fix pre-commit

deepspeech/exps/deepspeech2/config.py

@@ -13,12 +13,11 @@
 # limitations under the License.
 from yacs.config import CfgNode
 
-from deepspeech.models.deepspeech2 import DeepSpeech2Model
-from deepspeech.io.dataset import ManifestDataset
-from deepspeech.io.collator import SpeechCollator
-from deepspeech.exps.deepspeech2.model import DeepSpeech2Trainer
 from deepspeech.exps.deepspeech2.model import DeepSpeech2Tester
-
+from deepspeech.exps.deepspeech2.model import DeepSpeech2Trainer
+from deepspeech.io.collator import SpeechCollator
+from deepspeech.io.dataset import ManifestDataset
+from deepspeech.models.deepspeech2 import DeepSpeech2Model
 
 _C = CfgNode()
 

deepspeech/exps/deepspeech2/model.py

@@ -15,11 +15,13 @@
 import time
 from collections import defaultdict
 from pathlib import Path
+from typing import Optional
 
 import numpy as np
 import paddle
 from paddle import distributed as dist
 from paddle.io import DataLoader
+from yacs.config import CfgNode
 
 from deepspeech.io.collator import SpeechCollator
 from deepspeech.io.dataset import ManifestDataset
@@ -33,9 +35,6 @@ from deepspeech.utils import error_rate
 from deepspeech.utils import layer_tools
 from deepspeech.utils import mp_tools
 from deepspeech.utils.log import Log
-
-from typing import Optional
-from yacs.config import CfgNode
 logger = Log(__name__).getlog()
 
 
@@ -44,13 +43,13 @@ class DeepSpeech2Trainer(Trainer):
     def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
         # training config
         default = CfgNode(
-                    dict(
-                        lr=5e-4,  # learning rate
-                        lr_decay=1.0,  # learning rate decay
-                        weight_decay=1e-6,  # the coeff of weight decay
-                        global_grad_clip=5.0,  # the global norm clip
-                        n_epoch=50,  # train epochs
-                    ))
+            dict(
+                lr=5e-4,  # learning rate
+                lr_decay=1.0,  # learning rate decay
+                weight_decay=1e-6,  # the coeff of weight decay
+                global_grad_clip=5.0,  # the global norm clip
+                n_epoch=50,  # train epochs
+            ))
 
         if config is not None:
             config.merge_from_other_cfg(default)
@@ -184,7 +183,6 @@ class DeepSpeech2Trainer(Trainer):
 
         collate_fn_train = SpeechCollator.from_config(config)
 
-
         config.collator.augmentation_config = ""
         collate_fn_dev = SpeechCollator.from_config(config)
         self.train_loader = DataLoader(
@@ -206,18 +204,18 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
     def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
         # testing config
         default = CfgNode(
-                dict(
-                    alpha=2.5,  # Coef of LM for beam search.
-                    beta=0.3,  # Coef of WC for beam search.
-                    cutoff_prob=1.0,  # Cutoff probability for pruning.
-                    cutoff_top_n=40,  # Cutoff number for pruning.
-                    lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm',  # Filepath for language model.
-                    decoding_method='ctc_beam_search',  # Decoding method. Options: ctc_beam_search, ctc_greedy
-                    error_rate_type='wer',  # Error rate type for evaluation. Options `wer`, 'cer'
-                    num_proc_bsearch=8,  # # of CPUs for beam search.
-                    beam_size=500,  # Beam search width.
-                    batch_size=128,  # decoding batch size
-                ))
+            dict(
+                alpha=2.5,  # Coef of LM for beam search.
+                beta=0.3,  # Coef of WC for beam search.
+                cutoff_prob=1.0,  # Cutoff probability for pruning.
+                cutoff_top_n=40,  # Cutoff number for pruning.
+                lang_model_path='models/lm/common_crawl_00.prune01111.trie.klm',  # Filepath for language model.
+                decoding_method='ctc_beam_search',  # Decoding method. Options: ctc_beam_search, ctc_greedy
+                error_rate_type='wer',  # Error rate type for evaluation. Options `wer`, 'cer'
+                num_proc_bsearch=8,  # # of CPUs for beam search.
+                beam_size=500,  # Beam search width.
+                batch_size=128,  # decoding batch size
+            ))
 
         if config is not None:
             config.merge_from_other_cfg(default)
@@ -235,7 +233,13 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             trans.append(''.join([chr(i) for i in ids]))
         return trans
 
