语音合成与识别服务（以下简称“服务”）提供高质量的文本转语音（TTS）和语音转文本（ASR）功能。为了方便用户快速部署和使用该服务，我们提供了基于Docker的部署方案。本文档将详细介绍如何使用Docker部署该服务。
本文提供的语音合成与识别服务，是基于paddlespeech开发的TTS和ASR服务(纯CPU服务)，用户可以通过Docker快速部署并使用该服务。
在开始部署之前，请确保您的环境满足以下要求：
1. 已安装Docker和Docker Compose。
2. 具备一定的Linux命令行操作基础。
3. 确保您的服务器具备足够的计算资源（CPU、内存、GPU等）。
我们提供了预构建的Docker镜像，地址位于七牛云，下载地址为：
https://datacdn.data-it.tech/HomeAssistant/dokerimages/paddlespeech1.1/paddlespeech.tar
curl -fsSL https://datacdn.data-it.tech/HomeAssistant/dokerimages/paddlespeech1.1/install-paddlespeech.sh | sudo bash
使用TTS或asr服务，首次运行会自动下载模型文件，模型文件较大,***下载过程约需10分钟***，请耐心等待。
我们可以如用下命令实时查看运行日志:
docker logs -f paddlespeech
TTS模型首次使用时侍自动从网络下载约2G的模型文件，下载完成后会自动启动服务。后续再次使用时，不会重复下载模型文件。
Asr模型首次使用时侍自动从网络下载约1G的模型文件，下载完成后会自动启动服务。后续再次使用时，不会重复下载模型文件。
离线安装包分两部份，一部份是容器镜像，一部份是数据卷文件，安装启动容器前先替换对据卷绑定的目录，这样容器启动后无须下载模型，可以在无互联网的情况下直接使用
如下示例，我们将文件放到用户目录的down下,请保持相应的目录结构，如下所示：
tst@tst-VMware-Virtual-Platform:~/down$ pwd
/home/tst/down
tst@tst-VMware-Virtual-Platform:~/down$ ls
data install-paddlespeech-localh.sh
tst@tst-VMware-Virtual-Platform:~/down$ tree
.
├── data
│ ├── home_dtuser_opt_paddlespeech_data_logs.tar.gz
│ ├── home_dtuser_opt_paddlespeech_data_nltk_data.tar.gz
│ ├── home_dtuser_opt_paddlespeech_data_output.tar.gz
│ ├── home_dtuser_opt_paddlespeech_data_paddlenlp_models.tar.gz
│ └── home_dtuser_opt_paddlespeech_data_paddlespeech_models.tar.gz
├── home_dtuser_opt_paddlespeech_data_paddlespeech_models.tar.gz
└── install-paddlespeech-localh.sh
2 directories, 7 files
chmod +x install-paddlespeech-localh.sh
sudo ./install-paddlespeech-localh.sh
按装脚本将会先解压文件，并加载docker镜像，然后启动容器，启动完成后，可以使用docker ps命令查看运行状态，如下所示：
tst@tst-VMware-Virtual-Platform:~/down$ sudo ./install-paddlespeech-localh.sh
[INFO] 开始 PaddleSpeech 离线安装...
[SUCCESS] 磁盘空间检查通过（55GB）
[INFO] 检测网络连接...
[SUCCESS] 网络连接正常
[INFO] 创建目录结构...
[SUCCESS] 目录结构创建完成
[INFO] 解压数据卷...
