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NVIDIA Nemotron 3.5 ASR API

nvidia /

NVIDIA Nemotron 3.5 ASR is a fast AI speech-to-text model that transcribes multilingual audio into text with automatic language detection. Ready-to-use REST inference API for audio transcription, podcast processing, video subtitles, meeting notes, voice analytics, content localization, and professional ASR workflows with simple integration, no coldstarts, and affordable pricing.

speech-to-text
इनपुट

निष्क्रिय

{
  "output": "Ah, the garden is blooming again just like life with a little patience and care, everything finds its time to shine."
}

$0.008प्रति रन·~125 / $1

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README

NVIDIA Nemotron 3.5 ASR

NVIDIA Nemotron 3.5 ASR is a multilingual speech-to-text model that transcribes uploaded audio into text with automatic language detection. It is designed for audio transcription, podcast processing, subtitle preparation, meeting notes, voice analytics, localization workflows, and other production-ready ASR use cases.

Why Choose This?

  • Multilingual speech recognition
    Transcribe spoken audio into text across multiple languages.

  • Automatic language detection
    Leave language on auto when you want the model to detect the spoken language automatically.

  • Simple transcription workflow
    Upload one audio file, optionally choose a language, and generate the transcript.

  • Useful for content and productivity workflows
    Suitable for podcasts, subtitles, interviews, lectures, meetings, and other spoken-content pipelines.

  • Production-ready API
    Easy to integrate into transcription, indexing, search, and accessibility workflows.

Parameters

ParameterRequiredDescription
audioYesInput audio file to transcribe.
languageNoLanguage setting for transcription. Use auto for automatic detection.

How to Use

  1. Upload your audio — provide the audio clip you want to transcribe.
  2. Choose language (optional) — leave it as auto for automatic detection, or select a fixed language if needed.
  3. Submit — run the model and get the transcription result.

Example Output

{
  "output": "Ah, the garden is blooming again just like life with a little patience and care, everything finds its time to shine."
}

Pricing

Pricing is billed per started minute of input audio.

Audio DurationCost
1s–60s$0.008
61s–120s$0.016
121s–180s$0.024

Billing Rules

  • Pricing is $0.008 per started minute
  • Audio duration is billed in started 60-second units
  • Audio shorter than 60 seconds is billed as 1 minute
  • language does not affect pricing

Best Use Cases

  • Audio transcription — Convert speech recordings into text.
  • Podcast processing — Generate transcripts for episodes and clips.
  • Subtitle preparation — Extract spoken content before caption formatting.
  • Meeting notes — Turn recorded discussions into readable text.
  • Voice analytics — Prepare transcripts for search, tagging, or downstream analysis.

Pro Tips

  • Use clean audio for better transcription accuracy.
  • Leave language as auto when the spoken language is clear and consistent.
  • Set a specific language when automatic detection may be ambiguous.
  • Short clips are useful for fast testing before processing longer recordings.
  • Review the output before publishing if the audio contains names, jargon, or strong accents.

Notes

  • audio is required.
  • language is optional.
  • Pricing is based on input audio duration and billed per started minute.
  • Better audio quality generally improves transcription quality.

Related Models

  • Other NVIDIA speech and multimodal workflows — Useful when you need text, audio, video, or vision processing beyond ASR.
  • Text-to-speech workflows — Useful when you need voice generation instead of transcription.
  • Subtitle and caption workflows — Useful when you need styled subtitle output rather than plain transcript text.
नोट:यह वेबसाइट तृतीय पक्षों द्वारा प्रदान किए गए AI मॉडलों का उपयोग करती है।

Nemotron 3.5 Asr API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/nvidia/nemotron-3.5-asr with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Nemotron 3.5 Asr below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "language": "auto"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/nvidia/nemotron-3.5-asr" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/nvidia/nemotron-3.5-asr";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
        "language": "auto"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3",
    "language": "auto"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/nvidia/nemotron-3.5-asr", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Nemotron 3.5 Asr API — Frequently asked questions

What is the Nemotron 3.5 Asr API?

Nemotron 3.5 Asr is a NVIDIA model for AI inference, exposed as a REST API on WaveSpeedAI. NVIDIA Nemotron 3.5 ASR is a fast AI speech-to-text model that transcribes multilingual audio into text with automatic language detection. Ready-to-use REST inference API for audio transcription, podcast processing, video subtitles, meeting notes, voice analytics, content localization, and professional ASR workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Nemotron 3.5 Asr API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/nvidia/nvidia-nemotron-3.5-asr.

How much does Nemotron 3.5 Asr cost per run?

Nemotron 3.5 Asr starts at $0.008 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Nemotron 3.5 Asr accept?

Key inputs: `audio`, `language`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/nvidia/nvidia-nemotron-3.5-asr.

How long does Nemotron 3.5 Asr take to generate?

Median end-to-end generation time on WaveSpeedAI is around 4 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Nemotron 3.5 Asr outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (NVIDIA). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

NVIDIA Nemotron 3.5 ASR API | Multilingual Speech To Text | WaveSpeedAI