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修复gpt的loss计算问题

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ChasonJiang 2025-07-18 12:41:30 +08:00
parent b9211657d8
commit e9475921d0
2 changed files with 8 additions and 8 deletions
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12
GPT_SoVITS/AR/models/t2s_model.py
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@ -356,7 +356,7 @@ class Text2SemanticDecoder(nn.Module):
x = self.ar_text_embedding(x) x = self.ar_text_embedding(x)
x = x + self.bert_proj(bert_feature.transpose(1, 2)) x = x + self.bert_proj(bert_feature.transpose(1, 2))
x = self.ar_text_position(x) x = self.ar_text_position(x)
x_mask = make_pad_mask(x_lens) x_mask = make_pad_mask_left(x_lens)
y_mask = make_pad_mask(y_lens) y_mask = make_pad_mask(y_lens)
y_mask_int = y_mask.type(torch.int64) y_mask_int = y_mask.type(torch.int64)
@ -420,7 +420,7 @@ class Text2SemanticDecoder(nn.Module):
mask=xy_attn_mask, mask=xy_attn_mask,
) )
x_len = x_lens.max() x_len = x_lens.max()
logits = self.ar_predict_layer(xy_dec[:, x_len:]) logits = self.ar_predict_layer(xy_dec[:, x_len-1:])
###### DPO ############# ###### DPO #############
reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data( reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(
@ -432,7 +432,7 @@ class Text2SemanticDecoder(nn.Module):
mask=reject_xy_attn_mask, mask=reject_xy_attn_mask,
) )
x_len = x_lens.max() x_len = x_lens.max()
reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:]) reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len-1:])
# loss # loss
# from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
@ -455,7 +455,7 @@ class Text2SemanticDecoder(nn.Module):
x = self.ar_text_embedding(x) x = self.ar_text_embedding(x)
x = x + self.bert_proj(bert_feature.transpose(1, 2)) x = x + self.bert_proj(bert_feature.transpose(1, 2))
x = self.ar_text_position(x) x = self.ar_text_position(x)
x_mask = make_pad_mask(x_lens) x_mask = make_pad_mask_left(x_lens)
y_mask = make_pad_mask(y_lens) y_mask = make_pad_mask(y_lens)
y_mask_int = y_mask.type(torch.int64) y_mask_int = y_mask.type(torch.int64)
@ -502,7 +502,7 @@ class Text2SemanticDecoder(nn.Module):
(xy_pos, None), (xy_pos, None),
mask=xy_attn_mask, mask=xy_attn_mask,
) )
logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1) logits = self.ar_predict_layer(xy_dec[:, x_len-1:]).permute(0, 2, 1)
# loss # loss
# from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum # from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
loss = F.cross_entropy(logits, targets, reduction="sum") loss = F.cross_entropy(logits, targets, reduction="sum")
@ -578,7 +578,7 @@ class Text2SemanticDecoder(nn.Module):
def pad_y_eos(self, y, y_mask_int, eos_id): def pad_y_eos(self, y, y_mask_int, eos_id):
targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(y_mask_int, (0, 1), value=1) targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(y_mask_int, (0, 1), value=1)
# 错位 # 错位
return targets[:, :-1], targets[:, 1:] return targets[:, :-1], targets
def infer_panel_batch_infer( def infer_panel_batch_infer(
self, self,

4
GPT_SoVITS/configs/tts_infer.yaml
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@ -3,9 +3,9 @@ custom:
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
device: cuda device: cuda
is_half: true is_half: true
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt t2s_weights_path: "GPT_weights_v2Pro/\u97F5\u513F-e15.ckpt"
version: v2 version: v2
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth vits_weights_path: "SoVITS_weights_v2/\u97F5\u513F_e25_s9475.pth"
v1: v1:
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base