Release set of skills
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# Whisper Language Support Guide
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Complete guide to Whisper's multilingual capabilities.
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## Supported languages (99 total)
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### Top-tier support (WER < 10%)
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- English (en)
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- Spanish (es)
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- French (fr)
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- German (de)
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- Italian (it)
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- Portuguese (pt)
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- Dutch (nl)
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- Polish (pl)
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- Russian (ru)
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- Japanese (ja)
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- Korean (ko)
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- Chinese (zh)
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### Good support (WER 10-20%)
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- Arabic (ar)
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- Turkish (tr)
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- Vietnamese (vi)
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- Swedish (sv)
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- Finnish (fi)
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- Czech (cs)
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- Romanian (ro)
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- Hungarian (hu)
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- Danish (da)
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- Norwegian (no)
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- Thai (th)
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- Hebrew (he)
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- Greek (el)
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- Indonesian (id)
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- Malay (ms)
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### Full list (99 languages)
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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
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## Usage examples
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### Auto-detect language
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```python
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import whisper
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model = whisper.load_model("turbo")
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# Auto-detect language
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result = model.transcribe("audio.mp3")
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print(f"Detected language: {result['language']}")
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print(f"Text: {result['text']}")
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```
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### Specify language (faster)
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```python
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# Specify language for faster transcription
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result = model.transcribe("audio.mp3", language="es") # Spanish
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result = model.transcribe("audio.mp3", language="fr") # French
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result = model.transcribe("audio.mp3", language="ja") # Japanese
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```
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### Translation to English
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```python
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# Translate any language to English
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result = model.transcribe(
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"spanish_audio.mp3",
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task="translate" # Translates to English
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)
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print(f"Original language: {result['language']}")
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print(f"English translation: {result['text']}")
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```
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## Language-specific tips
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### Chinese
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```python
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# Chinese works well with larger models
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model = whisper.load_model("large")
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result = model.transcribe(
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"chinese_audio.mp3",
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language="zh",
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initial_prompt="这是一段关于技术的讨论" # Context helps
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)
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```
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### Japanese
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```python
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# Japanese benefits from initial prompt
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result = model.transcribe(
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"japanese_audio.mp3",
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language="ja",
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initial_prompt="これは技術的な会議の録音です"
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)
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```
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### Arabic
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```python
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# Arabic: Use large model for best results
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model = whisper.load_model("large")
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result = model.transcribe(
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"arabic_audio.mp3",
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language="ar"
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)
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```
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## Model size recommendations
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| Language Tier | Recommended Model | WER |
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|---------------|-------------------|-----|
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| Top-tier (en, es, fr, de) | base/turbo | < 10% |
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| Good (ar, tr, vi) | medium/large | 10-20% |
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| Lower-resource | large | 20-30% |
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## Performance by language
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### English
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- **tiny**: WER ~15%
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- **base**: WER ~8%
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- **small**: WER ~5%
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- **medium**: WER ~4%
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- **large**: WER ~3%
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- **turbo**: WER ~3.5%
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### Spanish
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- **tiny**: WER ~20%
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- **base**: WER ~12%
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- **medium**: WER ~6%
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- **large**: WER ~4%
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### Chinese
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- **small**: WER ~15%
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- **medium**: WER ~8%
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- **large**: WER ~5%
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## Best practices
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1. **Use English-only models** - Better for small models (tiny/base)
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2. **Specify language** - Faster than auto-detect
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3. **Add initial prompt** - Improves accuracy for technical terms
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4. **Use larger models** - For low-resource languages
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5. **Test on sample** - Quality varies by accent/dialect
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6. **Consider audio quality** - Clear audio = better results
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7. **Check language codes** - Use ISO 639-1 codes (2 letters)
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## Language detection
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```python
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# Detect language only (no transcription)
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import whisper
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model = whisper.load_model("base")
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# Load audio
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audio = whisper.load_audio("audio.mp3")
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audio = whisper.pad_or_trim(audio)
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# Make log-Mel spectrogram
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mel = whisper.log_mel_spectrogram(audio).to(model.device)
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# Detect language
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_, probs = model.detect_language(mel)
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detected_language = max(probs, key=probs.get)
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print(f"Detected language: {detected_language}")
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print(f"Confidence: {probs[detected_language]:.2%}")
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```
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## Resources
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- **Paper**: https://arxiv.org/abs/2212.04356
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- **GitHub**: https://github.com/openai/whisper
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- **Model Card**: https://github.com/openai/whisper/blob/main/model-card.md
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