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Overview

Whisper includes built-in language detection that can identify the spoken language in audio files. This feature analyzes the first 30 seconds of audio and returns probability distributions over all supported languages.

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Automatic Detection

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CLI

By default, Whisper automatically detects the language when not specified: 
whisper audio.mp3
Output: 
Detecting language using up to the first 30 seconds. Use `--language` to specify the language
Detected language: Japanese
[transcription follows...]
To skip detection and improve performance, specify the language: 
whisper audio.mp3 --language Japanese

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Python API

Omit the language parameter for automatic detection: 
import whisper

model = whisper.load_model("turbo")

# Language will be detected automatically
result = model.transcribe("audio.mp3")

print(f"Detected language: {result['language']}")
print(f"Transcription: {result['text']}")

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The detect_language() Function

For standalone language detection without transcription, use the detect_language() function:

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Function Signature

def detect_language(
    model: Whisper,
    mel: Tensor,
    tokenizer: Tokenizer = None
) -> Tuple[Tensor, List[dict]]

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Basic Usage

import whisper

model = whisper.load_model("turbo")

# Load and preprocess audio (first 30 seconds)
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)

# Generate Mel spectrogram
mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)

# Detect language
_, probs = model.detect_language(mel)

print(f"Detected language: {max(probs, key=probs.get)}")
print(f"\nTop 5 languages:")
for lang, prob in sorted(probs.items(), key=lambda x: x[1], reverse=True)[:5]:
    print(f"  {lang}: {prob:.2%}")

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Return Values

The function returns:
  1. language_tokens - Tensor of most probable language token IDs
  2. language_probs - Dictionary mapping language codes to probabilities
{
    'en': 0.95,
    'es': 0.02,
    'fr': 0.01,
    'de': 0.01,
    'ja': 0.005,
    # ... all 99 languages
}

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Practical Examples

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1. Batch Language Detection

import whisper
from pathlib import Path

def detect_languages_in_directory(audio_dir: str):
    model = whisper.load_model("turbo")
    results = {}
    
    for audio_file in Path(audio_dir).glob("*.mp3"):
        # Load audio
        audio = whisper.load_audio(str(audio_file))
        audio = whisper.pad_or_trim(audio)
        
        # Generate Mel spectrogram
        mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels)
        mel = mel.to(model.device)
        
        # Detect language
        _, probs = model.detect_language(mel)
        detected = max(probs, key=probs.get)
        confidence = probs[detected]
        
        results[audio_file.name] = {
            'language': detected,
            'confidence': confidence
        }
        
        print(f"{audio_file.name}: {detected} ({confidence:.1%})")
    
    return results

results = detect_languages_in_directory("./audio_files")

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2. Language-Specific Routing

import whisper

def process_by_language(audio_file: str):
    model = whisper.load_model("turbo")
    
    # Detect language first
    audio = whisper.load_audio(audio_file)
    audio = whisper.pad_or_trim(audio)
    mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
    
    _, probs = model.detect_language(mel)
    detected_language = max(probs, key=probs.get)
    
    # Route based on language
    if detected_language == 'en':
        # Use English-specific model for better accuracy
        model_en = whisper.load_model("medium.en")
        result = model_en.transcribe(audio_file)
    elif detected_language in ['zh', 'ja', 'ko']:
        # Handle Asian languages with specific settings
        result = model.transcribe(audio_file, language=detected_language)
    else:
        # Standard transcription
        result = model.transcribe(audio_file, language=detected_language)
    
    return result

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3. Confidence Threshold

import whisper

def transcribe_with_confidence_check(audio_file: str, min_confidence: float = 0.5):
    model = whisper.load_model("turbo")
    
    # Detect language with confidence
    audio = whisper.load_audio(audio_file)
    audio = whisper.pad_or_trim(audio)
    mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
    
    _, probs = model.detect_language(mel)
    detected_language = max(probs, key=probs.get)
    confidence = probs[detected_language]
    
    if confidence < min_confidence:
        # Low confidence - get top candidates
        top_3 = sorted(probs.items(), key=lambda x: x[1], reverse=True)[:3]
        print(f"Warning: Low confidence ({confidence:.1%})")
        print("Top candidates:")
        for lang, prob in top_3:
            print(f"  {lang}: {prob:.1%}")
        
        # Try transcription with top 2 candidates
        results = {}
        for lang, _ in top_3[:2]:
            result = model.transcribe(audio_file, language=lang)
            results[lang] = result
        
        return results
    else:
        print(f"Detected {detected_language} with {confidence:.1%} confidence")
        return model.transcribe(audio_file, language=detected_language)

result = transcribe_with_confidence_check("unclear_audio.mp3", min_confidence=0.7)

