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emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md ADDED
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+ ---
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+ language: en
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+ datasets:
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+ - msp-podcast
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+ inference: true
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+ tags:
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+ - speech
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+ - audio
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+ - wav2vec2
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+ - audio-classification
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+ - emotion-recognition
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+ license: cc-by-nc-sa-4.0
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+ pipeline_tag: audio-classification
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+ ---
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+
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+ # Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
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+
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+ The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
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+ Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
20
+
21
+ # Usage
22
+
23
+ ```python
24
+ import numpy as np
25
+ import torch
26
+ import torch.nn as nn
27
+ from transformers import Wav2Vec2Processor
28
+ from transformers.models.wav2vec2.modeling_wav2vec2 import (
29
+ Wav2Vec2Model,
30
+ Wav2Vec2PreTrainedModel,
31
+ )
32
+
33
+
34
+ class RegressionHead(nn.Module):
35
+ r"""Classification head."""
36
+
37
+ def __init__(self, config):
38
+
39
+ super().__init__()
40
+
41
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
42
+ self.dropout = nn.Dropout(config.final_dropout)
43
+ self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
44
+
45
+ def forward(self, features, **kwargs):
46
+
47
+ x = features
48
+ x = self.dropout(x)
49
+ x = self.dense(x)
50
+ x = torch.tanh(x)
51
+ x = self.dropout(x)
52
+ x = self.out_proj(x)
53
+
54
+ return x
55
+
56
+
57
+ class EmotionModel(Wav2Vec2PreTrainedModel):
58
+ r"""Speech emotion classifier."""
59
+
60
+ def __init__(self, config):
61
+
62
+ super().__init__(config)
63
+
64
+ self.config = config
65
+ self.wav2vec2 = Wav2Vec2Model(config)
66
+ self.classifier = RegressionHead(config)
67
+ self.init_weights()
68
+
69
+ def forward(
70
+ self,
71
+ input_values,
72
+ ):
73
+
74
+ outputs = self.wav2vec2(input_values)
75
+ hidden_states = outputs[0]
76
+ hidden_states = torch.mean(hidden_states, dim=1)
77
+ logits = self.classifier(hidden_states)
78
+
79
+ return hidden_states, logits
80
+
81
+
82
+
83
+ # load model from hub
84
+ device = 'cpu'
85
+ model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
86
+ processor = Wav2Vec2Processor.from_pretrained(model_name)
87
+ model = EmotionModel.from_pretrained(model_name)
88
+
89
+ # dummy signal
90
+ sampling_rate = 16000
91
+ signal = np.zeros((1, sampling_rate), dtype=np.float32)
92
+
93
+
94
+ def process_func(
95
+ x: np.ndarray,
96
+ sampling_rate: int,
97
+ embeddings: bool = False,
98
+ ) -> np.ndarray:
99
+ r"""Predict emotions or extract embeddings from raw audio signal."""
100
+
101
+ # run through processor to normalize signal
102
+ # always returns a batch, so we just get the first entry
103
+ # then we put it on the device
104
+ y = processor(x, sampling_rate=sampling_rate)
105
+ y = y['input_values'][0]
106
+ y = y.reshape(1, -1)
107
+ y = torch.from_numpy(y).to(device)
108
+
109
+ # run through model
110
+ with torch.no_grad():
