Merge remote-tracking branch 'dan/master' into dataset-parallel-augmentation-example

README.md

@@ -383,3 +383,7 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
 [vctk]: egs/vctk/TTS
 [ljspeech]: egs/ljspeech/TTS
 [libritts_tts]: egs/libritts/TTS
+
+## Acknowledgements
+
+Some contributors to this project were supported by Xiaomi Corporation. Others were supported by National Science Foundation CCRI award 2120435.  This is not an exhaustive list of sources of support.

egs/librispeech/ASR/zipformer/finetune.py

@@ -140,8 +140,8 @@ def add_finetune_arguments(parser: argparse.ArgumentParser):
         type=str2bool,
         default=False,
         help="""
-        Whether to adapt. If true, we will mix 5% of the new data
+        Whether to adapt. If true, we will mix 5%% of the new data
-        with 95% of the original data to fine-tune. This is useful
+        with 95%% of the original data to fine-tune. This is useful
         if you want to maintain the performance on the original domain
         """,
     )
@@ -1134,7 +1134,7 @@ def train_one_epoch(
                 f"Epoch {params.cur_epoch}, "
                 f"batch {batch_idx}, loss[{loss_info}], "
                 f"tot_loss[{tot_loss}], batch size: {batch_size}, "
-                f"lr: {cur_lr:.2e}, "
+                f"lr: {cur_lr: .2e}, "
                 + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
             )
 

egs/librispeech/ASR/zipformer/model.py

@@ -210,10 +210,10 @@ class AsrModel(nn.Module):
         )
 
         # Compute consistency regularization loss
-        exchanged_targets = ctc_output.detach().chunk(2, dim=0)
+        batch_size = ctc_output.shape[0]
-        exchanged_targets = torch.cat(
+        assert batch_size % 2 == 0, batch_size
-            [exchanged_targets[1], exchanged_targets[0]], dim=0
+        # exchange: [x1, x2] -> [x2, x1]
-        )  # exchange: [x1, x2] -> [x2, x1]
+        exchanged_targets = torch.roll(ctc_output.detach(), batch_size // 2, dims=0)
         cr_loss = nn.functional.kl_div(
             input=ctc_output,
             target=exchanged_targets,

icefall/utils.py

@@ -186,7 +186,7 @@ class AttributeDict(dict):
         tmp = {}
         for k, v in self.items():
             # PosixPath is ont JSON serializable
-            if isinstance(v, pathlib.Path) or isinstance(v, torch.device):
+            if isinstance(v, (pathlib.Path, torch.device, torch.dtype)):
                 v = str(v)
             tmp[k] = v
         return json.dumps(tmp, indent=indent, sort_keys=True)