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PaddleSpeech/paddlespeech/s2t/exps/deepspeech2/bin/deploy/server.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Server-end for the ASR demo."""
import functools
import numpy as np
import paddle
from paddle.io import DataLoader
from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults
from paddlespeech.s2t.io.collator import SpeechCollator
from paddlespeech.s2t.io.dataset import ManifestDataset
from paddlespeech.s2t.models.ds2 import DeepSpeech2Model
from paddlespeech.s2t.training.cli import default_argument_parser
from paddlespeech.s2t.utils.socket_server import AsrRequestHandler
from paddlespeech.s2t.utils.socket_server import AsrTCPServer
from paddlespeech.s2t.utils.socket_server import warm_up_test
from paddlespeech.s2t.utils.utility import add_arguments
from paddlespeech.s2t.utils.utility import print_arguments
def start_server(config, args):
"""Start the ASR server"""
config.defrost()
config.data.manifest = config.data.test_manifest
dataset = ManifestDataset.from_config(config)
config.collator.augmentation_config = ""
config.collator.keep_transcription_text = True
config.collator.batch_size = 1
config.collator.num_workers = 0
collate_fn = SpeechCollator.from_config(config)
test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
model = DeepSpeech2Model.from_pretrained(test_loader, config,
args.checkpoint_path)
model.eval()
# prepare ASR inference handler
def file_to_transcript(filename):
feature = test_loader.collate_fn.process_utterance(filename, "")
audio = np.array([feature[0]]).astype('float32') #[1, T, D]
# audio = audio.swapaxes(1,2)
print('---file_to_transcript feature----')
print(audio.shape)
audio_len = feature[0].shape[0]
print(audio_len)
audio_len = np.array([audio_len]).astype('int64') # [1]
result_transcript = model.decode(
paddle.to_tensor(audio),
paddle.to_tensor(audio_len),
vocab_list=test_loader.collate_fn.vocab_list,
decoding_method=config.decoding.decoding_method,
lang_model_path=config.decoding.lang_model_path,
beam_alpha=config.decoding.alpha,
beam_beta=config.decoding.beta,
beam_size=config.decoding.beam_size,
cutoff_prob=config.decoding.cutoff_prob,
cutoff_top_n=config.decoding.cutoff_top_n,
num_processes=config.decoding.num_proc_bsearch)
return result_transcript[0]
# warming up with utterrances sampled from Librispeech
print('-----------------------------------------------------------')
print('Warming up ...')
warm_up_test(
audio_process_handler=file_to_transcript,
manifest_path=args.warmup_manifest,
num_test_cases=3)
print('-----------------------------------------------------------')
# start the server
server = AsrTCPServer(
server_address=(args.host_ip, args.host_port),
RequestHandlerClass=AsrRequestHandler,
speech_save_dir=args.speech_save_dir,
audio_process_handler=file_to_transcript)
print("ASR Server Started.")
server.serve_forever()
def main(config, args):
start_server(config, args)
if __name__ == "__main__":
parser = default_argument_parser()
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('host_ip', str,
'localhost',
"Server's IP address.")
add_arg('host_port', int, 8088, "Server's IP port.")
add_arg('speech_save_dir', str,
'demo_cache',
"Directory to save demo audios.")
add_arg('warmup_manifest', str, None, "Filepath of manifest to warm up.")
args = parser.parse_args()
print_arguments(args, globals())
# https://yaml.org/type/float.html
config = get_cfg_defaults()
if args.config:
config.merge_from_file(args.config)
if args.opts:
config.merge_from_list(args.opts)
config.freeze()
print(config)
args.warmup_manifest = config.data.test_manifest
print_arguments(args, globals())
if args.dump_config:
with open(args.dump_config, 'w') as f:
print(config, file=f)
main(config, args)