Instructions to use Drigoro/audio_wav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drigoro/audio_wav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Drigoro/audio_wav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Drigoro/audio_wav2vec") model = AutoModelForAudioClassification.from_pretrained("Drigoro/audio_wav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,637 Bytes
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"_name_or_path": "facebook/wav2vec2-base",
"activation_dropout": 0.0,
"adapter_attn_dim": null,
"adapter_kernel_size": 3,
"adapter_stride": 2,
"add_adapter": false,
"apply_spec_augment": true,
"architectures": [
"Wav2Vec2ForSequenceClassification"
],
"attention_dropout": 0.1,
"bos_token_id": 1,
"classifier_proj_size": 256,
"codevector_dim": 256,
"contrastive_logits_temperature": 0.1,
"conv_bias": false,
"conv_dim": [
512,
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],
"conv_kernel": [
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],
"conv_stride": [
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],
"ctc_loss_reduction": "sum",
"ctc_zero_infinity": false,
"diversity_loss_weight": 0.1,
"do_stable_layer_norm": false,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_norm": "group",
"feat_proj_dropout": 0.1,
"feat_quantizer_dropout": 0.0,
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"freeze_feat_extract_train": true,
"hidden_act": "gelu",
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"hidden_size": 768,
"id2label": {
"0": "ANGER",
"1": "DISGUST",
"2": "FEAR",
"3": "JOY",
"4": "SADNESS",
"5": "SURPRISE"
},
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"intermediate_size": 3072,
"label2id": {
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"FEAR": 2,
"JOY": 3,
"SADNESS": 4,
"SURPRISE": 5
},
"layer_norm_eps": 1e-05,
"layerdrop": 0.0,
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"mask_channel_selection": "static",
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"mask_time_length": 10,
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"mask_time_prob": 0.05,
"mask_time_selection": "static",
"model_type": "wav2vec2",
"no_mask_channel_overlap": false,
"no_mask_time_overlap": false,
"num_adapter_layers": 3,
"num_attention_heads": 12,
"num_codevector_groups": 2,
"num_codevectors_per_group": 320,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 12,
"num_negatives": 100,
"output_hidden_size": 768,
"pad_token_id": 0,
"proj_codevector_dim": 256,
"tdnn_dilation": [
1,
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],
"tdnn_dim": [
512,
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],
"tdnn_kernel": [
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],
"torch_dtype": "float32",
"transformers_version": "4.41.2",
"use_weighted_layer_sum": false,
"vocab_size": 32,
"xvector_output_dim": 512
}
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