Skip to main content

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.

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).

​
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.

​
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], "...")