Agent skill

Piper Tts Training

Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Piper Tts Training

skills CLI

$ npx skills add sammcj/agentic-coding --skill piper-tts-training -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install sammcj/agentic-coding piper-tts-training --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Skills_disabled/piper-tts-training .claude/skills/piper-tts-training && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "piper-tts-training" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training into .claude/skills/piper-tts-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "piper-tts-training", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add sammcj/agentic-coding --skill piper-tts-training -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install sammcj/agentic-coding piper-tts-training --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Skills_disabled/piper-tts-training .agents/skills/piper-tts-training && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "piper-tts-training" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training into .agents/skills/piper-tts-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "piper-tts-training", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add sammcj/agentic-coding --skill piper-tts-training -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install sammcj/agentic-coding piper-tts-training --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Skills_disabled/piper-tts-training .cursor/skills/piper-tts-training && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "piper-tts-training" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training into .cursor/skills/piper-tts-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "piper-tts-training", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/sammcj/agentic-coding.git --path Skills_disabled/piper-tts-training

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add sammcj/agentic-coding --skill piper-tts-training -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install sammcj/agentic-coding piper-tts-training --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Skills_disabled/piper-tts-training .gemini/skills/piper-tts-training && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "piper-tts-training" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training into .gemini/skills/piper-tts-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "piper-tts-training", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install sammcj/agentic-coding piper-tts-training

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add sammcj/agentic-coding --skill piper-tts-training -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .github/skills && cp -r skills-src/Skills_disabled/piper-tts-training .github/skills/piper-tts-training && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "piper-tts-training" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training into .github/skills/piper-tts-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "piper-tts-training", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add sammcj/agentic-coding --skill piper-tts-training -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install sammcj/agentic-coding piper-tts-training --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/sammcj/agentic-coding.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Skills_disabled/piper-tts-training .opencode/skills/piper-tts-training && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "piper-tts-training" agent skill from https://github.com/sammcj/agentic-coding/tree/main/Skills_disabled/piper-tts-training into .opencode/skills/piper-tts-training/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "piper-tts-training", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: piper-tts-training
GitHub stars: 162
Token cost: ~1.4k tokens
SKILL.md length: 461 words
Files: 4 (incl. scripts, references)
Skills in repo: 64
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches.

Works in 7 steps: Corpus Preparation → Audio Generation → Quality Validation with Whisper → …
Creating new synthetic voices
SKILL.md covers Overview, Workflow, Localisation for Australian,… and Dependencies, plus 2 more sections
Runs Python scripts from its folder; calls python3

What it does

Piper Tts Training is an agent skill from sammcj/agentic-coding. Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/american_spellings.json`, `references/localisation.md` and `scripts/convert_spelling.py`).

It sits in AI & LLM Engineering, covering Text to speech and voice, Fine-tuning and Speech recognition and synthesis. It works with ONNX and Home Assistant. The repository describes itself as: Agentic Coding Rules, Templates etc... The licence is Apache-2.0.

When your agent uses it

Creating new synthetic voices
Fine-tuning existing Piper checkpoints
Preparing audio datasets for TTS training
Deploying voice models to devices like Raspberry Pi

Example prompts

“/piper-tts-training”

Requirements

Python 3
Docker

Workflow steps

7 steps, taken from the step headings in SKILL.md.

Corpus Preparation
Audio Generation
Quality Validation with Whisper
Dataset Format (LJSpeech)
Preprocessing
Training
ONNX Export

What it can do on your machine

Read from SKILL.md and the folder at commit 2f25ced. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Piper Tts Training loads about 1.4k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 461 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~101
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~16k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from sammcj/agentic-coding at commit 2f25ced, republished under its Apache-2.0 licence (© sammcj). 461 words, ~1,449 tokens.

Download SKILL.mdSave it as .claude/skills/piper-tts-training/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: piper-tts-training
description: Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.

Piper TTS Voice Training

Train custom text-to-speech voices compatible with Piper's lightweight ONNX runtime.