-    def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout = None):
+    def compute_metrics(self,
+                        utts,
+                        audio,
+                        audio_len,
+                        texts,
+                        texts_len,
+                        fout=None):
         cfg = self.config.decoding
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
@@ -257,7 +261,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
             cutoff_top_n=cfg.cutoff_top_n,
             num_processes=cfg.num_proc_bsearch)
 
-        for utt, target, result in zip(utts, target_transcripts, result_transcripts):
+        for utt, target, result in zip(utts, target_transcripts,
+                                       result_transcripts):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref
@@ -287,7 +292,8 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
         with open(self.args.result_file, 'w') as fout:
             for i, batch in enumerate(self.test_loader):
                 utts, audio, audio_len, texts, texts_len = batch
-                metrics = self.compute_metrics(utts, audio, audio_len, texts, texts_len, fout)
+                metrics = self.compute_metrics(utts, audio, audio_len, texts,
+                                               texts_len, fout)
                 errors_sum += metrics['errors_sum']
                 len_refs += metrics['len_refs']
                 num_ins += metrics['num_ins']

deepspeech/exps/u2/config.py

@@ -15,9 +15,9 @@ from yacs.config import CfgNode
 
 from deepspeech.exps.u2.model import U2Tester
 from deepspeech.exps.u2.model import U2Trainer
+from deepspeech.io.collator import SpeechCollator
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.models.u2 import U2Model
-from deepspeech.io.collator import SpeechCollator
 
 _C = CfgNode()
 

deepspeech/exps/u2/model.py

@@ -78,7 +78,8 @@ class U2Trainer(Trainer):
         start = time.time()
         utt, audio, audio_len, text, text_len = batch_data
 
-        loss, attention_loss, ctc_loss = self.model(audio, audio_len, text, text_len)
+        loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
+                                                    text_len)
         # loss div by `batch_size * accum_grad`
         loss /= train_conf.accum_grad
         loss.backward()
@@ -121,7 +122,8 @@ class U2Trainer(Trainer):
         total_loss = 0.0
         for i, batch in enumerate(self.valid_loader):
             utt, audio, audio_len, text, text_len = batch
-            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text, text_len)
+            loss, attention_loss, ctc_loss = self.model(audio, audio_len, text,
+                                                        text_len)
             if paddle.isfinite(loss):
                 num_utts = batch[1].shape[0]
                 num_seen_utts += num_utts
@@ -221,7 +223,7 @@ class U2Trainer(Trainer):
         dev_dataset = ManifestDataset.from_config(config)
 
         collate_fn_train = SpeechCollator.from_config(config)
-        
+
         config.collator.augmentation_config = ""
         collate_fn_dev = SpeechCollator.from_config(config)
 
@@ -372,7 +374,13 @@ class U2Tester(U2Trainer):
             trans.append(''.join([chr(i) for i in ids]))
         return trans
 
-    def compute_metrics(self, utts, audio, audio_len, texts, texts_len, fout=None):
+    def compute_metrics(self,
+                        utts,
+                        audio,
+                        audio_len,
+                        texts,
+                        texts_len,
+                        fout=None):
         cfg = self.config.decoding
         errors_sum, len_refs, num_ins = 0.0, 0, 0
         errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
@@ -399,7 +407,8 @@ class U2Tester(U2Trainer):
             simulate_streaming=cfg.simulate_streaming)
         decode_time = time.time() - start_time
 
-        for utt, target, result in zip(utts, target_transcripts, result_transcripts):
+        for utt, target, result in zip(utts, target_transcripts,
+                                       result_transcripts):
             errors, len_ref = errors_func(target, result)
             errors_sum += errors
             len_refs += len_ref

deepspeech/io/collator.py

@@ -11,21 +11,21 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import io
+import time
+from collections import namedtuple
+from typing import Optional
+
 import numpy as np
+from yacs.config import CfgNode
 
