[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CAARY92X3NE7VTNN9WCFS9":3,"skill-same:01M6CAARY92X3NE7VTNN9WCFS9":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":6,"ownerHtmlUrl":63,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":64,"install":65,"resources":78},"01M6CAARY92X3NE7VTNN9WCFS9","whisper",null,"OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.","adnichols\u002Fai-configs","adnichols","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F493746?v=4","01M6C33VRY9WF6XWYPT1AFA7JJ","Aaron Nichols","01M6C33VWTNKGRM92E9G643V4W",5,"MIT","https:\u002F\u002Fgithub.com\u002Fadnichols\u002Fai-configs\u002Fblob\u002Fmain\u002F_hermes\u002Fdefault\u002Fprofiles\u002Fnerd\u002Fskills\u002Fmlops\u002Fmodels\u002Fwhisper\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34],"research","openai","speech","multimodal","Transformers","ASR","Audio","text","model","speech recognition","use","2026-11-10 10:12:30",false,"---\nname: whisper\ndescription: OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.\nversion: 1.0.0\nauthor: Orchestra Research\nlicense: MIT\ndependencies: [openai-whisper, transformers, torch]\nmetadata:\n  hermes:\n    tags: [Whisper, Speech Recognition, ASR, Multimodal, Multilingual, OpenAI, Speech-To-Text, Transcription, Translation, Audio Processing]\n\n---\n\n# Whisper - Robust Speech Recognition\n\nOpenAI's multilingual speech recognition model.\n\n## When to use Whisper\n\n**Use when:**\n- Speech-to-text transcription (99 languages)\n- Podcast\u002Fvideo transcription\n- Meeting notes automation\n- Translation to English\n- Noisy audio transcription\n- Multilingual audio processing\n\n**Metrics**:\n- **72,900+ GitHub stars**\n- 99 languages supported\n- Trained on 680,000 hours of audio\n- MIT License\n\n**Use alternatives instead**:\n- **AssemblyAI**: Managed API, speaker diarization\n- **Deepgram**: Real-time streaming ASR\n- **Google Speech-to-Text**: Cloud-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Requires Python 3.8-3.11\npip install -U openai-whisper\n\n# Requires ffmpeg\n# macOS: brew install ffmpeg\n# Ubuntu: sudo apt install ffmpeg\n# Windows: choco install ffmpeg\n```\n\n### Basic transcription\n\n```python\nimport whisper\n\n# Load model\nmodel = whisper.load_model(\"base\")\n\n# Transcribe\nresult = model.transcribe(\"audio.mp3\")\n\n# Print text\nprint(result[\"text\"])\n\n# Access segments\nfor segment in result[\"segments\"]:\n    print(f\"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}\")\n```\n\n## Model sizes\n\n```python\n# Available models\nmodels = [\"tiny\", \"base\", \"small\", \"medium\", \"large\", \"turbo\"]\n\n# Load specific model\nmodel = whisper.load_model(\"turbo\")  # Fastest, good quality\n```\n\n| Model | Parameters | English-only | Multilingual | Speed | VRAM |\n|-------|------------|--------------|--------------|-------|------|\n| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |\n| base | 74M | ✓ | ✓ | ~16x | ~1 GB |\n| small | 244M | ✓ | ✓ | ~6x | ~2 GB |\n| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |\n| large | 1550M | ✗ | ✓ | 1x | ~10 GB |\n| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |\n\n**Recommendation**: Use `turbo` for best speed\u002Fquality, `base` for prototyping\n\n## Transcription options\n\n### Language specification\n\n```python\n# Auto-detect language\nresult = model.transcribe(\"audio.mp3\")\n\n# Specify language (faster)\nresult = model.transcribe(\"audio.mp3\", language=\"en\")\n\n# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more\n```\n\n### Task selection\n\n```python\n# Transcription (default)\nresult = model.transcribe(\"audio.mp3\", task=\"transcribe\")\n\n# Translation to English\nresult = model.transcribe(\"spanish.mp3\", task=\"translate\")\n# Input: Spanish audio → Output: English text\n```\n\n### Initial prompt\n\n```python\n# Improve accuracy with context\nresult = model.transcribe(\n    \"audio.mp3\",\n    initial_prompt=\"This is a technical podcast about machine learning and AI.