Nemotron Asr Multilingual
Integrieren
nvidia/nemotron-asr-multilingual/asr
# 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/nvidia/nemotron-asr-multilingual/asr?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/nvidia/nemotron-asr-multilingual/asr?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/nvidia/nemotron-asr-multilingual/asr", 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/de/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": "nvidia/nemotron-asr-multilingual/asr",
"input": {
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}
}
Rufe das Modell Nemotron Asr Multilingual (nvidia/nemotron-asr-multilingual/asr) über die Doitong API auf. Dokumentation für Agenten: https://doitong.com/de/developers/models/nvidia/nemotron-asr-multilingual/asr.md
POST https://api.doitong.ru/v1/run/nvidia/nemotron-asr-multilingual/asr?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}
Wenn der Antwortstatus "queued" oder "running" lautet, frage GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 ab, bis er "succeeded" lautet; die Dateien befinden sich in outputs[].url. POST https://api.doitong.ru/v1/quote/nvidia/nemotron-asr-multilingual/asr mit demselben Body gibt den exakten Preis vor der Ausführung zurück.
Dateien wie das Beispiel: 0,002 $ 2 API-Credits · ≈ 0,22 ₽ Deine Dateien werden vor der Ausführung gemessen Auf dieser Seite ausführen
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Die Steuerelemente werden aus dem Modellschema generiert. Code und Preis werden aktualisiert, während du sie änderst.
/run/nvidia/nemotron-asr-multilingual/asr?wait=20
Preis → 0,002 $
# 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/nvidia/nemotron-asr-multilingual/asr?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}'
# 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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/nvidia/nemotron-asr-multilingual/asr?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/nvidia/nemotron-asr-multilingual/asr", 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": "nvidia/nemotron-asr-multilingual/asr",
"input": {
"audio": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
},
"wait_seconds": 20
}
}
}
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Über Nemotron Asr Multilingual
Transkribiert eine Audiodatei aus `audio_url` in Text. Die Eingabe `language` verwendet standardmäßig `auto` zur Erkennung, oder du kannst ein Gebietsschema wie en-US, de-DE oder ja-JP aus der Liste auswählen. `acceleration` tauscht Genauigkeit gegen Geschwindigkeit: `none` ist am genauesten, `regular` ausgewogen, `high` schneller und `full` am schnellsten.
Parameter
Sende entweder den kanonischen Namen oder den nativen Namen, nicht beide. Kanonische Namen
audio_urlerforderlich
audio
languageoptional
+25 weitere
zh-CNvi-VNhe-ILnl-NLcs-CZda-DKpl-PLnn-NOnb-NOsv-SEth-THtr-TRbg-BGel-GRet-EEfi-FIhr-HRhu-HUlt-LTlv-LVro-ROsk-SKuk-UAmt-MTsl-SIaccelerationoptional
Dateiparameter akzeptieren eine öffentliche https://-URL, eine data:-URI bis 10 MB oder die file_url eines Uploads. Dateien
Ausgabe
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "nvidia/nemotron-asr-multilingual/asr",
"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": 2,
"usd": "0.002",
"charged": 2,
"refunded": 0
},
"input": {
"audio_url": "https://content.doitong.com/audio/showcase/v3/tools/dub_es.mp3"
},
"output": {
"output": "Actually, I'm second guessing myself a single coherent passage might be cleaner and more representative of real world speech performance, which is what benchmarking typically uses. I'll write an engaging flowing piece on something like a journey or a day in a tech forward city. That weaves in all the varied elements naturally, rather than splitting it into sections. I'm deciding to keep the targeted tricky sentences section after all. It'll directly test homophones and number heavy lines that are most prone to errors, so I'll include it as a concise, clearly marked addition at the end now. I'm drafting the full passage, starting with a detailed travel narrative that weaves in dates, times, currency, and specific details by the time we pulled into Waverley Station, my phone had buzzed forty seven times, mostly from doctor Amara Akon Reyes at Neurosynt Technologies fretting over our board presentation. We'd spent three weeks refining a transformer model with one point three billion parameters, and the improvements were stunning. Our word error rate plummeted from fourteen point two percent down to three point eight percent. There's something surreal about watching those number shift like that. They're not just metrics they're the payoff from countless late nights and too much coffee.",
"partial": false
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"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"
}
}
Ausgabefelder
- output
- stringDurch Transkription des Audios gewonnener Text.
- partial
- booleanTrue, wenn das Streaming-Ergebnis vorläufig ist.
Weitere Modelle: Sprache zu Text
Für KI-Agenten: Diese Seite ist als Markdown verfügbar unter https://doitong.com/de/developers/models/nvidia/nemotron-asr-multilingual/asr.md. Alle Modelle und Anleitungen: https://doitong.com/developers/llms.txt