-from deepspeech.frontend.utility import IGNORE_ID
-from deepspeech.io.utility import pad_sequence
-from deepspeech.utils.log import Log
 from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
 from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
 from deepspeech.frontend.normalizer import FeatureNormalizer
 from deepspeech.frontend.speech import SpeechSegment
-import io
-import time
-from yacs.config import CfgNode
-from typing import Optional
-
-from collections import namedtuple
+from deepspeech.frontend.utility import IGNORE_ID
+from deepspeech.io.utility import pad_sequence
+from deepspeech.utils.log import Log
 
 __all__ = ["SpeechCollator"]
 
@@ -34,6 +34,7 @@ logger = Log(__name__).getlog()
 # namedtupe need global for pickle.
 TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
 
+
 class SpeechCollator():
     @classmethod
     def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
@@ -56,8 +57,7 @@ class SpeechCollator():
                 use_dB_normalization=True,
                 target_dB=-20,
                 dither=1.0,  # feature dither
-                keep_transcription_text=False
-            ))
+                keep_transcription_text=False))
 
         if config is not None:
             config.merge_from_other_cfg(default)
@@ -84,7 +84,9 @@ class SpeechCollator():
         if isinstance(config.collator.augmentation_config, (str, bytes)):
             if config.collator.augmentation_config:
                 aug_file = io.open(
-                    config.collator.augmentation_config, mode='r', encoding='utf8')
+                    config.collator.augmentation_config,
+                    mode='r',
+                    encoding='utf8')
             else:
                 aug_file = io.StringIO(initial_value='{}', newline='')
         else:
@@ -92,43 +94,46 @@ class SpeechCollator():
             assert isinstance(aug_file, io.StringIO)
 
         speech_collator = cls(
-                aug_file=aug_file,
-                random_seed=0,
-                mean_std_filepath=config.collator.mean_std_filepath,
-                unit_type=config.collator.unit_type,
-                vocab_filepath=config.collator.vocab_filepath,
-                spm_model_prefix=config.collator.spm_model_prefix,
-                specgram_type=config.collator.specgram_type, 
-                feat_dim=config.collator.feat_dim, 
-                delta_delta=config.collator.delta_delta, 
-                stride_ms=config.collator.stride_ms, 
-                window_ms=config.collator.window_ms, 
-                n_fft=config.collator.n_fft, 
-                max_freq=config.collator.max_freq, 
-                target_sample_rate=config.collator.target_sample_rate, 
-                use_dB_normalization=config.collator.use_dB_normalization,
-                target_dB=config.collator.target_dB,
-                dither=config.collator.dither, 
-                keep_transcription_text=config.collator.keep_transcription_text
-            )
+            aug_file=aug_file,
+            random_seed=0,
+            mean_std_filepath=config.collator.mean_std_filepath,
+            unit_type=config.collator.unit_type,
+            vocab_filepath=config.collator.vocab_filepath,
+            spm_model_prefix=config.collator.spm_model_prefix,
+            specgram_type=config.collator.specgram_type,
+            feat_dim=config.collator.feat_dim,
+            delta_delta=config.collator.delta_delta,
+            stride_ms=config.collator.stride_ms,
+            window_ms=config.collator.window_ms,
+            n_fft=config.collator.n_fft,
+            max_freq=config.collator.max_freq,
+            target_sample_rate=config.collator.target_sample_rate,
+            use_dB_normalization=config.collator.use_dB_normalization,
+            target_dB=config.collator.target_dB,
+            dither=config.collator.dither,
+            keep_transcription_text=config.collator.keep_transcription_text)
         return speech_collator
 