\"\n)\n\n# Helps with:\n# - Technical terms\n# - Proper nouns\n# - Domain-specific vocabulary\n```\n\n### Timestamps\n\n```python\n# Word-level timestamps\nresult = model.transcribe(\"audio.mp3\", word_timestamps=True)\n\nfor segment in result[\"segments\"]:\n    for word in segment[\"words\"]:\n        print(f\"{word['word']} ({word['start']:.2f}s - {word['end']:.2f}s)\")\n```\n\n### Temperature fallback\n\n```python\n# Retry with different temperatures if confidence low\nresult = model.transcribe(\n    \"audio.mp3\",\n    temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)\n)\n```\n\n## Command line usage\n\n```bash\n# Basic transcription\nwhisper audio.mp3\n\n# Specify model\nwhisper audio.mp3 --model turbo\n\n# Output formats\nwhisper audio.mp3 --output_format txt     # Plain text\nwhisper audio.mp3 --output_format srt     # Subtitles\nwhisper audio.mp3 --output_format vtt     # WebVTT\nwhisper audio.mp3 --output_format json    # JSON with timestamps\n\n# Language\nwhisper audio.mp3 --language Spanish\n\n# Translation\nwhisper spanish.mp3 --task translate\n```\n\n## Batch processing\n\n```python\nimport os\n\naudio_files = [\"file1.mp3\", \"file2.mp3\", \"file3.mp3\"]\n\nfor audio_file in audio_files:\n    print(f\"Transcribing {audio_file}...\")\n    result = model.transcribe(audio_file)\n\n    # Save to file\n    output_file = audio_file.replace(\".mp3\", \".txt\")\n    with open(output_file, \"w\") as f:\n        f.write(result[\"text\"])\n```\n\n## Real-time transcription\n\n```python\n# For streaming audio, use faster-whisper\n# pip install faster-whisper\n\nfrom faster_whisper import WhisperModel\n\nmodel = WhisperModel(\"base\", device=\"cuda\", compute_type=\"float16\")\n\n# Transcribe with streaming\nsegments, info = model.transcribe(\"audio.mp3\", beam_size=5)\n\nfor segment in segments:\n    print(f\"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}\")\n```\n\n## GPU acceleration\n\n```python\nimport whisper\n\n# Automatically uses GPU if available\nmodel = whisper.load_model(\"turbo\")\n\n# Force CPU\nmodel = whisper.load_model(\"turbo\", device=\"cpu\")\n\n# Force GPU\nmodel = whisper.load_model(\"turbo\", device=\"cuda\")\n\n# 10-20× faster on GPU\n```\n\n## Integration with other tools\n\n### Subtitle generation\n\n```bash\n# Generate SRT subtitles\nwhisper video.mp4 --output_format srt --language English\n\n# Output: video.srt\n```\n\n### With LangChain\n\n```python\nfrom langchain.document_loaders import WhisperTranscriptionLoader\n\nloader = WhisperTranscriptionLoader(file_path=\"audio.mp3\")\ndocs = loader.load()\n\n# Use transcription in RAG\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\n\nvectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())\n```\n\n### Extract audio from video\n\n```bash\n# Use ffmpeg to extract audio\nffmpeg -i video.mp4 -vn -acodec pcm_s16le audio.wav\n\n# Then transcribe\nwhisper audio.wav\n```\n\n## Best practices\n\n1. **Use turbo model** - Best speed\u002Fquality for English\n2. **Specify language** - Faster than auto-detect\n3. **Add initial prompt** - Improves technical terms\n4. **Use GPU** - 10-20× faster\n5. **Batch process** - More efficient\n6. **Convert to WAV** - Better compatibility\n7. **Split long audio** - \u003C30 min chunks\n8. **Check language support** - Quality varies by language\n9. **Use faster-whisper** - 4× faster than openai-whisper\n10. **Monitor VRAM** - Scale model size to hardware\n\n## Performance\n\n| Model | Real-time factor (CPU) | Real-time factor (GPU) |\n|-------|------------------------|------------------------|\n| tiny | ~0.32 | ~0.01 |\n| base | ~0.16 | ~0.01 |\n| turbo | ~0.08 | ~0.01 |\n| large | ~1.0 | ~0.05 |\n\n*Real-time