Overview

Piper produces fast, offline TTS suitable for embedded devices. Training involves:

Corpus preparation (text covering phonetic range)
Audio generation or recording
Quality validation via Whisper transcription
Fine-tuning from existing checkpoint (recommended) or training from scratch
ONNX export for deployment

Fine-tuning vs from-scratch:

Fine-tuning: ~1,300 phrases + 1,000 epochs (days on modest GPU)
From scratch: ~13,000+ phrases + 2,000+ epochs (weeks/months)

Workflow

1. Corpus Preparation

Gather 1,300-1,500+ phrases covering broad phonetic range:

Use piper-recording-studio corpus as base
Add domain-specific phrases for your use case
Include varied sentence structures and lengths

Critical for non-US English: Ensure corpus uses correct regional spelling. See Localisation.

2. Audio Generation

Generate or record training audio at 22050Hz mono WAV.

If using voice cloning (e.g., Chatterbox TTS):

Generate at source sample rate (often 24kHz)
Convert to 22050Hz: sox -v 0.95 input.wav -r 22050 -t wav output.wav
The -v 0.95 prevents clipping during resampling

Recording requirements:

Consistent microphone position and room acoustics
Minimal background noise
Natural speaking pace (not reading voice)

3. Quality Validation with Whisper

Automate quality checks rather than manual listening:

python

import whisper
from piper_phonemize import phonemize_text

model = whisper.load_model("base")

def validate_sample(audio_path, expected_text):
    result = model.transcribe(audio_path)
    transcribed = result["text"].strip()

    # Compare phonemically to handle spelling/punctuation differences
    expected_phonemes = phonemize_text(expected_text, "en-gb")
    transcribed_phonemes = phonemize_text(transcribed, "en-gb")

    return expected_phonemes == transcribed_phonemes

Retry failed samples up to 3 times. Target 95%+ dataset coverage.

4. Dataset Format (LJSpeech)

Structure your dataset:

dataset/
├── metadata.csv
└── wavs/
    ├── sample_0001.wav
    ├── sample_0002.wav
    └── ...

metadata.csv format: {id}|{text} (pipe-separated, no headers)

sample_0001|The quick brown fox jumps over the lazy dog.
sample_0002|Pack my box with five dozen liquor jugs.

5. Preprocessing

Convert to PyTorch tensors:

bash

python3 -m piper_train.preprocess \
    --language en-gb \
    --input-dir dataset/ \
    --output-dir piper_training_dir/ \
    --dataset-format ljspeech

Use en-gb for Australian/NZ/UK voices (espeak-ng phoneme set).

6. Training

Fine-tuning (recommended):

bash

python3 -m piper_train \
    --dataset-dir piper_training_dir/ \
    --accelerator gpu \
    --devices 1 \
    --batch-size 12 \
    --max_epochs 3000 \
    --resume_from_checkpoint ljspeech-2000.ckpt \
    --checkpoint-epochs 100 \
    --quality high \
    --precision 32

Key parameters:

--batch-size: Reduce if VRAM limited (12 works on 8GB)
--resume_from_checkpoint: Start from LJSpeech high-quality checkpoint
--precision 32: More stable than mixed precision
--validation-split 0.0 --num-test-examples 0: Skip validation for small datasets

Monitor with TensorBoard: watch loss_disc_all for convergence.

Show full SKILL.md (186 more words)Show less

7. ONNX Export

bash

python3 -m piper_train.export_onnx checkpoint.ckpt output.onnx.unoptimized
onnxsim output.onnx.unoptimized output.onnx

Create metadata file output.onnx.json from training config.json.

Localisation for Australian, New Zealand and UK English

Piper uses espeak-ng for phonemisation. American pronunciations in training data cause accent drift.

Corpus preparation:

Run scripts/convert_spelling.py on corpus text before training
Use en-gb or en-au espeak-ng voice for phonemisation
Review generated phonemes for Americanisms

Common spelling conversions:

American	Australian/UK
-ize	-ise
-or	-our
-er	-re
-og	-ogue
-ense	-ence

Phoneme considerations:

/r/ linking and intrusion patterns differ
Vowel sounds in words like "dance", "bath", "castle"
Final -ile pronunciation (hostile, missile)

For complete word lists and phonetic details, see references/localisation.md.

Validation: Use Whisper with language="en" and verify transcriptions match expected regional forms.

