Train
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("fal-ai/stable-audio-3-trainer", {
input: {
audio_data_url: ""
},
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);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("fal-ai/stable-audio-3-trainer", {
input: {
audio_data_url: ""
},
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("fal-ai/stable-audio-3-trainer", {
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("fal-ai/stable-audio-3-trainer", {
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_data_url string* requiredURL to a zip archive containing audio files and matching .txt captions. Each audio file must have a sibling caption file with the same basename, for example clip.wav and clip.txt.
model ModelEnumStable Audio 3 base checkpoint to fine-tune. Default value: "medium-base"
Possible enum values: medium-base, small-music-base, small-sfx-base
number_of_steps integerNumber of LoRA training steps. Default value: 1000
learning_rate floatAdamW learning rate for LoRA parameters. Default value: 0.0001
rank integerLoRA rank. Default value: 16
adapter_type AdapterTypeEnumLoRA adapter family to train. Default value: "dora-rows"
Possible enum values: lora, dora, dora-rows, dora-cols, bora, lora-xs, dora-rows-xs, dora-cols-xs, bora-xs
duration floatClip duration in seconds for crop/pad sizing. Leave unset to auto-detect from the dataset (the longest clip). Always capped at the chosen model's native training length.
batch_size integerTraining batch size. Runs with batch_size > 1 are billed additional units proportional to batch_size x clip duration. Default value: 1
seed integerRandom seed. Default value: 42
base_precision BasePrecisionEnumPrecision for frozen base weights; LoRA params stay fp32. Default value: "bf16"
Possible enum values: bf16, bfloat16, fp16, float16
Only add LoRA to modules whose names contain these substrings.
Skip modules whose names contain these substrings.
lora_checkpoint_url stringOptional .safetensors LoRA checkpoint URL to resume from.
pre_encode booleanPre-encode the audio archive to SAME latents before LoRA training.
{
"audio_data_url": "",
"model": "medium-base",
"number_of_steps": 1000,
"learning_rate": 0.0001,
"rank": 16,
"adapter_type": "dora-rows",
"batch_size": 1,
"seed": 42,
"base_precision": "bf16"
}Trained Stable Audio 3 LoRA weights.
JSON metadata for the training run and compatible inference model.
{
"lora_file": {
"url": "",
"content_type": "image/png",
"file_name": "z9RV14K95DvU.png",
"file_size": 4404019
},
"config_file": {
"url": "",
"content_type": "image/png",
"file_name": "z9RV14K95DvU.png",
"file_size": 4404019
}
}url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.