Hui Zhang
5 years ago
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00147dc060
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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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"""Server-end for the ASR demo."""
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import os
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import time
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import argparse
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import functools
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import paddle
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import numpy as np
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from deepspeech.utils.socket_server import warm_up_test
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from deepspeech.utils.socket_server import AsrTCPServer
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from deepspeech.utils.socket_server import AsrRequestHandler
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.exps.deepspeech2.config import get_cfg_defaults
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from deepspeech.frontend.utility import read_manifest
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from deepspeech.utils.utility import add_arguments, print_arguments
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from deepspeech.models.deepspeech2 import DeepSpeech2Model
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from deepspeech.io.dataset import ManifestDataset
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from paddle.inference import Config
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from paddle.inference import create_predictor
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def init_predictor(args):
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if args.model_dir is not None:
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config = Config(args.model_dir)
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else:
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config = Config(args.model_file, args.params_file)
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config.enable_memory_optim()
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if args.use_gpu:
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config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0)
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else:
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# If not specific mkldnn, you can set the blas thread.
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# The thread num should not be greater than the number of cores in the CPU.
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config.set_cpu_math_library_num_threads(4)
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config.enable_mkldnn()
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predictor = create_predictor(config)
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return predictor
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def run(predictor, img):
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# copy img data to input tensor
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input_names = predictor.get_input_names()
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for i, name in enumerate(input_names):
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input_tensor = predictor.get_input_handle(name)
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#input_tensor.reshape(img[i].shape)
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#input_tensor.copy_from_cpu(img[i].copy())
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# do the inference
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predictor.run()
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results = []
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# get out data from output tensor
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output_names = predictor.get_output_names()
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for i, name in enumerate(output_names):
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output_tensor = predictor.get_output_handle(name)
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output_data = output_tensor.copy_to_cpu()
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results.append(output_data)
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return results
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def inference(config, args):
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predictor = init_predictor(args)
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def start_server(config, args):
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"""Start the ASR server"""
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dataset = ManifestDataset(
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config.data.test_manifest,
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config.data.vocab_filepath,
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config.data.mean_std_filepath,
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augmentation_config="{}",
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max_duration=config.data.max_duration,
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min_duration=config.data.min_duration,
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stride_ms=config.data.stride_ms,
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window_ms=config.data.window_ms,
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n_fft=config.data.n_fft,
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max_freq=config.data.max_freq,
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target_sample_rate=config.data.target_sample_rate,
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specgram_type=config.data.specgram_type,
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use_dB_normalization=config.data.use_dB_normalization,
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target_dB=config.data.target_dB,
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random_seed=config.data.random_seed,
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keep_transcription_text=True)
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model = DeepSpeech2Model.from_pretrained(dataset, 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 = dataset.process_utterance(filename, "")
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audio = np.array([feature[0]]).astype('float32') #[1, D, T]
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audio_len = feature[0].shape[1]
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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=dataset.vocab_list,
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decoding_method=config.decoding.decoding_method,
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lang_model_path=config.decoding.lang_model_path,
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beam_alpha=config.decoding.alpha,
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beam_beta=config.decoding.beta,
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beam_size=config.decoding.beam_size,
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cutoff_prob=config.decoding.cutoff_prob,
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cutoff_top_n=config.decoding.cutoff_top_n,
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num_processes=config.decoding.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, 8086, "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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add_arg(
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"--model_file",
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type=str,
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default="",
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help="Model filename, Specify this when your model is a combined model."
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)
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add_arg(
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"--params_file",
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type=str,
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default="",
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help=
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"Parameter filename, Specify this when your model is a combined model."
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)
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add_arg(
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"--model_dir",
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type=str,
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default=None,
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help=
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"Model dir, If you load a non-combined model, specify the directory of the model."
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)
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add_arg("--use_gpu",
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type=bool,
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default=False,
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help="Whether use gpu.")
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args = parser.parse_args()
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print_arguments(args)
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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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args.warmup_manifest = config.data.test_manifest
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print_arguments(args)
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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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