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You help developers build voice-enabled applications with natural-sounding speech, custom voice creation, multilingual support, and real-time streaming TTS for voice agents, audiobooks, podcasts, and accessibility features.\n\n## Instructions\n\n### Text-to-Speech\n\n```python\n# Basic TTS — generate audio from text\nfrom elevenlabs import ElevenLabs\n\nclient = ElevenLabs(api_key=os.environ[\"ELEVENLABS_API_KEY\"])\n\n# Generate and save audio\naudio = client.text_to_speech.convert(\n    voice_id=\"pNInz6obpgDQGcFmaJgB\",    # \"Rachel\" — warm, professional\n    text=\"Welcome to Bright Smile Dental. How can I help you today?\",\n    model_id=\"eleven_turbo_v2_5\",         # Optimized for low latency (~200ms)\n    voice_settings={\n        \"stability\": 0.6,                 # Lower = more expressive, higher = more consistent\n        \"similarity_boost\": 0.8,           # How closely to match the original voice\n        \"style\": 0.3,                      # Style exaggeration (0-1)\n        \"use_speaker_boost\": True,         # Enhance clarity\n    },\n)\n\n# Save to file\nwith open(\"greeting.mp3\", \"wb\") as f:\n    for chunk in audio:\n        f.write(chunk)\n\n# Streaming TTS — for real-time applications\naudio_stream = client.text_to_speech.convert_as_stream(\n    voice_id=\"pNInz6obpgDQGcFmaJgB\",\n    text=\"Let me check our available appointments for next Tuesday.\",\n    model_id=\"eleven_turbo_v2_5\",\n    output_format=\"pcm_24000\",            # Raw PCM for WebRTC\u002FLiveKit\n)\n\nfor chunk in audio_stream:\n    send_to_audio_output(chunk)            # Stream directly to speaker\n```\n\n### Voice Cloning\n\n```python\n# Instant voice clone — from a single audio sample\nvoice = client.voices.add(\n    name=\"Dr. Smith\",\n    files=[open(\"dr_smith_sample.mp3\", \"rb\")],\n    description=\"Calm, authoritative male voice for medical context\",\n    labels={\"use_case\": \"voice_agent\", \"language\": \"en\"},\n)\nprint(f\"Cloned voice ID: {voice.voice_id}\")\n\n# Professional voice clone (higher quality, requires consent)\n# Needs 30+ minutes of clean audio for best results\n```\n\n### Conversational AI Agent\n\n```python\n# ElevenLabs Conversational AI — fully managed voice agent\nfrom elevenlabs import ConversationalAI\n\nagent = ConversationalAI(\n    api_key=os.environ[\"ELEVENLABS_API_KEY\"],\n    agent_id=\"your-agent-id\",             # Created in ElevenLabs dashboard\n)\n\n# WebSocket connection for real-time conversation\nasync def handle_call(websocket):\n    async for audio_chunk in websocket:\n        # Send caller audio to ElevenLabs\n        response = await agent.process_audio(audio_chunk)\n        # Send AI response audio back to caller\n        await websocket.send(response.audio)\n```\n\n### JavaScript \u002F React\n\n```typescript\n\u002F\u002F Browser-based TTS\nimport { ElevenLabsClient } from \"elevenlabs\";\n\nconst client = new ElevenLabsClient({ apiKey: process.env.ELEVENLABS_KEY });\n\n\u002F\u002F Stream audio in browser\nconst response = await client.textToSpeech.convertAsStream(voiceId, {\n  text: \"Hello! How can I assist you?\",\n  model_id: \"eleven_turbo_v2_5\",\n  output_format: \"mp3_44100_128\",\n});\n\n\u002F\u002F Play audio using Web Audio API\nconst audioContext = new AudioContext();\nconst reader = response.getReader();\n\u002F\u002F ... decode and play chunks\n```\n\n## Available Models\n\n| Model | Latency | Quality | Best For |\n|-------|---------|---------|----------|\n| `eleven_turbo_v2_5` | ~200ms | High | Voice agents, real-time apps |\n| `eleven_multilingual_v2` | ~400ms | Highest | Multilingual, audiobooks |\n| `eleven_english_v1` | ~300ms | Good | English-only, cost-sensitive |\n\n## Installation\n\n```bash\npip install elevenlabs                    # Python\nnpm install elevenlabs                    # Node.js\n```\n\n## Examples\n\n**Example 1: User asks to set up elevenlabs**\n\nUser: \"Help me set up elevenlabs for my project\"\n\nThe agent should:\n1. Check system requirements and prerequisites\n2. Install or configure elevenlabs\n3. Set up initial project structure\n4. Verify the setup works correctly\n\n**Example 2: User asks to build a feature with elevenlabs**\n\nUser: \"Create a dashboard using elevenlabs\"\n\nThe agent should:\n1. Scaffold the component or configuration\n2. Connect to the appropriate data source\n3. Implement the requested feature\n4. Test and validate the output\n\n## Guidelines\n\n1. **Turbo model for voice agents** — Use `eleven_turbo_v2_5` for real-time conversations; 200ms latency feels instant\n2. **Streaming for real-time** — Use `convert_as_stream` instead of `convert` for voice agents; first audio chunk arrives in ~200ms\n3. **Voice settings tuning** — Lower stability (0.3-0.5) for expressive narration; higher (0.7-0.9) for consistent voice agents\n4. **PCM output for WebRTC** — Use `pcm_24000` or `pcm_16000` output format when feeding into WebRTC\u002FLiveKit; no decoding overhead\n5. **Voice library** — Browse ElevenLabs' voice library (1000+ voices) before cloning; many professional voices are already available\n6. **Pronunciation dictionary** — Upload custom pronunciation rules for medical terms, brand names, and technical jargon\n7. **Character count billing** — ElevenLabs bills per character; cache common phrases and greetings to reduce costs\n8. **SSML-like control** — Use `\u003Cbreak 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