Hui Zhang
3 years ago
parent
cd34e733a4
commit
ab23eb5710
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Alignment for U2 model."""
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from deepspeech.exps.u2.model import get_cfg_defaults
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from deepspeech.exps.u2.model import U2Tester as Tester
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.utils.dynamic_import import dynamic_import
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from deepspeech.utils.utility import print_arguments
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def main_sp(config, args):
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exp = Tester(config, args)
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exp.setup()
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exp.run_align()
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def main(config, args):
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main_sp(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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parser.add_arguments(
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'--model-name',
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type=str,
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default='u2',
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help='model name, e.g: deepspeech2, u2, u2_kaldi, u2_st')
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args = parser.parse_args()
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print_arguments(args, globals())
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# https://yaml.org/type/float.html
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config = get_cfg_defaults()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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main(config, args)
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Export for U2 model."""
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from deepspeech.exps.u2.model import get_cfg_defaults
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from deepspeech.exps.u2.model import U2Tester as Tester
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.utils.utility import print_arguments
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def main_sp(config, args):
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exp = Tester(config, args)
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exp.setup()
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exp.run_export()
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def main(config, args):
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main_sp(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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args = parser.parse_args()
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print_arguments(args, globals())
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# https://yaml.org/type/float.html
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config = get_cfg_defaults()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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main(config, args)
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Evaluation for U2 model."""
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import cProfile
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from deepspeech.exps.u2.model import get_cfg_defaults
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from deepspeech.exps.u2.model import U2Tester as Tester
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.utils.utility import print_arguments
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# TODO(hui zhang): dynamic load
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def main_sp(config, args):
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exp = Tester(config, args)
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exp.setup()
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exp.run_test()
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def main(config, args):
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main_sp(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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args = parser.parse_args()
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print_arguments(args, globals())
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# https://yaml.org/type/float.html
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config = get_cfg_defaults()
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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# Setting for profiling
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pr = cProfile.Profile()
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pr.runcall(main, config, args)
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pr.dump_stats('test.profile')
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Trainer for U2 model."""
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import cProfile
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import os
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from paddle import distributed as dist
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from yacs.config import CfgNode
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.utils.dynamic_import import dynamic_import
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from deepspeech.utils.utility import print_arguments
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model_alias = {
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"u2": "deepspeech.exps.u2.model:U2Trainer",
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"u2_kaldi": "deepspeech.exps.u2_kaldi.model:U2Trainer",
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}
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def main_sp(config, args):
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trainer_cls = dynamic_import(args.model_name, model_alias)
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exp = trainer_cls(config, args)
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exp.setup()
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exp.run()
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def main(config, args):
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if args.device == "gpu" and args.nprocs > 1:
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dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
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else:
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main_sp(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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parser.add_argument(
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'--model-name',
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type=str,
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default='u2_kaldi',
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help='model name, e.g: deepspeech2, u2, u2_kaldi, u2_st')
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args = parser.parse_args()
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print_arguments(args, globals())
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config = CfgNode()
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config.set_new_allowed(True)
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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# Setting for profiling
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pr = cProfile.Profile()
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pr.runcall(main, config, args)
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pr.dump_stats(os.path.join(args.output, 'train.profile'))
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Contains U2 model."""
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import json
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import os
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import sys
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import time
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from collections import defaultdict
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import paddle
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from paddle import distributed as dist
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from yacs.config import CfgNode
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from deepspeech.io.dataloader import BatchDataLoader
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from deepspeech.models.u2 import U2Model
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from deepspeech.training.optimizer import OptimizerFactory
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from deepspeech.training.scheduler import LRSchedulerFactory
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from deepspeech.training.trainer import Trainer
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from deepspeech.utils import ctc_utils
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from deepspeech.utils import error_rate
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from deepspeech.utils import layer_tools
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from deepspeech.utils import mp_tools
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from deepspeech.utils import text_grid
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from deepspeech.utils import utility
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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def get_cfg_defaults():
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"""Get a yacs CfgNode object with default values for my_project."""
