Medix-AI/coqui-tts
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fix Lint check errors

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Edresson 2020-07-31 01:12:17 -03:00 committed by erogol
parent f37159c135
commit f3b8ef4272
1 changed files with 6 additions and 6 deletions
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12
mozilla_voice_tts/speaker_encoder/losses.py
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@ -124,14 +124,14 @@ class GE2ELoss(nn.Module):
# adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py # adapted from https://github.com/clovaai/voxceleb_trainer/blob/master/loss/angleproto.py
class AngleProtoLoss(nn.Module): class AngleProtoLoss(nn.Module):
""" """
Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982 Implementation of the Angular Prototypical loss defined in https://arxiv.org/abs/2003.11982
Accepts an input of size (N, M, D) Accepts an input of size (N, M, D)
where N is the number of speakers in the batch, where N is the number of speakers in the batch,
M is the number of utterances per speaker, M is the number of utterances per speaker,
and D is the dimensionality of the embedding vector and D is the dimensionality of the embedding vector
Args: Args:
- init_w (float): defines the initial value of w - init_w (float): defines the initial value of w
- init_b (float): definies the initial value of b - init_b (float): definies the initial value of b
""" """
def __init__(self, init_w=10.0, init_b=-5.0): def __init__(self, init_w=10.0, init_b=-5.0):
@ -148,13 +148,13 @@ class AngleProtoLoss(nn.Module):
""" """
Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats) Calculates the AngleProto loss for an input of dimensions (num_speakers, num_utts_per_speaker, dvec_feats)
""" """
out_anchor = torch.mean(x[:,1:,:],1) out_anchor = torch.mean(x[:, 1:, :], 1)
out_positive = x[:,0,:] out_positive = x[:, 0, :]
num_speakers = out_anchor.size()[0] num_speakers = out_anchor.size()[0]
cos_sim_matrix = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1,-1,num_speakers),out_anchor.unsqueeze(-1).expand(-1,-1,num_speakers).transpose(0,2)) cos_sim_matrix = F.cosine_similarity(out_positive.unsqueeze(-1).expand(-1, -1, num_speakers),out_anchor.unsqueeze(-1).expand(-1, -1,num_speakers).transpose(0, 2))
torch.clamp(self.w, 1e-6) torch.clamp(self.w, 1e-6)
cos_sim_matrix = cos_sim_matrix * self.w + self.b cos_sim_matrix = cos_sim_matrix * self.w + self.b
label = torch.from_numpy(np.asarray(range(0,num_speakers))).to(cos_sim_matrix.device) label = torch.from_numpy(np.asarray(range(0, num_speakers))).to(cos_sim_matrix.device)
L = self.criterion(cos_sim_matrix, label) L = self.criterion(cos_sim_matrix, label)
return L return L