Google tts

Skill sanjay3290/ai-skills/skills/google-tts

Convert documents and text to audio using Google Cloud Text-to-Speech. Use this skill when the user wants to: narrate a document, read aloud text, generate audio from a file, convert text to speech, create a recording of documentation or analysis, create a podcast from a document, or use Google TTS/text-to-speech. Trigger phrases: "read this aloud", "narrate this", "create a recording", "text to speech", "TTS", "convert to audio", "audio from document", "listen to this", "generate audio", "google tts", "create a podcast".From its SKILL.md

Install

npx -y skills add sanjay3290/ai-skills --skill google-tts

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

reads credentialsReads from 2 credential sources: `GOOGLE_TTS_API_KEY` and 1 more.
runs commandsInstructs the agent to run 8 commands, including `python skills/google-tts/scripts/google_tts.py voices --language en-US --type Neural2` and 7 more.

SKILL.md

3.4 KB, 798 tokens by cl100k_base, as published. Nobody here has run it

Google Cloud Text-to-Speech

Converts text and documents into audio using Google Cloud TTS API. Supports Neural2, WaveNet, Studio, and Standard voices across 40+ languages.

Setup

API key via GOOGLE_TTS_API_KEY env var or skills/google-tts/config.json with {"api_key": "..."}. Requires ffmpeg for multi-chunk documents. Optional: pip install PyPDF2 python-docx for PDF/DOCX.

Commands

List Voices

python skills/google-tts/scripts/google_tts.py voices --language en-US --type Neural2
python skills/google-tts/scripts/google_tts.py voices --json

Text-to-Speech

# From text or document (PDF, DOCX, MD, TXT)
python skills/google-tts/scripts/google_tts.py tts --text "Hello world" --output ~/Downloads/hello.mp3
python skills/google-tts/scripts/google_tts.py tts --file /path/to/doc.pdf --output ~/Downloads/narration.mp3

# With voice, rate, pitch, encoding options
python skills/google-tts/scripts/google_tts.py tts --file doc.md --voice en-US-Neural2-F --rate 0.9 --encoding MP3 --output ~/Downloads/out.mp3

Podcast Generation

Takes a JSON script with alternating speakers, synthesizes each with a different voice.

[
  {"speaker": "host1", "text": "Welcome to our podcast!"},
  {"speaker": "host2", "text": "Thanks for having me..."}
]

python skills/google-tts/scripts/google_tts.py podcast --script /tmp/script.json --output ~/Downloads/podcast.mp3
python skills/google-tts/scripts/google_tts.py podcast --script /tmp/script.json --voice1 en-US-Neural2-J --voice2 en-US-Neural2-H --rate 0.9 --output ~/Downloads/podcast.mp3

Workflow

Single-Voice Narration

If user provides a file path, use --file. For generated content, write clean prose to /tmp/tts_input.md first.
Default voice: en-US-Neural2-D (male) or en-US-Neural2-F (female). Use Neural2 for best quality/cost balance.
Generate: python skills/google-tts/scripts/google_tts.py tts --file /tmp/tts_input.md --output ~/Downloads/recording.mp3
Report file location and size. Default output to ~/Downloads/.

Podcast from Document

Extract text: python skills/google-tts/scripts/extract.py /path/to/document.pdf
Generate a two-host conversation script as JSON:
- Natural discussion, not verbatim reading. Host 1 leads, Host 2 reacts/analyzes.
- Include intro and outro. Vary turn lengths. Keep turns under 4000 chars.
Write script to /tmp/podcast_script.json
Generate: python skills/google-tts/scripts/google_tts.py podcast --script /tmp/podcast_script.json --output ~/Downloads/podcast.mp3
Clean up temp files.

Reference

Recommended voice type: Neural2 (~$4/1M chars, high quality)
Speaking rate: 0.25-4.0 (0.85-0.95 good for technical content)
Pitch: -20.0 to 20.0 semitones
Encodings: MP3 (default), LINEAR16 (.wav), OGG_OPUS (.ogg)
API limit: 5000 bytes/request. Script auto-chunks at sentence boundaries.

What ships with it: 3 files

26.5 KB alongside SKILL.md, 2 of them executable

scripts/

extract.pyruns4.8 KB
google_tts.pyruns21.6 KB

.gitignore31 B

Gives 0 of the 12 instructions most docs writing skills give in 798 tokens

Counted across 1,951 of the 3,904 authors here whose files we hold, read 2026-09-06

Use third-person for skill descriptionsin 54 of 1951, across 35 files
Start descriptions with Use whenin 43 of 1951, across 29 files
Run baseline scenarios before writing any skillin 40 of 1951, across 26 files
Use active voicein 40 of 1951, across 36 files
Map file responsibilities before defining tasksin 36 of 1951, across 29 files
Use checkbox syntax for tracking stepsin 35 of 1951, across 27 files
Ask one question at a timein 35 of 1951
Offer execution options after saving the planin 33 of 1951, across 24 files
Include complete code in every stepin 33 of 1951, across 27 files
Design units with clear boundaries and interfacesin 31 of 1951, across 23 files
Announce the skill usage at the startin 30 of 1951
Verify agent compliance after adding the skillin 29 of 1951, across 17 files

Said here and by no other author read

use Neural2 voices for quality
use file flag for document paths
extract text from documents before podcasting
generate two host conversation script as JSON

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.