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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md new file mode 100644 index 0000000..dc42770 --- /dev/null +++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md @@ -0,0 +1,127 @@ +--- +language: en +datasets: +- msp-podcast +inference: true +tags: +- speech +- audio +- wav2vec2 +- audio-classification +- emotion-recognition +license: cc-by-nc-sa-4.0 +pipeline_tag: audio-classification +--- + +# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0 + +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 [ +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). + +# Usage + +```python +import numpy as np +import torch +import torch.nn as nn +from transformers import Wav2Vec2Processor +from transformers.models.wav2vec2.modeling_wav2vec2 import ( + Wav2Vec2Model, + Wav2Vec2PreTrainedModel, +) + + +class RegressionHead(nn.Module): + r"""Classification head.""" + + def __init__(self, config): + + super().__init__() + + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.final_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + + x = features + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + + return x + + +class EmotionModel(Wav2Vec2PreTrainedModel): + r"""Speech emotion classifier.""" + + def __init__(self, config): + + super().__init__(config) + + self.config = config + self.wav2vec2 = Wav2Vec2Model(config) + self.classifier = RegressionHead(config) + self.init_weights() + + def forward( + self, + input_values, + ): + + outputs = self.wav2vec2(input_values) + hidden_states = outputs[0] + hidden_states = torch.mean(hidden_states, dim=1) + logits = self.classifier(hidden_states) + + return hidden_states, logits + + + +# load model from hub +device = 'cpu' +model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim' +processor = Wav2Vec2Processor.from_pretrained(model_name) +model = EmotionModel.from_pretrained(model_name) + +# dummy signal +sampling_rate = 16000 +signal = np.zeros((1, sampling_rate), dtype=np.float32) + + +def process_func( + x: np.ndarray, + sampling_rate: int, + embeddings: bool = False, +) -> np.ndarray: + r"""Predict emotions or extract embeddings from raw audio signal.""" + + # run through processor to normalize signal + # always returns a batch, so we just get the first entry + # then we put it on the device + y = processor(x, sampling_rate=sampling_rate) + y = y['input_values'][0] + y = y.reshape(1, -1) + y = torch.from_numpy(y).to(device) + + # run through model + with torch.no_grad(): + y = model(y)[0 if embeddings else 1] + + # convert to numpy + y = y.detach().cpu().numpy() + + return y + + +print(process_func(signal, sampling_rate)) +# Arousal dominance valence +# [[0.5460754 0.6062266 0.40431657]] + +print(process_func(signal, sampling_rate, embeddings=True)) +# Pooled hidden states of last transformer layer +# [[-0.00752167 0.0065819 -0.00746342 ... 0.00663632 0.00848748 +# 0.00599211]] +``` diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json new file mode 100644 index 0000000..a9596a6 --- /dev/null +++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json @@ -0,0 +1,122 @@ +{ + "_name_or_path": "torch", + "activation_dropout": 0.1, + "adapter_kernel_size": 3, + "adapter_stride": 2, + "add_adapter": false, + "apply_spec_augment": true, + "architectures": [ + "Wav2Vec2ForSpeechClassification" + ], + "attention_dropout": 0.1, + "bos_token_id": 1, + "classifier_proj_size": 256, + "codevector_dim": 768, + "contrastive_logits_temperature": 0.1, + "conv_bias": true, + "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": true, + "eos_token_id": 2, + "feat_extract_activation": "gelu", + "feat_extract_dropout": 0.0, + "feat_extract_norm": "layer", + "feat_proj_dropout": 0.1, + "feat_quantizer_dropout": 0.0, + "final_dropout": 0.1, + "finetuning_task": "wav2vec2_reg", + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout": 0.1, + "hidden_dropout_prob": 0.1, + "hidden_size": 1024, + "id2label": { + "0": "arousal", + "1": "dominance", + "2": "valence" + }, + "initializer_range": 0.02, + "intermediate_size": 4096, + "label2id": { + "arousal": 0, + "dominance": 1, + "valence": 2 + }, + "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": 0.05, + "model_type": "wav2vec2", + "num_adapter_layers": 3, + "num_attention_heads": 16, + "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": 1024, + "pad_token_id": 0, + "pooling_mode": "mean", + "problem_type": "regression", + "proj_codevector_dim": 768, + "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": "4.17.0.dev0", + "use_weighted_layer_sum": false, + "vocab_size": null, + "xvector_output_dim": 512 +} diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json new file mode 100644 index 0000000..73caa15 --- /dev/null +++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json @@ -0,0 +1,9 @@ +{ + "do_normalize": true, + "feature_extractor_type": "Wav2Vec2FeatureExtractor", + "feature_size": 1, + "padding_side": "right", + "padding_value": 0.0, + "return_attention_mask": true, + "sampling_rate": 16000 +} diff --git a/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json new file mode 100644 index 0000000..0967ef4 --- /dev/null +++ b/emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json @@ -0,0 +1 @@ +{} diff --git a/infer.py b/infer.py index 9e689e7..7262f18 100644 --- a/infer.py +++ b/infer.py @@ -29,7 +29,7 @@ from oldVersion.V110.text import symbols as V110symbols from oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn from oldVersion.V101.text import symbols as V101symbols -from oldVersion import V111, V110, V101, V200 +from oldVersion import V111, V110, V101, V200, V210 # 当前版本信息 latest_version = "2.2" @@ -157,9 +157,9 @@ def infer( ): # 2.2版本参数位置变了 # 2.1 参数新增 emotion reference_audio skip_start skip_end - # inferMap_V3 = { - # "2.1": V210.infer, - # } + inferMap_V3 = { + "2.1": V210.infer, + } # 支持中日英三语版本 inferMap_V2 = { "2.0.2-fix": V200.infer, @@ -180,23 +180,23 @@ def infer( version = hps.version if hasattr(hps, "version") else latest_version # 非当前版本，根据版本号选择合适的infer if version != latest_version: - # if version in inferMap_V3.keys(): - # return inferMap_V3[version]( - # text, - # sdp_ratio, - # noise_scale, - # noise_scale_w, - # length_scale, - # sid, - # language, - # hps, - # net_g, - # device, - # reference_audio, - # emotion, - # skip_start, - # skip_end, - # ) + if version in inferMap_V3.keys(): + return inferMap_V3[version]( + text, + sdp_ratio, + noise_scale, + noise_scale_w, + length_scale, + sid, + language, + hps, + net_g, + device, + reference_audio, + emotion, + skip_start, + skip_end, + ) if version in inferMap_V2.keys(): return inferMap_V2[version]( text, diff --git a/oldVersion/V210/models.py b/oldVersion/V210/models.py index f5a0d5c..60dffdc 100644 --- a/oldVersion/V210/models.py +++ b/oldVersion/V210/models.py @@ -13,7 +13,7 @@ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from vector_quantize_pytorch import VectorQuantize from commons import init_weights, get_padding -from text import symbols, num_tones, num_languages +from .text import symbols, num_tones, num_languages class DurationDiscriminator(nn.Module): # vits2