Upload model
Browse files- config.json +5 -3
- modeling_wav2vec2_spkreg.py +3 -3
config.json
CHANGED
@@ -1,4 +1,5 @@
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{
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"activation_dropout": 0.0,
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"adapter_attn_dim": null,
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"adapter_kernel_size": 3,
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@@ -6,11 +7,12 @@
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"
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],
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"attention_dropout": 0.1,
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"auto_map": {
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"AutoConfig": "configuration_wav2vec2_spkreg.Wav2Vec2SpkRegConfig"
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},
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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@@ -56,7 +58,6 @@
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"freeze_feat_extract_train": true,
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-
"gradient_checkpointing": true,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 768,
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@@ -119,6 +120,7 @@
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1,
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1
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],
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"transformers_version": "4.46.2",
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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{
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"_name_or_path": "facebook/wav2vec2-base",
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"activation_dropout": 0.0,
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"adapter_attn_dim": null,
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"adapter_kernel_size": 3,
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"add_adapter": false,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2SpkRegModel"
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],
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"attention_dropout": 0.1,
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"auto_map": {
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"AutoConfig": "configuration_wav2vec2_spkreg.Wav2Vec2SpkRegConfig",
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"AutoModel": "modeling_wav2vec2_spkreg.Wav2Vec2SpkRegModel"
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},
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"bos_token_id": 1,
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"classifier_proj_size": 256,
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"feat_quantizer_dropout": 0.0,
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"final_dropout": 0.0,
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"freeze_feat_extract_train": true,
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"hidden_act": "gelu",
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"hidden_dropout": 0.1,
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"hidden_size": 768,
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1,
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1
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],
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"torch_dtype": "float32",
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"transformers_version": "4.46.2",
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"use_weighted_layer_sum": false,
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"vocab_size": 32,
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modeling_wav2vec2_spkreg.py
CHANGED
@@ -732,16 +732,16 @@ class Wav2Vec2SpkRegForSequenceClassification(Wav2Vec2SpkRegPreTrainedModel):
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loss = None
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if labels is not None:
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if self.loss_fct == 'cross_entropy':
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loss_fct = nn.CrossEntropyLoss(
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label_smoothing=self.config.label_smoothing,
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reduction=self.config.reduction
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)
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elif self.loss_fct == 'additive_margin':
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loss_fct = AMSoftmaxLoss(
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self.config.num_labels, self.config.scale, self.config.margin
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)
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elif self.loss_fct == 'additive_angular_margin':
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loss_fct = AAMSoftmaxLoss(
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self.config.num_labels, self.config.scale, self.config.margin, self.config.easy_margin
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)
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loss = None
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if labels is not None:
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if self.config.loss_fct == 'cross_entropy':
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loss_fct = nn.CrossEntropyLoss(
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label_smoothing=self.config.label_smoothing,
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reduction=self.config.reduction
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)
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elif self.config.loss_fct == 'additive_margin':
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loss_fct = AMSoftmaxLoss(
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self.config.num_labels, self.config.scale, self.config.margin
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)
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elif self.config.loss_fct == 'additive_angular_margin':
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loss_fct = AAMSoftmaxLoss(
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self.config.num_labels, self.config.scale, self.config.margin, self.config.easy_margin
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)
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