feat: Blinkist-style audio summary bot (MAT-74)

Add interactive article summary feature: user pastes URL → bot asks
language/duration/topics → generates audio summary via LLM + ElevenLabs
TTS → posts MP3 inline with transcript and follow-up Q&A.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

article_summary/tts.py

@@ -0,0 +1,108 @@
+"""ElevenLabs TTS — direct API calls to generate MP3 audio."""
+
+from __future__ import annotations
+
+import io
+import logging
+
+import httpx
+
+logger = logging.getLogger("article-summary.tts")
+
+ELEVENLABS_API = "https://api.elevenlabs.io/v1"
+CHUNK_SIZE = 5000  # Max chars per TTS request
+
+
+async def generate_audio(
+    text: str,
+    api_key: str,
+    voice_id: str,
+    language: str = "en",
+) -> tuple[bytes, float]:
+    """Generate MP3 audio from text via ElevenLabs API.
+
+    Args:
+        text: Text to convert to speech.
+        api_key: ElevenLabs API key.
+        voice_id: ElevenLabs voice ID.
+        language: Language code ("en" or "de").
+
+    Returns:
+        Tuple of (mp3_bytes, estimated_duration_seconds).
+    """
+    chunks = _split_text(text, CHUNK_SIZE)
+    mp3_parts: list[bytes] = []
+
+    for i, chunk in enumerate(chunks):
+        logger.info("Generating TTS chunk %d/%d (%d chars)", i + 1, len(chunks), len(chunk))
+        mp3_data = await _tts_request(chunk, api_key, voice_id, language)
+        mp3_parts.append(mp3_data)
+
+    combined = b"".join(mp3_parts)
+
+    # Estimate duration: ~150 words per minute
+    word_count = len(text.split())
+    est_duration = (word_count / 150) * 60
+
+    logger.info("TTS complete: %d bytes, ~%.0fs estimated", len(combined), est_duration)
+    return combined, est_duration
+
+
+async def _tts_request(
+    text: str,
+    api_key: str,
+    voice_id: str,
+    language: str,
+) -> bytes:
+    """Single TTS API call."""
+    url = f"{ELEVENLABS_API}/text-to-speech/{voice_id}"
+    headers = {
+        "xi-api-key": api_key,
+        "Content-Type": "application/json",
+        "Accept": "audio/mpeg",
+    }
+    payload = {
+        "text": text,
+        "model_id": "eleven_multilingual_v2",
+        "voice_settings": {
+            "stability": 0.5,
+            "similarity_boost": 0.75,
+        },
+    }
+    # Add language hint for non-English
+    if language == "de":
+        payload["language_code"] = "de"
+
+    async with httpx.AsyncClient(timeout=120.0) as client:
+        resp = await client.post(url, json=payload, headers=headers)
+        resp.raise_for_status()
+        return resp.content
+
+
+def _split_text(text: str, max_chars: int) -> list[str]:
+    """Split text at sentence boundaries for TTS chunking."""
+    if len(text) <= max_chars:
+        return [text]
+
+    chunks: list[str] = []
+    current = ""
+
+    for sentence in _sentence_split(text):
+        if len(current) + len(sentence) > max_chars and current:
+            chunks.append(current.strip())
+            current = sentence
+        else:
+            current += sentence
+
+    if current.strip():
+        chunks.append(current.strip())
+
+    return chunks or [text[:max_chars]]
+
+
+def _sentence_split(text: str) -> list[str]:
+    """Split text into sentences, keeping delimiters attached."""
+    import re
+    parts = re.split(r'(?<=[.!?])\s+', text)
+    # Re-add trailing space for joining
+    return [p + " " for p in parts]