Upload Wav2Vec2ForMultiHeadMultiLabelClassification

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+### Model Sources [optional]
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+<!-- Provide the basic links for the model. -->
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+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
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+### Recommendations
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+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
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+[More Information Needed]
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+
+#### Training Hyperparameters
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+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+[More Information Needed]
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+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
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+[More Information Needed]
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+#### Factors
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+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+[More Information Needed]
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+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+[More Information Needed]
+
+### Results
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+[More Information Needed]
+
+#### Summary
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+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
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+[More Information Needed]
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+## Environmental Impact
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+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
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+[More Information Needed]
+
+### Compute Infrastructure
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+[More Information Needed]
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+#### Hardware
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+#### Software
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+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+**BibTeX:**
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+**APA:**
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+## Glossary [optional]
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
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+## More Information [optional]
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config.json

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+{
+  "activation_dropout": 0.0,
+  "adapter_attn_dim": null,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForMultiHeadMultiLabelClassification"
+  ],
+  "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_wav2vec2multihead.Wav2Vec2MultiHeadConfig",
+    "AutoModelForSequenceClassification": "modeling_wav2vec2multihead.Wav2Vec2ForMultiHeadMultiLabelClassification"
+  },
+  "bos_token_id": 1,
+  "classifier_proj_size": 256,
+  "codevector_dim": 256,
+  "contrastive_logits_temperature": 0.1,
+  "conv_bias": false,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "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,
+  "final_dropout": 0.0,
+  "freeze_feat_extract_train": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11",
+    "12": "LABEL_12",
+    "13": "LABEL_13",
+    "14": "LABEL_14",
+    "15": "LABEL_15",
+    "16": "LABEL_16",
+    "17": "LABEL_17",
+    "18": "LABEL_18",
+    "19": "LABEL_19",
+    "20": "LABEL_20",
+    "21": "LABEL_21",
+    "22": "LABEL_22",
+    "23": "LABEL_23"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_12": 12,
+    "LABEL_13": 13,
+    "LABEL_14": 14,
+    "LABEL_15": 15,
+    "LABEL_16": 16,
+    "LABEL_17": 17,
+    "LABEL_18": 18,
+    "LABEL_19": 19,
+    "LABEL_2": 2,
+    "LABEL_20": 20,
+    "LABEL_21": 21,
+    "LABEL_22": 22,
+    "LABEL_23": 23,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.0,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "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_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2multihead_3class",
+  "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_labels_1": 6,
+  "num_labels_2": 14,
+  "num_labels_3": 4,
+  "num_negatives": 100,
+  "output_hidden_size": 768,
+  "pad_token_id": 0,
+  "proj_codevector_dim": 256,
+  "tdnn_dilation": [
+    1,
+    2,
+    3,
+    1,
+    1
+  ],
+  "tdnn_dim": [
+    512,
+    512,
+    512,
+    512,
+    1500
+  ],
+  "tdnn_kernel": [
+    5,
+    3,
+    3,
+    1,
+    1
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 32,
+  "xvector_output_dim": 512
+}

configuration_wav2vec2multihead.py

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+from transformers import Wav2Vec2Config
+
+
+class Wav2Vec2MultiHeadConfig(Wav2Vec2Config):
+    model_type = "wav2vec2multihead_3class"
+    is_encoder_decoder = False
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.num_labels_1 = kwargs.pop("num_labels_1", 0)
+        self.num_labels_2 = kwargs.pop("num_labels_2", 0)
+        self.num_labels_3 = kwargs.pop("num_labels_3", 0)
+        self.num_labels = self.num_labels_1 + self.num_labels_2 + self.num_labels_3
+
+        

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aea9c650ef257cc7feb5ba66af251bdda873adf7d91713c24b58f22c4904a777
+size 377586832

modeling_wav2vec2multihead.py

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+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from torch import nn
+from transformers import (
+    Wav2Vec2Model,
+    Wav2Vec2PreTrainedModel,
+)
+from transformers.utils import ModelOutput
+
+
+@dataclass
+class Wav2Vec2MultiHeadMultiLabelOutput(ModelOutput):
+    loss: Optional[torch.FloatTensor] = None
+    logits1: torch.FloatTensor = None
+    logits2: torch.FloatTensor = None
+    logits3: torch.FloatTensor = None
+    hidden_states: Optional[tuple[torch.FloatTensor]] = None
+    attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+class Wav2Vec2ForMultiHeadMultiLabelClassification(Wav2Vec2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.wav2vec2 = Wav2Vec2Model(config)
+        self.dropout = nn.Dropout(config.final_dropout)
+        self.classifier1 = nn.Linear(config.hidden_size, config.num_labels_1)
+        self.classifier2 = nn.Linear(config.hidden_size, config.num_labels_2)
+        self.classifier3 = nn.Linear(config.hidden_size, config.num_labels_3)
+        self.init_weights()
+
+    def freeze_feature_extractor(self):
+        self.wav2vec2.feature_extractor._freeze_parameters()
+
+    def freeze_cnn_projection(self):
+        for param in self.wav2vec2.feature_projection.parameters():
+            param.requires_grad = False
+
+    def forward(
+        self,
+        input_values,
+        attention_mask=None,
+        labels1=None,
+        labels2=None,
+        labels3=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        outputs = self.wav2vec2(
+            input_values,
+            attention_mask=attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = torch.mean(hidden_states, dim=1)
+
+        logits1 = self.classifier1(hidden_states)
+        logits2 = self.classifier2(hidden_states)
+        logits3 = self.classifier3(hidden_states)
+
+        loss = None
+        if labels1 is not None and labels2 is not None and labels3 is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss1 = loss_fct(
+                logits1.view(-1, self.config.num_labels_1), labels1.view(-1)
+            )
+            loss2 = loss_fct(
+                logits2.view(-1, self.config.num_labels_2), labels2.view(-1)
+            )
+            loss3 = loss_fct(
+                logits3.view(-1, self.config.num_labels_3), labels3.view(-1)
+            )
+            loss = loss1 + loss2 + loss3
+
+        return Wav2Vec2MultiHeadMultiLabelOutput(
+            loss=loss,
+            logits1=logits1,
+            logits2=logits2,
+            logits3=logits3,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )