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| import librosa | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from torch.utils.data import Dataset | |
| from torch.utils.data import Dataset | |
| from transformers import Wav2Vec2Processor | |
| from transformers.models.wav2vec2.modeling_wav2vec2 import ( | |
| Wav2Vec2Model, | |
| Wav2Vec2PreTrainedModel, | |
| ) | |
| from config import config | |
| 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 | |
| class AudioDataset(Dataset): | |
| def __init__(self, list_of_wav_files, sr, processor): | |
| self.list_of_wav_files = list_of_wav_files | |
| self.processor = processor | |
| self.sr = sr | |
| def __len__(self): | |
| return len(self.list_of_wav_files) | |
| def __getitem__(self, idx): | |
| wav_file = self.list_of_wav_files[idx] | |
| audio_data, _ = librosa.load(wav_file, sr=self.sr) | |
| processed_data = self.processor(audio_data, sampling_rate=self.sr)[ | |
| "input_values" | |
| ][0] | |
| return torch.from_numpy(processed_data) | |
| device = config.emo_gen_config.device | |
| model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim" | |
| processor = Wav2Vec2Processor.from_pretrained(model_name) | |
| model = EmotionModel.from_pretrained(model_name).to(device) | |
| def process_func( | |
| x: np.ndarray, | |
| sampling_rate: int, | |
| model: EmotionModel, | |
| processor: Wav2Vec2Processor, | |
| device: str, | |
| embeddings: bool = False, | |
| ) -> np.ndarray: | |
| r"""Predict emotions or extract embeddings from raw audio signal.""" | |
| model = model.to(device) | |
| y = processor(x, sampling_rate=sampling_rate) | |
| y = y["input_values"][0] | |
| y = torch.from_numpy(y).unsqueeze(0).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 | |
| def get_emo(path): | |
| wav, sr = librosa.load(path, 16000) | |
| return process_func( | |
| np.expand_dims(wav, 0).astype(np.float64), | |
| sr, | |
| model, | |
| processor, | |
| device, | |
| embeddings=True, | |
| ).squeeze(0) | |