Instructions to use LyngualLabs/YorubaEnglish-CodeSwitching-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use LyngualLabs/YorubaEnglish-CodeSwitching-TTS with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("LyngualLabs/YorubaEnglish-CodeSwitching-TTS") wav = model.generate( text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.", prompt_wav_path=None, # optional: path to a prompt speech for voice cloning prompt_text=None, # optional: reference text cfg_value=2.0, # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse inference_timesteps=10, # LocDiT inference timesteps, higher for better result, lower for fast speed normalize=True, # enable external TN tool denoise=True, # enable external Denoise tool retry_badcase=True, # enable retrying mode for some bad cases (unstoppable) retry_badcase_max_times=3, # maximum retrying times retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech ) sf.write("output.wav", wav, 16000) print("saved: output.wav") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebd550957d5a76c515b60b7ded3848117dd8b7ef4bdcefb8da6cbd70a2a861c1
- Size of remote file:
- 2.92 MB
- SHA256:
- 8b1dc0cf06bfb0c79d6a134c39ff6b5cd4e50050bb2bdd403c22dbe5978ea1a5
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