Instructions to use might2901/train_super_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use might2901/train_super_01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="might2901/train_super_01")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("might2901/train_super_01", dtype="auto") - Notebooks
- Google Colab
- Kaggle
train_super_01
Self-distillation LoRA SFT checkpoint (loop 1) for the Vocence subnet.
- Base:
gold24k/vocence-new-tts - Training data:
eval_train.jsonl(validator-style prompts + miner audio) - Checkpoint: epoch 1 merged weights
Prompt-driven English TTS on Qwen3-TTS-12Hz-1.7B-VoiceDesign. Served via canonical miner.py on Chutes.
from qwen_tts import Qwen3TTSModel
m = Qwen3TTSModel.from_pretrained("might2901/train_super_01")
wavs, sr = m.generate_voice_design(
text="Hello world.",
instruct="A calm adult male with an American accent.",
language="english",
)
CC BY-NC-SA 4.0.
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Model tree for might2901/train_super_01
Base model
Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign