feat: 实现新的基于http协议API服务接口

asrserver_http.py

@@ -0,0 +1,176 @@
+#!/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HTTP协议的API服务器程序
+"""
+
+import base64
+import json
+from flask import Flask, Response, request
+
+from speech_model import ModelSpeech
+from speech_model_zoo import SpeechModel251
+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_FORMAT = 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
+
+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
+sm251 = SpeechModel251(
+    input_shape=(AUDIO_LENGTH, AUDIO_FEATURE_LENGTH, CHANNELS),
+    output_size=OUTPUT_SIZE
+    )
+feat = Spectrogram()
+ms = ModelSpeech(sm251, feat, max_label_length=64)
+ms.load_model('save_models/' + sm251.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('/<level>', 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('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()
+        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='0.0.0.0', port=20001)

assets/default.html

@@ -0,0 +1,25 @@
+<!DOCTYPE html>
+<html>
+	<head>
+		<meta charset="utf-8">
+		<title>ASRT Speech Recognition API</title>
+		<style>
+		body {
+			width: 35em;
+			margin: 0 auto;
+			font-family: Tahoma, Verdana, Arial, sans-serif;
+    	}
+		</style>
+	</head>
+	<body>
+		<h1>ASRT Speech Recognition API</h1>
+		<h3>framework version: 1.0</h3>
+		<p>If you see this page, the ASRT api server is successfully installed and working. </p>
+		<p>For online documentation and support please refer to <a href="https://asrt.ailemon.net">ASRT Project Page</a>.</p>
+		<p>Please call this web api by post menthod. </p>
+		<em>Thank you for using ASRT.</em>
+		<footer>
+			<p style="text-align: center;">Copyright © <a href="https://www.ailemon.net">ailemon.net</a></p>
+		</footer>
+	</body>
+</html>

client_http.py

@@ -0,0 +1,49 @@
+#!/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asrserver http协议测试专用客户端
+'''
+import base64
+import json
+import time
+import requests
+from utils.ops import read_wav_bytes
+
+URL = 'http://127.0.0.1:20001/all'
+
+wav_bytes, sample_rate, channels, sample_width = read_wav_bytes('Y:\\SpeechData\\语音数据集\\data_thchs30\\train\\A11_0.wav')
+datas = {
+    'channels': channels,
+    'sample_rate': sample_rate,
+    'byte_width': sample_width,
+    'samples': str(base64.urlsafe_b64encode(wav_bytes), encoding='utf-8')
+}
+headers = {'Content-Type': 'application/json'}
+
+t0=time.time()
+r = requests.post(URL, headers=headers, data=json.dumps(datas))
+t1=time.time()
+r.encoding='utf-8'
+
+result = json.loads(r.text)
+print(result)
+print('time:', t1-t0, 's')

utils/ops.py

@@ -44,6 +44,18 @@ def read_wav_data(filename: str) -> tuple:
     wave_data = wave_data.T # 将矩阵转置
     return wave_data, framerate, num_channel, num_sample_width
 
+def read_wav_bytes(filename: str) -> tuple:
+    '''
+    读取一个wav文件，返回声音信号的时域谱矩阵和播放时间
+    '''
+    wav = wave.open(filename,"rb") # 打开一个wav格式的声音文件流
+    num_frame = wav.getnframes() # 获取帧数
+    num_channel=wav.getnchannels() # 获取声道数
+    framerate=wav.getframerate() # 获取帧速率
+    num_sample_width=wav.getsampwidth() # 获取实例的比特宽度，即每一帧的字节数
+    str_data = wav.readframes(num_frame) # 读取全部的帧
+    wav.close() # 关闭流
+    return str_data, framerate, num_channel, num_sample_width
 
 def get_edit_distance(str1, str2) -> int:
     '''
@@ -89,3 +101,59 @@ def visual_2D(img):
     plt.imshow(img)
     plt.colorbar(cax=None, ax=None, shrink=0.5)
     plt.show() 
+
+def decode_wav_bytes(samples_data: bytes, channels: int = 1, byte_width: int = 2) -> list:
+    '''
+    解码wav格式样本点字节流，得到numpy数组
+    '''
+    numpy_type = np.short
+    if byte_width == 4:
+        numpy_type = np.int
+    elif byte_width != 2:
+        raise Exception('error: unsurpport byte width `' + str(byte_width) + '`')
+    wave_data = np.fromstring(samples_data, dtype = numpy_type) # 将声音文件数据转换为数组矩阵形式
+    wave_data.shape = -1, channels # 按照声道数将数组整形，单声道时候是一列数组，双声道时候是两列的矩阵
+    wave_data = wave_data.T # 将矩阵转置
+    return wave_data
+
+def get_symbol_dict(dict_filename):
+    '''
+    读取拼音汉字的字典文件
+    返回读取后的字典
+    '''
+    txt_obj = open(dict_filename, 'r', encoding='UTF-8') # 打开文件并读入
+    txt_text = txt_obj.read()
+    txt_obj.close()
+    txt_lines = txt_text.split('\n') # 文本分割
+
+    dic_symbol = {} # 初始化符号字典
+    for i in txt_lines:
+        list_symbol=[] # 初始化符号列表
+        if i!='':
+            txt_l=i.split('\t')
+            pinyin = txt_l[0]
+            for word in txt_l[1]:
+                list_symbol.append(word)
+        dic_symbol[pinyin] = list_symbol
+
+    return dic_symbol
+
+def get_language_model(model_language_filename):
+    '''
+    读取语言模型的文件
+    返回读取后的模型
+    '''
+    txt_obj = open(model_language_filename, 'r', encoding='UTF-8') # 打开文件并读入
+    txt_text = txt_obj.read()
+    txt_obj.close()
+    txt_lines = txt_text.split('\n') # 文本分割
+
+    dic_model = {} # 初始化符号字典
+    for i in txt_lines:
+        if i!='':
+            txt_l=i.split('\t')
+            if len(txt_l) == 1:
+                continue
+            dic_model[txt_l[0]] = txt_l[1]
+
+    return dic_model