[INFO] 正在解压: /home/tst/down/data/home_dtuser_opt_paddlespeech_data_logs.tar.gz 到 /dt_opt/paddlespeech/data/logs
./
[INFO] 正在解压: /home/tst/down/data/home_dtuser_opt_paddlespeech_data_nltk_data.tar.gz 到 /dt_opt/paddlespeech/data/nltk_data
./
./taggers/
./taggers/averaged_perceptron_tagger/
./taggers/averaged_perceptron_tagger/averaged_perceptron_tagger.pickle
./taggers/averaged_perceptron_tagger.zip
./corpora/
./corpora/cmudict/
./corpora/cmudict/cmudict
./corpora/cmudict/README
./corpora/cmudict.zip
[INFO] 正在解压: /home/tst/down/data/home_dtuser_opt_paddlespeech_data_output.tar.gz 到 /dt_opt/paddlespeech/data/output
./
./2753d658-b228-4ae6-94fc-3404a41f4526.wav
./9f24d71e-9f29-4d60-aa13-677fd19bba46.wav
./38ea94c6-e19b-4e41-8cca-454d8e374a15.wav
./d44aa891-c36b-473f-aef3-44484f0cd74b.wav
./c5ef53d3-502b-4f81-9fdd-583836ba7096.wav
./3931cd47-e022-4385-b588-4a61e64e5eed.wav
./1d213380-eee6-43ba-88db-99ba9b5609df.wav
./c85f65de-a4c1-43aa-a251-52ce4e9df271.wav
./7cd5fc65-665b-444c-9c9f-41f1ce8036f8.wav
./f74dbda7-1cf8-4056-bd30-523b74af8b0e.wav
[INFO] 正在解压: /home/tst/down/data/home_dtuser_opt_paddlespeech_data_paddlenlp_models.tar.gz 到 /dt_opt/paddlespeech/data/paddlenlp_models
./
./packages/
./models/
./models/bert-base-chinese/
./models/bert-base-chinese/bert-base-chinese-vocab.txt
./models/bert-base-chinese/tokenizer_config.json
./models/bert-base-chinese/vocab.txt
./models/bert-base-chinese/special_tokens_map.json
./models/embeddings/
./models/.locks/
./models/.locks/bert-base-chinese/
./datasets/
[INFO] 正在解压: /home/tst/down/data/home_dtuser_opt_paddlespeech_data_paddlespeech_models.tar.gz 到 /dt_opt/paddlespeech/data/paddlespeech_models
./
./conf/
./conf/cache.yaml
./models/
./models/G2PWModel_1.1/
./models/G2PWModel_1.1/config.py
./models/G2PWModel_1.1/MONOPHONIC_CHARS.txt
./models/G2PWModel_1.1/__pycache__/
./models/G2PWModel_1.1/__pycache__/config.cpython-39.pyc
./models/G2PWModel_1.1/__pycache__/config.cpython-310.pyc
./models/G2PWModel_1.1/record.log
./models/G2PWModel_1.1/bopomofo_to_pinyin_wo_tune_dict.json
./models/G2PWModel_1.1/POLYPHONIC_CHARS.txt
./models/G2PWModel_1.1/char_bopomofo_dict.json
./models/G2PWModel_1.1/g2pW.onnx
./models/G2PWModel_1.1/version
./models/G2PWModel_1.1.zip
./models/conformer_wenetspeech-zh-16k/
./models/conformer_wenetspeech-zh-16k/1.0/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/conf/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/conf/preprocess.yaml
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/conf/tuning/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/conf/tuning/decode.yaml
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/conf/conformer.yaml
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/conf/conformer_infer.yaml
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/data/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/data/lang_char/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/data/lang_char/vocab.txt
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/data/mean_std.json
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/model.yaml
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/exp/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/exp/conformer/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/exp/conformer/checkpoints/
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar/exp/conformer/checkpoints/wenetspeech.pdparams
./models/conformer_wenetspeech-zh-16k/1.0/asr1_conformer_wenetspeech_ckpt_0.1.1.model.tar.gz
./models/fastspeech2_csmsc-zh/
./models/fastspeech2_csmsc-zh/1.0/
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4.zip
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/snapshot_iter_76000.pdz
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/phone_id_map.txt
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/default.yaml
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/speech_stats.npy
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/pitch_stats.npy
./models/fastspeech2_csmsc-zh/1.0/fastspeech2_nosil_baker_ckpt_0.4/energy_stats.npy
./models/hifigan_csmsc-zh/
./models/hifigan_csmsc-zh/1.0/
./models/hifigan_csmsc-zh/1.0/hifigan_csmsc_ckpt_0.1.1.zip
./models/hifigan_csmsc-zh/1.0/hifigan_csmsc_ckpt_0.1.1/
./models/hifigan_csmsc-zh/1.0/hifigan_csmsc_ckpt_0.1.1/default.yaml
./models/hifigan_csmsc-zh/1.0/hifigan_csmsc_ckpt_0.1.1/snapshot_iter_2500000.pdz
./models/hifigan_csmsc-zh/1.0/hifigan_csmsc_ckpt_0.1.1/feats_stats.npy
./datasets/
[SUCCESS] 数据卷解压完成
[INFO] 解压镜像包...
