eugenehp/tiny-tts-rlx

TinyTTS / MeloTTS RLX bundle

MeloTTS/VITS2 English nested .rlxp subgraphs + frontend for RLX TinyTTS and MeloTTS. CPU / Metal / MLX / CUDA / wgpu.

Field	Value
Hub id	eugenehp/tiny-tts-rlx
Kind	RLX-native weight bundle (graphs + sidecars ready for rlx-* crates).
RLX crate	rlx-tiny-tts
Upstream	https://github.com/tronghieuit/tiny-tts

Quick start

just fetch-tiny-tts   # or: hf download eugenehp/tiny-tts-rlx tiny-tts.rlxp --local-dir weights/tts/tiny-tts-rlx
just fetch-tiny-tts && cargo run -p rlx-tiny-tts --release --features apple-silicon -- --data weights/tts/tiny-tts-rlx --text "Hi." --device metal --out /tmp/tiny.wav

Primary files (use these)

• tiny-tts.rlxp — 29.4 MiB

Contents

Hub ships tiny-tts.rlxp only (nested graph packs + frontend). Also used by rlx-melotts / just fetch-melotts. Runtime does not load .onnx from Hub.

Pack layout (.rlxp)

Outer RLXPFLAT bundle: nested native subgraph packs under graphs/ (hot weight tensors + graph IR sidecars) plus English frontend assets. No .onnx on Hub. rlx-tiny-tts materializes the outer pack, then lowers each nested pack to HIR for the utterance length.

Official RLX package format (RLXPFLAT, container v2).

[0..8)   magic          RLXPFLAT
[8..12)  version        u32 LE (= 2)
[12..16) flags          u32 LE (hybrid hot/warm/cold)
[16..24) toc_len        u64 LE
[24..)   TOC            JSON table of contents
         data region    64-byte aligned payloads

The TOC lists tensors (named weight blobs) and/or sidecars (files: ONNX, tokenizers, manifests, …). Sidecars are usually cold + zstd; model weights in tensor packs are hot + uncompressed for mmap. Runtime crates open the pack directly (or materialize sidecars to a temp dir for asset-only packs).

This pack

Field	Value
File	tiny-tts.rlxp (29.4 MiB)
Manifest name	tiny-tts
Producer	rlx-assets
Container	RLXPFLAT v2, flags=0x1
Tensors	0
Sidecars	16

Sidecars (file assets)

Outer TOC is file sidecars only. Neural weights live inside each graphs/<name>.rlxp as hot mmap tensors (not safetensors).

Paths below are logical ids inside the pack (__flat__/sidecar/<id>). Cold sidecars are zstd-compressed; sizes show raw → stored.

Sidecar	Raw	Stored	Role
config.json	325 B	220 B	Model / sample-rate config (44.1 kHz)
frontend/bert/tokenizer.json	694.7 KiB	214.5 KiB	BERT WordPiece tokenizer
frontend/bert/tokenizer_config.json	351 B	218 B	Tokenizer config
frontend/cmudict_rep.txt	3.9 MiB	1.0 MiB	CMUdict repair / variants
frontend/g2p_checkpoint.safetensors	3.2 MiB	3.0 MiB	G2P checkpoint
frontend/g2p_cmudict.txt	3.3 MiB	995.6 KiB	CMUdict for G2P
frontend/homographs.en	18.3 KiB	5.0 KiB	Homograph list
frontend/perceptron_tagger.json	5.4 MiB	1.5 MiB	POS tagger
frontend/symbols.json	2.1 KiB	726 B	Symbol table
graphs/decoder.rlxp	13.7 MiB	12.5 MiB	Nested pack: hot tensors + graph.json
graphs/duration_predictor.rlxp	1.0 MiB	598.8 KiB	Nested pack: hot tensors + graph.json
graphs/flow.rlxp	13.7 MiB	8.0 MiB	Nested pack: hot tensors + graph.json
graphs/text_encoder.rlxp	2.9 MiB	1.7 MiB	Nested pack: hot tensors + graph.json

Architecture

Pipeline: text → English frontend (G2P) → text_encoder → duration_predictor → monotonic alignment + latent sample (Rust) → flow → decoder → 44.1 kHz mono WAV.

Module	Role	Typical dtype	Notes
text_encoder	phone/tone/lang → latent	f32	length = phoneme count T
duration_predictor	per-phone duration	f32
flow	prior / flow	f32
decoder	HiFi-GAN-style vocoder	f32	ConvTranspose upsample ×512
frontend	G2P + BERT tokenizer + CMUdict	—	file sidecars

Logical tree

tiny-tts.rlxp
├── graphs/
│   ├── text_encoder.rlxp      # hot f32/i64 + graph.json
│   ├── duration_predictor.rlxp
│   ├── flow.rlxp
│   └── decoder.rlxp
├── config.json
└── frontend/                 # G2P / tokenizer (not neural)

How it is packed

just export-tiny-tts-rlxp / pack_rlxp example: ONNX pack-time source → nested graphs/*.rlxp via rlx-assets native-pack, then outer bundle. Hub artifact has zero .onnx.

Note

MeloTTS (rlx-melotts) loads this same bundle — locally weights/tts/melotts is a symlink.

Note

Hub ships no ONNX. Nested graphs/*.rlxp hold hot f32/i64 tensors + graph.json; the crate lowers to HIR per utterance length.

Run with RLX

Clone rlx-models, place this repo under weights/tts/tiny-tts-rlx (or pass the path explicitly), then:

just fetch-tiny-tts && cargo run -p rlx-tiny-tts --release --features apple-silicon -- --data weights/tts/tiny-tts-rlx --text "Hi." --device metal --out /tmp/tiny.wav

License

MIT — see LICENSE. Inherit upstream terms when redistributing.

Original weights and authorship: https://github.com/tronghieuit/tiny-tts

Maintenance

Cards and LFS attrs are regenerated from the local weights/ tree in rlx-models via python3 scripts/prepare_weights_hf.py.