Instructions to use coder543/ming-3.0-tts-tiny-glade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use coder543/ming-3.0-tts-tiny-glade with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download coder543/ming-3.0-tts-tiny-glade --local-dir ming-3.0-tts-tiny-glade
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download speaker/speaker-network.json from coder543/ming-3.0-tts-tiny-glade: direct link, hf CLI and curl.
- Browser
- Download file 295 kB
-
https://huggingface.co/coder543/ming-3.0-tts-tiny-glade/resolve/main/speaker/speaker-network.json
- Command line
-
hf download hf://coder543/ming-3.0-tts-tiny-glade/speaker/speaker-network.json
-
curl -L -o speaker-network.json https://huggingface.co/coder543/ming-3.0-tts-tiny-glade/resolve/main/speaker/speaker-network.json
295 kB
| { | |
| "embeddingDimension": 192, | |
| "featureBins": 80, | |
| "input": "input", | |
| "modelFamily": "ming_3_tts_tiny_speaker", | |
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| "output": "/head/Unsqueeze_output_0" | |
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| "output": "/head/conv1/Conv_output_0", | |
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| "stride": [ | |
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| "weight": "onnx::Conv_4423" | |
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| "/head/conv1/Conv_output_0" | |
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| "output": "/head/Relu_output_0" | |
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| "output": "/head/layer1/layer1.1/Relu_output_0" | |
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| "output": "/head/layer1/layer1.1/conv2/Conv_output_0", | |
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| "output": "/head/layer2/layer2.0/shortcut/shortcut.0/Conv_output_0", | |
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| "/head/layer2/layer2.0/shortcut/shortcut.0/Conv_output_0" | |
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| "kind": "add", | |
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| "output": "/head/layer2/layer2.0/Add_output_0" | |
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| { | |
| "inputs": [ | |
| "/head/layer2/layer2.0/Add_output_0" | |
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| "output": "/head/layer2/layer2.1/Relu_output_0" | |
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| "dilation": [ | |
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| "/head/layer2/layer2.1/Relu_output_0" | |
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| "output": "/head/layer2/layer2.1/conv2/Conv_output_0", | |
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| "/head/layer2/layer2.0/Relu_1_output_0" | |
| ], | |
| "kind": "add", | |
| "name": "/head/layer2/layer2.1/Add", | |
| "output": "/head/layer2/layer2.1/Add_output_0" | |
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| { | |
| "inputs": [ | |
| "/head/layer2/layer2.1/Add_output_0" | |
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| "name": "/head/layer2/layer2.1/Relu_1", | |
| "output": "/head/layer2/layer2.1/Relu_1_output_0" | |
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| "output": "/head/conv2/Conv_output_0", | |
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| "name": "/head/Relu_1", | |
| "output": "/head/Relu_1_output_0" | |
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| { | |
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| "/head/Relu_1_output_0" | |
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| "/xvector/tdnn/linear/Conv_output_0" | |
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| "name": "/xvector/tdnn/nonlinear/relu/Relu", | |
| "output": "/xvector/tdnn/nonlinear/relu/Relu_output_0" | |
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| { | |
| "bias": "xvector.block1.tdnnd1.nonlinear1.batchnorm.bias", | |
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| "name": "/xvector/block1/tdnnd1/nonlinear1/relu/Relu", | |
| "output": "/xvector/block1/tdnnd1/nonlinear1/relu/Relu_output_0" | |
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| "bias": "onnx::Conv_4463", | |
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| "kind": "conv", | |
| "name": "/xvector/block1/tdnnd1/linear1/Conv", | |
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| "name": "/xvector/block1/tdnnd1/nonlinear2/relu/Relu", | |
| "output": "/xvector/block1/tdnnd1/nonlinear2/relu/Relu_output_0" | |
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| "output": "/xvector/block1/tdnnd1/cam_layer/linear_local/Conv_output_0", | |
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| { | |
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| "kind": "contextSummary", | |
| "name": "/xvector/block1/tdnnd1/cam_layer/Add", | |
| "output": "/xvector/block1/tdnnd1/cam_layer/Add_output_0", | |
| "segmentFrames": 100 | |
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| "kind": "relu", | |
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| "output": "/xvector/block1/tdnnd1/cam_layer/relu/Relu_output_0" | |
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| { | |
| "bias": "xvector.block1.tdnnd1.cam_layer.linear2.bias", | |
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| "weight": "xvector.block1.tdnnd1.cam_layer.linear2.weight" | |
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| "kind": "sigmoid", | |
| "name": "/xvector/block1/tdnnd1/cam_layer/sigmoid/Sigmoid", | |
| "output": "/xvector/block1/tdnnd1/cam_layer/sigmoid/Sigmoid_output_0" | |
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| "/xvector/block1/tdnnd1/cam_layer/sigmoid/Sigmoid_output_0" | |
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| "kind": "multiply", | |
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| { | |
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| "/xvector/block1/tdnnd1/cam_layer/Mul_1_output_0" | |
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| "kind": "concatenate", | |
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| "output": "/xvector/block1/Concat_output_0" | |
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| "bias": "xvector.block1.tdnnd2.nonlinear1.batchnorm.bias", | |
| "epsilon": 9.999999747378752e-06, | |
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| "kind": "batchNorm", | |
| "mean": "xvector.block1.tdnnd2.nonlinear1.batchnorm.running_mean", | |
| "name": "/xvector/block1/tdnnd2/nonlinear1/batchnorm/BatchNormalization", | |
| "output": "/xvector/block1/tdnnd2/nonlinear1/batchnorm/BatchNormalization_output_0", | |
| "scale": "xvector.block1.tdnnd2.nonlinear1.batchnorm.weight", | |
| "variance": "xvector.block1.tdnnd2.nonlinear1.batchnorm.running_var" | |
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| "/xvector/block1/tdnnd2/nonlinear1/batchnorm/BatchNormalization_output_0" | |
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| "kind": "relu", | |
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| { | |
| "bias": "onnx::Conv_4466", | |
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| "inputs": [ | |
| "/xvector/block1/tdnnd2/nonlinear1/relu/Relu_output_0" | |
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| "name": "/xvector/block1/tdnnd2/linear1/Conv", | |
| "output": "/xvector/block1/tdnnd2/linear1/Conv_output_0", | |
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