Start media analysis
curl --request POST \
--url http://localhost:6699/api/v1/analyze \
--header 'Content-Type: application/json' \
--header 'X-License-Key: <api-key>' \
--data '
{
"cache_dir": "<string>",
"detect_speakers": true,
"speaker_count": "auto",
"preferred_qwen_model_key": "auto",
"visual_media_paths": [
"<string>"
],
"transcription_jobs": [
{
"path": "<string>",
"language": "english",
"detect_speakers": true,
"channels": null
}
],
"face_clustering_jobs": [
{
"cluster_job_name": "<string>",
"face_media_paths": [
"<string>"
],
"face_eps": 123,
"face_min_samples": 123,
"sample_faces": [
{
"name": "<string>",
"face_images": [
"<string>"
],
"face_paths": [
"<string>"
]
}
]
}
],
"sample_faces": [
{
"name": "<string>",
"face_images": [
"<string>"
],
"face_paths": [
"<string>"
]
}
],
"summary_requests": [
{
"path": "<string>",
"analysis_id": "<string>",
"analysis_name": "<string>",
"collection": {
"name": "<string>",
"id": "<string>",
"description": "<string>"
},
"footage_context": "<string>",
"summary_objective": {
"preset": "general_understanding",
"description": "<string>",
"priority_hints": []
},
"use_transcript": true,
"output_language": "auto",
"face_collection": "<string>"
}
]
}
'import requests
url = "http://localhost:6699/api/v1/analyze"
payload = {
"cache_dir": "<string>",
"detect_speakers": True,
"speaker_count": "auto",
"preferred_qwen_model_key": "auto",
"visual_media_paths": ["<string>"],
"transcription_jobs": [
{
"path": "<string>",
"language": "english",
"detect_speakers": True,
"channels": None
}
],
"face_clustering_jobs": [
{
"cluster_job_name": "<string>",
"face_media_paths": ["<string>"],
"face_eps": 123,
"face_min_samples": 123,
"sample_faces": [
{
"name": "<string>",
"face_images": ["<string>"],
"face_paths": ["<string>"]
}
]
}
],
"sample_faces": [
{
"name": "<string>",
"face_images": ["<string>"],
"face_paths": ["<string>"]
}
],
"summary_requests": [
{
"path": "<string>",
"analysis_id": "<string>",
"analysis_name": "<string>",
"collection": {
"name": "<string>",
"id": "<string>",
"description": "<string>"
},
"footage_context": "<string>",
"summary_objective": {
"preset": "general_understanding",
"description": "<string>",
"priority_hints": []
},
"use_transcript": True,
"output_language": "auto",
"face_collection": "<string>"
}
]
}
headers = {
"X-License-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-License-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
cache_dir: '<string>',
detect_speakers: true,
speaker_count: 'auto',
preferred_qwen_model_key: 'auto',
visual_media_paths: ['<string>'],
transcription_jobs: [{path: '<string>', language: 'english', detect_speakers: true, channels: null}],
face_clustering_jobs: [
{
cluster_job_name: '<string>',
face_media_paths: ['<string>'],
face_eps: 123,
face_min_samples: 123,
sample_faces: [{name: '<string>', face_images: ['<string>'], face_paths: ['<string>']}]
}
],
sample_faces: [{name: '<string>', face_images: ['<string>'], face_paths: ['<string>']}],
summary_requests: [
{
path: '<string>',
analysis_id: '<string>',
analysis_name: '<string>',
collection: {name: '<string>', id: '<string>', description: '<string>'},
footage_context: '<string>',
summary_objective: {preset: 'general_understanding', description: '<string>', priority_hints: []},
use_transcript: true,
output_language: 'auto',
face_collection: '<string>'
}
]
})
};
fetch('http://localhost:6699/api/v1/analyze', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_PORT => "6699",
CURLOPT_URL => "http://localhost:6699/api/v1/analyze",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'cache_dir' => '<string>',
'detect_speakers' => true,
'speaker_count' => 'auto',
'preferred_qwen_model_key' => 'auto',
'visual_media_paths' => [
'<string>'
],
'transcription_jobs' => [
[
'path' => '<string>',
'language' => 'english',
'detect_speakers' => true,
'channels' => null
]
],
'face_clustering_jobs' => [
[
'cluster_job_name' => '<string>',
'face_media_paths' => [
'<string>'
],
'face_eps' => 123,
'face_min_samples' => 123,
'sample_faces' => [
[
'name' => '<string>',
'face_images' => [
'<string>'
],
'face_paths' => [
'<string>'
]
]
]
]
],
'sample_faces' => [
[
'name' => '<string>',
'face_images' => [
'<string>'
],
'face_paths' => [
'<string>'
]
]
],
'summary_requests' => [
[
'path' => '<string>',
'analysis_id' => '<string>',
'analysis_name' => '<string>',
'collection' => [
'name' => '<string>',
'id' => '<string>',
'description' => '<string>'
],
'footage_context' => '<string>',
'summary_objective' => [
