> ## Documentation Index
> Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Use Automatic Language Detection as a Separate Step From Transcription

In this guide, we'll show you how to perform automatic language detection separately from the transcription process. For the transcription, the file then gets then routed to either our [Universal-3.5 Pro or Universal-2](/pre-recorded-audio/select-the-speech-model) 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 Universal-2 ALD, which detects 99 languages, at a cost of \$0.002 per transcript for this language detection workflow (not including the total transcription cost).

## Get started

Before we begin, make sure you have an AssemblyAI account and an API key. You can [sign up](https://assemblyai.com/dashboard/signup) for a free account and get your API key from your dashboard.

## Step-by-step instructions

Install the SDK:

```bash theme={null}
pip install assemblyai
```

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

```python theme={null}
import assemblyai as aai
aai.settings.api_key = "YOUR_API_KEY"
```

Create a set with all supported languages for *Universal*. You can find them in our [documentation here](/pre-recorded-audio/supported-languages).

```python expandable theme={null}
supported_languages_for_universal = {
    "en",
    "en_au",
    "en_uk",
    "en_us",
    "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.

```python theme={null}
transcriber = aai.Transcriber()
```

Define two helper functions:

* `detect_language()` performs language detection on the [first 60 seconds](/api-reference/transcripts/submit#request.body.audio_end_at) of the audio and returns the language code.
* `transcribe_file()` performs the transcription. For this, the identified language is applied and either Universal-3.5 Pro or Universal-2 is used depending on the supported language.

```python theme={null}
def detect_language(audio_url):
    config = aai.TranscriptionConfig(
        audio_end_at=60000,  # first 60 seconds (in milliseconds)
        language_detection=True,
        speech_models=["universal-2"],
    )
    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_models=(
            ["universal-3-5-pro", "universal-2"]
            if language_code in supported_languages_for_universal
            else ["universal-2"]
        ),
    )
    transcript = transcriber.transcribe(audio_url, config=config)
    return transcript
```

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

```python theme={null}
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], "...")
```