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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 Best or Nano 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).

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:

1import assemblyai as aai
2aai.settings.api_key = "YOUR_API_KEY"

Create a set with all supported languages for Best. You can find them in our documentation here.

1supported_languages_for_best = {
2    "en",
3    "en_au",
4    "en_uk",
5    "en_us",
6    "es",
7    "fr",
8    "de",
9    "it",
10    "pt",
11    "nl",
12    "hi",
13    "ja",
14    "zh",
15    "fi",
16    "ko",
17    "pl",
18    "ru",
19    "tr",
20    "uk",
21    "vi",
22}

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

1transcriber = aai.Transcriber()

Define two helper functions:

  • detect_language() performs language detection on the first 60 seconds of the audio using Nano and returns the language code.
  • transcribe_file() performs the transcription. For this, the identified language is applied and either Best or Nano is used depending on the supported language.
1def detect_language(audio_url):
2    config = aai.TranscriptionConfig(
3        audio_end_at=60000,  # first 60 seconds (in milliseconds)
4        language_detection=True,
5        speech_model=aai.SpeechModel.nano,
6    )
7    transcript = transcriber.transcribe(audio_url, config=config)
8    return transcript.json_response["language_code"]
9
10def transcribe_file(audio_url, language_code):
11    config = aai.TranscriptionConfig(
12        language_code=language_code,
13        speech_model=(
14            aai.SpeechModel.best
15            if language_code in supported_languages_for_best
16            else aai.SpeechModel.nano
17        ),
18    )
19    transcript = transcriber.transcribe(audio_url, config=config)
20    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.

1audio_urls = [
2    "https://storage.googleapis.com/aai-web-samples/public_benchmarking_portugese.mp3",
3    "https://storage.googleapis.com/aai-web-samples/public_benchmarking_spanish.mp3",
4    "https://storage.googleapis.com/aai-web-samples/slovenian_luka_doncic_interview.mp3",
5    "https://storage.googleapis.com/aai-web-samples/5_common_sports_injuries.mp3",
6]
7
8for audio_url in audio_urls:
9    language_code = detect_language(audio_url)
10    print("Identified language:", language_code)
11
12    transcript = transcribe_file(audio_url, language_code)
13    print("Transcript:", transcript.text[:100], "...")