piper-tts-training

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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.

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Sourcesammcj/agentic-coding

Added on2026-02-07

NPX Install

npx skill4agent add sammcj/agentic-coding piper-tts-training

SKILL.md Content

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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.

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

piper-tts-training

NPX Install

Tags

SKILL.md Content

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