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- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities. - Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools. - Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing. - Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format. - Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5. - Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills. - Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
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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