Instructions to use SPRINGLab/SPRING_F5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SPRINGLab/SPRING_F5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="SPRINGLab/SPRING_F5", trust_remote_code=True)# Load model directly from transformers import SPRING_F5 model = SPRING_F5.from_pretrained("SPRINGLab/SPRING_F5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| services: | |
| tts: | |
| image: soar97/triton-f5-tts:24.12 | |
| shm_size: '1gb' | |
| ports: | |
| - "8000:8000" | |
| - "8001:8001" | |
| - "8002:8002" | |
| environment: | |
| - PYTHONIOENCODING=utf-8 | |
| - MODEL_ID=${MODEL_ID} | |
| deploy: | |
| resources: | |
| reservations: | |
| devices: | |
| - driver: nvidia | |
| device_ids: ['0'] | |
| capabilities: [gpu] | |
| command: > | |
| /bin/bash -c "pip install vocos && rm -rf F5-TTS && git clone https://github.com/SWivid/F5-TTS.git && cd F5-TTS/src/f5_tts/runtime/triton_trtllm/ && bash run.sh 0 4 $MODEL" | |