fix dist batch sampler set_epcoh call

deepspeech/exps/u2/model.py

@@ -24,6 +24,7 @@ import numpy as np
 import paddle
 from paddle import distributed as dist
 from paddle.io import DataLoader
+from paddle.io import DistributedBatchSampler
 from yacs.config import CfgNode
 
 from deepspeech.io.collator import SpeechCollator
@@ -162,8 +163,10 @@ class U2Trainer(Trainer):
             self.save(tag='init')
 
         self.lr_scheduler.step(self.iteration)
-        if self.parallel:
-            self.train_loader.batch_sampler.set_epoch(self.epoch)
+        if hasattr(self.train_loader, "batch_sampler"):
+            batch_sampler = self.train_loader.batch_sampler
+            if isinstance(batch_sampler, DistributedBatchSampler):
+                batch_sampler.set_epoch(self.epoch)
 
         logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.training.n_epoch:
@@ -476,13 +479,6 @@ class U2Tester(U2Trainer):
             })
             f.write(data + '\n')
 
-    # def run_test(self):
-    #     self.resume_or_scratch()
-    #     try:
-    #         self.test()
-    #     except KeyboardInterrupt:
-    #         sys.exit(-1)
-
     def load_inferspec(self):
         """infer model and input spec.
 
@@ -491,7 +487,7 @@ class U2Tester(U2Trainer):
             List[paddle.static.InputSpec]: input spec.
         """
         from deepspeech.models.u2 import U2InferModel
-        infer_model = U2InferModel.from_pretrained(self.test_loader.dataset,
+        infer_model = U2InferModel.from_pretrained(self.test_loader,
                                                    self.config.model.clone(),
                                                    self.args.checkpoint_path)
         feat_dim = self.test_loader.dataset.feature_size
@@ -511,37 +507,3 @@ class U2Tester(U2Trainer):
         static_model = paddle.jit.to_static(infer_model, input_spec=input_spec)
         logger.info(f"Export code: {static_model.forward.code}")
         paddle.jit.save(static_model, self.args.export_path)
-
-    # def run_export(self):
-    #     try:
-    #         self.export()
-    #     except KeyboardInterrupt:
-    #         sys.exit(-1)
-
-    # def setup(self):
-    #     """Setup the experiment.
-    #     """
-    #     paddle.set_device(self.args.device)
-
-    #     self.setup_output_dir()
-    #     self.setup_checkpointer()
-
-    #     self.setup_dataloader()
-    #     self.setup_model()
-
-    #     self.iteration = 0
-    #     self.epoch = 0
-
-    # def setup_output_dir(self):
-    #     """Create a directory used for output.
-    #     """
-    #     # output dir
-    #     if self.args.output:
-    #         output_dir = Path(self.args.output).expanduser()
-    #         output_dir.mkdir(parents=True, exist_ok=True)
-    #     else:
-    #         output_dir = Path(
-    #             self.args.checkpoint_path).expanduser().parent.parent
-    #         output_dir.mkdir(parents=True, exist_ok=True)
-
-    #     self.output_dir = output_dir

deepspeech/frontend/augmentor/spec_augment.py

@@ -41,7 +41,8 @@ class SpecAugmentor(AugmentorBase):
                  W=40,
                  adaptive_number_ratio=0,
                  adaptive_size_ratio=0,
-                 max_n_time_masks=20):
+                 max_n_time_masks=20,
+                 **kwargs):
         """SpecAugment class.
         Args:
             rng (random.Random): random generator object.
@@ -121,7 +122,7 @@ class SpecAugmentor(AugmentorBase):
     def time_mask(self):
         return self._time_mask
 
-    def time_warp(xs, W=40):
+    def time_warp(self, xs, W=40):
         raise NotImplementedError
 
     def mask_freq(self, xs, replace_with_zero=False):

deepspeech/frontend/featurizer/text_featurizer.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 """Contains the text featurizer class."""
 import sentencepiece as spm
+from pprint import pformat
 
 from ..utility import EOS
 from ..utility import SPACE
@@ -206,7 +207,7 @@ class TextFeaturizer():
         """Load vocabulary from file."""
         vocab_list = load_dict(vocab_filepath, maskctc)
         assert vocab_list is not None
-        logger.info(f"Vocab: {vocab_list}")
+        logger.info(f"Vocab: {pformat(vocab_list)}")
 
         id2token = dict(
             [(idx, token) for (idx, token) in enumerate(vocab_list)])
@@ -220,10 +221,10 @@ class TextFeaturizer():
         sos_id = vocab_list.index(SOS) if SOS in vocab_list else -1
         space_id = vocab_list.index(SPACE) if SPACE in vocab_list else -1
 
