mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
2196844d1d
* multi tokenizer * support fsmn_kws, fsmn_kws_mt, sanm_kws, sanm_kws_streaming training * kws --------- Co-authored-by: pengteng.spt <pengteng.spt@alibaba-inc.com>
135 lines
3.7 KiB
Python
135 lines
3.7 KiB
Python
from __future__ import print_function
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import argparse
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import copy
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import logging
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import os
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from shutil import copyfile
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import torch
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import yaml
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from typing import Union
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from funasr.models.fsmn_kws.model import FsmnKWSConvert
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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def get_args():
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parser = argparse.ArgumentParser(
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description=
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'load and convert network to each other between kaldi/pytorch format')
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parser.add_argument('--config', required=True, help='config file')
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parser.add_argument(
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'--network_file',
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default='',
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required=True,
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help='input network, support kaldi.txt/pytorch.pt')
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parser.add_argument('--model_dir', required=True, help='save model dir')
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parser.add_argument('--model_name', required=True, help='save model name')
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parser.add_argument('--convert_to',
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default='kaldi',
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required=True,
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help='target network type, kaldi/pytorch')
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args = parser.parse_args()
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return args
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def convert_to_kaldi(
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configs,
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network_file,
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model_dir,
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model_name="convert.kaldi.txt"
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):
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copyfile(network_file, os.path.join(model_dir, 'origin.torch.pt'))
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model = FsmnKWSConvert(
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vocab_size=configs['encoder_conf']['output_dim'],
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encoder='FSMNConvert',
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encoder_conf=configs['encoder_conf'],
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ctc_conf=configs['ctc_conf'],
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)
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print(model)
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num_params = count_parameters(model)
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print('the number of model params: {}'.format(num_params))
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states= torch.load(network_file, map_location='cpu')
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model.load_state_dict(states["state_dict"])
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kaldi_text = os.path.join(model_dir, model_name)
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with open(kaldi_text, 'w', encoding='utf8') as fout:
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nnet_desp = model.to_kaldi_net()
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fout.write(nnet_desp)
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fout.close()
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def convert_to_pytorch(
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configs,
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network_file,
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model_dir,
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model_name="convert.torch.pt"
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):
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model = FsmnKWSConvert(
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vocab_size=configs['encoder_conf']['output_dim'],
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frontend=None,
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specaug=None,
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normalize=None,
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encoder='FSMNConvert',
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encoder_conf=configs['encoder_conf'],
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ctc_conf=configs['ctc_conf'],
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)
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num_params = count_parameters(model)
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print('the number of model params: {}'.format(num_params))
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copyfile(network_file, os.path.join(model_dir, 'origin.kaldi.txt'))
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model.to_pytorch_net(network_file)
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save_model_path = os.path.join(model_dir, model_name)
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torch.save({"model": model.state_dict()}, save_model_path)
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print('convert torch format back to kaldi')
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kaldi_text = os.path.join(model_dir, 'convert.kaldi.txt')
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with open(kaldi_text, 'w', encoding='utf8') as fout:
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nnet_desp = model.to_kaldi_net()
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fout.write(nnet_desp)
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fout.close()
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print('Done!')
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def main():
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args = get_args()
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s %(levelname)s %(message)s')
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print(args)
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with open(args.config, 'r') as fin:
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configs = yaml.load(fin, Loader=yaml.FullLoader)
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if args.convert_to == 'pytorch':
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print('convert kaldi net to pytorch...')
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convert_to_pytorch(
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configs,
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args.network_file,
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args.model_dir,
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args.model_name
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)
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elif args.convert_to == 'kaldi':
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print('convert pytorch net to kaldi...')
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convert_to_kaldi(
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configs,
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args.network_file,
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args.model_dir,
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args.model_name
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)
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else:
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print('unsupported target network type: {}'.format(args.convert_to))
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if __name__ == '__main__':
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main()
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