factor: 0.1 = 10× faster than real-time*\n\n## Language support\n\nTop-supported languages:\n- English (en)\n- Spanish (es)\n- French (fr)\n- German (de)\n- Italian (it)\n- Portuguese (pt)\n- Russian (ru)\n- Japanese (ja)\n- Korean (ko)\n- Chinese (zh)\n\nFull list: 99 languages total\n\n## Limitations\n\n1. **Hallucinations** - May repeat or invent text\n2. **Long-form accuracy** - Degrades on >30 min audio\n3. **Speaker identification** - No diarization\n4. **Accents** - Quality varies\n5. **Background noise** - Can affect accuracy\n6. **Real-time latency** - Not suitable for live captioning\n\n## Resources\n\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper ⭐ 72,900+\n- **Paper**: https:\u002F\u002Farxiv.org\u002Fabs\u002F2212.04356\n- **Model Card**: https:\u002F\u002Fgithub.com\u002Fopenai\u002Fwhisper\u002Fblob\u002Fmain\u002Fmodel-card.md\n- **Colab**: Available in repo\n- **License**: MIT\n\n\n","# Whisper - Robust Speech Recognition\n\nOpenAI's multilingual speech recognition model.\n\n## When to use Whisper\n\n**Use when:**\n- Speech-to-text transcription (99 languages)\n- Podcast\u002Fvideo transcription\n- Meeting notes automation\n- Translation to English\n- Noisy audio transcription\n- Multilingual audio processing\n\n**Metrics**:\n- **72,900+ GitHub stars**\n- 99 languages supported\n- Trained on 680,000 hours of audio\n- MIT License\n\n**Use alternatives instead**:\n- **AssemblyAI**: Managed API, speaker diarization\n- **Deepgram**: Real-time streaming ASR\n- **Google Speech-to-Text**: Cloud-based\n\n## Quick start\n\n### Installation\n\n```bash\n# Requires Python 3.8-3.11\npip install -U openai-whisper\n\n# Requires ffmpeg\n# macOS: brew install ffmpeg\n# Ubuntu: sudo apt install ffmpeg\n# Windows: choco install ffmpeg\n```\n\n### Basic transcription\n\n```python\nimport whisper\n\n# Load model\nmodel = whisper.load_model(\"base\")\n\n# Transcribe\nresult = model.transcribe(\"audio.mp3\")\n\n# Print text\nprint(result[\"text\"])\n\n# Access segments\nfor segment in result[\"segments\"]:\n    print(f\"[{segment['start']:.2f}s - {segment['end']:.2f}s] {segment['text']}\")\n```\n\n## Model sizes\n\n```python\n# Available models\nmodels = [\"tiny\", \"base\", \"small\", \"medium\", \"large\", \"turbo\"]\n\n# Load specific model\nmodel = whisper.load_model(\"turbo\")  # Fastest, good quality\n```\n\n| Model | Parameters | English-only | Multilingual | Speed | VRAM |\n|-------|------------|--------------|--------------|-------|------|\n| tiny | 39M | ✓ | ✓ | ~32x | ~1 GB |\n| base | 74M | ✓ | ✓ | ~16x | ~1 GB |\n| small | 244M | ✓ | ✓ | ~6x | ~2 GB |\n| medium | 769M | ✓ | ✓ | ~2x | ~5 GB |\n| large | 1550M | ✗ | ✓ | 1x | ~10 GB |\n| turbo | 809M | ✗ | ✓ | ~8x | ~6 GB |\n\n**Recommendation**: Use `turbo` for best speed\u002Fquality, `base` for prototyping\n\n## Transcription options\n\n### Language specification\n\n```python\n# Auto-detect language\nresult = model.transcribe(\"audio.mp3\")\n\n# Specify language (faster)\nresult = model.transcribe(\"audio.mp3\", language=\"en\")\n\n# Supported: en, es, fr, de, it, pt, ru, ja, ko, zh, and 89 more\n```\n\n### Task selection\n\n```python\n# Transcription (default)\nresult = model.transcribe(\"audio.mp3\", task=\"transcribe\")\n\n# Translation to English\nresult = model.transcribe(\"spanish.mp3\", task=\"translate\")\n# Input: Spanish audio → Output: English text\n```\n\n### Initial prompt\n\n```python\n# Improve accuracy with context\nresult = model.transcribe(\n    \"audio.mp3\",\n    initial_prompt=\"This is a technical podcast about machine learning and AI.