[SUCCESS] 镜像包已移动到 /dt_opt/paddlespeech/images
[INFO] 加载Docker镜像...
daf557c4f08e: Loading layer [==================================================>] 81.04MB/81.04MB
d593918d433d: Loading layer [==================================================>] 4.123MB/4.123MB
7c4cff21e743: Loading layer [==================================================>] 41.36MB/41.36MB
25f6bb353e99: Loading layer [==================================================>] 5.12kB/5.12kB
bc3a46414f17: Loading layer [==================================================>] 1.536kB/1.536kB
8fc7185920f1: Loading layer [==================================================>] 3.584kB/3.584kB
bac2a7f893df: Loading layer [==================================================>] 847MB/847MB
092bab498a3d: Loading layer [==================================================>] 13.31MB/13.31MB
19db93cce63e: Loading layer [==================================================>] 2.56kB/2.56kB
2fc7499dded4: Loading layer [==================================================>] 3.042GB/3.042GB
7da6b60d7eb7: Loading layer [==================================================>] 2.53MB/2.53MB
057242764992: Loading layer [==================================================>] 8.704kB/8.704kB
9a996f3d539d: Loading layer [==================================================>] 3.072kB/3.072kB
4cdf0570391d: Loading layer [==================================================>] 104.2MB/104.2MB
Loaded image: dt_iot/paddlespeech:latest
[SUCCESS] 镜像加载成功
[INFO] 生成docker-compose.yaml...
[SUCCESS] docker-compose.yaml创建完成
[INFO] 启动容器...
[+] Running 2/2
✔ Network dtnet Created 0.5s
✔ Container paddlespeech Started 4.6s
[SUCCESS] 容器启动成功
[SUCCESS] PaddleSpeech 离线安装完成！
docker ps
docker logs -f paddlespeech
进入容器器的部署目录：
/dt_opt/paddlespeech
docker ps up -d # 启动容器
docker ps down # 停止容器
docker ps restart # 重启容器
docker ps logs -f paddlespeech # 实时查看日志
docker ps exec -it paddlespeech /bin/bash # 进入容器
docker ps rm -f paddlespeech # 删除容器
docker rmi dt_iot/paddlespeech:latest # 删除镜像
docker volume rm paddlespeech_data # 删除数据卷
docker network rm dtnet # 删除网络
docker system prune -a # 清理无用的镜像、容器、数据卷、网络等
以上是常用命令，使用时根据需要灵活使用。
找到文件
/dt_opt/paddlespeech/docker-compose.yaml
修改对应的端口号，如下所示：
更改第一个端口为需要的端口号即可，如下所示:
docker-compse down
sudo nano ./docker-compose.yaml
# 修改对应的端口号，第6行
# - "8001:8001"
# 保存退出，重启容器即可生效
docker-compose up -d
为了方便的演示如何调用这个api，我们提供了一个测试网页,下载地址为:
https://datacdn.data-it.tech/HomeAssistant/dokerimages/paddlespeech1.1/tts_demo.zip
curl --request POST \
--url http://ikuai.m-iot.tech:58150/tts \
--header 'content-type: application/x-www-form-urlencoded' \
--data 'text=请张三到一室做检查' \
--data lang=zh \
--data spk_id=0
POST /tts HTTP/1.1
Content-Type: application/x-www-form-urlencoded
Host: ikuai.m-iot.tech:58150
Content-Length: 103
text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0
//asynchttp
AsyncHttpClient client = new DefaultAsyncHttpClient();
client.prepare("POST", "http://ikuai.m-iot.tech:58150/tts")
.setHeader("content-type", "application/x-www-form-urlencoded")
.setBody("text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0")
.execute()
.toCompletableFuture()
.thenAccept(System.out::println)
.join();
client.close();
//nethttp
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("http://ikuai.m-iot.tech:58150/tts"))
.header("content-type", "application/x-www-form-urlencoded")
.method("POST", HttpRequest.BodyPublishers.ofString("text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0"))
.build();
HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
//okhttp
OkHttpClient client = new OkHttpClient();
MediaType mediaType = MediaType.parse("application/x-www-form-urlencoded");
