ASRT语音识别asrserver http协议测试专用客户端
左丘昊天
测试代码
#!/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
import wave
URL = 'http://106.13.12.80:20001/speech'
# URL = 'http://106.13.12.80:20001/all'
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
wav_bytes, sample_rate, channels, sample_width = read_wav_bytes('.\\q7fmz-ix2hf.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'}
# print(wav_bytes)
# print(sample_rate)
# print(channels)
# print(sample_width)
print(datas)
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')
测试结果
python -u ".\client_http.py"
{'channels': 1, 'sample_rate': 16000, 'byte_width': 2, 'samples': 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD__wAAAAAAAAAA_____wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD__wAAAAAAAAAA__8AAAAAAAAAAAAAAAAAAAAAAAAAAAAA__8AAAAAAAAAAAAAAAABAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAA__8AAAAAAAD_______8AAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAAA__8AAAEAAAAAAAAA__8AAAEAAQAAAAAA__8AAAAAAQAAAP____8AAAAAAAAAAP__AAAAAAAAAAD_____AAAAAAEAAQAAAAAAAAABAAEAAQAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAD_______8AAAAA_____wAA__8AAAAAAAD_____AAAAAAAAAAD_____AAAAAAAAAAAAAAAAAAABAAEAAQAAAAAA__8BAAEAAAABAAEAAAABAAIAAQAAAAAA_____wAAAAD___7_AAAAAAAAAQABAAAAAAAAAAEAAQAAAP____8AAAEAAAAAAAAAAAAAAAEAAAAAAP___v___wAAAAD_____AAAAAAEAAQAAAAAAAQABAAAAAQAAAP____8AAAEAAAAAAAAAAAAAAAEAAAD__wAAAAAAAAAAAAD_______8BAAEAAAD_____AAAAAAAAAAAAAP__AAAAAAAAAAD__wAAAAAAAAAA_____wAAAQAAAAAAAAD__wAAAAABAAAAAAAAAAAAAAABAAAA__8AAAEAAQABAAEA_____wAAAAAAAAAAAAD_____AQAAAAEAAQD_____AQAAAP_______wAAAQABAAAA__8BAAEAAAAAAAEA__8AAAAA_____wAA__8AAAEAAgABAAAAAAABAAIAAwABAAAAAAD__wAAAQAAAAAAAAD__wEAAgAAAAAA_v_9____AAD______f_9____AAABAAEA_____wAAAQACAAIAAAD_____AAABAAIAAQD__wAAAQAAAAAAAQD__wAAAQD__wAAAQAAAP__AAAAAAAAAAAAAAAA_________v___wEAAAD__________wAAAAD__wAAAAD__wAAAQAAAAAAAAAAAAIABAABAAAAAQAAAAAAAQAAAP__AAD_____AAABAAAA_____wAAAQACAAAA_v_-_wAAAQABAP____8AAAAA__8AAAEA___-_wAAAAD__wAAAAD-____AQAAAAEAAQD___7_AAABAAAA_v_-_wAAAQACAAIAAAAAAAIAAQABAAIAAQABAAEAAAAAAAAA___-____AQACAAMAAgD__wEAAwABAAAAAQD___7__v___________wAAAAD__wEAAQAAAAAA_v_9__7____9__7___8AAAEAAgABAAIAAwAAAAAAAgACAAQAAwD9__3_AgADAAEA___-_wEAAgAAAAIABAAAAP__AQAAAAIABQACAAEAAgABAAEABAAEAAAA_P8AAAYABAABAAAAAQAAAP__AwAIAAQA_v_9__z_AQAHAAIA_P____3_-P_-_wYA_v_z__n_AgAAAP__AgD___3_AQD_____BgAEAPz__f___wIABAADAAYABAD9_wEACgAGAAMAAwD-__r__v8JABAAAgD2_wAACQADAP3__f8GABIABgDw__n_DQAFAPH_9P8FAAwABAD4____DgACAO7_-_8KAAUAAwD9__P__P8CAPP_8f_-____-f_2__n__v_5__j_AAD9_wEAFQAKAPH_BAAWAPn_6v8BAAkAAQAAAPr_9f_9__b_3f_j_wIACADv_9__7P8AAAEA_v8FAAUA_v8KABcADQABAAcAFAAYABAAFgAlAB4AFwAZAAQA-_8UAA0A-v8PAAMAwP-9_wUAGgDr_9P_1__U__j_IwAHANf_2P_m__j_KAA-AAMAwP_a_xwALQAkAAkA0f_S_xcAJwDw_8H_wv_r_xAAAwDe_-D_CAARAPb_7P_0_wIADgD___r_CwDt_7z_4P8vACcA2f_K__X__f8CAP__xf_D__7_7__b_woACgDT_87__P8AANr_7v_-_8L_wv_i_7n_s__E_7f_4P_z_8b_zP_c_-L____x_9v_2f_T_w8APAAKAOf_0__u_1QARwD7_w8ACwDv__j_AAAhAAoAzv8FABEA9P9BABcAu_8zAGQA6P_e_zIAPQDv_7b_2P_6_zMAUQDH_7r_VwAcAOT_YQArALX_9v8-AE8AKQDD_6r_8v9HAD8A2v_z_1IAIADY_9H__f9JAPP_e_8IALQAhwDf_4X_QAAuAd0A_v_R_0MAkABXACkAJwDR_2r_fP9hAFYBbAB9_o7-hAC3AbYA5v69_u__sQCxAEAADgAcAHn_Wf_UAOEB8AD7_mf-LgCnARsB0f8B_6v_sAAsAAMAqAD7_1L_tv8rANYAiQAW_-7-DwC_AAoA5v6f_7sAMwCQ__b-T_9DAbAAtv7O_7QAx__K_-X_8v96ABsAd_-a_6kAQAGd_x3_nwD3_13_MwB9_wUAOwEm_zz-GwCnADoAq_-L_zYAt_9-_2wAUQBHANP_k_7O_yMBVwAjAID_If9_AEIA_P_YAJb_a__WAMH_pf9vAQgBqv8p_-L_FQHIALAAeADf_tH_4AAB_-7_YwEf__T-TgBW_17_MQDe_4P_Wv_w_9f_6v5gAN4Alf7a_vX_k_-oALYAyv51_qL_RQGdAAX-X__aAHn-xv6u_1P-XQDWACD9cv6AAdL_AP8vAMz__P6A_10Acf9L_08B-v9H_vIAxwAd_5EBbQFh_4AASQB0_0QATgC2ANz_vf5pAYcBUP-mAEr_NP7HArQBg_0pACUB8_44AKcAGP--_5EBVgGo_3kAhQAE_v8ASgP-_Q3-QgGs_t__TQF4_Z__mwH9_D7-JAJiAOX-F__Q_xcBpwD2_4z_FAAsAsYAEP_VARACNQCOAPX_lwAhAuUAZwD1_7b_HgJiAN_9owGtAUT-2__N_4z-vQEMAeP7gv1FAwcBQPs7_u8Bav6p_h0Bv_5UAC0Cwf17__4EQAFz_HH_QAPjARH_ef9rALsAwgH__zD_IAMcAjD9