Instructions to use build-small-hackathon/baby-mynah-welsh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- VoxCPM
How to use build-small-hackathon/baby-mynah-welsh with VoxCPM:
import soundfile as sf from voxcpm import VoxCPM model = VoxCPM.from_pretrained("build-small-hackathon/baby-mynah-welsh") 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
Upload lora_config.json with huggingface_hub
Browse files- lora_config.json +28 -0
lora_config.json
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{
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"base_model": "C:/portfolio/foo/models/VoxCPM-0.5B",
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"lora_config": {
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"enable_lm": true,
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"enable_dit": true,
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"enable_proj": false,
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"r": 32,
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"alpha": 32,
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"dropout": 0.0,
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"target_modules_lm": [
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"q_proj",
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"v_proj",
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"k_proj",
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"o_proj"
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],
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"target_modules_dit": [
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"q_proj",
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"v_proj",
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"k_proj",
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"o_proj"
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],
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"target_proj_modules": [
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"enc_to_lm_proj",
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"lm_to_dit_proj",
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"res_to_dit_proj"
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]
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}
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}
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