#!/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 .
# ============================================================================
"""
@author: nl8590687
ASRT语音识别基于HTTP协议的API服务器程序
"""
import argparse
import base64
import json
from flask import Flask, Response, request
from speech_model import ModelSpeech
from speech_model_zoo import SpeechModel251BN
from speech_features import Spectrogram
from LanguageModel2 import ModelLanguage
from utils.ops import decode_wav_bytes
API_STATUS_CODE_OK = 200000 # OK
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 HTTP+Json RESTful API Service')
parser.add_argument('--listen', default='0.0.0.0', type=str, help='the network to listen')
parser.add_argument('--port', default='20001', type=str, help='the port to listen')
args = parser.parse_args()
app = Flask("ASRT API Service")
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.LoadModel()
class AsrtApiResponse:
'''
ASRT语音识别基于HTTP协议的API接口响应类
'''
def __init__(self, status_code, status_message='', result=''):
self.status_code = status_code
self.status_message = status_message
self.result = result
def to_json(self):
'''
类转json
'''
return json.dumps(self, default=lambda o: o.__dict__,
sort_keys=True)
# api接口根url:GET
@app.route('/', methods=["GET"])
def index_get():
'''
根路径handle GET方法
'''
buffer = ''
with open('assets/default.html', 'r', encoding='utf-8') as file_handle:
buffer = file_handle.read()
return Response(buffer, mimetype='text/html; charset=utf-8')
# api接口根url:POST
@app.route('/', methods=["POST"])
def index_post():
'''
根路径handle POST方法
'''
json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'ok')
buffer = json_data.to_json()
return Response(buffer, mimetype='application/json')
# 获取分类列表
@app.route('/', methods=["POST"])
def recognition_post(level):
'''
其他路径 POST方法
'''
#读取json文件内容
try:
if level == 'speech':
request_data = request.get_json()
samples = request_data['samples']
wavdata_bytes = base64.urlsafe_b64decode(bytes(samples,encoding='utf-8'))
sample_rate = request_data['sample_rate']
channels = request_data['channels']
byte_width = request_data['byte_width']
wavdata = decode_wav_bytes(samples_data=wavdata_bytes,
channels=channels, byte_width=byte_width)
result = ms.recognize_speech(wavdata, sample_rate)
json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'speech level')
json_data.result = result
buffer = json_data.to_json()
print('output:', buffer)
return Response(buffer, mimetype='application/json')
elif level == 'language':
request_data = request.get_json()
seq_pinyin = request_data['sequence_pinyin']
result = ml.SpeechToText(seq_pinyin)
json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'language level')
json_data.result = result
buffer = json_data.to_json()
print('output:', buffer)
return Response(buffer, mimetype='application/json')
elif level == 'all':
request_data = request.get_json()
samples = request_data['samples']
wavdata_bytes = base64.urlsafe_b64decode(samples)
sample_rate = request_data['sample_rate']
channels = request_data['channels']
byte_width = request_data['byte_width']
wavdata = decode_wav_bytes(samples_data=wavdata_bytes,
channels=channels, byte_width=byte_width)
result_speech = ms.recognize_speech(wavdata, sample_rate)
result = ml.SpeechToText(result_speech)
json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'all level')
json_data.result = result
buffer = json_data.to_json()
print('ASRT Result:', result,'output:', buffer)
return Response(buffer, mimetype='application/json')
else:
request_data = request.get_json()
print('input:', request_data)
json_data = AsrtApiResponse(API_STATUS_CODE_CLIENT_ERROR, '')
buffer = json_data.to_json()
print('output:', buffer)
return Response(buffer, mimetype='application/json')
except Exception as except_general:
request_data = request.get_json()
#print(request_data['sample_rate'], request_data['channels'],
# request_data['byte_width'], len(request_data['samples']),
# request_data['samples'][-100:])
json_data = AsrtApiResponse(API_STATUS_CODE_SERVER_ERROR, str(except_general))
buffer = json_data.to_json()
#print("input:", request_data, "\n", "output:", buffer)
print("output:", buffer, "error:", except_general)
return Response(buffer, mimetype='application/json')
if __name__ == '__main__':
# for development env
#app.run(host='0.0.0.0', port=20001)
# for production env
import waitress
waitress.serve(app, host=args.listen, port=args.port)