Zen3 Audio
Collection
Speech recognition + text-to-speech. • 7 items • Updated
How to use zenlm/zen3-tts with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-speech", model="zenlm/zen3-tts") # Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("zenlm/zen3-tts", dtype="auto")Zen3 text-to-speech base model. ~1.7B parameters, 12 Hz codec, multilingual synthesis (English and Chinese primary; the codec covers ten languages).
Derived by fine-tuning Qwen/Qwen3-TTS-12Hz-1.7B-Base (Alibaba Cloud, Apache-2.0).
Qwen3TTSForConditionalGeneration (qwen3_tts)Qwen/Qwen3-TTS-12Hz-1.7B-BaseThis 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-Base.
Fine-tuned from Qwen/Qwen3-TTS-12Hz-1.7B-Base (Apache-2.0). See NOTICE for full attribution.
Base model
Qwen/Qwen3-TTS-12Hz-1.7B-Base