can train

deepspeech/exps/deepspeech2/bin/deploy/runtime.py

@@ -79,9 +79,9 @@ def inference(config, args):
 
 def start_server(config, args):
     """Start the ASR server"""
+    config.defrost()
     config.data.manfiest = config.data.test_manifest
-    config.data.augmentation_config = io.StringIO(
-        initial_value='{}', newline='')
+    config.data.augmentation_config = ""
     config.data.keep_transcription_text = True
     dataset = ManifestDataset.from_config(config)
 

deepspeech/exps/deepspeech2/bin/deploy/server.py

@@ -31,9 +31,9 @@ from deepspeech.io.dataset import ManifestDataset
 
 def start_server(config, args):
     """Start the ASR server"""
+    config.defrost()
     config.data.manfiest = config.data.test_manifest
-    config.data.augmentation_config = io.StringIO(
-        initial_value='{}', newline='')
+    config.data.augmentation_config = ""
     config.data.keep_transcription_text = True
     dataset = ManifestDataset.from_config(config)
 

deepspeech/exps/deepspeech2/bin/tune.py

@@ -36,10 +36,9 @@ def tune(config, args):
         raise ValueError("num_alphas must be non-negative!")
     if not args.num_betas >= 0:
         raise ValueError("num_betas must be non-negative!")
-
+    config.defrost()
     config.data.manfiest = config.data.dev_manifest
-    config.data.augmentation_config = io.StringIO(
-        initial_value='{}', newline='')
+    config.data.augmentation_config = ""
     config.data.keep_transcription_text = True
     dev_dataset = ManifestDataset.from_config(config)
 

deepspeech/exps/deepspeech2/model.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 """Contains DeepSpeech2 model."""
 
-import io
 import time
 import logging
 import numpy as np
@@ -24,7 +23,7 @@ import paddle
 from paddle import distributed as dist
 from paddle.io import DataLoader
 
-from deepspeech.training import Trainer
+from deepspeech.training.trainer import Trainer
 from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
 
 from deepspeech.utils import mp_tools
@@ -140,15 +139,15 @@ class DeepSpeech2Trainer(Trainer):
         self.logger.info("Setup model/optimizer/lr_scheduler!")
 
     def setup_dataloader(self):
-        config = self.config
+        config = self.config.clone()
+        config.defrost()
         config.data.keep_transcription_text = False
 
-        config.data.manfiest = config.data.train_manifest
+        config.data.manifest = config.data.train_manifest
         train_dataset = ManifestDataset.from_config(config)
 
-        config.data.manfiest = config.data.dev_manifest
-        config.data.augmentation_config = io.StringIO(
-            initial_value='{}', newline='')
+        config.data.manifest = config.data.dev_manifest
+        config.data.augmentation_config = ""
         dev_dataset = ManifestDataset.from_config(config)
 
         if self.parallel:
@@ -324,13 +323,12 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         self.logger.info("Setup model!")
 
     def setup_dataloader(self):
-        config = self.config
+        config = self.config.clone()
+        config.defrost()
         # return raw text
 
-        config.data.manfiest = config.data.test_manifest
-        config.data.augmentation_config = io.StringIO(
-            initial_value='{}', newline='')
-        config.data.keep_transcription_text = True
+        config.data.manifest = config.data.test_manifest
+        config.data.augmentation_config = ""
         test_dataset = ManifestDataset.from_config(config)
 
         # return text ord id

deepspeech/exps/u2/config.py

@@ -14,23 +14,20 @@
 
 from yacs.config import CfgNode
 
+from deepspeech.io.dataset import ManifestDataset
 from deepspeech.models.u2 import U2Model
 from deepspeech.exps.u2.model import U2Trainer
 from deepspeech.exps.u2.model import U2Tester
 
 _C = CfgNode()
 
-_C.data = CfgNode()
-ManifestDataset.params(_C.data)
+_C.data = ManifestDataset.params()
 
-_C.model = CfgNode()
-U2Model.params(_C.model)
+_C.model = U2Model.params()
 
-_C.training = CfgNode()
-U2Trainer.params(_C.training)
+_C.training = U2Trainer.params()
 
-_C.decoding = CfgNode()
-U2Tester.params(_C.training)
+_C.decoding = U2Tester.params()
 
 
 def get_cfg_defaults():

deepspeech/exps/u2/model.py

@@ -18,12 +18,14 @@ import logging
 import numpy as np
 from collections import defaultdict
 from pathlib import Path
+from typing import Optional
+from yacs.config import CfgNode
 
 import paddle
 from paddle import distributed as dist
 from paddle.io import DataLoader
 
-from deepspeech.training import Trainer
+from deepspeech.training.trainer import Trainer
 from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
 from deepspeech.training.scheduler import WarmupLR
 