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# Return a clone so that the defaults will not be altered
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# This is for the "local variable" use pattern
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_C = CfgNode()
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_C.model = U2Model.params()
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_C.training = U2Trainer.params()
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_C.decoding = U2Tester.params()
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config = _C.clone()
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config.set_new_allowed(True)
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return config
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class U2Trainer(Trainer):
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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# training config
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default = CfgNode(
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dict(
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n_epoch=50, # train epochs
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log_interval=100, # steps
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accum_grad=1, # accum grad by # steps
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checkpoint=dict(
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kbest_n=50,
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latest_n=5, ), ))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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def __init__(self, config, args):
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super().__init__(config, args)
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def train_batch(self, batch_index, batch_data, msg):
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train_conf = self.config.training
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start = time.time()
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utt, audio, audio_len, text, text_len = batch_data
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loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
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text_len)
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# loss div by `batch_size * accum_grad`
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loss /= train_conf.accum_grad
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loss.backward()
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layer_tools.print_grads(self.model, print_func=None)
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losses_np = {'loss': float(loss) * train_conf.accum_grad}
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if attention_loss:
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losses_np['att_loss'] = float(attention_loss)
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if ctc_loss:
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losses_np['ctc_loss'] = float(ctc_loss)
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if (batch_index + 1) % train_conf.accum_grad == 0:
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self.optimizer.step()
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self.optimizer.clear_grad()
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self.lr_scheduler.step()
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self.iteration += 1
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iteration_time = time.time() - start
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if (batch_index + 1) % train_conf.log_interval == 0:
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msg += "train time: {:>.3f}s, ".format(iteration_time)
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msg += "batch size: {}, ".format(self.config.collator.batch_size)
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msg += "accum: {}, ".format(train_conf.accum_grad)
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msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in losses_np.items())
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logger.info(msg)
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if dist.get_rank() == 0 and self.visualizer:
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losses_np_v = losses_np.copy()
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losses_np_v.update({"lr": self.lr_scheduler()})
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self.visualizer.add_scalars("step", losses_np_v,
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self.iteration - 1)
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@paddle.no_grad()
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def valid(self):
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self.model.eval()
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logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
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valid_losses = defaultdict(list)
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num_seen_utts = 1
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total_loss = 0.0
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for i, batch in enumerate(self.valid_loader):
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utt, audio, audio_len, text, text_len = batch
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loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
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text_len)
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if paddle.isfinite(loss):
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num_utts = batch[1].shape[0]
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num_seen_utts += num_utts
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total_loss += float(loss) * num_utts
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valid_losses['val_loss'].append(float(loss))
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if attention_loss:
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valid_losses['val_att_loss'].append(float(attention_loss))
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if ctc_loss:
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valid_losses['val_ctc_loss'].append(float(ctc_loss))
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if (i + 1) % self.config.training.log_interval == 0:
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valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
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valid_dump['val_history_loss'] = total_loss / num_seen_utts
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# logging
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msg = f"Valid: Rank: {dist.get_rank()}, "
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msg += "epoch: {}, ".format(self.epoch)
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msg += "step: {}, ".format(self.iteration)
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msg += "batch: {}/{}, ".format(i + 1, len(self.valid_loader))
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msg += ', '.join('{}: {:>.6f}'.format(k, v)
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for k, v in valid_dump.items())
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logger.info(msg)
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logger.info('Rank {} Val info val_loss {}'.format(
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dist.get_rank(), total_loss / num_seen_utts))
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return total_loss, num_seen_utts
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def train(self):
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"""The training process control by step."""
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# !!!IMPORTANT!!!