+        logger.info(f"BLANK id: {blank_id}")
         logger.info(f"UNK id: {unk_id}")
         logger.info(f"EOS id: {eos_id}")
         logger.info(f"SOS id: {sos_id}")
         logger.info(f"SPACE id: {space_id}")
-        logger.info(f"BLANK id: {blank_id}")
         logger.info(f"MASKCTC id: {maskctc_id}")
         return token2id, id2token, vocab_list, unk_id, eos_id

deepspeech/models/u2/u2.py

@@ -911,8 +911,10 @@ class U2Model(U2BaseModel):
             DeepSpeech2Model: The model built from pretrained result.
         """
         with UpdateConfig(config):
-            config.input_dim = dataloader.collate_fn.feature_size
-            config.output_dim = dataloader.collate_fn.vocab_size
+            #config.input_dim = dataloader.collate_fn.feature_size
+            #config.output_dim = dataloader.collate_fn.vocab_size
+            config.input_dim = dataloader.dataset.feature_size
+            config.output_dim = dataloader.dataset.vocab_size
 
         model = cls.from_config(config)
 

deepspeech/training/trainer.py

@@ -17,6 +17,7 @@ from pathlib import Path
 
 import paddle
 from paddle import distributed as dist
+from paddle.io import DistributedBatchSampler
 from tensorboardX import SummaryWriter
 
 from deepspeech.utils import mp_tools
@@ -179,8 +180,10 @@ class Trainer():
         """Reset the train loader seed and increment `epoch`.
         """
         self.epoch += 1
-        if self.parallel:
-            self.train_loader.batch_sampler.set_epoch(self.epoch)
+        if hasattr(self.train_loader, "batch_sampler"):
+            batch_sampler = self.train_loader.batch_sampler
+            if isinstance(batch_sampler, DistributedBatchSampler):
+                batch_sampler.set_epoch(self.epoch)
 
     def train(self):
         """The training process control by epoch."""
@@ -190,8 +193,10 @@ class Trainer():
             self.save(tag='init')
 
         self.lr_scheduler.step(self.iteration)
-        if self.parallel:
-            self.train_loader.batch_sampler.set_epoch(self.epoch)
+        if hasattr(self.train_loader, "batch_sampler"):
+            batch_sampler = self.train_loader.batch_sampler
+            if isinstance(batch_sampler, DistributedBatchSampler):
+                batch_sampler.set_epoch(self.epoch)
 
         logger.info(f"Train Total Examples: {len(self.train_loader.dataset)}")
         while self.epoch < self.config.training.n_epoch:

examples/tiny/s0/conf/augmentation.json

 [
+  {
+    "type": "speed",
+    "params": {
+      "min_speed_rate": 0.9,
+      "max_speed_rate": 1.1,
+      "num_rates": 3
+    },
+    "prob": 0.0
+  },
   {
     "type": "shift",
     "params": {
@@ -6,5 +15,22 @@
       "max_shift_ms": 5
     },
     "prob": 1.0
+  },
+  {
+    "type": "specaug",
+    "params": {
+      "W": 5,
+      "warp_mode": "PIL",
+      "F": 30,
+      "n_freq_masks": 2,
+      "T": 40,
+      "n_time_masks": 2,
+      "p": 1.0,
+      "adaptive_number_ratio": 0,
+      "adaptive_size_ratio": 0,
+      "max_n_time_masks": 20,
+      "replace_with_zero": true
+    },
+    "prob": 1.0
   }
 ]

examples/tiny/s0/local/data.sh

-#! /usr/bin/env bash
+#!/bin/bash
 
 stage=-1
 stop_stage=100

examples/tiny/s1/path.sh

-export MAIN_ROOT=${PWD}/../../../
+export MAIN_ROOT=`realpath ${PWD}/../../../`
 
 export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
 export LC_ALL=C