more avg and test info

deepspeech/__init__.py

@@ -125,6 +125,7 @@ if not hasattr(paddle, 'cat'):
 def item(x: paddle.Tensor):
     return x.numpy().item()
 
+
 if not hasattr(paddle.Tensor, 'item'):
     logger.warn(
         "override item of paddle.Tensor if exists or register, remove this when fixed!"

deepspeech/exps/u2/model.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Contains U2 model."""
+import json
+import os
 import sys
 import time
 from collections import defaultdict
@@ -439,6 +441,31 @@ class U2Tester(U2Trainer):
             error_rate_type, num_ins, num_ins, errors_sum / len_refs)
         logger.info(msg)
 
+        # test meta results
+        err_meta_path = os.path.splitext(self.args.checkpoint_path)[0] + '.err'
+        err_type_str = "{}".format(error_rate_type)
+        with open(err_meta_path, 'w') as f:
+            data = json.dumps({
+                "epoch":
+                self.epoch,
+                "step":
+                self.iteration,
+                "rtf":
+                rtf,
+                error_rate_type:
+                errors_sum / len_refs,
+                "dataset_hour": (num_frames * stride_ms) / 1000.0 / 3600.0,
+                "process_hour":
+                num_time / 1000.0 / 3600.0,
+                "num_examples":
+                num_ins,
+                "err_sum":
+                errors_sum,
+                "ref_len":
+                len_refs,
+            })
+            f.write(data + '\n')
+
     def run_test(self):
         self.resume_or_scratch()
         try:

examples/aishell/s1/local/avg.sh

@@ -7,7 +7,7 @@ fi
 
 ckpt_path=${1}
 average_num=${2}
-decode_checkpoint=${ckpt_path}/avg_${average_num}.pt
+decode_checkpoint=${ckpt_path}/avg_${average_num}.pdparams
 
 python3 -u ${MAIN_ROOT}/utils/avg_model.py \
 --dst_model ${decode_checkpoint} \
@@ -21,4 +21,4 @@ if [ $? -ne 0 ]; then
 fi
 
 
-exit 0
\ No newline at end of file
+exit 0

utils/avg_model.py

@@ -21,14 +21,15 @@ import paddle
 
 
 def main(args):
-    checkpoints = []
     val_scores = []
-
+    beat_val_scores = []
+    selected_epochs = []
     if args.val_best:
         jsons = glob.glob(f'{args.ckpt_dir}/[!train]*.json')
         for y in jsons:
-            dic_json = json.load(y)
-            loss = dic_json['valid_loss']
+            with open(y, 'r') as f:
+                dic_json = json.load(f)
+            loss = dic_json['val_loss']
             epoch = dic_json['epoch']
             if epoch >= args.min_epoch and epoch <= args.max_epoch:
                 val_scores.append((epoch, loss))
@@ -40,9 +41,11 @@ def main(args):
             args.ckpt_dir + '/{}.pdparams'.format(int(epoch))
             for epoch in sorted_val_scores[:args.num, 0]
         ]
-        print("best val scores = " + str(sorted_val_scores[:args.num, 1]))
-        print("selected epochs = " + str(sorted_val_scores[:args.num, 0].astype(
-            np.int64)))
+
+        beat_val_scores = sorted_val_scores[:args.num, 1]
+        selected_epochs = sorted_val_scores[:args.num, 0].astype(np.int64)
+        print("best val scores = " + str(beat_val_scores))
+        print("selected epochs = " + str(selected_epochs))
     else:
         path_list = glob.glob(f'{args.ckpt_dir}/[!avg][!final]*.pdparams')
         path_list = sorted(path_list, key=os.path.getmtime)
@@ -64,11 +67,21 @@ def main(args):
     # average
     for k in avg.keys():
         if avg[k] is not None:
-            avg[k] = paddle.divide(avg[k], num)
+            avg[k] /= num
 
     paddle.save(avg, args.dst_model)
     print(f'Saving to {args.dst_model}')
 
+    meta_path = os.path.splitext(args.dst_model)[0] + '.avg.json'
+    with open(meta_path, 'w') as f:
+        data = json.dumps({
+            "avg_ckpt": args.dst_model,
+            "ckpt": path_list,
+            "epoch": selected_epochs.tolist(),
+            "val_loss": beat_val_scores.tolist(),
+        })
+        f.write(data + "\n")
+
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='average model')