190 lines
6.6 KiB
Python
190 lines
6.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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#
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# Copyright 2016-2099 Ailemon.net
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#
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# This file is part of ASRT Speech Recognition Tool.
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#
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# ASRT is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# ASRT is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with ASRT. If not, see <https://www.gnu.org/licenses/>.
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# ============================================================================
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"""
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@author: nl8590687
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ASRT语音识别基于HTTP协议的API服务器程序
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"""
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import argparse
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import base64
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import json
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from flask import Flask, Response, request
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from speech_model import ModelSpeech
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from model_zoo.speech_model.keras_backend import SpeechModel251BN
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from speech_features import Spectrogram
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from language_model3 import ModelLanguage
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from utils.ops import decode_wav_bytes
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API_STATUS_CODE_OK = 200000 # OK
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API_STATUS_CODE_CLIENT_ERROR = 400000
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API_STATUS_CODE_CLIENT_ERROR_FORMAT = 400001 # 请求数据格式错误
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API_STATUS_CODE_CLIENT_ERROR_CONFIG = 400002 # 请求数据配置不支持
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API_STATUS_CODE_SERVER_ERROR = 500000
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API_STATUS_CODE_SERVER_ERROR_RUNNING = 500001 # 服务器运行中出错
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parser = argparse.ArgumentParser(description='ASRT HTTP+Json RESTful API Service')
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parser.add_argument('--listen', default='0.0.0.0', type=str, help='the network to listen')
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parser.add_argument('--port', default='20001', type=str, help='the port to listen')
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args = parser.parse_args()
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app = Flask("ASRT API Service")
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AUDIO_LENGTH = 1600
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AUDIO_FEATURE_LENGTH = 200
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CHANNELS = 1
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# 默认输出的拼音的表示大小是1428，即1427个拼音+1个空白块
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OUTPUT_SIZE = 1428
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sm251bn = SpeechModel251BN(
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input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS),
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output_size=OUTPUT_SIZE
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)
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feat = Spectrogram()
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ms = ModelSpeech(sm251bn, feat, max_label_length=64)
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ms.load_model('save_models/' + sm251bn.get_model_name() + '.model.h5')
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ml = ModelLanguage('model_language')
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ml.load_model()
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class AsrtApiResponse:
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"""
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ASRT语音识别基于HTTP协议的API接口响应类
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"""
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def __init__(self, status_code, status_message='', result=''):
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self.status_code = status_code
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self.status_message = status_message
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self.result = result
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def to_json(self):
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"""
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类转json
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"""
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return json.dumps(self, default=lambda o: o.__dict__, sort_keys=True)
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# api接口根url:GET
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@app.route('/', methods=["GET"])
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def index_get():
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"""
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根路径handle GET方法
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"""
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buffer = ''
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with open('assets/default.html', 'r', encoding='utf-8') as file_handle:
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buffer = file_handle.read()
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return Response(buffer, mimetype='text/html; charset=utf-8')
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# api接口根url:POST
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@app.route('/', methods=["POST"])
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def index_post():
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"""
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根路径handle POST方法
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"""
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json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'ok')
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buffer = json_data.to_json()
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return Response(buffer, mimetype='application/json')
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# 获取分类列表
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@app.route('/<level>', methods=["POST"])
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def recognition_post(level):
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"""
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其他路径 POST方法
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"""
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# 读取json文件内容
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try:
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if level == 'speech':
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request_data = request.get_json()
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samples = request_data['samples']
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wavdata_bytes = base64.urlsafe_b64decode(bytes(samples, encoding='utf-8'))
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sample_rate = request_data['sample_rate']
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channels = request_data['channels']
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byte_width = request_data['byte_width']
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wavdata = decode_wav_bytes(samples_data=wavdata_bytes,
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channels=channels, byte_width=byte_width)
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result = ms.recognize_speech(wavdata, sample_rate)
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json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'speech level')
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json_data.result = result
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buffer = json_data.to_json()
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print('output:', buffer)
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return Response(buffer, mimetype='application/json')
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elif level == 'language':
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request_data = request.get_json()
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seq_pinyin = request_data['sequence_pinyin']
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result = ml.pinyin_to_text(seq_pinyin)
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json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'language level')
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json_data.result = result
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buffer = json_data.to_json()
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print('output:', buffer)
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return Response(buffer, mimetype='application/json')
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elif level == 'all':
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request_data = request.get_json()
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samples = request_data['samples']
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wavdata_bytes = base64.urlsafe_b64decode(samples)
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sample_rate = request_data['sample_rate']
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channels = request_data['channels']
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byte_width = request_data['byte_width']
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wavdata = decode_wav_bytes(samples_data=wavdata_bytes,
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channels=channels, byte_width=byte_width)
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result_speech = ms.recognize_speech(wavdata, sample_rate)
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result = ml.pinyin_to_text(result_speech)
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json_data = AsrtApiResponse(API_STATUS_CODE_OK, 'all level')
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json_data.result = result
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buffer = json_data.to_json()
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print('ASRT Result:', result, 'output:', buffer)
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return Response(buffer, mimetype='application/json')
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else:
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request_data = request.get_json()
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print('input:', request_data)
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json_data = AsrtApiResponse(API_STATUS_CODE_CLIENT_ERROR, '')
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buffer = json_data.to_json()
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print('output:', buffer)
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return Response(buffer, mimetype='application/json')
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except Exception as except_general:
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request_data = request.get_json()
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# print(request_data['sample_rate'], request_data['channels'],
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# request_data['byte_width'], len(request_data['samples']),
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# request_data['samples'][-100:])
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json_data = AsrtApiResponse(API_STATUS_CODE_SERVER_ERROR, str(except_general))
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buffer = json_data.to_json()
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# print("input:", request_data, "\n", "output:", buffer)
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print("output:", buffer, "error:", except_general)
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return Response(buffer, mimetype='application/json')
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if __name__ == '__main__':
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# for development env
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# app.run(host='0.0.0.0', port=20001)
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# for production env
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import waitress
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waitress.serve(app, host=args.listen, port=args.port)
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