> ## Documentation Index
> Fetch the complete documentation index at: https://assembly-preview.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Separating automatic language detection from transcription

> In this guide, you'll learn how to perform automatic language detection (ALD) separately from the transcription process. For the transcription, the file then gets routed to either the  model class, depending on the supported language.

This workflow is designed to be cost-effective, slicing the first 60 seconds of audio and running it through Nano ALD, which detects 99 languages, at a cost of \$0.002 per transcript for this language detection workflow (not including the total transcription cost).

Performing ALD with this workflow has a few benefits:

* Cost-effective language detection
* Ability to detect 99 languages
* Ability to use Nano as fallback if the language is not supported in Best
* Ability to enable [Audio Intelligence models](/docs/audio-intelligence) if the [language is supported](/docs/getting-started/supported-languages)
* Ability to use [LeMUR](/docs/lemur) with LLM prompts in Spanish for Spanish audio

## Before you begin[​](#before-you-begin "Direct link to Before you begin")

To complete this tutorial, you need:

* [Python](https://www.python.org/) installed.
* A .

The entire source code of this guide can be viewed [here](https://github.com/AssemblyAI/cookbook/blob/master/core-transcription/automatic-language-detection-separate.ipynb).

## Step-by-step instructions[​](#step-by-step-instructions "Direct link to Step-by-step instructions")

Install the Python SDK:

```
pip install assemblyai
```

Import the `assemblyai` package and set your API key:

```
import assemblyai as aaiaai.settings.api_key = "YOUR_API_KEY"
```

Create a set with all supported languages for Best. You can find them in our [documentation here](/docs/getting-started/supported-languages#supported-languages-for-best).

```
supported_languages_for_best = {    "en",    "es",    "fr",    "de",    "it",    "pt",    "nl",    "hi",    "ja",    "zh",    "fi",    "ko",    "pl",    "ru",    "tr",    "uk",    "vi",}
```

Define a `Transcriber`. Note that here we don't pass in a global `TranscriptionConfig`, but later apply different ones during the `transcribe()` call.

```
transcriber = aai.Transcriber()
```

Define two helper functions:

* `detect_language()` performs language detection on the [first 60 seconds](/docs/api-reference/transcripts/submit#request.body.audio_end_at) of the audio using Nano and returns the language code.
* `transcribe_file()` performs the transcription using Best or Nano depending on the identified language.

```
def detect_language(audio_url):    config = aai.TranscriptionConfig(        audio_end_at=60000,  # first 60 seconds (in milliseconds)        language_detection=True,        speech_model=aai.SpeechModel.nano,    )    transcript = transcriber.transcribe(audio_url, config=config)    return transcript.json_response["language_code"]def transcribe_file(audio_url, language_code):    config = aai.TranscriptionConfig(        language_code=language_code,        speech_model=(            aai.SpeechModel.best            if language_code in supported_languages_for_best            else aai.SpeechModel.nano        ),    )    transcript = transcriber.transcribe(audio_url, config=config)    return transcript
```

Test the code with different audio files. Apply both helper functions sequentially to each file to first identify the language and then transcribe the file.

```
audio_urls = [    "https://storage.googleapis.com/aai-web-samples/public_benchmarking_portugese.mp3",    "https://storage.googleapis.com/aai-web-samples/public_benchmarking_spanish.mp3",    "https://storage.googleapis.com/aai-web-samples/slovenian_luka_doncic_interview.mp3",    "https://storage.googleapis.com/aai-web-samples/5_common_sports_injuries.mp3",]for audio_url in audio_urls:    language_code = detect_language(audio_url)    print("Identified language:", language_code)    transcript = transcribe_file(audio_url, language_code)    print("Transcript:", transcript.text[:100], "...")
```

Output:

```
Identified language: ptTranscript: e aí Olá pessoal, sejam bem-vindos a mais um vídeo e hoje eu vou ensinar-vos como fazer esta espada  ...Identified language: esTranscript: Precisamente sobre este caso, el diario estadounidense New York Times reveló este sábado un conjunto ...Identified language: slTranscript: Ni lepška, kaj videl tega otroka v mrekoj svojga okolja, da mu je uspil in to v takimi miri, da pač  ...Identified language: enTranscript: Runner's knee runner's knee is a condition characterized by pain behind or around the kneecap. It is ...
```