Text-to-Speech
Transformers
Safetensors
Qwen3-TTS
English
text-generation
tts
qwen
qwen3
qwen3-tts
voice-design
merged-lora
fine-tuned
audio
Instructions to use handsometiger0202/f-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use handsometiger0202/f-llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="handsometiger0202/f-llm")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("handsometiger0202/f-llm", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: cc-by-nc-sa-4.0
base_model: macminix/qwen3_voice_design_t1
pipeline_tag: text-to-speech
library_name: transformers
language:
- en
tags:
- tts
- qwen
- qwen3
- qwen3-tts
- voice-design
- merged-lora
- fine-tuned
- audio
ckpt-600 — merged
Self-contained snapshot of macminix/qwen3_voice_design_t1
with the ckpt-600 LoRA adapter folded into the Talker weights.
No PEFT layers at runtime — load directly with Qwen3TTSModel.from_pretrained.
Quick start
from qwen_tts import Qwen3TTSModel
wrap = Qwen3TTSModel.from_pretrained("<this-repo-id>")
wavs, sr = wrap.generate_voice_design(
text="Hello, this is a test.",
instruct="A young adult female speaker speaks calmly at a normal pace.",
language="english",
temperature=0.9, top_p=1.0, top_k=50,
repetition_penalty=1.05, max_new_tokens=600,
)
Repository layout
Same shape as the upstream macminix repo:
config.json,model.safetensors— Qwen3-TTS Talker + Code Predictor (merged)speech_tokenizer/— 12.5 fps × 16 codebook neural codec (unchanged)tokenizer.*,vocab.json,merges.txt,added_tokens.json,special_tokens_map.json— Qwen2 BPE tokenizergeneration_config.json,preprocessor_config.jsonvocence_config.yaml,chute_config.yml— runtime + Chutes deploy hints
You can drop your own miner.py into this repo (same contract as macminix's:
class Miner with __init__(path_hf_repo: Path), warmup(),
generate_wav(instruction, text) → (np.ndarray, int)); the standard
Vocence chute wrapper will load this model unchanged.
Provenance
See merge_info.json for the exact base path, adapter
path, LoRA hyperparameters, and merge timestamp.