Instructions to use Avdpro/MLX-RVC-Serena-E70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Avdpro/MLX-RVC-Serena-E70 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download Avdpro/MLX-RVC-Serena-E70 --local-dir MLX-RVC-Serena-E70
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,161 Bytes
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"schema": "ai2apps.mlx-rvc-checkpoint/v1",
"source": {
"name": "native-mlx-training",
"synthetic": false
},
"weights": {
"name": "model.safetensors",
"sha256": "f48790573f69181a6c484ef42b7c3c53e2e0c9aef20bb9fcdbe1b308f5fcb51b",
"tensor_count": 353,
"parameter_count": 28694338,
"dtype": "float32"
},
"model": {
"spec_channels": 1025,
"segment_size": 36,
"inter_channels": 192,
"hidden_channels": 192,
"filter_channels": 768,
"heads": 2,
"layers": 6,
"kernel_size": 3,
"dropout": 0.0,
"resblock": "1",
"resblock_kernel_sizes": [
3,
7,
11
],
"resblock_dilation_sizes": [
[
1,
3,
5
],
[
1,
3,
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],
[
1,
3,
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]
],
"upsample_rates": [
12,
10,
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],
"upsample_initial_channels": 512,
"upsample_kernel_sizes": [
24,
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4,
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],
"speaker_count": 109,
"speaker_embedding_channels": 256,
"sample_rate": 48000,
"feature_version": "v2",
"uses_f0": true
}
}
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