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134 lines
4.8 KiB
134 lines
4.8 KiB
# 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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"""Server-end for the ASR demo."""
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import functools
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import numpy as np
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import paddle
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from paddle.io import DataLoader
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from yacs.config import CfgNode
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from paddlespeech.s2t.io.collator import SpeechCollator
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from paddlespeech.s2t.io.dataset import ManifestDataset
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from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.utils.socket_server import AsrRequestHandler
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from paddlespeech.s2t.utils.socket_server import AsrTCPServer
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from paddlespeech.s2t.utils.socket_server import warm_up_test
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from paddlespeech.s2t.utils.utility import add_arguments
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from paddlespeech.s2t.utils.utility import print_arguments
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def start_server(config, args):
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"""Start the ASR server"""
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config.defrost()
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config.manifest = config.test_manifest
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dataset = ManifestDataset.from_config(config)
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config.augmentation_config = ""
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config.keep_transcription_text = True
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config.batch_size = 1
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config.num_workers = 0
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collate_fn = SpeechCollator.from_config(config)
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test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
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model = DeepSpeech2Model.from_pretrained(test_loader, config,
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args.checkpoint_path)
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model.eval()
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# prepare ASR inference handler
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def file_to_transcript(filename):
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feature = test_loader.collate_fn.process_utterance(filename, "")
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audio = np.array([feature[0]]).astype('float32') #[1, T, D]
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# audio = audio.swapaxes(1,2)
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print('---file_to_transcript feature----')
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print(audio.shape)
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audio_len = feature[0].shape[0]
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print(audio_len)
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audio_len = np.array([audio_len]).astype('int64') # [1]
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result_transcript = model.decode(
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paddle.to_tensor(audio),
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paddle.to_tensor(audio_len),
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vocab_list=test_loader.collate_fn.vocab_list,
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decoding_method=config.decode.decoding_method,
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lang_model_path=config.decode.lang_model_path,
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beam_alpha=config.decode.alpha,
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beam_beta=config.decode.beta,
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beam_size=config.decode.beam_size,
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cutoff_prob=config.decode.cutoff_prob,
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cutoff_top_n=config.decode.cutoff_top_n,
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num_processes=config.decode.num_proc_bsearch)
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return result_transcript[0]
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# warming up with utterrances sampled from Librispeech
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print('-----------------------------------------------------------')
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print('Warming up ...')
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warm_up_test(
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audio_process_handler=file_to_transcript,
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manifest_path=args.warmup_manifest,
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num_test_cases=3)
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print('-----------------------------------------------------------')
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# start the server
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server = AsrTCPServer(
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server_address=(args.host_ip, args.host_port),
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RequestHandlerClass=AsrRequestHandler,
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speech_save_dir=args.speech_save_dir,
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audio_process_handler=file_to_transcript)
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print("ASR Server Started.")
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server.serve_forever()
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def main(config, args):
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start_server(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('host_ip', str,
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'localhost',
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"Server's IP address.")
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add_arg('host_port', int, 8088, "Server's IP port.")
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add_arg('speech_save_dir', str,
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'demo_cache',
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"Directory to save demo audios.")
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add_arg('warmup_manifest', str, None, "Filepath of manifest to warm up.")
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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 = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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if args.decode_cfg:
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decode_confs = CfgNode(new_allowed=True)
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decode_confs.merge_from_file(args.decode_cfg)
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config.decode = decode_confs
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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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args.warmup_manifest = config.test_manifest
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print_arguments(args, globals())
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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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