RequestBody body = RequestBody.create(mediaType, "text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0");
Request request = new Request.Builder()
.url("http://ikuai.m-iot.tech:58150/tts")
.post(body)
.addHeader("content-type", "application/x-www-form-urlencoded")
.build();
Response response = client.newCall(request).execute();
//unirest
HttpResponse<String> response = Unirest.post("http://ikuai.m-iot.tech:58150/tts")
.header("content-type", "application/x-www-form-urlencoded")
.body("text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0")
.asString();
//Xhr
const data = 'text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0';
const xhr = new XMLHttpRequest();
xhr.withCredentials = true;
xhr.addEventListener('readystatechange', function () {
if (this.readyState === this.DONE) {
console.log(this.responseText);
}
});
xhr.open('POST', 'http://ikuai.m-iot.tech:58150/tts');
xhr.setRequestHeader('content-type', 'application/x-www-form-urlencoded');
xhr.send(data);
//Axios
import axios from 'axios';
const encodedParams = new URLSearchParams();
encodedParams.set('text', '请张三到一室做检查');
encodedParams.set('lang', 'zh');
encodedParams.set('spk_id', '0');
const options = {
method: 'POST',
url: 'http://ikuai.m-iot.tech:58150/tts',
headers: {'content-type': 'application/x-www-form-urlencoded'},
data: encodedParams,
};
try {
const { data } = await axios.request(options);
console.log(data);
} catch (error) {
console.error(error);
}
//Fetch
const url = 'http://ikuai.m-iot.tech:58150/tts';
const options = {
method: 'POST',
headers: {'content-type': 'application/x-www-form-urlencoded'},
body: new URLSearchParams({text: '请张三到一室做检查', lang: 'zh', spk_id: '0'})
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}
//JQuery
const settings = {
async: true,
crossDomain: true,
url: 'http://ikuai.m-iot.tech:58150/tts',
method: 'POST',
headers: {
'content-type': 'application/x-www-form-urlencoded'
},
data: {
text: '请张三到一室做检查',
lang: 'zh',
spk_id: '0'
}
};
$.ajax(settings).done(function (response) {
console.log(response);
});
//httpclient
using System.Net.Http.Headers;
var client = new HttpClient();
var request = new HttpRequestMessage
{
Method = HttpMethod.Post,
RequestUri = new Uri("http://ikuai.m-iot.tech:58150/tts"),
Content = new FormUrlEncodedContent(new Dictionary<string, string>
{
{ "text", "请张三到一室做检查" },
{ "lang", "zh" },
{ "spk_id", "0" },
}),
};
using (var response = await client.SendAsync(request))
{
response.EnsureSuccessStatusCode();
var body = await response.Content.ReadAsStringAsync();
Console.WriteLine(body);
}
//restsharp
var client = new RestClient("http://ikuai.m-iot.tech:58150/tts");
var request = new RestRequest("", Method.Post);
request.AddHeader("content-type", "application/x-www-form-urlencoded");
request.AddParameter("application/x-www-form-urlencoded", "text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0", ParameterType.RequestBody);
var response = client.Execute(request);
package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "http://ikuai.m-iot.tech:58150/tts"
payload := strings.NewReader("text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("content-type", "application/x-www-form-urlencoded")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}
Preparing request to http://ikuai.m-iot.tech:58150/tts
Current time is 2025-09-21T07:49:17.236Z
POST http://ikuai.m-iot.tech:58150/tts
Accept: application/json, text/plain, */*
Content-Type: application/x-www-form-urlencoded
User-Agent: bruno-runtime/2.3.0
text=%E8%AF%B7%E5%BC%A0%E4%B8%89%E5%88%B0%E4%B8%80%E5%AE%A4%E5%81%9A%E6%A3%80%E6%9F%A5&lang=zh&spk_id=0