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# Try to export the model by script, if fails, we should refine
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# the code to satisfy the script export requirements
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# script_model = paddle.jit.to_static(self.model)
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# script_model_path = str(self.checkpoint_dir / 'init')
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# paddle.jit.save(script_model, script_model_path)
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from_scratch = self.resume_or_scratch()
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if from_scratch:
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# save init model, i.e. 0 epoch
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self.save(tag='init')
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self.lr_scheduler.step(self.iteration)
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if self.parallel:
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self.train_loader.batch_sampler.set_epoch(self.epoch)
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logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
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while self.epoch < self.config.training.n_epoch:
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self.model.train()
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try:
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data_start_time = time.time()
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for batch_index, batch in enumerate(self.train_loader):
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dataload_time = time.time() - data_start_time
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msg = "Train: Rank: {}, ".format(dist.get_rank())
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msg += "epoch: {}, ".format(self.epoch)
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msg += "step: {}, ".format(self.iteration)
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msg += "batch : {}/{}, ".format(batch_index + 1,
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len(self.train_loader))
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msg += "lr: {:>.8f}, ".format(self.lr_scheduler())
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msg += "data time: {:>.3f}s, ".format(dataload_time)
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self.train_batch(batch_index, batch, msg)
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data_start_time = time.time()
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except Exception as e:
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logger.error(e)
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raise e
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total_loss, num_seen_utts = self.valid()
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if dist.get_world_size() > 1:
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num_seen_utts = paddle.to_tensor(num_seen_utts)
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# the default operator in all_reduce function is sum.
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dist.all_reduce(num_seen_utts)
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total_loss = paddle.to_tensor(total_loss)
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dist.all_reduce(total_loss)
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cv_loss = total_loss / num_seen_utts
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cv_loss = float(cv_loss)
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else:
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cv_loss = total_loss / num_seen_utts
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logger.info(
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'Epoch {} Val info val_loss {}'.format(self.epoch, cv_loss))
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if self.visualizer:
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self.visualizer.add_scalars(
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'epoch', {'cv_loss': cv_loss,
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'lr': self.lr_scheduler()}, self.epoch)
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self.save(tag=self.epoch, infos={'val_loss': cv_loss})
|
||||
self.new_epoch()
|
||||
|
||||
def setup_dataloader(self):
|
||||
config = self.config.clone()
|
||||
# train/valid dataset, return token ids
|
||||
self.train_loader = BatchDataLoader(
|
||||
json_file=config.data.train_manifest,
|
||||
train_mode=True,
|
||||
sortagrad=False,
|
||||
batch_size=config.collator.batch_size,
|
||||
maxlen_in=float('inf'),
|
||||
maxlen_out=float('inf'),
|
||||
minibatches=0,
|
||||
mini_batch_size=1,
|
||||
batch_count='auto',
|
||||
batch_bins=0,
|
||||
batch_frames_in=0,
|
||||
batch_frames_out=0,
|
||||
batch_frames_inout=0,
|
||||
preprocess_conf=config.collator.augmentation_config,
|
||||
n_iter_processes=config.collator.num_workers,
|
||||
subsampling_factor=1,
|
||||
num_encs=1)
|
||||
|
||||
self.valid_loader = BatchDataLoader(
|
||||
json_file=config.data.dev_manifest,
|
||||
train_mode=False,
|
||||
sortagrad=False,
|
||||
batch_size=config.collator.batch_size,
|
||||
maxlen_in=float('inf'),
|
||||
maxlen_out=float('inf'),
|
||||
minibatches=0,
|
||||
mini_batch_size=1,
|
||||
batch_count='auto',
|
||||
batch_bins=0,
|
||||
batch_frames_in=0,
|
||||
batch_frames_out=0,
|
||||
batch_frames_inout=0,
|
||||
preprocess_conf=None,
|
||||
n_iter_processes=1,
|
||||
subsampling_factor=1,
|
||||
num_encs=1)
|
||||
|
||||
# test dataset, return raw text
|
||||
self.test_loader = BatchDataLoader(
|
||||
json_file=config.data.test_manifest,
|
||||
train_mode=False,
|
||||
sortagrad=False,
|
||||
batch_size=config.collator.batch_size,
|
||||
maxlen_in=float('inf'),
|
||||
maxlen_out=float('inf'),
|
||||
minibatches=0,
|
||||
mini_batch_size=1,
|
||||
batch_count='auto',
|
||||
batch_bins=0,
|
||||
batch_frames_in=0,
|
||||
batch_frames_out=0,
|
||||
batch_frames_inout=0,
|
||||
preprocess_conf=None,
|
||||
n_iter_processes=1,
|
||||
subsampling_factor=1,
|
||||
num_encs=1)
|
||||
|
||||
self.align_loader = BatchDataLoader(
|
||||
json_file=config.data.test_manifest,
|
||||
train_mode=False,
|
||||
sortagrad=False,
|
||||
batch_size=config.collator.batch_size,
|
||||
maxlen_in=float('inf'),
|
||||
maxlen_out=float('inf'),
|
||||
minibatches=0,
|
||||
mini_batch_size=1,
|
||||
batch_count='auto',
|
||||
batch_bins=0,
|
||||
batch_frames_in=0,
|
||||
batch_frames_out=0,
|
||||
batch_frames_inout=0,
|
||||
preprocess_conf=None,
|
||||
n_iter_processes=1,
|
||||
subsampling_factor=1,
|
||||
num_encs=1)
|
||||
logger.info("Setup train/valid/test/align Dataloader!")
