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Automatic language detection identifies the dominant language in your audio and routes the request to the best available model based on the detected language and the models you specify in speech_models. You can check which model processed your request using the speech_model_used field in the response. For best results, your audio should contain at least 15–90 seconds of spoken audio.

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Quickstart

import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    language_detection=True
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

print(transcript.text)
print(transcript.json_response["language_code"])

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Set a list of expected languages

If you’re confident the audio is in one of a few languages, provide that list via language_detection_options.expected_languages. Detection is restricted to these candidates and the model will choose the language with the highest confidence from this list. This can eliminate scenarios where Automatic Language Detection selects an unexpected language for transcription.
  • Use our language codes (e.g., "en", "es", "fr").
  • If expected_languages is not specified, it is set to ["all"] by default.
import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = "https://assembly.ai/wildfires.mp3"

options = aai.LanguageDetectionOptions(
    expected_languages=["en", "es", "fr", "de"],
    fallback_language="auto"
)

config = aai.TranscriptionConfig(
    language_detection=True,
    language_detection_options=options
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

print(transcript.text)
print(transcript.json_response["language_code"])

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Choose a fallback language

Control what language transcription should fall back to when detection cannot confidently select a language from the expected_languages list.
  • Set language_detection_options.fallback_language to a specific language code (e.g., "en").
  • fallback_language must be one of the language codes in expected_languages or "auto".
  • When fallback_language is unspecified, it is set to "auto" by default. This tells our model to choose the fallback language from expected_languages with the highest confidence score.
import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = "https://assembly.ai/wildfires.mp3"

options = aai.LanguageDetectionOptions(
    expected_languages=["en", "es", "fr", "de"],
    fallback_language="auto"
)

config = aai.TranscriptionConfig(
    language_detection=True,
    language_detection_options=options
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

print(transcript.text)
print(transcript.json_response["language_code"])

​
Confidence score

If language detection is enabled, the API returns a confidence score for the detected language. The score ranges from 0.0 (low confidence) to 1.0 (high confidence). 
import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    language_detection=True
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

print(transcript.text)
print(transcript.json_response["language_confidence"])

​
Set a language confidence threshold

You can set the confidence threshold that must be reached if language detection is enabled. An error will be returned if the language confidence is below this threshold. Valid values are in the range [0,1] inclusive. 
import assemblyai as aai

aai.settings.api_key = "<YOUR_API_KEY>"

# audio_file = "./local_file.mp3"
audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    language_detection=True,
    language_confidence_threshold=0.8
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

if transcript.status == "error":
  raise RuntimeError(f"Transcription failed: {transcript.error}")
else:
  print(transcript.json_response["language_confidence"])
  print(transcript.text)
If the language_confidence_threshold you specify is not met you will receive an error message like detected language 'bg', confidence 0.2949, is below the requested confidence threshold value of '0.4'.

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Troubleshooting

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Accented speech detected as the wrong language

Automatic Language Detection uses Whisper-based language identification, which can sometimes misidentify heavily accented speech as a different language. For example, English spoken with a strong accent may be detected as Finnish, Latvian, Latin, or Arabic. When this happens, the model might not just return a wrong language label — it might also transcribe the audio in the incorrectly detected language. This effectively translates the speech rather than transcribing it, producing output in a language the speaker wasn’t using.
The exact transcription behavior can vary depending on the detected language and speech model used.
Use expected_languages to constrain detection (most effective). If you know which languages your audio may contain, set expected_languages to only those languages. This prevents the model from selecting an unexpected language entirely. For example, if your application processes interviews in English, Spanish, and French: 
{
  "language_detection": true,
  "language_detection_options": {
    "expected_languages": ["en", "es", "fr"],
    "fallback_language": "en"
  }
}
Setting fallback_language to your most common language (e.g., "en") ensures that if the model can’t confidently choose between the expected languages, it defaults to the language most likely to produce a useful transcript. Use language_confidence_threshold to reject low-confidence detections. Setting a threshold (e.g., 0.7) causes the API to return an error instead of a transcript when confidence is low. This helps catch some misdetections, but not cases where the model is confidently wrong. Monitor language_confidence in responses. Log the language_code and language_confidence fields from your transcript responses. Unexpected language codes or unusual confidence patterns can help you identify misdetection issues early and decide whether to retry with expected_languages or flag the transcript for review.