Gf8MA-EBav4a_YD_cAHHAEkAMP9l_nT_gP-N_wUAnv78_Zj-z_9MAkIBY_0F_Tb_rQGhAXz-c_7O_4X_yAFSAun_8f-B_mL-XgMCA4v-Rv6H_1sBIAH__iUB6wDV_QYAuf9V_xME4v6z-DABlQT7_YL9LgAqAY8AHP4v_5ABFQL5Aab9FP2yA_QBIf1pAIgADf6T_6H_JQGAAxcAc_3h_lAC0gUmAUb8FAK3AoD9tgIrBeT9m_7rAaX-pAHYBYr-G_pBAOcCy_9r_7YAM_-8_df_QAGcANcABP3l-cEB3wSJ_F_8PQBi_s0AgwFw_UQAvAFv_on_9f-kAK4Bl_z1_XsDX_9hABgEXP2O_lEEI_-V_6ADU_4v_fMAUwFWAjABT_24-83-gQZDBHP5ev3nA7r-ugF1BGn7sfw_Agr-bADtBF_-Svqu_0UFvAHS-ur_awNW-7b_SwY3_Dn-LweQ_IT7UwjsAVX6lAK7Aqn86QAfBGr-9f5JBsoAW_qSBTsGRfnX_ycGkfzz_yoEC_wfAfAGVP3I-8UC5ANQAcX8m_2iAzAC2v5e_1b-4_95AXH-___KAqT-nPwfAGoDxwPm_RD6YgLlBoL-VPwdAZ__X__3ABP-fQD7Asn8Tv6OBRkCBf4uANj_zf2o_iwBHgAM_msBhAB-_EkDCAcAAZH_vP35_YsG4wRc_I798gB1ArIBIACJA7UAj_qx_zgExQRQAkT4Kf38CQ4C8f2ZBBH_if3fAp4A5gCjATL-Rf2o_kAE2gKT-aP-CgOx_dcCqgDD-MsDagVp-kEA-gVwAMv8Cv-3BVoDrf2-ALn9cwDOCKj6h_jZCpgBAPg7BBADev4YALj8GgA9A-sAKwA2-10APggf_RD9OgZs_Un9PwS9_XD_VgUm_077TAGsB7b_1PcUBUoHzfohAUUD8fuVBLgFTvyq_3AE1AHe-1f-AwjW_g755gagAOj7_gl7_U70rAfUBUz6-_5xAIwBMgI-_1kBGQFPAmQBLvj4AK0Kyfx1-j4CCQFdAur9Df2lBasA9AAIBAb4egHoCvn3t_qyBMD7EQFKAUf5mwNKATn5EwKBAUv_WP_D-PoBmgYY-yf-2v8g_n8EePwd_TkJb_wM-scHBwB6_bEBFf3QBV4GvPge_sYEPQJPAUX8TQAuBUf97__kAer7EwMBAK72aQIBBDb7BwBT_yP_PgXf_V362gBx_xH-5v4PAIkCf_1v_TYD7v2m_6wFZPu3-1kH4wDd-98DFgJ1_FwDCAc--wT72wrZA333VgaVBhX4tALGBJj55wQpBpX4fAHSBrn7Nf4JB3AFT_3y-skE0QbSAOsDGv4P-GMHLAes9-_-BQMA-dX-4QXkAIz-mPt3-jQDgQiQAHf2W_vYBhUDYPwI_43_rgL4Alj4yv_YDcL86_F4Ba0JO_2e_lYAmv3MBOsEefnP_l0I_Pod9vEHcAg6-yD99_79_MsF6wXe9ob4AwjAA336AAR1AjD15v9QCPb7xf6XA6D4af19CN8B3fpd_GgBWgFe_MECRQTr-XUABwQR-k8BMQEl9qQDkAb19nX-dQT__K8BgQKX_D3_FAGwAFcA1wBIAtf7PPyLBZEAcP--BCL6J_yOCNEA2P-8Aiv3d_82DFn-DPvIBt4Cd_sbAO4GPQSe_skAef9__tIGZAAr9rwBDwQ8-sD_qQGm_GQB4ADk_PMASAEg_yH_w_0SAFYBnf70_wv_e_1QAz8BXPna_p4DX_16_AL_SP3M_XP-Jf66_1P-Rf9iAmr8SvzsBRoBDPoUAIL__vxeAuoANv7lAIL_W_5F_yr_8f6l-8j8QQBN_AQAXAPK-IH9Ogcu_kwAwwN--EECOgoP-D771wna_7v4lv2wAUYGSAAo-E8AhAd5ANb4w_3SBWz-NPdvADsEMv5t-1T7ewSNDDgBu