remove cosyvoice scheduler

egs/wenetspeech4tts/TTS/f5-tts/model/cfm.py

@@ -290,8 +290,6 @@ def forward(
 
         # time step
         time = torch.rand((batch,), dtype=dtype, device=self.device)
-        # add cosyvoice cosine scheduler
-        time = 1 - torch.cos(time * 0.5 * torch.pi)
         # TODO. noise_scheduler
 
         # sample xt (φ_t(x) in the paper)

egs/wenetspeech4tts/TTS/f5-tts/train.py

@@ -998,24 +998,14 @@ def run(rank, world_size, args):
             weight_decay=1e-2,
             eps=1e-8,
         )
-    elif params.optimizer_name == "Adam":
-        optimizer = torch.optim.Adam(
-            model_parameters,
-            lr=params.base_lr,
-            betas=(0.9, 0.95),
-            eps=1e-8,
-        )
     else:
         raise NotImplementedError()
-    if params.decay_steps:
-        warmup_scheduler = LinearLR(optimizer, start_factor=1e-8, end_factor=1.0, total_iters=params.warmup_steps)
-        decay_scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=1e-8, total_iters=params.decay_steps)
-        scheduler = SequentialLR(
-            optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[params.warmup_steps]
-        )
-        assert 1==2
-    else:
-        scheduler = Eden(optimizer, 50000, 10, warmup_batches=params.warmup_steps)
+
+    warmup_scheduler = LinearLR(optimizer, start_factor=1e-8, end_factor=1.0, total_iters=params.warmup_steps)
+    decay_scheduler = LinearLR(optimizer, start_factor=1.0, end_factor=1e-8, total_iters=params.decay_steps)
+    scheduler = SequentialLR(
+        optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[params.warmup_steps]
+    )
 
     optimizer.zero_grad()