'preset' => 'general_understanding',
'description' => '<string>',
'priority_hints' => [
]
],
'use_transcript' => true,
'output_language' => 'auto',
'face_collection' => '<string>'
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-License-Key: <api-key>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "http://localhost:6699/api/v1/analyze"
payload := strings.NewReader("{\n \"cache_dir\": \"<string>\",\n \"detect_speakers\": true,\n \"speaker_count\": \"auto\",\n \"preferred_qwen_model_key\": \"auto\",\n \"visual_media_paths\": [\n \"<string>\"\n ],\n \"transcription_jobs\": [\n {\n \"path\": \"<string>\",\n \"language\": \"english\",\n \"detect_speakers\": true,\n \"channels\": null\n }\n ],\n \"face_clustering_jobs\": [\n {\n \"cluster_job_name\": \"<string>\",\n \"face_media_paths\": [\n \"<string>\"\n ],\n \"face_eps\": 123,\n \"face_min_samples\": 123,\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ]\n }\n ],\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ],\n \"summary_requests\": [\n {\n \"path\": \"<string>\",\n \"analysis_id\": \"<string>\",\n \"analysis_name\": \"<string>\",\n \"collection\": {\n \"name\": \"<string>\",\n \"id\": \"<string>\",\n \"description\": \"<string>\"\n },\n \"footage_context\": \"<string>\",\n \"summary_objective\": {\n \"preset\": \"general_understanding\",\n \"description\": \"<string>\",\n \"priority_hints\": []\n },\n \"use_transcript\": true,\n \"output_language\": \"auto\",\n \"face_collection\": \"<string>\"\n }\n ]\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-License-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("http://localhost:6699/api/v1/analyze")
.header("X-License-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"cache_dir\": \"<string>\",\n \"detect_speakers\": true,\n \"speaker_count\": \"auto\",\n \"preferred_qwen_model_key\": \"auto\",\n \"visual_media_paths\": [\n \"<string>\"\n ],\n \"transcription_jobs\": [\n {\n \"path\": \"<string>\",\n \"language\": \"english\",\n \"detect_speakers\": true,\n \"channels\": null\n }\n ],\n \"face_clustering_jobs\": [\n {\n \"cluster_job_name\": \"<string>\",\n \"face_media_paths\": [\n \"<string>\"\n ],\n \"face_eps\": 123,\n \"face_min_samples\": 123,\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ]\n }\n ],\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ],\n \"summary_requests\": [\n {\n \"path\": \"<string>\",\n \"analysis_id\": \"<string>\",\n \"analysis_name\": \"<string>\",\n \"collection\": {\n \"name\": \"<string>\",\n \"id\": \"<string>\",\n \"description\": \"<string>\"\n },\n \"footage_context\": \"<string>\",\n \"summary_objective\": {\n \"preset\": \"general_understanding\",\n \"description\": \"<string>\",\n \"priority_hints\": []\n },\n \"use_transcript\": true,\n \"output_language\": \"auto\",\n \"face_collection\": \"<string>\"\n }\n ]\n}")
.asString();require 'uri'
require 'net/http'
url = URI("http://localhost:6699/api/v1/analyze")
http = Net::HTTP.new(url.host, url.port)
request = Net::HTTP::Post.new(url)
request["X-License-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"cache_dir\": \"<string>\",\n \"detect_speakers\": true,\n \"speaker_count\": \"auto\",\n \"preferred_qwen_model_key\": \"auto\",\n \"visual_media_paths\": [\n \"<string>\"\n ],\n \"transcription_jobs\": [\n {\n \"path\": \"<string>\",\n \"language\": \"english\",\n \"detect_speakers\": true,\n \"channels\": null\n }\n ],\n \"face_clustering_jobs\": [\n {\n \"cluster_job_name\": \"<string>\",\n \"face_media_paths\": [\n \"<string>\"\n ],\n \"face_eps\": 123,\n \"face_min_samples\": 123,\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ]\n }\n ],\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ],\n \"summary_requests\": [\n {\n \"path\": \"<string>\",\n \"analysis_id\": \"<string>\",\n \"analysis_name\": \"<string>\",\n \"collection\": {\n \"name\": \"<string>\",\n \"id\": \"<string>\",\n \"description\": \"<string>\"\n },\n \"footage_context\": \"<string>\",\n \"summary_objective\": {\n \"preset\": \"general_understanding\",\n \"description\": \"<string>\",\n \"priority_hints\": []\n },\n \"use_transcript\": true,\n \"output_language\": \"auto\",\n \"face_collection\": \"<string>\"\n }\n ]\n}"
response = http.request(request)
puts response.read_body{
"message": "Analysis started",
"task_id": "<string>"
}Analysis
Start media analysis
Starts an asynchronous analysis pipeline. Returns immediately with a task_id
that can be used to track progress via SocketIO.