SSL validation: enabled
HTTP/1.1 200 OK
date: Sun, 21 Sep 2025 07:49:18 GMT
server: uvicorn
content-type: audio/wav
content-disposition: attachment; filename="7b41c15b-e0a4-4ba5-aeb1-ceff058c90e6.wav"
content-length: 89444
last-modified: Sun, 21 Sep 2025 07:49:20 GMT
etag: d85eaa2cadf7182ac9b54763e6a97e2c
request-duration: 2224
Request completed in 2224 ms
curl --request POST \
--url http://ikuai.m-iot.tech:58150/asr \
--header 'content-type: multipart/form-data' \
--form 'file=@C:\Users\trphoenix\Documents\录音\录音 (6).m4a' \
--form lang=zh \
--form format=wav \
--form sample_rate=16000
直接上传文件，文件格式支持wav、m4a、mp3等常见音频格式，服务端会自动转换为wav格式进行识别。curl curl --request POST \ --url http://ikuai.m-iot.tech:58150/asr \ --header 'content-type: multipart/form-data' \ --form 'file=@C:\Users\trphoenix\Documents\录音\录音 (6).m4a' \ --form lang=zh
传入参数可以只有file与lang这两个参数，其它参数，可以忽略，如下图所示
POST /asr HTTP/1.1
Content-Type: multipart/form-data; boundary=---011000010111000001101001
Host: ikuai.m-iot.tech:58150
Content-Length: 239
-----011000010111000001101001
Content-Disposition: form-data; name="file"
C:\Users\trphoenix\Documents\录音\录音 (6).m4a
-----011000010111000001101001
Content-Disposition: form-data; name="lang"
zh
-----011000010111000001101001--
// asynchttp
AsyncHttpClient client = new DefaultAsyncHttpClient();
client.prepare("POST", "http://ikuai.m-iot.tech:58150/asr")
.setHeader("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.setBody("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n")
.execute()
.toCompletableFuture()
.thenAccept(System.out::println)
.join();
client.close();
// nethttp
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("http://ikuai.m-iot.tech:58150/asr"))
.header("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.method("POST", HttpRequest.BodyPublishers.ofString("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n"))
.build();
HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
// okhttp
OkHttpClient client = new OkHttpClient();
MediaType mediaType = MediaType.parse("multipart/form-data; boundary=---011000010111000001101001");
RequestBody body = RequestBody.create(mediaType, "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n");
Request request = new Request.Builder()
.url("http://ikuai.m-iot.tech:58150/asr")
.post(body)
.addHeader("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.build();
Response response = client.newCall(request).execute();
// unirest
HttpResponse<String> response = Unirest.post("http://ikuai.m-iot.tech:58150/asr")
.header("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.body("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n")
.asString();
//xhr
const data = new FormData();
data.append('file', 'C:\Users\trphoenix\Documents\录音\录音 (6).m4a');
data.append('lang', 'zh');
const xhr = new XMLHttpRequest();
xhr.withCredentials = true;
xhr.addEventListener('readystatechange', function () {
if (this.readyState === this.DONE) {
console.log(this.responseText);
}
});
xhr.open('POST', 'http://ikuai.m-iot.tech:58150/asr');
xhr.send(data);
// Axios
import axios from 'axios';
const form = new FormData();
form.append('file', 'C:\Users\trphoenix\Documents\录音\录音 (6).m4a');
form.append('lang', 'zh');
const options = {
method: 'POST',
url: 'http://ikuai.m-iot.tech:58150/asr',
headers: {'content-type': 'multipart/form-data; boundary=---011000010111000001101001'},
data: '[form]'
};
try {
const { data } = await axios.request(options);
console.log(data);
} catch (error) {
console.error(error);
}
// fetch
const url = 'http://ikuai.m-iot.tech:58150/asr';
const form = new FormData();