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4. Multilingual Audio Segmentation

import whisper
import numpy as np

def detect_language_segments(audio_file: str, segment_duration: int = 30):
    """
    Detect language for each segment of a long audio file.
    Useful for files with multiple speakers or language switches.
    """
    model = whisper.load_model("turbo")
    
    # Load full audio
    audio = whisper.load_audio(audio_file)
    sample_rate = 16000
    segment_samples = segment_duration * sample_rate
    
    segments = []
    for i in range(0, len(audio), segment_samples):
        segment_audio = audio[i:i + segment_samples]
        
        if len(segment_audio) < sample_rate:  # Skip very short segments
            continue
        
        # Pad to 30 seconds
        segment_audio = whisper.pad_or_trim(segment_audio)
        
        # Detect language
        mel = whisper.log_mel_spectrogram(segment_audio, n_mels=model.dims.n_mels)
        mel = mel.to(model.device)
        _, probs = model.detect_language(mel)
        
        detected = max(probs, key=probs.get)
        confidence = probs[detected]
        
        start_time = i / sample_rate
        end_time = (i + segment_samples) / sample_rate
        
        segments.append({
            'start': start_time,
            'end': end_time,
            'language': detected,
            'confidence': confidence
        })
        
        print(f"[{start_time:.1f}s - {end_time:.1f}s]: {detected} ({confidence:.1%})")
    
    return segments

segments = detect_language_segments("multilingual_conversation.mp3")

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5. Integration with Transcription

import whisper

def smart_transcribe(audio_file: str):
    """
    Transcribe with automatic language detection and logging.
    """
    model = whisper.load_model("turbo")
    
    # Enable verbose mode to see language detection
    result = model.transcribe(audio_file, verbose=True)
    
    # The language is automatically detected and included in result
    detected_language = result['language']
    
    print(f"\n=== Transcription Summary ===")
    print(f"Language: {detected_language}")
    print(f"Duration: {result['segments'][-1]['end']:.1f}s")
    print(f"Segments: {len(result['segments'])}")
    print(f"\nFull text:\n{result['text']}")
    
    return result

result = smart_transcribe("unknown_language.mp3")

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Advanced: Language-Specific Tokenizers

Different languages use different tokenizers. Understanding this helps with lower-level API usage: 
import whisper
from whisper.tokenizer import get_tokenizer

model = whisper.load_model("turbo")

# Get language-specific tokenizer
tokenizer_en = get_tokenizer(
    multilingual=model.is_multilingual,
    num_languages=model.num_languages,
    language="en",
    task="transcribe"
)

tokenizer_ja = get_tokenizer(
    multilingual=model.is_multilingual,
    num_languages=model.num_languages,
    language="ja",
    task="transcribe"
)

print(f"English SOT sequence: {tokenizer_en.sot_sequence}")
print(f"Japanese SOT sequence: {tokenizer_ja.sot_sequence}")

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Supported Languages

Whisper supports 99 languages. Common language codes:
  • en - English
  • es - Spanish
  • fr - French
  • de - German
  • it - Italian
  • pt - Portuguese
For the complete list, see tokenizer.py.

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Model Requirements

Language detection only works with multilingual models. English-only models (tiny.en, base.en, small.en, medium.en) cannot detect languages.
import whisper

# This works - multilingual model
model = whisper.load_model("turbo")
result = model.transcribe("audio.mp3")  # Language detected

# This doesn't detect language - English-only model
model_en = whisper.load_model("medium.en")
result = model_en.transcribe("audio.mp3")  # Always assumes English

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Performance Considerations

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Detection Speed

Language detection analyzes only the first 30 seconds of audio: 
# Detection uses first 30 seconds
audio = whisper.load_audio("long_file.mp3")
audio_30s = whisper.pad_or_trim(audio)  # Trim to 30 seconds

# This is fast regardless of file length
mel = whisper.log_mel_spectrogram(audio_30s, n_mels=model.dims.n_mels)
_, probs = model.detect_language(mel)

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Skip Detection for Known Languages

If you know the language, skip detection to save time: 
# Slower - detects language
result = model.transcribe("english_audio.mp3")

# Faster - skips detection
result = model.transcribe("english_audio.mp3", language="en")
For batch processing of files in the same language, detect once and reuse the language code for subsequent files.

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