You can run visual analysis, speech transcription, face clustering, and hierarchical Summary Analysis in a single call. The REST endpoint runs directly and does not require an MCP client, agent, or approval step.
Summary Analysis requires a supported Apple silicon or Windows device and
a locally downloaded Summary model. Each summary path must already have
complete visual analysis or be included in visual_media_paths in the same
request.
For throughput, batch many files into one request instead of sending many single-file requests. Jumper loads ML models per analysis request, so one 50-file request is much faster than 50 one-file requests on the same node.
Only one analysis task can run at a time per backend instance. A new analyze
request first asks the current task to stop. If the previous task is still
unwinding, Jumper returns 409.
POST
/
analyze
Start media analysis
curl --request POST \
--url http://localhost:6699/api/v1/analyze \
--header 'Content-Type: application/json' \
--header 'X-License-Key: <api-key>' \
--data '
{
"cache_dir": "<string>",
"detect_speakers": true,
"speaker_count": "auto",
"preferred_qwen_model_key": "auto",
"visual_media_paths": [
"<string>"
],
"transcription_jobs": [
{
"path": "<string>",
"language": "english",
"detect_speakers": true,
"channels": null
}
],
"face_clustering_jobs": [
{
"cluster_job_name": "<string>",
"face_media_paths": [
"<string>"
],
"face_eps": 123,
"face_min_samples": 123,
"sample_faces": [
{
"name": "<string>",
"face_images": [
"<string>"
],
"face_paths": [
"<string>"
]
}
]
}
],
"sample_faces": [
{
"name": "<string>",
"face_images": [
"<string>"
],
"face_paths": [
"<string>"
]
}
],
"summary_requests": [
{
"path": "<string>",
"analysis_id": "<string>",
"analysis_name": "<string>",
"collection": {
"name": "<string>",
"id": "<string>",
"description": "<string>"
},
"footage_context": "<string>",
"summary_objective": {
"preset": "general_understanding",
"description": "<string>",
"priority_hints": []
},
"use_transcript": true,
"output_language": "auto",
"face_collection": "<string>"
}
]
}
'import requests
url = "http://localhost:6699/api/v1/analyze"
payload = {
"cache_dir": "<string>",
"detect_speakers": True,
"speaker_count": "auto",
"preferred_qwen_model_key": "auto",
"visual_media_paths": ["<string>"],
"transcription_jobs": [
{
"path": "<string>",
"language": "english",
"detect_speakers": True,
"channels": None
}
],
"face_clustering_jobs": [
{
"cluster_job_name": "<string>",
"face_media_paths": ["<string>"],
"face_eps": 123,
"face_min_samples": 123,
"sample_faces": [
{
"name": "<string>",
"face_images": ["<string>"],
"face_paths": ["<string>"]
}
]
}
],
"sample_faces": [
{
"name": "<string>",
"face_images": ["<string>"],
"face_paths": ["<string>"]
}
],
"summary_requests": [
{
"path": "<string>",
"analysis_id": "<string>",
"analysis_name": "<string>",
"collection": {
"name": "<string>",
"id": "<string>",
"description": "<string>"
},
"footage_context": "<string>",
"summary_objective": {
"preset": "general_understanding",
"description": "<string>",
"priority_hints": []
},
"use_transcript": True,
"output_language": "auto",
"face_collection": "<string>"
}
]
}
headers = {
"X-License-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-License-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
cache_dir: '<string>',
detect_speakers: true,
speaker_count: 'auto',
preferred_qwen_model_key: 'auto',
visual_media_paths: ['<string>'],
transcription_jobs: [{path: '<string>', language: 'english', detect_speakers: true, channels: null}],
face_clustering_jobs: [
{
cluster_job_name: '<string>',
face_media_paths: ['<string>'],
face_eps: 123,
face_min_samples: 123,
sample_faces: [{name: '<string>', face_images: ['<string>'], face_paths: ['<string>']}]
