Nemotron Asr Multilingual
統合
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/ja/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"
}
}
Doitong APIを通じてNemotron Asr Multilingualモデル(nvidia/nemotron-asr-multilingual/asr)を呼び出します。エージェント向けドキュメント: https://doitong.com/ja/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"
}
レスポンスのステータスが"queued"または"running"の場合、"succeeded"になるまでGET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20をポーリングしてください。ファイルはoutputs[].urlにあります。同じボディでPOST https://api.doitong.ru/v1/quote/nvidia/nemotron-asr-multilingual/asrを実行すると、実行前の正確な価格が返されます。
例と同様のファイル: $0.002 2 APIクレジット · ≈ ₽0.22 実行前にファイルを計測します このページで実行
新しいAPIキーです。 これは今だけ表示され、コードと下記のAPIキー欄にすでに入力されています。秘密にしてください。ブラウザで実行されるコードに決して含めないでください。
最初の結果がここに表示されます
以下でリクエストを作成し、APIキーで実行してください。出力はストレージにコピーされ、永久保存または無料で7日保存されます。
リクエストを作成
コントロールはモデルスキーマから生成されます。値を変更するとコードと価格が更新されます。
/run/nvidia/nemotron-asr-multilingual/asr?wait=20
見積もり → $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
}
}
}
Nemotron Asr Multilingualについて
`audio_url`の音声ファイルをテキストに文字起こしします。`language`のデフォルトは検出用の`auto`です。リストからen-US、de-DE、ja-JPなどのロケールを選択することもできます。`acceleration`は精度と速度のトレードオフを設定します。`none`が最も高精度、`regular`がバランス型、`high`が高速、`full`が最速です。
パラメータ
正式名称またはネイティブ名のいずれか一方を送信してください。両方は送信しないでください。 正式名称
audio_url必須
audio
language任意
さらに25件
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-SIacceleration任意
ファイルパラメータには、公開されたhttps:// URL、10 MBまでのdata: URI、またはアップロードしたファイルのfile_urlを指定します。 ファイル
出力
{
"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"
}
}
出力フィールド
- output
- string音声の文字起こしによって得られたテキスト。
- partial
- booleanストリーミング結果が中間結果の場合にtrue。
その他のモデル: 音声認識
AIエージェント向け:このページのMarkdown版はこちらです: https://doitong.com/ja/developers/models/nvidia/nemotron-asr-multilingual/asr.md. すべてのモデルとガイド: https://doitong.com/developers/llms.txt