|
||||
|
||||
def setup_model(self):
|
||||
config = self.config
|
||||
|
||||
# model
|
||||
model_conf = config.model
|
||||
model_conf.defrost()
|
||||
model_conf.input_dim = self.train_loader.feat_dim
|
||||
model_conf.output_dim = self.train_loader.vocab_size
|
||||
model_conf.freeze()
|
||||
model = U2Model.from_config(model_conf)
|
||||
|
||||
if self.parallel:
|
||||
model = paddle.DataParallel(model)
|
||||
|
||||
logger.info(f"{model}")
|
||||
layer_tools.print_params(model, logger.info)
|
||||
|
||||
# lr
|
||||
scheduler_conf = config.scheduler_conf
|
||||
scheduler_args = {
|
||||
"learning_rate": scheduler_conf.lr,
|
||||
"warmup_steps": scheduler_conf.warmup_steps,
|
||||
"gamma": scheduler_conf.lr_decay,
|
||||
"d_model": model_conf.encoder_conf.output_size,
|
||||
"verbose": False,
|
||||
}
|
||||
lr_scheduler = LRSchedulerFactory.from_args(config.scheduler,
|
||||
scheduler_args)
|
||||
|
||||
# opt
|
||||
def optimizer_args(
|
||||
config,
|
||||
parameters,
|
||||
lr_scheduler=None, ):
|
||||
optim_conf = config.optim_conf
|
||||
return {
|
||||
"grad_clip": optim_conf.global_grad_clip,
|
||||
"weight_decay": optim_conf.weight_decay,
|
||||
"learning_rate": lr_scheduler,
|
||||
"parameters": parameters,
|
||||
}
|
||||
|
||||
optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
|
||||
optimizer = OptimizerFactory.from_args(config.optim, optimzer_args)
|
||||
|
||||
self.model = model
|
||||
self.lr_scheduler = lr_scheduler
|
||||
self.optimizer = optimizer
|
||||
logger.info("Setup model/optimizer/lr_scheduler!")
|
||||
|
||||
|
||||
class U2Tester(U2Trainer):
|
||||
@classmethod
|
||||
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
|
||||
# decoding config
|
||||
default = CfgNode(
|
||||
dict(
|
||||
alpha=2.5, # Coef of LM for beam search.
|
||||
beta=0.3, # Coef of WC for beam search.
|
||||
cutoff_prob=1.0, # Cutoff probability for pruning.
|
||||
cutoff_top_n=40, # Cutoff number for pruning.
|
||||
lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm', # Filepath for language model.
|
||||
decoding_method='attention', # Decoding method. Options: 'attention', 'ctc_greedy_search',
|
||||
# 'ctc_prefix_beam_search', 'attention_rescoring'
|
||||
error_rate_type='wer', # Error rate type for evaluation. Options `wer`, 'cer'
|
||||
num_proc_bsearch=8, # # of CPUs for beam search.
|
||||
beam_size=10, # Beam search width.