}
],
sample_faces: [{name: '<string>', face_images: ['<string>'], face_paths: ['<string>']}],
summary_requests: [
{
path: '<string>',
analysis_id: '<string>',
analysis_name: '<string>',
collection: {name: '<string>', id: '<string>', description: '<string>'},
footage_context: '<string>',
summary_objective: {preset: 'general_understanding', description: '<string>', priority_hints: []},
use_transcript: true,
output_language: 'auto',
face_collection: '<string>'
}
]
})
};
fetch('http://localhost:6699/api/v1/analyze', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_PORT => "6699",
CURLOPT_URL => "http://localhost:6699/api/v1/analyze",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'cache_dir' => '<string>',
'detect_speakers' => true,
'speaker_count' => 'auto',
'preferred_qwen_model_key' => 'auto',
'visual_media_paths' => [
'<string>'
],
'transcription_jobs' => [
[
'path' => '<string>',
'language' => 'english',
'detect_speakers' => true,
'channels' => null
]
],
'face_clustering_jobs' => [
[
'cluster_job_name' => '<string>',
'face_media_paths' => [
'<string>'
],
'face_eps' => 123,
'face_min_samples' => 123,
'sample_faces' => [
[
'name' => '<string>',
'face_images' => [
'<string>'
],
'face_paths' => [
'<string>'
]
]
]
]
],
'sample_faces' => [
[
'name' => '<string>',
'face_images' => [
'<string>'
],
'face_paths' => [
'<string>'
]
]
],
'summary_requests' => [
[
'path' => '<string>',
'analysis_id' => '<string>',
'analysis_name' => '<string>',
'collection' => [
'name' => '<string>',
'id' => '<string>',
'description' => '<string>'
],
'footage_context' => '<string>',
'summary_objective' => [
'preset' => 'general_understanding',
'description' => '<string>',
'priority_hints' => [
]
],
'use_transcript' => true,
'output_language' => 'auto',
'face_collection' => '<string>'
]
]
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-License-Key: <api-key>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "http://localhost:6699/api/v1/analyze"
payload := strings.NewReader("{\n \"cache_dir\": \"<string>\",\n \"detect_speakers\": true,\n \"speaker_count\": \"auto\",\n \"preferred_qwen_model_key\": \"auto\",\n \"visual_media_paths\": [\n \"<string>\"\n ],\n \"transcription_jobs\": [\n {\n \"path\": \"<string>\",\n \"language\": \"english\",\n \"detect_speakers\": true,\n \"channels\": null\n }\n ],\n \"face_clustering_jobs\": [\n {\n \"cluster_job_name\": \"<string>\",\n \"face_media_paths\": [\n \"<string>\"\n ],\n \"face_eps\": 123,\n \"face_min_samples\": 123,\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ]\n }\n ],\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ],\n \"summary_requests\": [\n {\n \"path\": \"<string>\",\n \"analysis_id\": \"<string>\",\n \"analysis_name\": \"<string>\",\n \"collection\": {\n \"name\": \"<string>\",\n \"id\": \"<string>\",\n \"description\": \"<string>\"\n },\n \"footage_context\": \"<string>\",\n \"summary_objective\": {\n \"preset\": \"general_understanding\",\n \"description\": \"<string>\",\n \"priority_hints\": []\n },\n \"use_transcript\": true,\n \"output_language\": \"auto\",\n \"face_collection\": \"<string>\"\n }\n ]\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-License-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("http://localhost:6699/api/v1/analyze")