form.append('file', 'C:\Users\trphoenix\Documents\录音\录音 (6).m4a');
form.append('lang', 'zh');
const options = {method: 'POST'};
options.body = form;
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}
//jquery
const form = new FormData();
form.append('file', 'C:\Users\trphoenix\Documents\录音\录音 (6).m4a');
form.append('lang', 'zh');
const settings = {
async: true,
crossDomain: true,
url: 'http://ikuai.m-iot.tech:58150/asr',
method: 'POST',
headers: {},
processData: false,
contentType: false,
mimeType: 'multipart/form-data',
data: form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
//httpclient
using System.Net.Http.Headers;
var client = new HttpClient();
var request = new HttpRequestMessage
{
Method = HttpMethod.Post,
RequestUri = new Uri("http://ikuai.m-iot.tech:58150/asr"),
Content = new MultipartFormDataContent
{
new StringContent(["C:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a"])
{
Headers =
{
ContentDisposition = new ContentDispositionHeaderValue("form-data")
{
Name = "file",
FileName = "C:\Users\trphoenix\Documents\录音\录音 (6).m4a",
}
}
},
new StringContent("zh")
{
Headers =
{
ContentDisposition = new ContentDispositionHeaderValue("form-data")
{
Name = "lang",
}
}
},
},
};
using (var response = await client.SendAsync(request))
{
response.EnsureSuccessStatusCode();
var body = await response.Content.ReadAsStringAsync();
Console.WriteLine(body);
}
//Restsharp
var client = new RestClient("http://ikuai.m-iot.tech:58150/asr");
var request = new RestRequest("", Method.Post);
request.AddHeader("content-type", "multipart/form-data; boundary=---011000010111000001101001");
request.AddParameter("multipart/form-data; boundary=---011000010111000001101001", "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n", ParameterType.RequestBody);
var response = client.Execute(request);
package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "http://ikuai.m-iot.tech:58150/asr"
payload := strings.NewReader("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("content-type", "multipart/form-data; boundary=---011000010111000001101001")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(res)
fmt.Println(string(body))
}
// asynchttp
AsyncHttpClient client = new DefaultAsyncHttpClient();
client.prepare("POST", "http://ikuai.m-iot.tech:58150/asr")
.setHeader("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.setBody("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n")
.execute()
.toCompletableFuture()
.thenAccept(System.out::println)
.join();
client.close();
// nethttp
HttpRequest request = HttpRequest.newBuilder()
.uri(URI.create("http://ikuai.m-iot.tech:58150/asr"))
.header("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.method("POST", HttpRequest.BodyPublishers.ofString("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n"))
.build();
HttpResponse<String> response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
// okhttp
OkHttpClient client = new OkHttpClient();
MediaType mediaType = MediaType.parse("multipart/form-data; boundary=---011000010111000001101001");
RequestBody body = RequestBody.create(mediaType, "-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n");
Request request = new Request.Builder()
.url("http://ikuai.m-iot.tech:58150/asr")
.post(body)
.addHeader("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.build();
Response response = client.newCall(request).execute();
// unirest
HttpResponse<String> response = Unirest.post("http://ikuai.m-iot.tech:58150/asr")
.header("content-type", "multipart/form-data; boundary=---011000010111000001101001")
