_model(transducer_model.encoder, params)
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print(model)
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'''
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for n, p in model.named_parameters():
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if 'layer' not in n:
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