Dependencies

Pin versions to avoid API breakage:

pytorch-lightning==1.9.3
torch<2.6.0
piper-phonemize
onnxruntime-gpu
onnxsim

Docker containerisation recommended for reproducibility.

Hardware Requirements

Minimum (fine-tuning):

8GB VRAM GPU (Pascal or newer)
8GB system RAM
~5 days for 1,000 epochs on Tesla P4

From scratch: Multiply time by ~200x.

Troubleshooting

Issue	Solution
CUDA OOM	Reduce batch-size (try 8 or 4)
Checkpoint won't load	Check pytorch-lightning version matches checkpoint
Garbled output	Insufficient training epochs or dataset too small
Wrong accent	Check espeak-ng language code and corpus spelling

Files

SKILL.md and 3 other files (scripts, references) in Skills_disabled/piper-tts-training of sammcj/agentic-coding.

SKILL.md
references/american_spellings.json
references/localisation.md
scripts/convert_spelling.py

Open the folder on GitHubat commit 2f25ced

Compare with similar skills

Piper Tts Training next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Piper Tts Training compared with similar skills
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Xybrid Initxybrid-ai/xybrid	469	—	~3k	Automated safety check: Pass	Apache-2.0	yesterday
Agentstadaspetra/loop	296	1 repos	~2.5k	Automated safety check: Pass	MIT	8 days ago
Agentselevenlabs/skills	482	—	~6.5k	Automated safety check: Pass	MIT	yesterday
Parakeet Sttsundial-org/awesome-openclaw-skills	663	—	~771	Automated safety check: Pass	None	7 mo ago

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Questions about Piper Tts Training

What does Piper Tts Training do?

Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Piper Tts Training is an agent skill from sammcj/agentic-coding. Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches.

When should I use Piper Tts Training?

Piper Tts Training fits situations like: creating new synthetic voices; fine-tuning existing Piper checkpoints; preparing audio datasets for TTS training; deploying voice models to devices like Raspberry Pi.

How do I install Piper Tts Training in Claude Code?

Run `npx skills add sammcj/agentic-coding --skill piper-tts-training -a claude-code`. Or copy the skill folder (Skills_disabled/piper-tts-training in sammcj/agentic-coding) into .claude/skills/piper-tts-training in your project. Claude Code loads it when a task matches its description.

How do I install Piper Tts Training in Codex?

Run `npx skills add sammcj/agentic-coding --skill piper-tts-training -a codex`. Or copy the skill folder (Skills_disabled/piper-tts-training in sammcj/agentic-coding) into .agents/skills/piper-tts-training in your project. Codex loads it when a task matches its description.

Can I use Piper Tts Training in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sammcj/agentic-coding --skill piper-tts-training -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/piper-tts-training, .gemini/skills/piper-tts-training, .github/skills/piper-tts-training and .opencode/skills/piper-tts-training in your project.

What does Piper Tts Training need to run?

Going by SKILL.md and its folder, Piper Tts Training needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3; Docker.

Does Piper Tts Training access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Piper Tts Training safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Piper Tts Training use?

Piper Tts Training is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Piper Tts Training use?

About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 15k tokens, read only when the agent opens those files.

What are the alternatives to Piper Tts Training?

Skills that share tags, products or a category with Piper Tts Training: Lilly Community Research (ssaaffaakk/Lilly, 171 stars), Xybrid Init (xybrid-ai/xybrid, 469 stars), Agents (tadaspetra/loop, 296 stars) and Agents (elevenlabs/skills, 482 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Piper Tts Training?

sammcj (a GitHub user) maintains it in sammcj/agentic-coding, which has 162 GitHub stars. The repository holds 64 skills in this directory. The repository was last updated on October 9, 2026.

Source: sammcj/agentic-coding on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Piper Tts Training

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Piper TTS Voice Training

Overview

Workflow

1. Corpus Preparation

2. Audio Generation

3. Quality Validation with Whisper

4. Dataset Format (LJSpeech)

5. Preprocessing

6. Training

7. ONNX Export

Localisation for Australian, New Zealand and UK English

Dependencies

Hardware Requirements

Troubleshooting

Files

Compare with similar skills

Similar skills

More from sammcj/agentic-coding

Related

Questions about Piper Tts Training