hermes-sync/skills/mlops/models/whisper/references/languages.md

Whisper Language Support Guide

Complete guide to Whisper's multilingual capabilities.

Supported languages (99 total)

Top-tier support (WER < 10%)

• English (en)
• Spanish (es)
• French (fr)
• German (de)
• Italian (it)
• Portuguese (pt)
• Dutch (nl)
• Polish (pl)
• Russian (ru)
• Japanese (ja)
• Korean (ko)
• Chinese (zh)

Good support (WER 10-20%)

• Arabic (ar)
• Turkish (tr)
• Vietnamese (vi)
• Swedish (sv)
• Finnish (fi)
• Czech (cs)
• Romanian (ro)
• Hungarian (hu)
• Danish (da)
• Norwegian (no)
• Thai (th)
• Hebrew (he)
• Greek (el)
• Indonesian (id)
• Malay (ms)

Full list (99 languages)

Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Bashkir, Basque, Belarusian, Bengali, Bosnian, Breton, Bulgarian, Burmese, Cantonese, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Faroese, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Lao, Latin, Latvian, Lingala, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Moldavian, Mongolian, Myanmar, Nepali, Norwegian, Nynorsk, Occitan, Pashto, Persian, Polish, Portuguese, Punjabi, Pushto, Romanian, Russian, Sanskrit, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Tibetan, Turkish, Turkmen, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Yiddish, Yoruba

Usage examples

Auto-detect language

import whisper

model = whisper.load_model("turbo")

# Auto-detect language
result = model.transcribe("audio.mp3")

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

Specify language (faster)

# Specify language for faster transcription
result = model.transcribe("audio.mp3", language="es")  # Spanish
result = model.transcribe("audio.mp3", language="fr")  # French
result = model.transcribe("audio.mp3", language="ja")  # Japanese

Translation to English

# Translate any language to English
result = model.transcribe(
    "spanish_audio.mp3",
    task="translate"  # Translates to English
)

print(f"Original language: {result['language']}")
print(f"English translation: {result['text']}")

Language-specific tips

Chinese

# Chinese works well with larger models
model = whisper.load_model("large")

result = model.transcribe(
    "chinese_audio.mp3",
    language="zh",
    initial_prompt="这是一段关于技术的讨论"  # Context helps
)

Japanese

# Japanese benefits from initial prompt
result = model.transcribe(
    "japanese_audio.mp3",
    language="ja",
    initial_prompt="これは技術的な会議の録音です"
)

Arabic

# Arabic: Use large model for best results
model = whisper.load_model("large")

result = model.transcribe(
    "arabic_audio.mp3",
    language="ar"
)

Model size recommendations

Language Tier	Recommended Model	WER
Top-tier (en, es, fr, de)	base/turbo	< 10%
Good (ar, tr, vi)	medium/large	10-20%
Lower-resource	large	20-30%

Performance by language

English

• tiny: WER ~15%
• base: WER ~8%
• small: WER ~5%
• medium: WER ~4%
• large: WER ~3%
• turbo: WER ~3.5%

Spanish

• tiny: WER ~20%
• base: WER ~12%
• medium: WER ~6%
• large: WER ~4%

Chinese

• small: WER ~15%
• medium: WER ~8%
• large: WER ~5%

Best practices

1. Use English-only models - Better for small models (tiny/base)
2. Specify language - Faster than auto-detect
3. Add initial prompt - Improves accuracy for technical terms
4. Use larger models - For low-resource languages
5. Test on sample - Quality varies by accent/dialect
6. Consider audio quality - Clear audio = better results
7. Check language codes - Use ISO 639-1 codes (2 letters)

Language detection

# Detect language only (no transcription)
import whisper

model = whisper.load_model("base")

# Load audio
audio = whisper.load_audio("audio.mp3")
audio = whisper.pad_or_trim(audio)

# Make log-Mel spectrogram
mel = whisper.log_mel_spectrogram(audio).to(model.device)

# Detect language
_, probs = model.detect_language(mel)
detected_language = max(probs, key=probs.get)

print(f"Detected language: {detected_language}")
print(f"Confidence: {probs[detected_language]:.2%}")

Resources

• Paper: https://arxiv.org/abs/2212.04356
• GitHub: https://github.com/openai/whisper
• Model Card: https://github.com/openai/whisper/blob/main/model-card.md