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# !/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2016-2099 Ailemon.net
#
# This file is part of ASRT Speech Recognition Tool.
#
# ASRT is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# ASRT is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with ASRT. If not, see <https://www.gnu.org/licenses/>.
# ============================================================================
"""
@author: nl8590687
ASRT语音识别基于gRPC协议的API服务器程序
"""
import argparse
import time
from concurrent import futures
import grpc
from assets.asrt_pb2_grpc import AsrtGrpcServiceServicer, add_AsrtGrpcServiceServicer_to_server
from assets.asrt_pb2 import SpeechResponse, TextResponse
from speech_model import ModelSpeech
from model_zoo.speech_model.keras_backend import SpeechModel251BN
from speech_features import Spectrogram
from language_model3 import ModelLanguage
from utils.ops import decode_wav_bytes
API_STATUS_CODE_OK = 200000 # OK
API_STATUS_CODE_OK_PART = 206000 # 部分结果OK，用于stream
API_STATUS_CODE_CLIENT_ERROR = 400000
API_STATUS_CODE_CLIENT_ERROR_FORMAT = 400001 # 请求数据格式错误
API_STATUS_CODE_CLIENT_ERROR_CONFIG = 400002 # 请求数据配置不支持
API_STATUS_CODE_SERVER_ERROR = 500000
API_STATUS_CODE_SERVER_ERROR_RUNNING = 500001 # 服务器运行中出错
parser = argparse.ArgumentParser(description='ASRT gRPC Protocol API Service')
parser.add_argument('--listen', default='0.0.0.0', type=str, help='the network to listen')
parser.add_argument('--port', default='20002', type=str, help='the port to listen')
args = parser.parse_args()
AUDIO_LENGTH = 1600
AUDIO_FEATURE_LENGTH = 200
CHANNELS = 1
# 默认输出的拼音的表示大小是1428，即1427个拼音+1个空白块
OUTPUT_SIZE = 1428
sm251bn = SpeechModel251BN(
input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS),
output_size=OUTPUT_SIZE
)
feat = Spectrogram()
ms = ModelSpeech(sm251bn, feat, max_label_length=64)
ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5')
ml = ModelLanguage('model_language')
ml.load_model()
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
class ApiService(AsrtGrpcServiceServicer):
"""
继承AsrtGrpcServiceServicer,实现hello方法
"""
def __init__(self):
pass
def Speech(self, request, context):
"""
具体实现Speech的方法, 并按照pb的返回对象构造SpeechResponse返回
:param request:
:param context:
:return:
"""
wav_data = request.wav_data
wav_samples = decode_wav_bytes(samples_data=wav_data.samples,
channels=wav_data.channels, byte_width=wav_data.byte_width)
result = ms.recognize_speech(wav_samples, wav_data.sample_rate)
print("语音识别声学模型结果:", result)
return SpeechResponse(status_code=API_STATUS_CODE_OK, status_message='',
result_data=result)
def Language(self, request, context):
"""
具体实现Language的方法, 并按照pb的返回对象构造TextResponse返回
:param request:
:param context:
:return:
"""
print('Language收到了请求:', request)
result = ml.pinyin_to_text(list(request.pinyins))
print('Language结果:', result)
return TextResponse(status_code=API_STATUS_CODE_OK, status_message='',
text_result=result)
def All(self, request, context):
"""
具体实现All的方法, 并按照pb的返回对象构造TextResponse返回
:param request:
:param context:
:return:
"""
wav_data = request.wav_data
wav_samples = decode_wav_bytes(samples_data=wav_data.samples,
channels=wav_data.channels, byte_width=wav_data.byte_width)
result_speech = ms.recognize_speech(wav_samples, wav_data.sample_rate)
result = ml.pinyin_to_text(result_speech)
print("语音识别结果:", result)
return TextResponse(status_code=API_STATUS_CODE_OK, status_message='',
text_result=result)
def Stream(self, request_iterator, context):
"""
具体实现Stream的方法, 并按照pb的返回对象构造TextResponse返回
:param request_iterator:
:param context:
:return:
"""
result = list()
tmp_result_last = list()
beam_size = 100
for request in request_iterator:
wav_data = request.wav_data
wav_samples = decode_wav_bytes(samples_data=wav_data.samples,
channels=wav_data.channels,
byte_width=wav_data.byte_width)
result_speech = ms.recognize_speech(wav_samples, wav_data.sample_rate)
for item_pinyin in result_speech:
tmp_result = ml.pinyin_stream_decode(tmp_result_last, item_pinyin, beam_size)
if len(tmp_result) == 0 and len(tmp_result_last) > 0:
result.append(tmp_result_last[0][0])
print("流式语音识别结果：", ''.join(result))
yield TextResponse(status_code=API_STATUS_CODE_OK, status_message='',
text_result=''.join(result))
result = list()
tmp_result = ml.pinyin_stream_decode([], item_pinyin, beam_size)
tmp_result_last = tmp_result
yield TextResponse(status_code=API_STATUS_CODE_OK_PART, status_message='',
text_result=''.join(tmp_result[0][0]))
if len(tmp_result_last) > 0:
result.append(tmp_result_last[0][0])
print("流式语音识别结果：", ''.join(result))
yield TextResponse(status_code=API_STATUS_CODE_OK, status_message='',
text_result=''.join(result))
def run(host, port):
"""
gRPC API服务启动
:return:
"""
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
add_AsrtGrpcServiceServicer_to_server(ApiService(), server)
server.add_insecure_port(''.join([host, ':', port]))
server.start()
print("start service...")
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
if __name__ == '__main__':
run(host=args.listen, port=args.port)
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