Text-to-Speech
Transformers
Safetensors
English
llama
text-generation
tts
voice-synthesis
emotional-speech
fine-tuned
advanced-training
vocence
qwen3-tts
text-generation-inference
Instructions to use shiningstar1128/llm-trainer-v02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shiningstar1128/llm-trainer-v02 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="shiningstar1128/llm-trainer-v02")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shiningstar1128/llm-trainer-v02") model = AutoModelForCausalLM.from_pretrained("shiningstar1128/llm-trainer-v02") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3273ed58228a90a2915161deef43ad19090956e8c44c2d2ee359a78eb4807056
- Size of remote file:
- 22.9 MB
- SHA256:
- 6c5e5b1d89b7e3738e5a5a4f93c326d8f3292ea83f9c560b8dbb6d66fb851973
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