111
+ y = model(y)[0 if embeddings else 1]
112
+
113
+ # convert to numpy
114
+ y = y.detach().cpu().numpy()
115
+
116
+ return y
117
+
118
+
119
+ print(process_func(signal, sampling_rate))
120
+ # Arousal dominance valence
121
+ # [[0.5460754 0.6062266 0.40431657]]
122
+
123
+ print(process_func(signal, sampling_rate, embeddings=True))
124
+ # Pooled hidden states of last transformer layer
125
+ # [[-0.00752167 0.0065819 -0.00746342 ... 0.00663632 0.00848748
126
+ # 0.00599211]]
127
+ ```
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json ADDED
@@ -0,0 +1,122 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "torch",
3
+ "activation_dropout": 0.1,
4
+ "adapter_kernel_size": 3,
5
+ "adapter_stride": 2,
6
+ "add_adapter": false,
7
+ "apply_spec_augment": true,
8
+ "architectures": [
9
+ "Wav2Vec2ForSpeechClassification"
10
+ ],
11
+ "attention_dropout": 0.1,
12
+ "bos_token_id": 1,
13
+ "classifier_proj_size": 256,
14
+ "codevector_dim": 768,
15
+ "contrastive_logits_temperature": 0.1,
16
+ "conv_bias": true,
17
+ "conv_dim": [
18
+ 512,
19
+ 512,
20
+ 512,
21
+ 512,
22
+ 512,
23
+ 512,
24
+ 512
25
+ ],
26
+ "conv_kernel": [
27
+ 10,
28
+ 3,
29
+ 3,
30
+ 3,
31
+ 3,
32
+ 2,
33
+ 2
34
+ ],
35
+ "conv_stride": [
36
+ 5,
37
+ 2,
38
+ 2,
39
+ 2,
40
+ 2,
41
+ 2,
42
+ 2
43
+ ],
44
+ "ctc_loss_reduction": "sum",
45
+ "ctc_zero_infinity": false,
46
+ "diversity_loss_weight": 0.1,
47
+ "do_stable_layer_norm": true,
48
+ "eos_token_id": 2,
49
+ "feat_extract_activation": "gelu",
50
+ "feat_extract_dropout": 0.0,
51
+ "feat_extract_norm": "layer",
52
+ "feat_proj_dropout": 0.1,
53
+ "feat_quantizer_dropout": 0.0,
54
+ "final_dropout": 0.1,
55
+ "finetuning_task": "wav2vec2_reg",
56
+ "gradient_checkpointing": false,
57
+ "hidden_act": "gelu",
58
+ "hidden_dropout": 0.1,
59
+ "hidden_dropout_prob": 0.1,
60
+ "hidden_size": 1024,
61
+ "id2label": {
62
+ "0": "arousal",
63
+ "1": "dominance",
64
+ "2": "valence"
65
+ },
66
+ "initializer_range": 0.02,
67
+ "intermediate_size": 4096,
68
+ "label2id": {
69
+ "arousal": 0,
70
+ "dominance": 1,
71
+ "valence": 2
72
+ },
73
+ "layer_norm_eps": 1e-05,
74
+ "layerdrop": 0.1,
75
+ "mask_feature_length": 10,
76
+ "mask_feature_min_masks": 0,
77
+ "mask_feature_prob": 0.0,
78
+ "mask_time_length": 10,
79
+ "mask_time_min_masks": 2,
80
+ "mask_time_prob": 0.05,
81
+ "model_type": "wav2vec2",
82
+ "num_adapter_layers": 3,
83
+ "num_attention_heads": 16,
84
+ "num_codevector_groups": 2,
85
+ "num_codevectors_per_group": 320,
86
+ "num_conv_pos_embedding_groups": 16,
87
+ "num_conv_pos_embeddings": 128,
88
+ "num_feat_extract_layers": 7,
89
+ "num_hidden_layers": 12,
90
+ "num_negatives": 100,
91
+ "output_hidden_size": 1024,
92
+ "pad_token_id": 0,
93
+ "pooling_mode": "mean",
94
+ "problem_type": "regression",
95
+ "proj_codevector_dim": 768,
96
+ "tdnn_dilation": [
97
+ 1,
98
+ 2,
99
+ 3,
100
+ 1,
101
+ 1
102
+ ],
103
+ "tdnn_dim": [
104
+ 512,
105
+ 512,
106
+ 512,
107
+ 512,
108
+ 1500
109
+ ],
110
+ "tdnn_kernel": [
111
+ 5,
112
+ 3,
113
+ 3,
114
+ 1,
115
+ 1
116
+ ],
117
+ "torch_dtype": "float32",
118
+ "transformers_version": "4.17.0.dev0",
119
+ "use_weighted_layer_sum": false,
120
+ "vocab_size": null,
121
+ "xvector_output_dim": 512
122
+ }
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "do_normalize": true,
3
+ "feature_extractor_type": "Wav2Vec2FeatureExtractor",
4
+ "feature_size": 1,
5
+ "padding_side": "right",
6
+ "padding_value": 0.0,
7
+ "return_attention_mask": true,
8
+ "sampling_rate": 16000
9
+ }
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:176d9d1ce29a8bddbab44068b9c1c194c51624c7f1812905e01355da58b18816
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+ size 661436013
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json ADDED
@@ -0,0 +1 @@
 
 
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+ {}