From 822fe21a837ba6ed8e888283fb2a22d5f31d5389 Mon Sep 17 00:00:00 2001
From: luomingshuang <739314837@qq.com>
Date: Tue, 17 May 2022 09:38:25 +0800
Subject: [PATCH] use dynamicbucketsampler for decoding

---
 .../ASR/pruned_transducer_stateless2/asr_datamodule.py     | 5 +++--
 .../ASR/pruned_transducer_stateless2/decode.py             | 7 -------
 2 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py
index de83cef5a..343baf65a 100644
--- a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py
+++ b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py
@@ -34,6 +34,7 @@ from lhotse.dataset import (
     BucketingSampler,
     CutConcatenate,
     CutMix,
+    DynamicBucketingSampler,
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
     SingleCutSampler,
@@ -350,7 +351,7 @@ class Aidatatang_200zhAsrDataModule:
                 cut_transforms=transforms,
                 return_cuts=self.args.return_cuts,
             )
-        valid_sampler = BucketingSampler(
+        valid_sampler = DynamicBucketingSampler(
             cuts_valid,
             max_duration=self.args.max_duration,
             rank=0,
@@ -382,7 +383,7 @@ class Aidatatang_200zhAsrDataModule:
             else PrecomputedFeatures(),
             return_cuts=self.args.return_cuts,
         )
-        sampler = BucketingSampler(
+        sampler = DynamicBucketingSampler(
             cuts,
             max_duration=self.args.max_duration,
             rank=0,
diff --git a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py
index 83a442b90..b78c600c3 100755
--- a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py
+++ b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py
@@ -508,13 +508,6 @@ def main():
         model.to(device)
         model.load_state_dict(average_checkpoints(filenames, device=device))
 
-        average = average_checkpoints(filenames, device=device)
-        checkpoint = {"model": average}
-        torch.save(
-            checkpoint,
-            "pruned_transducer_stateless2/pretrained_average_11_to_29.pt",
-        )
-
     model.to(device)
     model.eval()
     model.device = device