Use torch.expm1

Author: cyyever

src/transformers/models/seamless_m4t/modeling_seamless_m4t.py

@@ -2578,7 +2578,7 @@ def forward(
         lang = self.language_embedding(lang_id).transpose(1, 2)
 
         log_dur_pred = self.dur_predictor(hidden_states.transpose(1, 2))
-        dur_out = torch.clamp(torch.round((torch.exp(log_dur_pred) - 1)).long(), min=1)
+        dur_out = torch.clamp(torch.round((torch.expm1(log_dur_pred))).long(), min=1)
         # B x C x T
         if hidden_states.size(0) == 1:
             hidden_states = torch.repeat_interleave(hidden_states, dur_out.view(-1), dim=2)

src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py

@@ -2292,7 +2292,7 @@ def forward(
 
         # predict duration
         log_dur_pred = self.duration_predictor(char_hidden_states, padding_mask=char_padding_mask)
-        dur_out = torch.clamp(torch.round((torch.exp(log_dur_pred) - 1)).long(), min=1)
+        dur_out = torch.clamp(torch.round((torch.expm1(log_dur_pred))).long(), min=1)
         dur_out = dur_out.masked_fill(~char_padding_mask.bool(), 0.0)
 
         # upsample char hidden states according to predicted duration
@@ -2854,7 +2854,7 @@ def forward(
         lang = self.language_embedding(lang_id).transpose(1, 2)
 
         log_dur_pred = self.dur_predictor(hidden_states.transpose(1, 2))
-        dur_out = torch.clamp(torch.round((torch.exp(log_dur_pred) - 1)).long(), min=1)
+        dur_out = torch.clamp(torch.round((torch.expm1(log_dur_pred))).long(), min=1)
         # B x C x T
         if hidden_states.size(0) == 1:
             hidden_states = torch.repeat_interleave(hidden_states, dur_out.view(-1), dim=2)