Elevenlabs Tts

ElevenLabs TTS — Voice Clone Audio Generation

Generate production-quality AI voiceovers using ElevenLabs cloned voices with automatic QC, chunking, and crossfade stitching.

When to Use

Use this skill when the user wants to:

• Generate AI voiceover audio from a script
• Create TTS audio using a cloned voice (Ryan Magin, Luis, or any clone)
• Run audio QC on generated speech
• Convert a YAP script or any text into spoken audio
• Produce audio for HeyGen avatar videos

Triggers: "generate audio", "elevenlabs", "TTS", "text to speech", "voiceover", "voice clone audio", "read this in Ryan's voice", "make audio from this script"

API Key

ElevenLabs API key: aba208275bd6b2a39fc033067eda714bb9a40f6be0a9d518eb3c08cac674cc53

Critical Voice Settings (Learned from Production)

These settings were dialed in through multiple production sessions with Ryan Magin's cloned voice. DO NOT deviate unless testing.

VOICE_SETTINGS = {
    "stability": 0.55,        # Balanced — not monotone, not random
    "similarity_boost": 0.75,  # Sweet spot for clones (higher = distortion)
    "style": 0.0,             # MUST BE ZERO for cloned voices
    "use_speaker_boost": True,
}

Why These Settings

Model Selection

ALWAYS use eleven_multilingual_v2 for cloned voices.

Script Preprocessing Rules

Before sending ANY script to the API, apply these transformations:

1. Remove em-dashes (— and –) → replace with commas
2. Remove ALL CAPS → convert to Title Case (caps cause unnatural emphasis artifacts)
3. Write out all numbers → "twelve" not "12", "five figure" not "5-figure"
4. Expand contractions → "do not" instead of "don't", "can not" instead of "can't" (multilingual model misreads contractions as other languages)
5. No special punctuation → no ellipsis (...), no semicolons, no colons mid-sentence
6. Keep sentences short → one idea per sentence for natural pacing

How It Works:

1. Split on sentence boundaries — never mid-sentence
2. Each chunk under 700 characters
3. Context seeding — chunks 2+ include the last sentence of the previous chunk as a lead-in so the model carries the same tone/energy forward
4. STT timestamp trimming — transcribe the context-seeded chunk, find where the new content starts using word-level timestamps, trim the lead-in
5. Crossfade stitch — 150ms triangular crossfade between chunks (NOT silence gaps — those create audible pauses)
6. Tail trim — cut last 0.4s from final chunk to remove end-of-generation artifacts

Why Context Seeding

Without it, each chunk generates independently and can drift in tone/energy. By prepending the last sentence of the previous chunk, the model "warms up" on the established tone before hitting the new content.

Complete Workflow

Step 1: Preprocess the Script

Apply preprocessing rules above. Remove caps, em-dashes, expand contractions, write out numbers.

Step 2: Generate Audio

# Short script (under 700 chars) — single pass
python3 ~/Desktop/Vision-Agents/audio_qc.py \
  --script script.txt \
  --elevenlabs-key "aba208275bd6b2a39fc033067eda714bb9a40f6be0a9d518eb3c08cac674cc53" \
  --voice-id "FaqEcpH1etobBPMBMfJg" \
  --output-dir ./output \
  --threshold 0.92

# Long script — auto-chunks, context-seeds, crossfade stitches
# (same command — chunking is automatic based on length)

Step 3: QC Loop (Automatic)

The script automatically:

1. Generates TTS audio
2. Transcribes it back via ElevenLabs STT (scribe_v1)
3. Diffs transcript against original script
4. If similarity < 92%, bumps stability +0.05 and retries (max 3 attempts)
5. Reports all word-level issues found

Step 4: Manual Listen Check

Even with 92%+ similarity, ALWAYS listen to the final audio. The QC catches word accuracy but NOT:

• Unnatural pacing
• Weird tonal shifts between chunks
• Robotic delivery
• Trailing artifacts

Step 5 (Optional): HeyGen Video

python3 ~/Desktop/Vision-Agents/audio_qc.py \
  --script script.txt \
  --elevenlabs-key "API_KEY" \
  --heygen \
  --heygen-key "sk_V2_hgu_ktgAobLdFV4_R7sCHf1JE0DJYsG8UUp6YmPtHVs56nsC" \
  --avatar-id "f1edc6880c3e49b0835f975b7f8d26f1" \
  --output-dir ./output

HeyGen Watermark Note: Watermark removal via API requires Enterprise plan. For watermark-free videos on Creator plan, upload the audio manually through the HeyGen web UI and toggle watermark off there.

Production Learnings (Clone Content)

Stability Sweet Spot by Content Type

Key Learning (2026-02-03): When generating Ryan Magin clone content (AI clone responding to comments/haters), stability 0.65 with similarity 0.75 produced the best "soul" — punchy, confident, grounded. Stability 0.45 went too loose and lost the conviction. The script matters as much as the settings — short punchy sentences ("You are telling on yourself right now." / "You are good. Go away.") give the model room to breathe and hit.

Tail Trim Fix

Default pipeline trims 0.4s from the end of the final chunk to remove generation artifacts. This can cut off the last word. Fix: Restitch manually with only 0.1s trim:

# Get raw chunks from best attempt, trim context seed from chunk 2,
# only 0.1s off the very end, crossfade stitch at 150ms
ffmpeg -y -i chunk0.wav -i chunk1_trimmed.wav \
  -filter_complex "[0][1]acrossfade=d=0.15:c1=tri:c2=tri" \
  -b:a 192k output.mp3

QC Score False Alarms

The QC similarity score will report low (10-55%) when the script uses written-out numbers ("twenty five") because STT transcribes them back as digits ("25"). Also "CRI" transcribes as "cry" and "can not" as "cannot." These are transcription interpretation differences, NOT mispronunciations. Listen to the audio — if the words sound right, the score is misleading.

Files

• QC Script: ~/Desktop/Vision-Agents/audio_qc.py
• Viral Scripts DB: ~/.claude/skills/yap-generator/references/viral-scripts-database.md

Asset Registry (Backups on Backups)

Every asset lives in ALL FOUR locations. When updating, update ALL of them.

Notion API Token

ntn_X62823410449D6uWLOLVFYAyo0pQPnxzGGFt59gWUPk8AI

Key IDs

Full Specification

Complete details, decision trees, protocols, and implementation specs: references/full-details.md