Instructions to use zenlm/zen3-tts-custom-voice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenlm/zen3-tts-custom-voice with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="zenlm/zen3-tts-custom-voice")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("zenlm/zen3-tts-custom-voice", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Zen3 TTS Custom Voice
Zen3 TTS variant for zero-shot voice cloning from a short reference sample, carrying speaker identity and prosody across long-form synthesis.
Derived by fine-tuning Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice (Alibaba Cloud, Apache-2.0).
- Architecture:
Qwen3TTSForConditionalGeneration(qwen3_tts) - Parameters: ~1.7B
- Base model:
Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
Weights
This repository contains the model weights: model.safetensors (talker) plus a speech_tokenizer/ module (12 Hz codec), config and tokenizer files.
The model uses the qwen3_tts architecture and loads with transformers (>= 4.57). It is API-compatible with the upstream base — follow the inference recipe on the base model card Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice.
Provenance
Fine-tuned from Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice (Apache-2.0). See NOTICE for full attribution.
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Model tree for zenlm/zen3-tts-custom-voice
Base model
Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice