Instructions to use michael-chan-000/tts-m-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michael-chan-000/tts-m-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="michael-chan-000/tts-m-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("michael-chan-000/tts-m-v2") model = AutoModelForCausalLM.from_pretrained("michael-chan-000/tts-m-v2") - Notebooks
- Google Colab
- Kaggle
| # Vocence chute config (example / mock). Must be in your HF repo as chute_config.yml. | |
| # Image, NodeSelector, and Chute settings. Adapt to your model's dependencies. | |
| Image: | |
| from_base: parachutes/base-python:3.12.9 | |
| run_command: | |
| - pip install torch torchaudio transformers accelerate huggingface_hub pyyaml soundfile snac | |
| set_workdir: /app | |
| NodeSelector: | |
| gpu_count: 1 | |
| min_vram_gb_per_gpu: 16 | |
| include: ["pro_6000"] | |
| exclude: [] | |
| Chute: | |
| tagline: vocence prompttts miner | |
| readme: vocence chute example | |
| shutdown_after_seconds: 86400 | |
| concurrency: 1 | |
| max_instances: 1 | |
| scaling_threshold: 0.5 | |
| tee: true | |