.body("-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"file\"\r\n\r\nC:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a\r\n-----011000010111000001101001\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\nzh\r\n-----011000010111000001101001--\r\n\r\n")
.asString();
{
"result": "喂语音测试语音测试录音测试录音测试",
"file_size": 185334,
"wav_info": {
"n_channels": 1,
"sample_width": 2,
"framerate": 16000,
"n_frames": 115029,
"duration": 7.1893125
},
"ffmpeg_log": ""
}
body
{
"_overheadLength": 252,
"_valueLength": 2,
"_valuesToMeasure": [
{
"fd": null,
"path": "C:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a",
"flags": "r",
"mode": 438,
"end": null,
"bytesRead": 0,
"_events": {},
"_readableState": {
"highWaterMark": 65536,
"buffer": [],
"bufferIndex": 0,
"length": 0,
"pipes": [],
"awaitDrainWriters": null
},
"_eventsCount": 3
}
],
"writable": false,
"readable": true,
"dataSize": 0,
"maxDataSize": 2097152,
"pauseStreams": true,
"_released": false,
"_streams": [
"----------------------------147555471668548578107544\r\nContent-Disposition: form-data; name=\"file\"; filename=\"录音 (6).m4a\"\r\nContent-Type: wav\r\n\r\n",
{
"source": "[Circular]",
"dataSize": 0,
"maxDataSize": null,
"pauseStream": true,
"_maxDataSizeExceeded": false,
"_released": false,
"_bufferedEvents": [
{
"0": "pause"
}
],
"_events": {},
"_eventsCount": 1
},
null,
"----------------------------147555471668548578107544\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\n",
"zh",
null
],
"_currentStream": null,
"_insideLoop": false,
"_pendingNext": false,
"_boundary": "--------------------------147555471668548578107544"
}
***Network Logs *** Preparing request to http://ikuai.m-iot.tech:58150/asr Current time is 2025-09-21T08:22:20.590Z POST http://ikuai.m-iot.tech:58150/asr Accept: application/json, text/plain, */* Content-Type: multipart/form-data; boundary=--------------------------147555471668548578107544 User-Agent: bruno-runtime/2.3.0 { "_overheadLength": 252, "_valueLength": 2, "_valuesToMeasure": [ { "fd": null, "path": "C:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a", "flags": "r", "mode": 438, "end": null, "bytesRead": 0, "_events": {}, "_readableState": { "highWaterMark": 65536, "buffer": [], "bufferIndex": 0, "length": 0, "pipes": [], "awaitDrainWriters": null }, "_eventsCount": 3 } ], "writable": false, "readable": true, "dataSize": 0, "maxDataSize": 2097152, "pauseStreams": true, "_released": false, "_streams": [ "----------------------------147555471668548578107544\r\nContent-Disposition: form-data; name=\"file\"; filename=\"录音 (6).m4a\"\r\nContent-Type: wav\r\n\r\n", { "source": { "fd": null, "path": "C:\\Users\\trphoenix\\Documents\\录音\\录音 (6).m4a", "flags": "r", "mode": 438, "end": null, "bytesRead": 0, "_events": {}, "_readableState": { "highWaterMark": 65536, "buffer": [], "bufferIndex": 0, "length": 0, "pipes": [], "awaitDrainWriters": null }, "_eventsCount": 3 }, "dataSize": 0, "maxDataSize": null, "pauseStream": true, "_maxDataSizeExceeded": false, "_released": false, "_bufferedEvents": [ { "0": "pause" } ], "_events": {}, "_eventsCount": 1 }, null, "----------------------------147555471668548578107544\r\nContent-Disposition: form-data; name=\"lang\"\r\n\r\n", "zh", null ], "_currentStream": null, "_insideLoop": false, "_pendingNext": false, "_boundary": "--------------------------147555471668548578107544" } SSL validation: enabled HTTP/1.1 200 OK date: Sun, 21 Sep 2025 08:22:21 GMT server: uvicorn content-length: 201 content-type: application/json request-duration: 6989 Request completed in 6989 ms
说明
paddlespeech中用的一个库有可能是单线程的，所以在高并发场景下，可能会出现响应变慢的情况，这个时候，可以考虑增加实例来解决这个问题。
实际使用时,如果想提高并发能力，可以尝试更改环境变量中的PYTHONUNBUFFFERED=4,或更高一个适合的数字，来提高并发能力。
《完》
