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The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("nvidia/nemotron-asr-multilingual/asr", {
input: {
audio_url: "https://v3b.fal.media/files/b/0a9c95c6/qxxx5skDQl8fPqbkjpxBc_speech.mp3"
},
logs: true,
onQueueUpdate: (update) => {
if (update.status === "IN_PROGRESS") {
update.logs.map((log) => log.message).forEach(console.log);
}
},
});
console.log(result.data);
console.log(result.requestId);This model supports streaming requests. You can stream data directly to the model and get the result in real-time.
import { fal } from "@fal-ai/client";
const stream = await fal.stream("nvidia/nemotron-asr-multilingual/asr", {
input: {
audio_url: "https://v3b.fal.media/files/b/0a9c95c6/qxxx5skDQl8fPqbkjpxBc_speech.mp3"
}
});
for await (const event of stream) {
console.log(event);
}
const result = await stream.done();The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.
The client API provides a convenient way to submit requests to the model.
import { fal } from "@fal-ai/client";
const { request_id } = await fal.queue.submit("nvidia/nemotron-asr-multilingual/asr", {
input: {
audio_url: "https://v3b.fal.media/files/b/0a9c95c6/qxxx5skDQl8fPqbkjpxBc_speech.mp3"
},
webhookUrl: "https://optional.webhook.url/for/results",
});You can fetch the status of a request to check if it is completed or still in progress.
import { fal } from "@fal-ai/client";
const status = await fal.queue.status("nvidia/nemotron-asr-multilingual/asr", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.
import { fal } from "@fal-ai/client";
const result = await fal.queue.result("nvidia/nemotron-asr-multilingual/asr", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.
You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.
You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.
We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.
import { fal } from "@fal-ai/client";
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).
Read more about file handling in our file upload guide.
audio_url string* requiredURL of the audio file to transcribe.
language LanguageEnumTarget language for transcription (language-ID prompt). 'auto' lets the model detect the language. Default value: "auto"
Possible enum values: auto, en-US, en-GB, es-US, es-ES, de-DE, fr-FR, fr-CA, it-IT, ar-AR, ja-JP, ko-KR, pt-BR, pt-PT, ru-RU, hi-IN, zh-CN, vi-VN, he-IL, nl-NL, cs-CZ, da-DK, pl-PL, nn-NO, nb-NO, sv-SE, th-TH, tr-TR, bg-BG, el-GR, et-EE, fi-FI, hr-HR, hu-HU, lt-LT, lv-LV, ro-RO, sk-SK, uk-UA, mt-MT, sl-SI
acceleration AccelerationEnumControls the speed/accuracy trade-off. 'none' = best accuracy (1.12s chunks), 'regular' = balanced (0.56s chunks), 'high' = faster (0.32s chunks), 'full' = fastest (0.08s chunks). Default value: "regular"
Possible enum values: none, regular, high, full
{
"audio_url": "https://v3b.fal.media/files/b/0a9c95c6/qxxx5skDQl8fPqbkjpxBc_speech.mp3",
"language": "auto",
"acceleration": "regular"
}output string* requiredThe transcribed text from the audio.
partial booleanTrue if this is an intermediate result during streaming.
{
"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."
}