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| # | |
| import torch | |
| import torch.nn.functional as F | |
| def tpr_loss(disc_real_outputs, disc_generated_outputs, tau): | |
| loss = 0 | |
| for dr, dg in zip(disc_real_outputs, disc_generated_outputs): | |
| m_DG = torch.median((dr - dg)) | |
| L_rel = torch.mean((((dr - dg) - m_DG) ** 2)[dr < dg + m_DG]) | |
| loss += tau - F.relu(tau - L_rel) | |
| return loss | |
| def mel_loss(real_speech, generated_speech, mel_transforms): | |
| loss = 0 | |
| for transform in mel_transforms: | |
| mel_r = transform(real_speech) | |
| mel_g = transform(generated_speech) | |
| loss += F.l1_loss(mel_g, mel_r) | |
| return loss | |
| def OrthogonalityLos_old(speaker_embedding, emotion_embedding): #speaker_embedding[270,192] emotion_embedding[270,192] | |
| #print("OrthogonalityLoss") | |
| speaker_embedding_t = speaker_embedding.t() #[192,270] | |
| dot_product_matrix = torch.matmul(emotion_embedding, speaker_embedding_t)#[270,270] | |
| emotion_norms = torch.norm(emotion_embedding, dim=1, keepdim=True) #[270,1] | |
| speaker_norms = torch.norm(speaker_embedding, dim=1, keepdim=True).t() #[1,270] | |
| normalized_dot_product_matrix = dot_product_matrix / (emotion_norms * speaker_norms) #[270,270] | |
| ort_loss = torch.norm(normalized_dot_product_matrix, p='fro')**2 #说话人与情感正交 | |
| cosine_sim = F.cosine_similarity(emotion_embedding.unsqueeze(2), speaker_embedding.unsqueeze(1), dim=-1) #【270，192，1】与[270, 1, 192]在最后一个维度计算cos相似度 cosine_sim形状是[270, 192] | |
| cosine_ort_loss = torch.norm(cosine_sim.mean(dim=-1), p='fro') ** 2 | |
| #return 0.01 * (ort_loss + cosine_ort_loss) #ort_loss是2292.1016 cosine_ort_loss是0.0554 直接加是否合理？ 目的就是是的情感与说话人正交？ 那么就是分离解耦？ | |
| return 1 * (ort_loss * 0.001 + cosine_ort_loss*10) | |
| #flow时候 ort_loss 2749.5303 cosine_ort_loss 0.0005 | |
| def OrthogonalityLoss(speaker_embedding, emotion_embedding): #speaker_embedding[270,192] emotion_embedding[270,192] | |
| #print("OrthogonalityLoss") | |
| speaker_embedding_t = speaker_embedding.t() #[192,270] | |
| dot_product_matrix = torch.matmul(emotion_embedding, speaker_embedding_t)#[270,270] | |
| emotion_norms = torch.norm(emotion_embedding, dim=1, keepdim=True) #[270,1] | |
| speaker_norms = torch.norm(speaker_embedding, dim=1, keepdim=True).t() #[1,270] | |
| normalized_dot_product_matrix = dot_product_matrix / (emotion_norms * speaker_norms) #[270,270] | |
| ort_loss = torch.norm(normalized_dot_product_matrix, p='fro')**2 #说话人与情感正交 | |
| cosine_sim = F.cosine_similarity(emotion_embedding.unsqueeze(2), speaker_embedding.unsqueeze(1), dim=-1) #【270，192，1】与[270, 1, 192]在最后一个维度计算cos相似度 cosine_sim形状是[270, 192] | |
| cosine_ort_loss = torch.norm(cosine_sim.mean(dim=-1), p='fro') ** 2 | |
| return 0.01 * (ort_loss + cosine_ort_loss) #ort_loss是2292.1016 cosine_ort_loss是0.0554 直接加是否合理？ 目的就是是的情感与说话人正交？ 那么就是分离解耦？ | |
| #return 1 * (ort_loss * 0.001 + cosine_ort_loss*10) | |
| #flow时候 ort_loss 2749.5303 cosine_ort_loss 0.0005 | |
| def ContrastiveOrthogonalLoss(spk_pure, emo_pure, temperature=0.1): | |
| # spk_pure, emo_pure: [B, D] | |
| spk_pure = F.normalize(spk_pure, dim=-1) | |
| emo_pure = F.normalize(emo_pure, dim=-1) | |
| # 相似度矩阵 | |
| sim_matrix = torch.mm(spk_pure, emo_pure.T) / temperature # [B, B] | |
| # 对角线是正样本对（同一个样本的 speaker vs emotion），但我们希望它们不相似！ | |
| # 所以我们最小化正样本对得分，最大化负样本对得分 → 反向 InfoNCE | |
| labels = torch.arange(sim_matrix.size(0)).to(spk_pure.device) | |
| loss = F.cross_entropy(-sim_matrix, labels) # 负号：让正样本对得分低 | |
| return loss |