@@ -77,7 +79,7 @@ class U2Trainer(Trainer):
         self.model.train()
 
         start = time.time()
-        loss = self.model(*batch_data)
+        loss, attention_loss, ctc_loss = self.model(*batch_data)
         loss.backward()
         layer_tools.print_grads(self.model, print_func=None)
         if self.iteration % train_conf.accum_grad == 0:
@@ -88,13 +90,15 @@ class U2Trainer(Trainer):
 
         losses_np = {
             'train_loss': float(loss),
-            'train_loss_div_batchsize':
-            float(loss) / self.config.data.batch_size
+            'train_att_loss': float(attention_loss),
+            'train_ctc_loss': float(ctc_loss),
         }
         msg = "Train: Rank: {}, ".format(dist.get_rank())
         msg += "epoch: {}, ".format(self.epoch)
         msg += "step: {}, ".format(self.iteration)
         msg += "time: {:>.3f}s, ".format(iteration_time)
+        msg += f"batch size: {self.config.data.batch_size}, "
+        msg += f"accum: {train_config.accum_grad}, "
         msg += ', '.join('{}: {:>.6f}'.format(k, v)
                          for k, v in losses_np.items())
         if self.iteration % train_conf.log_interval == 0:
@@ -113,11 +117,11 @@ class U2Trainer(Trainer):
             f"Valid Total Examples: {len(self.valid_loader.dataset)}")
         valid_losses = defaultdict(list)
         for i, batch in enumerate(self.valid_loader):
-            loss = self.model(*batch)
+            total_loss, attention_loss, ctc_loss = self.model(*batch)
 
-            valid_losses['val_loss'].append(float(loss))
-            valid_losses['val_loss_div_batchsize'].append(
-                float(loss) / self.config.data.batch_size)
+            valid_losses['val_loss'].append(float(total_loss))
+            valid_losses['val_att_loss'].append(float(attention_loss))
+            valid_losses['val_ctc_loss'].append(float(ctc_loss))
 
         # write visual log
         valid_losses = {k: np.mean(v) for k, v in valid_losses.items()}
@@ -137,13 +141,14 @@ class U2Trainer(Trainer):
 
     def setup_dataloader(self):
         config = self.config.clone()
+        config.defrost()
         config.data.keep_transcription_text = False
 
         # train/valid dataset, return token ids
-        config.data.manfiest = config.data.train_manifest
+        config.data.manifest = config.data.train_manifest
         train_dataset = ManifestDataset.from_config(config)
 
-        config.data.manfiest = config.data.dev_manifest
+        config.data.manifest = config.data.dev_manifest
         config.data.augmentation_config = ""
         dev_dataset = ManifestDataset.from_config(config)
 
@@ -181,7 +186,7 @@ class U2Trainer(Trainer):
         # test dataset, return raw text
         config.data.keep_transcription_text = True
         config.data.augmentation_config = ""
-        config.data.manfiest = config.data.test_manifest
+        config.data.manifest = config.data.test_manifest
         test_dataset = ManifestDataset.from_config(config)
         # return text ord id
         self.test_loader = DataLoader(
@@ -193,10 +198,12 @@ class U2Trainer(Trainer):
         self.logger.info("Setup train/valid/test Dataloader!")
 
     def setup_model(self):
-        config = self.config.clone()
+        config = self.config
         model_conf = config.model
+        model_conf.defrost()
         model_conf.input_dim = self.train_loader.dataset.feature_size
         model_conf.output_dim = self.train_loader.dataset.vocab_size
+        model_conf.freeze()
         model = U2Model.from_config(model_conf)
 
         if self.parallel:
@@ -206,12 +213,12 @@ class U2Trainer(Trainer):
 
         train_config = config.training
         optim_type = train_config.optim
-        optim_conf = train_config.train_config
+        optim_conf = train_config.optim_conf
         scheduler_type = train_config.scheduler
         scheduler_conf = train_config.scheduler_conf
 
         grad_clip = ClipGradByGlobalNormWithLog(train_config.global_grad_clip)
-        weight_decay = paddle.regularizer.L2Decay(train_config.weight_decay)
+        weight_decay = paddle.regularizer.L2Decay(optim_conf.weight_decay)
 
         if scheduler_type == 'expdecaylr':
             lr_scheduler = paddle.optimizer.lr.ExponentialDecay(

deepspeech/frontend/normalizer.py

@@ -93,5 +93,5 @@ class FeatureNormalizer(object):
             features.append(
                 featurize_func(AudioSegment.from_file(instance["feat"])))
         features = np.hstack(features)  #(D, T)
-        self._mean = np.mean(features, axis=1).reshape([-1, 1])  #(D, 1)
-        self._std = np.std(features, axis=1).reshape([-1, 1])  #(D, 1)
+        self._mean = np.mean(features, axis=1).reshape([1, -1])  #(1, D)
+        self._std = np.std(features, axis=1).reshape([1, -1])  #(1, D)

deepspeech/frontend/utility.py

@@ -235,8 +235,8 @@ def _load_kaldi_cmvn(kaldi_cmvn_file):
 
