Text-to-Speech (TTS) Skill

Fully autonomous audio generation pipeline. No user intervention required.

What this skill produces

• MP3 / WAV audio files from any text input
• Multilingual narration (131+ languages via espeak-ng)
• Video-ready voiceovers for product demos and presentations
• Batch narration for automated workflows

Engine Selection Guide

Confirmed Working Engines (ordered by quality)

⚠️ Environment constraint: This agent has no internet access. Use pyttsx3/espeak-ng or Kokoro (offline). For production with internet access, prefer edge-tts or gTTS.

Quick Start

Standard Narration (always works)

import pyttsx3
import subprocess

def generate_tts(text: str, output_mp3: str, lang: str = "en", rate: int = 145, voice_id: str = None):
    """Generate TTS audio. lang = 'en', 'ru', 'de', 'fr', 'es', 'zh', etc."""
    engine = pyttsx3.init()
    engine.setProperty('rate', rate)      # 100–200, 145 = natural
    engine.setProperty('volume', 1.0)
    
    # Select voice by language
    if voice_id:
        engine.setProperty('voice', voice_id)
    else:
        voices = engine.getProperty('voices')
        for v in voices:
            if lang == 'en' and 'en-gb' in v.id.lower():
                engine.setProperty('voice', v.id)
                break
            elif lang != 'en' and lang in v.id.lower() and 'lv' not in v.id:
                engine.setProperty('voice', v.id)
                break
    
    wav_path = output_mp3.replace('.mp3', '.wav')
    engine.save_to_file(text, wav_path)
    engine.runAndWait()
    
    # Convert WAV → MP3 with audio enhancement
    subprocess.run([
        'ffmpeg', '-i', wav_path,
        '-af', 'aresample=44100,equalizer=f=3000:t=o:w=1:g=3,equalizer=f=200:t=o:w=1:g=-2',
        '-c:a', 'libmp3lame', '-b:a', '192k',
        output_mp3, '-y', '-loglevel', 'quiet'
    ], check=True)
    
    return output_mp3

# Example usage
generate_tts("Welcome to our product demo.", "/tmp/narration.mp3", lang="en")
generate_tts("Добро пожаловать в демонстрацию продукта.", "/tmp/narration_ru.mp3", lang="ru")

Installation (run once per session if needed)

pip install pyttsx3 --break-system-packages -q
apt-get install -y espeak-ng -q

# Verify
python3 -c "import pyttsx3; e=pyttsx3.init(); print('OK:', len(e.getProperty('voices')), 'voices')"

Engine Details

Read the appropriate reference file for the engine you're using:

• references/pyttsx3-espeak.md — Primary engine: full API, voice selection, SSML-like control, quality tips
• references/espeak-cli.md — Direct espeak-ng CLI usage, flags, phoneme control
• references/kokoro-onnx.md — High-quality neural TTS (offline, needs model download)
• references/online-engines.md — gTTS, edge-tts, OpenAI TTS (when internet available)

Multi-scene Narration (for videos)

For video narration with multiple scenes, generate per-scene audio then concatenate:

import pyttsx3, subprocess, os

scenes = [
    {"text": "Welcome to our AI-powered platform.", "duration_hint": 3},
    {"text": "Our system automatically detects and fixes issues.", "duration_hint": 5},
    {"text": "Get started today with a free trial.", "duration_hint": 3},
]

def scenes_to_audio(scenes: list, output_path: str, lang: str = "en") -> str:
    """Generate concatenated narration from scene list."""
    wav_files = []
    engine = pyttsx3.init()
    engine.setProperty('rate', 145)
    
    for i, scene in enumerate(scenes):
        wav = f"/tmp/scene_{i}.wav"
        engine.save_to_file(scene["text"], wav)
        engine.runAndWait()
        wav_files.append(wav)
    
    # Build concat list for ffmpeg
    concat_txt = "/tmp/concat_list.txt"
    with open(concat_txt, 'w') as f:
        for wav in wav_files:
            f.write(f"file '{wav}'\n")
    
    subprocess.run([
        'ffmpeg', '-f', 'concat', '-safe', '0', '-i', concat_txt,
        '-c:a', 'libmp3lame', '-b:a', '192k',
        output_path, '-y', '-loglevel', 'quiet'
    ], check=True)
    
    return output_path

scenes_to_audio(scenes, "/tmp/full_narration.mp3")

Language Reference (top languages)

Full list: espeak-ng --voices

FFmpeg Audio Post-processing

# WAV → MP3 (standard)
ffmpeg -i input.wav -c:a libmp3lame -b:a 192k output.mp3 -y

# WAV → MP3 with EQ enhancement (clearer speech)
ffmpeg -i input.wav \
  -af "aresample=44100,equalizer=f=3000:t=o:w=1:g=3,equalizer=f=200:t=o:w=1:g=-2" \
  -c:a libmp3lame -b:a 192k output.mp3 -y

# Adjust speech speed without pitch change (0.85 = slower, 1.15 = faster)
ffmpeg -i input.wav -af "atempo=0.90" output_slow.wav -y

# Add silence padding (0.5s before, 0.5s after)
ffmpeg -i input.wav -af "adelay=500|500,apad=pad_dur=0.5" output_padded.wav -y

# Normalize audio volume
ffmpeg -i input.wav -af "loudnorm=I=-16:TP=-1.5:LRA=11" output_norm.wav -y

Common Pitfalls

Text Pre-processing

Always clean text before TTS to avoid robotic artifacts:

import re

def clean_for_tts(text: str) -> str:
    """Remove markdown and symbols that confuse TTS engines."""
    text = re.sub(r'#{1,6}\s*', '', text)        # headers
    text = re.sub(r'\*{1,2}(.+?)\*{1,2}', r'\1', text)  # bold/italic
    text = re.sub(r'`{1,3}[^`]*`{1,3}', '', text)        # code blocks
    text = re.sub(r'\[(.+?)\]\(.+?\)', r'\1', text)       # links → link text
    text = re.sub(r'[|>]', '', text)              # table/quote chars
    text = re.sub(r'\s+', ' ', text).strip()
    return text

Integration with Screen Recording Skill

When used inside the screen-recording skill, replace the basic pyttsx3 call with this skill's generate_tts() function for better audio quality and language support. The audio pipeline is identical — just swap the TTS step.