Instructions to use zwaiwng/maneko with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Pocket-TTS
How to use zwaiwng/maneko with Pocket-TTS:
from pocket_tts import TTSModel import scipy.io.wavfile tts_model = TTSModel.load_model("zwaiwng/maneko") voice_state = tts_model.get_state_for_audio_prompt( "hf://kyutai/tts-voices/alba-mackenna/casual.wav" ) audio = tts_model.generate_audio(voice_state, "Hello world, this is a test.") # Audio is a 1D torch tensor containing PCM data. scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy()) - Notebooks
- Google Colab
- Kaggle
maneko β quantized TTS weights
Ready-to-run quantized weights for maneko, a Rust/Candle TTS engine, so you don't have to quantize locally. Two model families, each self-contained.
pocket-tts/ β pocket-tts v2, Q8_0
Q8_0 quantizations of the pocket-tts v2 per-language models (a derivative of
kyutai/pocket-tts). One GGUF per language:
pocket-tts/languages/<lang>/model.q8.gguf
english english_2026-01 english_2026-04
german german_24l french_24l italian italian_24l
portuguese portuguese_24l spanish spanish_24l
- What's quantized: Linear weights β
Q8_0; conv / norm / embedding / bias keptF16(the source dtype, so dequantβf32 is numerically identical). ~1.6β1.8Γ smaller than the f16 source (6-layer ~121β137 MB, 24-layer_24l~351 MB). - Quality:
Q8_0is transparent β word-for-word identical to f32 on Whisper round-trips where decoding is deterministic, and indistinguishable native speech at normal temperature. - Voices: the upstream per-language built-in voices work (the engine imports their precomputed
model_state). Native voices:juergen(de),lola(es),estelle(fr),giovanni(it),rafael(pt); the rest are English.
Usage (maneko)
let model = pocket::TTSModel::load_gguf("german_24l", "pocket-tts/languages/german_24l/model.q8.gguf")?;
irodori-tts/ β Irodori v3 (Japanese, 48 kHz)
A self-contained bundle for the Irodori engine β q8 DiT + f16 codec + tokenizer, so maneko needs no third-party repo at runtime:
irodori-tts/
model.q8.gguf # v3 DiT β Q8_0 Linear weights (F16 elsewhere), ~610 MB
dacvae.f16.safetensors # Semantic-DACVAE codec, f16, ~215 MB
tokenizer.json # llm-jp tokenizer (unmodified)
- DiT β
Q8_0quantization of the Irodori v3 DiT (derivative ofAratako/Irodori-TTS-500M-v3), ~4Γ smaller than f32. v3's integrated duration predictor auto-lengths each clip (no manualseconds). - DACVAE β f16 of the Semantic-DACVAE-Japanese codec
(
Aratako/Semantic-DACVAE-Japanese-32dim), half the f32 size. maneko runs it f16 on Metal, f32 on CPU. - Quality: q8 DiT + f16 codec are transparent on a Whisper round-trip vs the f32 originals.
Usage (maneko)
let iro = irodori::Irodori::from_hf(&device)?; // pulls this irodori-tts/ bundle
License & attribution
Derivatives of permissively-licensed upstream models, redistributed with attribution:
pocket-tts/β quantized derivative ofkyutai/pocket-tts, CC-BY-4.0 (released under the same).irodori-tts/β derivatives ofAratako/Irodori-TTS-500M-v3andAratako/Semantic-DACVAE-Japanese-32dim(MIT), plus the unmodifiedllm-jp/llm-jp-3-150mtokenizer (Apache-2.0).
Each GGUF also carries general.{license,source.url,description} metadata.
Acceptable use: the upstream policies carry over β do not use these models for non-consensual voice cloning or impersonation.
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We're not able to determine the quantization variants.
Model tree for zwaiwng/maneko
Base model
Aratako/Irodori-TTS-500M-v2