-    def __init__(self, aug_file, mean_std_filepath,  
-                vocab_filepath, spm_model_prefix,
-                random_seed=0,
-                unit_type="char",
-                specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
-                feat_dim=0,  # 'mfcc', 'fbank'
-                delta_delta=False,  # 'mfcc', 'fbank'
-                stride_ms=10.0,  # ms
-                window_ms=20.0,  # ms
-                n_fft=None,  # fft points
-                max_freq=None,  # None for samplerate/2
-                target_sample_rate=16000,  # target sample rate
-                use_dB_normalization=True,
-                target_dB=-20,
-                dither=1.0,
-                keep_transcription_text=True):
+    def __init__(
+            self,
+            aug_file,
+            mean_std_filepath,
+            vocab_filepath,
+            spm_model_prefix,
+            random_seed=0,
+            unit_type="char",
+            specgram_type='linear',  # 'linear', 'mfcc', 'fbank'
+            feat_dim=0,  # 'mfcc', 'fbank'
+            delta_delta=False,  # 'mfcc', 'fbank'
+            stride_ms=10.0,  # ms
+            window_ms=20.0,  # ms
+            n_fft=None,  # fft points
+            max_freq=None,  # None for samplerate/2
+            target_sample_rate=16000,  # target sample rate
+            use_dB_normalization=True,
+            target_dB=-20,
+            dither=1.0,
+            keep_transcription_text=True):
         """SpeechCollator Collator
 
         Args:
@@ -159,9 +164,8 @@ class SpeechCollator():
 
         self._local_data = TarLocalData(tar2info={}, tar2object={})
         self._augmentation_pipeline = AugmentationPipeline(
-            augmentation_config=aug_file.read(), 
-            random_seed=random_seed)
-        
+            augmentation_config=aug_file.read(), random_seed=random_seed)
+
         self._normalizer = FeatureNormalizer(
             mean_std_filepath) if mean_std_filepath else None
 
@@ -290,8 +294,6 @@ class SpeechCollator():
         text_lens = np.array(text_lens).astype(np.int64)
         return utts, padded_audios, audio_lens, padded_texts, text_lens
 
-
-    
     @property
     def manifest(self):
         return self._manifest
@@ -318,4 +320,4 @@ class SpeechCollator():
 
     @property
     def stride_ms(self):
-        return self._speech_featurizer.stride_ms
\ No newline at end of file
+        return self._speech_featurizer.stride_ms

deepspeech/io/dataset.py

@@ -12,19 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import io
-import tarfile
-import time
-from collections import namedtuple
 from typing import Optional
 
-import numpy as np
 from paddle.io import Dataset
 from yacs.config import CfgNode
 
-from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
-from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
-from deepspeech.frontend.normalizer import FeatureNormalizer
-from deepspeech.frontend.speech import SpeechSegment
 from deepspeech.frontend.utility import read_manifest
 from deepspeech.utils.log import Log
 
@@ -46,8 +38,7 @@ class ManifestDataset(Dataset):
                 max_output_len=float('inf'),
                 min_output_len=0.0,
                 max_output_input_ratio=float('inf'),
-                min_output_input_ratio=0.0,
-            ))
+                min_output_input_ratio=0.0, ))
 
         if config is not None:
             config.merge_from_other_cfg(default)
@@ -66,7 +57,6 @@ class ManifestDataset(Dataset):
         assert 'manifest' in config.data
         assert config.data.manifest
 
-
         dataset = cls(
             manifest_path=config.data.manifest,
             max_input_len=config.data.max_input_len,
@@ -74,8 +64,7 @@ class ManifestDataset(Dataset):
             max_output_len=config.data.max_output_len,
             min_output_len=config.data.min_output_len,
             max_output_input_ratio=config.data.max_output_input_ratio,
-            min_output_input_ratio=config.data.min_output_input_ratio,
-            )
+            min_output_input_ratio=config.data.min_output_input_ratio, )
         return dataset
 
     def __init__(self,
@@ -111,7 +100,6 @@ class ManifestDataset(Dataset):
             min_output_input_ratio=min_output_input_ratio)
         self._manifest.sort(key=lambda x: x["feat_shape"][0])
 
-
     def __len__(self):
         return len(self._manifest)
 

deepspeech/models/u2.py

@@ -905,7 +905,6 @@ class U2InferModel(U2Model):
     def __init__(self, configs: dict):
         super().__init__(configs)
 
-
     def forward(self,
                 feats,
                 feats_lengths,