.header("X-License-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"cache_dir\": \"<string>\",\n \"detect_speakers\": true,\n \"speaker_count\": \"auto\",\n \"preferred_qwen_model_key\": \"auto\",\n \"visual_media_paths\": [\n \"<string>\"\n ],\n \"transcription_jobs\": [\n {\n \"path\": \"<string>\",\n \"language\": \"english\",\n \"detect_speakers\": true,\n \"channels\": null\n }\n ],\n \"face_clustering_jobs\": [\n {\n \"cluster_job_name\": \"<string>\",\n \"face_media_paths\": [\n \"<string>\"\n ],\n \"face_eps\": 123,\n \"face_min_samples\": 123,\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ]\n }\n ],\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ],\n \"summary_requests\": [\n {\n \"path\": \"<string>\",\n \"analysis_id\": \"<string>\",\n \"analysis_name\": \"<string>\",\n \"collection\": {\n \"name\": \"<string>\",\n \"id\": \"<string>\",\n \"description\": \"<string>\"\n },\n \"footage_context\": \"<string>\",\n \"summary_objective\": {\n \"preset\": \"general_understanding\",\n \"description\": \"<string>\",\n \"priority_hints\": []\n },\n \"use_transcript\": true,\n \"output_language\": \"auto\",\n \"face_collection\": \"<string>\"\n }\n ]\n}")
.asString();require 'uri'
require 'net/http'
url = URI("http://localhost:6699/api/v1/analyze")
http = Net::HTTP.new(url.host, url.port)
request = Net::HTTP::Post.new(url)
request["X-License-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"cache_dir\": \"<string>\",\n \"detect_speakers\": true,\n \"speaker_count\": \"auto\",\n \"preferred_qwen_model_key\": \"auto\",\n \"visual_media_paths\": [\n \"<string>\"\n ],\n \"transcription_jobs\": [\n {\n \"path\": \"<string>\",\n \"language\": \"english\",\n \"detect_speakers\": true,\n \"channels\": null\n }\n ],\n \"face_clustering_jobs\": [\n {\n \"cluster_job_name\": \"<string>\",\n \"face_media_paths\": [\n \"<string>\"\n ],\n \"face_eps\": 123,\n \"face_min_samples\": 123,\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ]\n }\n ],\n \"sample_faces\": [\n {\n \"name\": \"<string>\",\n \"face_images\": [\n \"<string>\"\n ],\n \"face_paths\": [\n \"<string>\"\n ]\n }\n ],\n \"summary_requests\": [\n {\n \"path\": \"<string>\",\n \"analysis_id\": \"<string>\",\n \"analysis_name\": \"<string>\",\n \"collection\": {\n \"name\": \"<string>\",\n \"id\": \"<string>\",\n \"description\": \"<string>\"\n },\n \"footage_context\": \"<string>\",\n \"summary_objective\": {\n \"preset\": \"general_understanding\",\n \"description\": \"<string>\",\n \"priority_hints\": []\n },\n \"use_transcript\": true,\n \"output_language\": \"auto\",\n \"face_collection\": \"<string>\"\n }\n ]\n}"
response = http.request(request)
puts response.read_body{
"message": "Analysis started",
"task_id": "<string>"
}Authorizations
Raw Jumper Pro license key on every authenticated request, without a Bearer prefix
Body
application/json
Where to store analysis results
Set to false to skip final speaker diarization for transcription jobs.
Optional speaker-count hint for pyannote diarization. Use auto for unconstrained detection, 1-4 for num_speakers, or 5+ for min_speakers=5 and max_speakers=12.
Available options:
auto, auto-detect, 5+ Optional Qwen ASR preference for Qwen-supported languages. Use faster for the 0.6B model, more accurate for the 1.7B model, or auto/default to use Jumper's default.
Available options:
auto, faster, more accurate, qwen3-asr-0.6b, qwen3-asr-1.7b Files to run visual analysis on
Files to transcribe, with optional language and channel hints
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Face detection and clustering job definitions
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Optional named reference images used as defaults for all face jobs. Requires a valid face_clustering_jobs entry. Applied after grouping and before Summary Analysis.
Maximum array length:
100Show child attributes
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Named per-media hierarchical summaries. Requires a downloaded Summary model on a supported Apple silicon or Windows device.
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