|
||||
batch_size=16, # decoding batch size
|
||||
ctc_weight=0.0, # ctc weight for attention rescoring decode mode.
|
||||
decoding_chunk_size=-1, # decoding chunk size. Defaults to -1.
|
||||
# <0: for decoding, use full chunk.
|
||||
# >0: for decoding, use fixed chunk size as set.
|
||||
# 0: used for training, it's prohibited here.
|
||||
num_decoding_left_chunks=-1, # number of left chunks for decoding. Defaults to -1.
|
||||
simulate_streaming=False, # simulate streaming inference. Defaults to False.
|
||||
))
|
||||
|
||||
if config is not None:
|
||||
config.merge_from_other_cfg(default)
|
||||
return default
|
||||
|
||||
def __init__(self, config, args):
|
||||
super().__init__(config, args)
|
||||
|
||||
def ordid2token(self, texts, texts_len):
|
||||
""" ord() id to chr() chr """
|
||||
trans = []
|
||||
for text, n in zip(texts, texts_len):
|
||||
n = n.numpy().item()
|
||||
ids = text[:n]
|
||||
trans.append(''.join([chr(i) for i in ids]))
|
||||
return trans
|
||||
|
||||
def compute_metrics(self,
|
||||
utts,
|
||||
audio,
|
||||
audio_len,
|
||||
texts,
|
||||
texts_len,
|
||||
fout=None):
|
||||
cfg = self.config.decoding
|
||||
errors_sum, len_refs, num_ins = 0.0, 0, 0
|
||||
errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
|
||||
error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
|
||||
|
||||
start_time = time.time()
|
||||
text_feature = self.test_loader.collate_fn.text_feature
|
||||
target_transcripts = self.ordid2token(texts, texts_len)
|
||||
result_transcripts = self.model.decode(
|
||||
audio,
|
||||
audio_len,
|
||||
text_feature=text_feature,
|
||||
decoding_method=cfg.decoding_method,
|
||||
lang_model_path=cfg.lang_model_path,
|
||||
beam_alpha=cfg.alpha,
|
||||
beam_beta=cfg.beta,
|
||||
beam_size=cfg.beam_size,
|
||||
cutoff_prob=cfg.cutoff_prob,
|
||||
cutoff_top_n=cfg.cutoff_top_n,
|
||||
num_processes=cfg.num_proc_bsearch,
|
||||
ctc_weight=cfg.ctc_weight,
|
||||
decoding_chunk_size=cfg.decoding_chunk_size,
|
||||
num_decoding_left_chunks=cfg.num_decoding_left_chunks,
|
||||
simulate_streaming=cfg.simulate_streaming)
|
||||
decode_time = time.time() - start_time
|
||||
|
||||
for utt, target, result in zip(utts, target_transcripts,
|
||||
result_transcripts):
|
||||
errors, len_ref = errors_func(target, result)
|
||||
errors_sum += errors
|
||||
len_refs += len_ref
|
||||
num_ins += 1
|
||||
if fout:
|
||||
fout.write(utt + " " + result + "\n")
|
||||
logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
|
||||
(target, result))
|
||||
logger.info("One example error rate [%s] = %f" %
|
||||
(cfg.error_rate_type, error_rate_func(target, result)))
|
||||
|
||||
return dict(
|
||||
errors_sum=errors_sum,
|
||||
len_refs=len_refs,
|
||||
num_ins=num_ins, # num examples
|
||||
error_rate=errors_sum / len_refs,
|
||||
error_rate_type=cfg.error_rate_type,
|
||||
num_frames=audio_len.sum().numpy().item(),
|
||||
decode_time=decode_time)
|
||||
|
||||
@mp_tools.rank_zero_only
|
||||
@paddle.no_grad()
|
||||
def test(self):
|
||||
assert self.args.result_file
|
||||
self.model.eval()
|
||||
logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
|
||||
|
||||
stride_ms = self.test_loader.collate_fn.stride_ms