_iuAJoHPQhIAtn8CgN8BB38jf8-B5UAf_mP_XQBMgNZBXD8MPSsA28Oo_1v9WUA3gPm_y_9eP4MBZED-PlP--kHvQv8-2TyxQPuDDT_Ufy-AD7_iARpAx76bAHDBtX3afRsBj8N9f449WT6PAAsBKUE5voO-AkChgKz_U4C8wTO_1f7wf2BBb0FbPwA-c__1wWfAUL6R_5qBND-efv9AngE2fop-ewCBwOM-tv6ov4BBMUImvuv9M8INQp-9vz8ewkwAMX4L_qGAQgNGgNd7o76whH6BVf0F_-iBTH8ZP-sAlz8RwM-AvDxmQCSFRwAIO_y_7YI7gQeBfz-u_t_BsYGn_rmAIYLSvwT9DQHewoq_Nb8df3U_OwLiAjt8F75ygxB_g_1-QZJBwP4rvtuAskB1wiyBEn1mv87DB39IPwfCg__-PhTBqP_P_nMB5QB-vK_AWIJ7_vl_0sGoPhs-n4LCgMl9pgBKwNa-EACqQct-4wAUwbf-In_Eg6-AM76HgWT_Sf63wnXBe70Nv8KDTD--vYYCaoJw_uZ_xz_GvvnCwcJze_q-4EONPrA9KkJZgZd-wb__Pny_MQR6QbY7Mn_rhLr_P37WQ4H_1j3nQqrA2b4YAcpAe_vTwJuDSb5j_c9Aof6iv7VCoz8gPT_A5ACsPuXBo8CxvMc_SII1f77-joCnP4I-lIG5gmg-jr7uAQw_VUA7g0WAjn3cAWACX4AAwTsBnn-BP53BX4DMwETBCj7PPkgDX0ILPAR_X0Otf0R-usGI_-Z-qwDlf81_5gLdwBJ7bcAihZOAZXwVgHRBmn_jQXxBKn-zQa2Asn19gbnEZ724_HFC_YFBvirBooDYvWFB9wKPPSqAKoNofNp9pMS4AJY8UgDzAE8-O0Kcwef8UsCeAsr9GEA2xW6_E7zwAeNAHv-8w7W-Ubq7AswELrxYvxBDXf5Z_auBsMC1gE_BcfzrvQdECcKf_J2_cMJxf5RAHQH3v5WAVgIVfnS-KUNAAP571sCUQyS-iz8mwas__QBPQkC_mD8fQqqA_n4wAd-CgX38vkfB6EBhwKsA-b0Hv85E6n8S--oDHYN5_Ii-6gLegVpA3cA_vd2Bi0Q8_XI8FIR7g_h8KT0hgg6B3MCAP3Z9mAFpgwF9kD5nRcGCGfmQvlMEs8EmPw5_4D2yf2YDP7-_vqNEPoEX-xABUYbNgJf-DkENPur_ZoLhfjH9IUShATK6rIJyhYL-Bf-Eg8h_XgBpBDJ99b1NBbiA_DmTwWsEi_00vjMCxP_tQCkCFHzivpbG-UHC-8SCo8Oa_OWASUTs_x_9xwC-vQH_MwU9fx841gDbBEJ9f76mw2W-Bf09gk5Ae_8KxHr_9zs1g9PGaH0g_e4CnL7ufxoCmH4r_v5F94A9uevDf0X8u1F8GALUgC7-zYDYvQC_kMZqv4w5bYJ-Rap86X1qA-fB57_ywTs_PsGxBoOAIvrthBBG-T27PmkEHgBrvrkBxv8kfdYCrX78ufYBP4PFfEy950QtwJs_bsMzf_9-jcQIQPF8MUJBQw67sD7jhQhBY0AJgvL-2H7ZRIjBNTvoANxAw_qnfy1FlUBWfL5_hD_YgO9EBr_Me4JAbsDWvLf_3ELgPRO8NcErQJQ-9cBZvgw8RkHMwpu8qb4fAjd-Rb6lQ6zA7r15QNnAh34Dwe4BWru2fY2BiL3A_QIAcn4F_YuBD37SfJ3BaEE5vEeAREPMPmy850FwwRdAskI0Pwb9xMJrQUd9K7_OAgK9sv0TAT4APX5hfyF-Yb9VAxkB_H5bgSYDRADFAbtEHgEv_s3B7sFz_0LBHz_qPQnAU4KDv1K-cj9XvfB-7EKpAQw9w_5wfkp_GENNQ8T-sf1YwHeA5UJaBBxAw74Fv-HAtEEVw-uB9jyj_h3C1UJlgCE_-z6ifl6AaMCF_7