Instructions to use spsither/wav2vec2_run1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spsither/wav2vec2_run1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="spsither/wav2vec2_run1")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("spsither/wav2vec2_run1") model = AutoModelForCTC.from_pretrained("spsither/wav2vec2_run1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Wav2Vec2ForCTC
Browse files- config.json +3 -3
- pytorch_model.bin +2 -2
config.json
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{
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"_name_or_path": "
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"activation_dropout": 0.0,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id":
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"proj_codevector_dim": 768,
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"tdnn_dilation": [
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1,
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"torch_dtype": "float32",
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"transformers_version": "4.24.0",
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"use_weighted_layer_sum": false,
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"vocab_size":
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"xvector_output_dim": 512
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}
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{
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"_name_or_path": "wav2vec2_run1/checkpoint-5800",
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"activation_dropout": 0.0,
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"adapter_kernel_size": 3,
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"adapter_stride": 2,
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"num_hidden_layers": 24,
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"num_negatives": 100,
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"output_hidden_size": 1024,
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"pad_token_id": 72,
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"proj_codevector_dim": 768,
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"tdnn_dilation": [
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1,
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"torch_dtype": "float32",
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"transformers_version": "4.24.0",
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"use_weighted_layer_sum": false,
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"vocab_size": 73,
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"xvector_output_dim": 512
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:580d19fa733a222f78f512dd980698ab7868076dfc5cc4a9a12a6ab613657855
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size 1262201133
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