README.md

metadata

license: apache-2.0
base_model: FireRedTeam/FireRedTTS3
pipeline_tag: text-to-speech
library_name: comfyui
tags:
  - tts
  - text-to-speech
  - voice-cloning
  - voice-design
  - speech-editing
  - fireredtts
  - qwen3
  - comfyui-custom-node
  - bf16
language:
  - zh
  - en
  - yue
  - ja
  - ko
  - es
  - fr
  - ru
  - ar
  - tr
  - id
  - pt
  - it
  - nl
  - vi
  - de
  - uk
  - th
  - pl
  - ro
  - el
  - cs
  - fi
  - hi

FireRedTTS3-bf16 (community mixed-precision mirror)

Half-size copy of FireRedTeam/FireRedTTS3 for FireRedTTS3-ComfyUI.

Conversion policy - only the tensors the official inference code already computes under bf16 autocast are stored in bf16; everything that officially runs in fp32 stays fp32:

Component	bf16	fp32
fireredtts3_base	Qwen3 backbone LLM	PatchEncoder, DiT flow head, stop head, speaker projections
fireredtts3_instruct	Qwen3 backbone LLM	PatchEncoder, DiT flow head, stop head
redae	audio encoder	decoder + ISTFT head
campp	-	everything

Same-seed generation against the official fp32 weights produces identical waveforms (cosine 1.0000, SNR > 80 dB) because the stored bf16 values round exactly like the runtime autocast cast does. campp/, text_tokenizer/, and fasttext/lid.176.ftz are copied through unchanged; only the dtype field in each config.json changed.

Usage Disclaimer

• The project incorporates zero-shot voice cloning functionality; Please note that this capability is intended solely for academic research purposes.
• DO NOT use this model for ANY illegal activities❗️❗️
• The developers assume no liability for any misuse of this model.
• If you identify any instances of abuse, misuse, or fraudulent activities related to this project, please report them to our team immediately.

Citation

@article{fireredtts3,
  title   = {FireRedTTS3: Unified Speech Generation and Editing with Semantically Enriched Speech Representations},
  author  = {FireRed Team},
  journal = {arXiv preprint},
  year    = {2026},
}

Acknowledgements

• Qwen3 and Qwen2-Audio for the language model and audio understanding foundations
• DiTAR for the patch-level diffusion autoregressive formulation
• X-Codec for the discriminator design used in RedAE training
• CAM++ for speaker embedding extraction
• fastText for automatic language identification
• WeTextProcessing (wetext) for the Chinese / English text normalization front-end

All credit to the FireRed Team - see the upstream repo and model card. Apache-2.0.