|
||||
error_rate_type = None
|
||||
errors_sum, len_refs, num_ins = 0.0, 0, 0
|
||||
num_frames = 0.0
|
||||
num_time = 0.0
|
||||
with open(self.args.result_file, 'w') as fout:
|
||||
for i, batch in enumerate(self.test_loader):
|
||||
metrics = self.compute_metrics(*batch, fout=fout)
|
||||
num_frames += metrics['num_frames']
|
||||
num_time += metrics["decode_time"]
|
||||
errors_sum += metrics['errors_sum']
|
||||
len_refs += metrics['len_refs']
|
||||
num_ins += metrics['num_ins']
|
||||
error_rate_type = metrics['error_rate_type']
|
||||
rtf = num_time / (num_frames * stride_ms)
|
||||
logger.info(
|
||||
"RTF: %f, Error rate [%s] (%d/?) = %f" %
|
||||
(rtf, error_rate_type, num_ins, errors_sum / len_refs))
|
||||
|
||||
rtf = num_time / (num_frames * stride_ms)
|
||||
msg = "Test: "
|
||||
msg += "epoch: {}, ".format(self.epoch)
|
||||
msg += "step: {}, ".format(self.iteration)
|
||||
msg += "RTF: {}, ".format(rtf)
|
||||
msg += "Final error rate [%s] (%d/%d) = %f" % (
|
||||
error_rate_type, num_ins, num_ins, errors_sum / len_refs)
|
||||
logger.info(msg)
|
||||
|
||||
# test meta results
|
||||
err_meta_path = os.path.splitext(self.args.result_file)[0] + '.err'
|
||||
err_type_str = "{}".format(error_rate_type)
|
||||
with open(err_meta_path, 'w') as f:
|
||||
data = json.dumps({
|
||||
"epoch":
|
||||
self.epoch,
|
||||
"step":
|
||||
self.iteration,
|
||||
"rtf":
|
||||
rtf,
|
||||
error_rate_type:
|
||||
errors_sum / len_refs,
|
||||
"dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0,
|
||||
"process_hour":
|
||||
num_time / 1000.0 / 3600.0,
|
||||
"num_examples":
|
||||
num_ins,
|
||||
"err_sum":
|
||||
errors_sum,
|
||||
"ref_len":
|
||||
len_refs,
|
||||
"decode_method":
|
||||
self.config.decoding.decoding_method,
|
||||
})
|
||||
f.write(data + '\n')
|
||||
|
||||
def run_test(self):
|
||||
self.resume_or_scratch()
|
||||
try:
|
||||
self.test()
|
||||
except KeyboardInterrupt:
|
||||
sys.exit(-1)
|
||||
|
||||
@paddle.no_grad()
|
||||
def align(self):
|
||||
if self.config.decoding.batch_size > 1:
|
||||
logger.fatal('alignment mode must be running with batch_size == 1')
|
||||
sys.exit(1)
|
||||
|
||||
# xxx.align
|
||||
assert self.args.result_file and self.args.result_file.endswith(
|
||||
'.align')
|
||||
|
||||
self.model.eval()
|
||||
logger.info(f"Align Total Examples: {len(self.align_loader.dataset)}")
|
||||
|
||||
stride_ms = self.config.collate.stride_ms
|
||||
token_dict = self.align_loader.collate_fn.vocab_list
|
||||
with open(self.args.result_file, 'w') as fout:
|
||||
# one example in batch
|
||||
for i, batch in enumerate(self.align_loader):
|
||||
key, feat, feats_length, target, target_length = batch
|
||||
|
||||
# 1. Encoder
|
||||
encoder_out, encoder_mask = self.model._forward_encoder(
|
||||
feat, feats_length) # (B, maxlen, encoder_dim)
|
||||
maxlen = encoder_out.size(1)
|
||||
ctc_probs = self.model.ctc.log_softmax(
|
||||
encoder_out) # (1, maxlen, vocab_size)
|
||||
|
||||
# 2. alignment
|
||||
ctc_probs = ctc_probs.squeeze(0)
|
||||
target = target.squeeze(0)
|
||||
alignment = ctc_utils.forced_align(ctc_probs, target)
|
||||
logger.info("align ids", key[0], alignment)
