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Wizper (Whisper v3 -- fal.ai edition) Speech to Text

fal-ai/wizper
[Experimental] Whisper v3 Large -- but optimized by our inference wizards. Same WER, double the performance!
Inference
Commercial use
Schema
LLMs
PlaygroundAPI

About

Transcribe an audio file using the Whisper model.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

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/wizper", {
  input: {
    audio_url: "https://ihlhivqvotguuqycfcvj.supabase.co/storage/v1/object/public/public-text-to-speech/scratch-testing/earth-history-19mins.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);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.
import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

3. Queue#

Submit a request#

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/wizper", {
  input: {
    audio_url: "https://ihlhivqvotguuqycfcvj.supabase.co/storage/v1/object/public/public-text-to-speech/scratch-testing/earth-history-19mins.mp3"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

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/wizper", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

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/wizper", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

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.

Data URI (base64)#

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.

Hosted files (URL)#

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.

Uploading files#

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);

Read more about file handling in our file upload guide.

5. Schema#

Input#

audio_url string* required

URL of the audio file to transcribe. Supported formats: mp3, mp4, mpeg, mpga, m4a, wav or webm.

task TaskEnum

Task to perform on the audio file. Either transcribe or translate. Default value: "transcribe"

Possible enum values: transcribe, translate

language Enum

Language of the audio file. If translate is selected as the task, the audio will be translated to English, regardless of the language selected. If None is passed, the language will be automatically detected. This will also increase the inference time. Default value: en

Possible enum values: af, am, ar, as, az, ba, be, bg, bn, bo, br, bs, ca, cs, cy, da, de, el, en, es, et, eu, fa, fi, fo, fr, gl, gu, ha, haw, he, hi, hr, ht, hu, hy, id, is, it, ja, jw, ka, kk, km, kn, ko, la, lb, ln, lo, lt, lv, mg, mi, mk, ml, mn, mr, ms, mt, my, ne, nl, nn, no, oc, pa, pl, ps, pt, ro, ru, sa, sd, si, sk, sl, sn, so, sq, sr, su, sv, sw, ta, te, tg, th, tk, tl, tr, tt, uk, ur, uz, vi, yi, yo, zh

chunk_level string

Level of the chunks to return. Default value: "segment"

max_segment_len integer

Maximum speech segment duration in seconds before splitting. Default value: 29

merge_chunks boolean

Whether to merge consecutive chunks. When enabled, chunks are merged if their combined duration does not exceed max_segment_len. Default value: true

version string

Version of the model to use. All of the models are the Whisper large variant. Default value: "3"

{
  "audio_url": "https://ihlhivqvotguuqycfcvj.supabase.co/storage/v1/object/public/public-text-to-speech/scratch-testing/earth-history-19mins.mp3",
  "task": "transcribe",
  "language": null,
  "chunk_level": "segment",
  "max_segment_len": 29,
  "merge_chunks": true,
  "version": "3"
}

Output#

text string* required

Transcription of the audio file

chunks list<WhisperChunk>* required

Timestamp chunks of the audio file

languages list<Enum>* required

List of languages that the audio file is inferred to be. Defaults to null.

{
  "text": "",
  "chunks": [
    {
      "text": ""
    }
  ]
}

Other types#

WhisperChunk#

timestamp array* required

Start and end timestamp of the chunk

text string* required

Transcription of the chunk