\"\n)\n\n# Helps with:\n# - Technical terms\n# - Proper nouns\n# - Domain-specific vocabulary\n```\n\n### Timestamps\n\n```python\n# Word-level timestamps\nresult = model.transcribe(\"audio.mp3\", word_timestamps=True)\n\nfor segment in result[\"segments\"]:\n    for word in segment[\"words\"]:\n        print(f\"{word['word']} ({word['start']:.2f}s - {word['end']:.2f}s)\")\n```\n\n### Temperature fallback\n\n```python\n# Retry with different temperatures if confidence low\nresult = model.transcribe(\n    \"audio.mp3\",\n    temperature=(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)\n)\n```\n\n## Command line usage\n\n```bash\n# Basic transcription\nwhisper audio.mp3\n\n# Specify model\nwhisper audio.mp3 --model turbo\n\n# Output formats\nwhisper audio.mp3 --output_format txt     # Plain text\nwhisper audio.mp3 --output_format srt     # Subtitles\nwhisper audio.mp3 --output_format vtt     # WebVTT\nwhisper audio.mp3 --output_format json    # JSON with timestamps\n\n# Language\nwhisper audio.mp3 --language Spanish\n\n# Translation\nwhisper spanish.mp3 --task translate\n```\n\n## Batch processing\n\n```python\nimport os\n\naudio_files = [\"file1.mp3\", \"file2.mp3\", \"file3.mp3\"]\n\nfor audio_file in audio_files:\n    print(f\"Transcribing {audio_file}...\")\n    result = model.transcribe(audio_file)\n\n    # Save to file\n    output_file = audio_file.replace(\".mp3\", \".txt\")\n    with open(output_file, \"w\") as f:\n        f.write(result[\"text\"])\n```\n\n## Real-time transcription\n\n```python\n# For streaming audio, use faster-whisper\n# pip install faster-whisper\n\nfrom faster_whisper import WhisperModel\n\nmodel = WhisperModel(\"base\", device=\"cuda\", compute_type=\"float16\")\n\n# Transcribe with streaming\nsegments, info = model.transcribe(\"audio.mp3\", beam_size=5)\n\nfor segment in segments:\n    print(f\"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}\")\n```\n\n## GPU acceleration\n\n```python\nimport whisper\n\n# Automatically uses GPU if available\nmodel = whisper.load_model(\"turbo\")\n\n# Force CPU\nmodel = whisper.load_model(\"turbo\", device=\"cpu\")\n\n# Force GPU\nmodel = whisper.load_model(\"turbo\", device=\"cuda\")\n\n# 10-20× faster on GPU\n```\n\n## Integration with other tools\n\n### Subtitle generation\n\n```bash\n# Generate SRT subtitles\nwhisper video.mp4 --output_format srt --language English\n\n# Output: video.srt\n```\n\n### With LangChain\n\n```python\nfrom langchain.document_loaders import WhisperTranscriptionLoader\n\nloader = WhisperTranscriptionLoader(file_path=\"audio.mp3\")\ndocs = loader.load()\n\n# Use transcription in RAG\nfrom langchain_chroma import Chroma\nfrom langchain_openai import OpenAIEmbeddings\n\nvectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())\n```\n\n### Extract audio from video\n\n```bash\n# Use ffmpeg to extract audio\nffmpeg -i video.mp4 -vn -acodec pcm_s16le audio.wav\n\n# Then transcribe\nwhisper audio.wav\n```\n\n## Best practices\n\n1. **Use turbo model** - Best speed\u002Fquality for English\n2. **Specify language** - Faster than auto-detect\n3. **Add initial prompt** - Improves technical terms\n4. **Use GPU** - 10-20× faster\n5. **Batch process** - More efficient\n6. **Convert to WAV** - Better compatibility\n7. **Split long audio** - \u003C30 min chunks\n8. **Check language support** - Quality varies by language\n9. **Use faster-whisper** - 4× faster than openai-whisper\n10. **Monitor VRAM** - Scale model size to hardware\n\n## Performance\n\n| Model | Real-time factor (CPU) | Real-time factor (GPU) |\n|-------|------------------------|------------------------|\n| tiny | ~0.32 | ~0.01 |\n| base | ~0.16 | ~0.01 |\n| turbo | ~0.08 | ~0.01 |\n| large | ~1.0 | ~0.05 |\n\n*Real-time factor: 0.1 = 10× faster than real-time*\n\n## Language support\n\nTop-supported languages:\n- English (en)\n- Spanish (es)\n- French (fr)\n- German (de)\n- Italian (it)\n- Portuguese (pt)\n- Russian (ru)\n- Japanese (ja)\n- Korean (ko)\n- Chinese (zh)\n\nFull list: 99 languages total\n\n## Limitations\n\n1. **Hallucinations** - 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