Elevenlabs
集成
模型 id
elevenlabs/text-to-dialogue/eleven-v3
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/elevenlabs/text-to-dialogue/eleven-v3?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/elevenlabs/text-to-dialogue/eleven-v3?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria",
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte",
},
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/elevenlabs/text-to-dialogue/eleven-v3", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/zh/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.ru/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "elevenlabs/text-to-dialogue/eleven-v3",
"input": {
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
}
}
通过 Doitong API 调用 Elevenlabs 模型(elevenlabs/text-to-dialogue/eleven-v3)。智能体文档:https://doitong.com/zh/developers/models/elevenlabs/text-to-dialogue/eleven-v3.md
POST https://api.doitong.ru/v1/run/elevenlabs/text-to-dialogue/eleven-v3?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
}
如果响应状态为 "queued" 或 "running",请轮询 GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20,直到状态变为 "succeeded";文件位于 outputs[].url。使用相同请求体 POST https://api.doitong.ru/v1/quote/elevenlabs/text-to-dialogue/eleven-v3,可在运行前返回确切价格。
此请求: ≈$0.10 100 API 积分 · ≈ ₽10.75 在此页面运行
你的新 API 密钥。 它仅在此时显示,并且已填入下方的代码和 API 密钥字段中。请妥善保密:切勿将其放入在浏览器中运行的代码。
构建请求
控件根据模型架构生成。更改控件时,代码和价格会同步更新。
POST
/run/elevenlabs/text-to-dialogue/eleven-v3?wait=20
报价 → $0.10
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/elevenlabs/text-to-dialogue/eleven-v3?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/elevenlabs/text-to-dialogue/eleven-v3?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria",
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte",
},
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/elevenlabs/text-to-dialogue/eleven-v3", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "elevenlabs/text-to-dialogue/eleven-v3",
"input": {
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
},
"wait_seconds": 20
}
}
}
关于 Elevenlabs
根据必需的 `inputs` 列表构建对话语音,每个条目将文本与 voice ID 配对。可使用 `stability`(0 到 1)和 `use_speaker_boost` 调整声音表现。可通过 `language_code` 指定语言,并应用最多 3 个发音词典定位器。支持使用 `seed` 保证可复现,并随音频返回。
参数
名称类型
默认值允许值
描述
inputs必填
类型object[]
要合成为语音的对话条目,每项将文本与语音 ID 配对。
inputs[].text必填
类型string
包含对话内容的文本。
inputs[].voice必填
类型string
为生成选择的语音名称或 ID。
stability可选
类型number | null
0 – 1
多次生成之间的语音稳定性和变化程度,仅限 0.0、0.5 或 1.0;超出此集合的输入会四舍五入到最接近的可接受值。较高设置可能限制情感并使声音单调,而较低设置会扩大语音的情感范围。
use_speaker_boost可选
类型boolean | null
说话人相似度增强功能会因计算负载略有增加而提高延迟。启用后,生成的语音会更接近原说话人。
pronunciation_dictionary_locators可选
类型object[]
默认值[]
按其影响文本的顺序提供最多 3 个发音词典定位符。每个定位符通过 id 和 version_id 标识一个词典。
pronunciation_dictionary_locators[].pronunciation_dictionary_id可选
类型string | null
发音词典的标识符。
pronunciation_dictionary_locators[].version_id可选
类型string | null
通过 ID 选择特定的发音词典版本,或留空以使用最新版本。
seed可选
类型integer | null
控制随机性的种子,以获得可复现的结果。
language_code可选
类型string | null
使用 ISO 639-1 代码指定所需的模型语言;除非模型支持该代码,否则请求会失败并返回错误。
必填
输出
GET /requests/req_8k2m4q7x1z9c3v5b6n0p2r4t
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "elevenlabs/text-to-dialogue/eleven-v3",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 100,
"usd": "0.10",
"charged": 100,
"refunded": 0
},
"input": {
"inputs": [
{
"text": "[applause] Thank you all for coming tonight! Today we have a very special guest with us.",
"voice": "Aria"
},
{
"text": "[gulps] ... [strong canadian accent] [excited] Hello everyone! Thank you all for having me tonight on this special day.",
"voice": "Charlotte"
}
]
},
"output": {
"audio": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp3",
"content_type": "audio/mpeg",
"file_name": "0-output.mp3",
"file_size": 1843200
},
"seed": 42
},
"outputs": [
{
"type": "audio",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.mp3",
"content_type": "audio/mpeg",
"file_size": 1843200,
"duration": 12,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
你的文件,由你选择每个 outputs[].url 都是存储中的副本:可永久保存(按 GB-月付费),也可免费保存 7 天,由账户设置或 ?storage= 决定。
每个模型的结构相同图像、视频和音频均包含 type、url、content_type、width、height 和 duration。output 是模型自身的结果对象。
输出字段
- audio
- object {url, content_type, file_name, file_size}生成的音频文件。
- seed
- integer控制随机性的种子,以获得可复现的结果。
更多模型:文本转语音
对于 AI 代理:此页面的 Markdown 版本位于 https://doitong.com/zh/developers/models/elevenlabs/text-to-dialogue/eleven-v3.md. 所有模型和指南: https://doitong.com/developers/llms.txt