Instructions to use mispeech/dasheng-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mispeech/dasheng-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="mispeech/dasheng-base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mispeech/dasheng-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- config.json +24 -0
- model.safetensors +3 -0
config.json
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{
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"architectures": [
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"DashengModel"
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],
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"encoder_kwargs": {
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"depth": 12,
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"embed_dim": 768,
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"num_heads": 12,
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"patch_size": [
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64,
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4
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],
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"patch_stride": [
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64,
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4
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],
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"target_length": 1008
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},
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"loss": "BCELoss",
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"model_type": "dasheng",
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"name": "dasheng-base",
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"torch_dtype": "float32",
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"transformers_version": "4.35.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:adaa439ebec13933501242364a29b7912c2695d0354061b278c873438b2736c3
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size 341807392
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