init

tts/piper/train.sh

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+#!/usr/bin/env bash
+# piper student — train FROM SCRATCH on the OmniVoice-generated dataset.
+# run on the workstation (gfx1100, ROCm) AFTER the LLM CPT frees the GPU.
+#
+# prereq: piper-train installed from rhasspy/piper (src/python): torch + pytorch-lightning.
+#         espeak-ng installed. piper/dataset/ built (build_dataset.py) & sanity-checked.
+set -euo pipefail
+cd "$(dirname "$0")/../.."          # → esp32-whisper-fine-tune/
+export HSA_OVERRIDE_GFX_VERSION=11.0.0
+
+DATASET=tts/piper/dataset
+TRAIN=tts/piper/train
+
+# 1. preprocess: text → espeak-ng phonemes → training cache
+python -m piper_train.preprocess \
+  --language ru --input-dir "$DATASET" --output-dir "$TRAIN" \
+  --dataset-format ljspeech --single-speaker --sample-rate 22050
+
+# 2. train from scratch (NO --resume_from_checkpoint), medium = CPU-real-time target
+python -m piper_train \
+  --dataset-dir "$TRAIN" --accelerator gpu --devices 1 \
+  --batch-size 16 --quality medium --precision 32 \
+  --max_epochs 4000 --checkpoint-epochs 100 --validation-split 0.02
+
+# 3. export best checkpoint → ONNX (adjust version_/last.ckpt path to your run)
+CKPT=$(ls -t "$TRAIN"/lightning_logs/version_*/checkpoints/*.ckpt | head -1)
+python -m piper_train.export_onnx "$CKPT" tts/piper/maven.onnx
+cp "$TRAIN"/config.json tts/piper/maven.onnx.json
+echo "[✓] exported → tts/piper/maven.onnx (+ .json). scp both to homesrv."