|
||||
fout.write('{} {}\n'.format(key[0], alignment))
|
||||
|
||||
# 3. gen praat
|
||||
# segment alignment
|
||||
align_segs = text_grid.segment_alignment(alignment)
|
||||
logger.info("align tokens", key[0], align_segs)
|
||||
# IntervalTier, List["start end token\n"]
|
||||
subsample = utility.get_subsample(self.config)
|
||||
tierformat = text_grid.align_to_tierformat(
|
||||
align_segs, subsample, token_dict)
|
||||
# write tier
|
||||
align_output_path = os.path.join(
|
||||
os.path.dirname(self.args.result_file), "align")
|
||||
tier_path = os.path.join(align_output_path, key[0] + ".tier")
|
||||
with open(tier_path, 'w') as f:
|
||||
f.writelines(tierformat)
|
||||
# write textgrid
|
||||
textgrid_path = os.path.join(align_output_path,
|
||||
key[0] + ".TextGrid")
|
||||
second_per_frame = 1. / (1000. /
|
||||
stride_ms) # 25ms window, 10ms stride
|
||||
second_per_example = (
|
||||
len(alignment) + 1) * subsample * second_per_frame
|
||||
text_grid.generate_textgrid(
|
||||
maxtime=second_per_example,
|
||||
intervals=tierformat,
|
||||
output=textgrid_path)
|
||||
|
||||
def run_align(self):
|
||||
self.resume_or_scratch()
|
||||
try:
|
||||
self.align()
|
||||
except KeyboardInterrupt:
|
||||
sys.exit(-1)
|
||||
|
||||
def load_inferspec(self):
|
||||
"""infer model and input spec.
|
||||
|
||||
Returns:
|
||||
nn.Layer: inference model
|
||||
List[paddle.static.InputSpec]: input spec.
|
||||
"""
|
||||
from deepspeech.models.u2 import U2InferModel
|
||||
infer_model = U2InferModel.from_pretrained(self.test_loader,
|
||||
self.config.model.clone(),
|
||||
self.args.checkpoint_path)
|
||||
feat_dim = self.test_loader.feat_dim
|
||||
input_spec = [
|
||||
paddle.static.InputSpec(shape=[1, None, feat_dim],
|
||||
dtype='float32'), # audio, [B,T,D]
|
||||
paddle.static.InputSpec(shape=[1],
|
||||
dtype='int64'), # audio_length, [B]
|
||||
]
|
||||
return infer_model, input_spec
|
||||
|
||||
def export(self):
|
||||
infer_model, input_spec = self.load_inferspec()
|
||||
assert isinstance(input_spec, list), type(input_spec)
|
||||
infer_model.eval()
|
||||
static_model = paddle.jit.to_static(infer_model, input_spec=input_spec)
|
||||
logger.info(f"Export code: {static_model.forward.code}")
|
||||
paddle.jit.save(static_model, self.args.export_path)
|
||||
|
||||
def run_export(self):
|
||||
try:
|
||||
self.export()
|
||||
except KeyboardInterrupt:
|
||||
sys.exit(-1)
|
||||
|
||||
def setup(self):
|
||||
"""Setup the experiment.
|
||||
"""
|
||||
paddle.set_device(self.args.device)
|
||||
|
||||
self.setup_output_dir()
|
||||
self.setup_checkpointer()
|
||||
|
||||
self.setup_dataloader()
|
||||
self.setup_model()
|
||||
|
||||
self.iteration = 0
|
||||
self.epoch = 0
|
||||
|
||||
def setup_output_dir(self):
|
||||
"""Create a directory used for output.
|
||||
"""
|
||||
# output dir
|
||||
if self.args.output:
|
||||
output_dir = Path(self.args.output).expanduser()
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
else:
|
||||
output_dir = Path(
|
||||
self.args.checkpoint_path).expanduser().parent.parent
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
self.output_dir = output_dir
|
||||
Loading…
Reference in new issue