O_Wb3kfH6_5MLx_3P9Hn-BgG3_5EGTQO--V7-nwP2AikJeQVS89H12AfuBVv-MwPZ_sD1dP6FB8UElwSd_7T1Cv8yDHQEkwEqC4YEOPvGBG8IHAEwA84ABPXf9wL9vvOR9TQAVPjQ8ZD86P7n-n0B2QFO-4UBhwX9_CL_vgYu_2z8LgifCFcBPwTPBBoDLwrMBjb4NfulBDr_QgAhDMAIqf5AA0MLJg0AC6_-ZfRc_ugGHv7T_PYCEvmO8WMA6Qw4CAD_6_ep-oUHoAcS--f6XP2z8sfzWAa1CT_8y_Y3-0oCjgaM_0z4jP1r_e7yBPaL_xX3Le0B9QH-FP5f--32T_npBL4FKf0FANwCovoyAOUV1h_YHscgPSNzLDo8XDyQMAwtlCRkEh0RYRk5D0j_efjp8XPyT_of87fnJe2S7DHgf-bU8troit-15qfrp_Ak-BDyQe_M_nYAK_Sz_t0KPPuy8zwC8gT6_wEGTgxGGkM0gjpfNlFMaF1jUH5SyWTLVqU6JjO2K1cfDxmKBNnr1uxb6H3Pt9Gk4qTP97oTyPfPT8tA05_UOs8a35rqsOOB84EJhfq88JYHtg2CApcITgiG_F0GSxBoCgodOjnuM4k0Y1KOW5pTrF4xYA5OMEk5PoofdBdnGEr5uuO87fvhQMfiyqfRXchdy6vLmbxnxOnWNc-U0qnt0OqR2wHzVgtLBvwHSg2MA7QHnBPvBwgDsRAaCOsAISP6POg08jyfUWVR3lUYY8FXPkeOQiYsLxRlFtQITeNx243j-9KgyBnQZsb3vJjKMs_4ypDWY9hezurgm_kn9KL09QfPCSQIKxhKGucOUxO3E6EH5w3bFhoLpw8wLQ48j0NuVxRffFsqZD9lcVWuTeI_ZhvwBHkDZvHU10DQu8hSvdm_YsOowA_Hccnmv0LJpeCp4aHcL-xL-W36dAQBEokXYhwCG2oT7BnPIvsV0go-EuYQoAdqE-Up2jaqQmROU1VGX5liBlQxSE1CFCndCAH80PAS26fOmMklv3i62rx-vTPGr9GXzJ7I0teY4_Tm8PRmAIP9MQI7EMoXmCDrJ30d3BLkFnQV4w8rFgsVigWZAzQPjBk1LqVGeUxvT-9b6Fu2UmJRFkQ3JMcLAPkI4QrUEs6avT-0kbkht8S2MMrC1ufQhdJO267f1OxH_p0CYwbMDj0NDRHGJrYvNyOaHG0Zlw4oDk4XkxQcDO4GawCNCUUrGkctUXRaJVzxUK1OGVEBQG8kFwpz6K3MnsYyw_a4Ira_tQKzTL0xzm7V9d4c68_mfuKC9BMH2wuZE3YccxyyHQIhVSHGJ9QpnxXkAw4JDwvaAogHlw_eCJgEqxE_LM9OE2SZXa1VXVleT8s5BS4eGl_vXMkUuQi29LpYvAS0dbbExzzSKNwb86H-9PEA6JntmPmtCO4RdA8pEQ8aWRoxGrslSSmGGmINgwkUBwcHXAjjB0oL6w4-C_QRVDG9URZdFVyjVvVJdjvhL0MhlAo76ibFZq5YsAO6Gr7CxGbO4NC90szfvvBn-V74LvHB7Nb0KAWSE-wd-SAXG1EZ5yKXKQglnhw7EZUCOPzXAaUINwvvCr4IRQlVD8EZry2lSedYGFKMRvc_wzXeJrcY5gZp6-zKPrNnsKK8OcZwyzHXSeUq67jvm_xnCakJjwBu-_n_1gaACaYM8BP2FyoULhHpFWcaeRRbCIsBtgHeAqQDGQYPCRAK7QrnEyUr60YKVgdXKFN7SgI6xCbYFF
{'result': ['xia4', 'yi1', 'shou3'], 'status_code': 200000, 'status_message': 'speech level'}
time: 1.506971836090088 s
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