 def _load_npz_cmvn(npz_cmvn_file, eps=1e-20):
     npzfile = np.load(npz_cmvn_file)
-    means = npzfile["mean"]  #(D, 1)
-    std = npzfile["std"]  #(D, 1)
+    means = npzfile["mean"]  #(1, D)
+    std = npzfile["std"]  #(1, D)
     std = np.clip(std, eps, None)
     variance = 1.0 / std
     cmvn = np.array([means, variance])

deepspeech/io/dataset.py

@@ -16,8 +16,9 @@ import io
 import random
 import tarfile
 import logging
-from collections import namedtuple
+from typing import Optional
 from yacs.config import CfgNode
+from collections import namedtuple
 
 from paddle.io import Dataset
 
@@ -42,6 +43,7 @@ class ManifestDataset(Dataset):
                 train_manifest="",
                 dev_manifest="",
                 test_manifest="",
+                manifest="",
                 unit_type="char",
                 vocab_filepath="",
                 spm_model_prefix="",
@@ -60,7 +62,7 @@ class ManifestDataset(Dataset):
                 raw_wav=True,  # use raw_wav or kaldi feature
                 specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
                 feat_dim=0,  # 'mfcc', 'fbank'
-                delat_delta=False,  # 'mfcc', 'fbank'
+                delta_delta=False,  # 'mfcc', 'fbank'
                 target_sample_rate=16000,  # target sample rate
                 use_dB_normalization=True,
                 target_dB=-20,
@@ -86,8 +88,9 @@ class ManifestDataset(Dataset):
         Returns:
             ManifestDataset: dataet object.
         """
-        assert manifest in config.data
-        assert keep_transcription_text in config.data
+        assert 'manifest' in config.data
+        assert config.data.manifest
+        assert 'keep_transcription_text' in config.data
 
         if isinstance(config.data.augmentation_config, (str, bytes)):
             if config.data.augmentation_config:
@@ -119,7 +122,7 @@ class ManifestDataset(Dataset):
             target_sample_rate=config.data.target_sample_rate,
             specgram_type=config.data.specgram_type,
             feat_dim=config.data.feat_dim,
-            delta_delta=config.data.delat_delta,
+            delta_delta=config.data.delta_delta,
             use_dB_normalization=config.data.use_dB_normalization,
             target_dB=config.data.target_dB,
             random_seed=config.data.random_seed,

deepspeech/models/u2.py

@@ -75,8 +75,8 @@ class U2BaseModel(nn.Module):
                 dropout_rate=0.1,
                 positional_dropout_rate=0.1,
                 attention_dropout_rate=0.0,
-                input_layer=conv2d,  # encoder input type, you can chose conv2d, conv2d6 and conv2d8
-                normalize_before=true,
+                input_layer='conv2d',  # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+                normalize_before=True,
                 cnn_module_kernel=15,
                 use_cnn_module=True,
                 activation_type='swish',
@@ -98,7 +98,7 @@ class U2BaseModel(nn.Module):
             dict(
                 ctc_weight=0.3,
                 lsm_weight=0.1,  # label smoothing option
-                length_normalized_loss=false, ))
+                length_normalized_loss=False, ))
 
         if config is not None:
             config.merge_from_other_cfg(default)
@@ -744,15 +744,9 @@ class U2Model(U2BaseModel):
             ValueError: raise when using not support encoder type.
 
         Returns:
-            int, nn.Layer, nn.Layer, nn.Layer: vocab size, encoder, decoder, ctc 
+            nn.Layer: U2Model
         """
-        vocab_size, encoder, decoder, ctc = U2Model._init_from_config(configs)
-
-        model = cls(vocab_size=vocab_size,
-                    encoder=encoder,
-                    decoder=decoder,
-                    ctc=ctc,
-                    **configs['model_conf'])
+        model = cls(configs)
         return model
 
     @classmethod

deepspeech/training/scheduler.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 import logging
+from typing import Union
+from typeguard import check_argument_types
 
 from paddle.optimizer.lr import LRScheduler
 

examples/tiny/s1/conf/augmentation.config

+[
+    {
+        "type": "shift",
+        "params": {"min_shift_ms": -5,
+                   "max_shift_ms": 5},
+        "prob": 1.0
+    }
+]

examples/tiny/s1/conf/conformer.yaml

@@ -5,7 +5,7 @@ data:
   test_manifest: data/manifest.tiny
   vocab_filepath: data/vocab.txt 
   unit_type: 'spm'
-  spm_model_prefix: 'bpe_unigram_200'
+  spm_model_prefix: 'data/bpe_unigram_200'
   mean_std_filepath: ""
   augmentation_config: conf/augmentation.config
   batch_size: 4
@@ -119,11 +119,11 @@ training:
   optim: adam
   optim_conf:
     lr: 0.002
-    lr_decay: 1.0 
     weight_decay: 1e-06
   scheduler: warmuplr     # pytorch v1.1.0+ required
   scheduler_conf:
     warmup_steps: 25000
+    lr_decay: 1.0
   log_interval: 100
 
 decoding: