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Built-in → optional-skills/: mlops/training/peft → optional-skills/mlops/peft mlops/training/pytorch-fsdp → optional-skills/mlops/pytorch-fsdp mlops/models/clip → optional-skills/mlops/clip mlops/models/stable-diffusion → optional-skills/mlops/stable-diffusion mlops/models/whisper → optional-skills/mlops/whisper mlops/cloud/modal → optional-skills/mlops/modal mcp/mcporter → optional-skills/mcp/mcporter Built-in mlops training kept: axolotl, trl-fine-tuning, unsloth. Built-in mlops models kept: audiocraft, segment-anything. Built-in mlops evaluation/research/huggingface-hub/inference all kept. native-mcp stays built-in (documents the native MCP tool); mcporter was a redundant alternative CLI. Also: removed now-empty skills/mlops/cloud/ dir, refreshed skills/mlops/models/DESCRIPTION.md and skills/mcp/DESCRIPTION.md to match what's left, and synchronized both catalog pages (skills-catalog.md, optional-skills-catalog.md).
189 lines
4.7 KiB
Markdown
189 lines
4.7 KiB
Markdown
# 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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