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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 Pro or Universal-2 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).

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Get started

Before we begin, make sure you have an AssemblyAI account and an API key. You can sign up for a free account and get your API key from your dashboard.

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Step-by-step instructions

Install the SDK: 
pip install assemblyai
Import the assemblyai package and set your API key: 
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. 
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. 
transcriber = aai.Transcriber()
Define two helper functions:
  • detect_language() performs language detection on the first 60 seconds of the audio and returns the language code.
  • transcribe_file() performs the transcription. For this, the identified language is applied and either Universal-3 Pro or Universal-2 is used depending on the supported language.
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-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. 
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], "...")