Wizper (Whisper v3 -- fal.ai edition)

fal-ai/wizper
Inference
Commercial use

1. Calling the API#

Install the client#

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

npm install --save @fal-ai/serverless-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 * as fal from "@fal-ai/serverless-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);
    }
  },
});

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 * as fal from "@fal-ai/serverless-client";

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

3. 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 * as fal from "@fal-ai/serverless-client";

// Upload a file (you can get a file reference from an input element or a drag-and-drop event)
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

// Use the URL in your request
const result = await fal.subscribe("fal-ai/wizper", { image_url: url });

Read more about file handling in our file upload guide.

4. Schema#

Input#

audio_url*string

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

taskTaskEnum

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

Possible values: "transcribe", "translate"

languageLanguageEnum

Language of the audio file. If translate is selected as the task, the audio will be translated to English, regardless of the language selected. Default value: "en"

Possible 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", "yue", "zh"

chunk_levelChunkLevelEnum

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

Possible values: "segment"

versionVersionEnum

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

Possible values: "3"

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

Output#

text*string

Transcription of the audio file

chunks*list<WhisperChunk>

Timestamp chunks of the audio file

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