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eugenehp/
rlx-tts

Name: rlx-tts
Availability: InStock

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RLX TTS (FastSpeech2 + WaveRNN)

Single-file RLX FastSpeech2 + WaveRNN TTS as rlx-tts.rlxp.

Field	Value
Hub id	`eugenehp/rlx-tts`
Kind	RLX-native weight bundle (graphs + sidecars ready for `rlx-*` crates).
RLX crate	`rlx-tts`

Quick start

bash

just fetch-rlx-tts   # or: hf download eugenehp/rlx-tts rlx-tts.rlxp --local-dir weights/tts/rlx-tts
just fetch-rlx-tts && just tts-demo

Primary files (use these)

rlx-tts.rlxp — 160.0 MiB

Hub ships rlx-tts.rlxp only. Pack locally with just export-rlx-tts-rlxp.

Pack layout (`.rlxp`)

Tensor + frontend pack: FastSpeech2 encoder/decoder and WaveRNN weights in the hot region; G2P / lexicon / neural frontend files as cold sidecars.

Official RLX package format (RLXPFLAT, container v2).

text

[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	`rlx-tts.rlxp` (160.0 MiB)
Manifest name	`rlx-tts`
Producer	`rlx-tts`
Container	RLXPFLAT v2, flags=0x1
Tensors	281
Sidecars	15

Tensors (hot weight region)

All acoustic weights are f32 row-major, hot (uncompressed).

All tensors are mmap'd from the hot region (schemes: f32×281).

Prefix	Tensors	Stored	Role
`encoder.*`	198	111.3 MiB	FastSpeech2 encoder + variance adaptor
`decoder.*`	55	18.5 MiB	FastSpeech2 mel decoder
`wavernn.*`	28	7.1 MiB	WaveRNN vocoder

Sidecars (file assets)

Text-normalization / G2P assets used before the acoustic model.

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

Sidecar	Raw	Stored	Role
`frontend/g2p_post_rule.dat`	2.8 KiB	416 B	G2P post rules
`frontend/rewrite_rule.dat`	7.0 MiB	472.1 KiB	Rewrite transducer rules
`frontend/g2p_seq2seq.safetensors`	28.9 MiB	22.1 MiB	Seq2seq G2P checkpoint
`frontend/g2p_lhp_rule.dat`	8.5 KiB	989 B	G2P LHP rules
`frontend/tn_prefix_rule.dat`	913.3 KiB	72.7 KiB	Text-norm prefix rules
`manifest.json`	3.7 KiB	1.4 KiB	Bundle manifest (sample rate, paths, versions)
`neural_fe_config.json`	3.6 KiB	1.2 KiB	Neural frontend config
`post.cfg`	8.3 KiB	1.7 KiB	Post-filter / WaveRNN post config
`symmap.json`	1.1 KiB	390 B	Symbol map
`frontend/gprm_index.json`	119 B	95 B	GPRM index
`frontend/rewrite_map.json`	102 B	87 B	Rewrite map
`frontend/nashville_isym_phones.json`	273.5 KiB	37.0 KiB	Phone inventory
`frontend/phonetic/to_lhp.json`	1.1 KiB	360 B	Phone → LHP map
`lexicon.txt`	200 B	146 B	Lexicon overrides
`frontend/g2p_bpe.json`	1.6 MiB	347.4 KiB	G2P BPE vocab

Architecture

Pipeline: text → G2P frontend → FastSpeech2 encoder → mel decoder → WaveRNN → WAV.

Prefix	Role	dtype
`encoder.*`	FastSpeech2 + variance adaptor	f32
`decoder.*`	mel decoder	f32
`wavernn.*`	vocoder	f32

Logical tree

text

rlx-tts.rlxp
├── tensors/
│   ├── encoder.*     # FastSpeech2
│   ├── decoder.*
│   └── wavernn.*     # vocoder
└── sidecars/
    ├── manifest.json / neural_fe_config.json / post.cfg / symmap.json
    ├── lexicon.txt
    └── frontend/     # G2P + TN rules + safetensors

How it is packed

just export-rlx-tts-rlxp → rlx-tts --pack-rlxp. Converts local rlx-tts.gguf or packs encoder/decoder/wavernn safetensors + frontend files from the bundle dir.

Note

Hub ships .rlxp only. A local legacy rlx-tts.gguf still loads if present.

Run with RLX

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

just fetch-rlx-tts && just tts-demo

License

Apache License 2.0 — see LICENSE. Inherit upstream terms when redistributing.

Maintenance

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

eugenehp/rlx-tts