Instructions to use tiny-random/omnivoice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/omnivoice with Transformers:
# Load model directly from transformers import OmniVoice model = OmniVoice.from_pretrained("tiny-random/omnivoice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,502 Bytes
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"acoustic_model_config": {
"codebook_dim": 8,
"codebook_loss_weight": 1.0,
"codebook_size": 1024,
"commitment_loss_weight": 0.25,
"decoder_hidden_size": 32,
"downsampling_ratios": [
8,
5,
4,
2,
3
],
"encoder_hidden_size": 4,
"hidden_size": 4,
"hop_length": 960,
"model_type": "dac",
"n_codebooks": 9,
"quantizer_dropout": 0,
"sampling_rate": 16000,
"upsampling_ratios": [
8,
5,
4,
2,
3
]
},
"architectures": [
"HiggsAudioV2TokenizerModel"
],
"block_dilations": [
1,
1
],
"channel_ratios": [
1,
1
],
"codebook_dim": 64,
"codebook_size": 1024,
"downsample_factor": 320,
"dtype": "float32",
"initializer_range": 0.02,
"kernel_size": 3,
"model_type": "higgs_audio_v2_tokenizer",
"sample_rate": 24000,
"semantic_model_config": {
"activation_dropout": 0.1,
"apply_spec_augment": true,
"attention_dropout": 0.1,
"bos_token_id": 1,
"classifier_proj_size": 256,
"conv_bias": false,
"conv_dim": [
8,
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8,
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8,
8,
8
],
"conv_kernel": [
10,
3,
3,
3,
3,
2,
2
],
"conv_pos_batch_norm": false,
"conv_stride": [
5,
2,
2,
2,
2,
2,
2
],
"ctc_loss_reduction": "sum",
"ctc_zero_infinity": false,
"do_stable_layer_norm": false,
"eos_token_id": 2,
"feat_extract_activation": "gelu",
"feat_extract_norm": "group",
"feat_proj_dropout": 0.0,
"feat_proj_layer_norm": true,
"final_dropout": 0.1,
"hidden_act": "gelu",
"hidden_dropout": 0.1,
"hidden_size": 64,
"initializer_range": 0.02,
"intermediate_size": 64,
"layer_norm_eps": 1e-05,
"layerdrop": 0.1,
"mask_feature_length": 10,
"mask_feature_min_masks": 0,
"mask_feature_prob": 0.0,
"mask_time_length": 10,
"mask_time_min_masks": 2,
"mask_time_prob": 0.0,
"model_type": "hubert",
"num_attention_heads": 4,
"num_conv_pos_embedding_groups": 16,
"num_conv_pos_embeddings": 128,
"num_feat_extract_layers": 7,
"num_hidden_layers": 2,
"pad_token_id": 0,
"use_weighted_layer_sum": false,
"vocab_size": 32
},
"semantic_sample_rate": 16000,
"strides": [
1,
1
],
"target_bandwidths": [
0.5,
1,
1.5,
2
],
"transformers_version": "5.5.0",
"unit_kernel_size": 3
}
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