spec augment

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

@@ -18,8 +18,10 @@ import numpy as np
 import paddle
 from paddle.inference import Config
 from paddle.inference import create_predictor
+from paddle.io import DataLoader
 
 from deepspeech.exps.deepspeech2.config import get_cfg_defaults
+from deepspeech.io.collator import SpeechCollator
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.models.deepspeech2 import DeepSpeech2Model
 from deepspeech.training.cli import default_argument_parser
@@ -78,26 +80,31 @@ 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 = ""
-    config.data.keep_transcription_text = True
+    config.data.manifest = config.data.test_manifest
     dataset = ManifestDataset.from_config(config)
 
-    model = DeepSpeech2Model.from_pretrained(dataset, config,
+    config.collator.augmentation_config = ""
+    config.collator.keep_transcription_text = True
+    config.collator.batch_size = 1
+    config.collator.num_workers = 0
+    collate_fn = SpeechCollator.from_config(config)
+    test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
+
+    model = DeepSpeech2Model.from_pretrained(test_loader, config,
                                              args.checkpoint_path)
     model.eval()
 
     # prepare ASR inference handler
     def file_to_transcript(filename):
-        feature = dataset.process_utterance(filename, "")
-        audio = np.array([feature[0]]).astype('float32')  #[1, D, T]
-        audio_len = feature[0].shape[1]
+        feature = test_loader.collate_fn.process_utterance(filename, "")
+        audio = np.array([feature[0]]).astype('float32')  #[1, T, D]
+        audio_len = feature[0].shape[0]
         audio_len = np.array([audio_len]).astype('int64')  # [1]
 
         result_transcript = model.decode(
             paddle.to_tensor(audio),
             paddle.to_tensor(audio_len),
-            vocab_list=dataset.vocab_list,
+            vocab_list=test_loader.collate_fn.vocab_list,
             decoding_method=config.decoding.decoding_method,
             lang_model_path=config.decoding.lang_model_path,
             beam_alpha=config.decoding.alpha,
@@ -138,7 +145,7 @@ if __name__ == "__main__":
     add_arg('host_ip',          str,
             'localhost',
             "Server's IP address.")
-    add_arg('host_port',        int,    8086,    "Server's IP port.")
+    add_arg('host_port',        int,    8089,    "Server's IP port.")
     add_arg('speech_save_dir',  str,
             'demo_cache',
             "Directory to save demo audios.")

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

@@ -16,8 +16,10 @@ import functools
 
 import numpy as np
 import paddle
+from paddle.io import DataLoader
 
 from deepspeech.exps.deepspeech2.config import get_cfg_defaults
+from deepspeech.io.collator import SpeechCollator
 from deepspeech.io.dataset import ManifestDataset
 from deepspeech.models.deepspeech2 import DeepSpeech2Model
 from deepspeech.training.cli import default_argument_parser
@@ -31,26 +33,35 @@ from deepspeech.utils.utility import print_arguments
 def start_server(config, args):
     """Start the ASR server"""
     config.defrost()
-    config.data.manfiest = config.data.test_manifest
-    config.data.augmentation_config = ""
-    config.data.keep_transcription_text = True
+    config.data.manifest = config.data.test_manifest
     dataset = ManifestDataset.from_config(config)
 
-    model = DeepSpeech2Model.from_pretrained(dataset, config,
+    config.collator.augmentation_config = ""
+    config.collator.keep_transcription_text = True
+    config.collator.batch_size = 1
+    config.collator.num_workers = 0
+    collate_fn = SpeechCollator.from_config(config)
+    test_loader = DataLoader(dataset, collate_fn=collate_fn, num_workers=0)
+
+    model = DeepSpeech2Model.from_pretrained(test_loader, config,
                                              args.checkpoint_path)
     model.eval()
 
     # prepare ASR inference handler
     def file_to_transcript(filename):
-        feature = dataset.process_utterance(filename, "")
-        audio = np.array([feature[0]]).astype('float32')  #[1, D, T]
-        audio_len = feature[0].shape[1]
+        feature = test_loader.collate_fn.process_utterance(filename, "")
+        audio = np.array([feature[0]]).astype('float32')  #[1, T, D]
+        # audio = audio.swapaxes(1,2)
+        print('---file_to_transcript feature----')
+        print(audio.shape)
+        audio_len = feature[0].shape[0]
+        print(audio_len)
         audio_len = np.array([audio_len]).astype('int64')  # [1]
 
         result_transcript = model.decode(
             paddle.to_tensor(audio),
             paddle.to_tensor(audio_len),
-            vocab_list=dataset.vocab_list,
+            vocab_list=test_loader.collate_fn.vocab_list,
             decoding_method=config.decoding.decoding_method,
             lang_model_path=config.decoding.lang_model_path,
             beam_alpha=config.decoding.alpha,
@@ -91,7 +102,7 @@ if __name__ == "__main__":
     add_arg('host_ip',          str,
             'localhost',
             "Server's IP address.")
-    add_arg('host_port',        int,    8086,    "Server's IP port.")
+    add_arg('host_port',        int,    8088,    "Server's IP port.")
     add_arg('speech_save_dir',  str,
             'demo_cache',
             "Directory to save demo audios.")

deepspeech/exps/deepspeech2/bin/tune.py

@@ -47,7 +47,7 @@ def tune(config, args):
         drop_last=False,
         collate_fn=SpeechCollator(keep_transcription_text=True))
 
-    model = DeepSpeech2Model.from_pretrained(dev_dataset, config,
+    model = DeepSpeech2Model.from_pretrained(valid_loader, config,
                                              args.checkpoint_path)
     model.eval()
 

deepspeech/exps/deepspeech2/model.py

@@ -318,7 +318,7 @@ class DeepSpeech2Tester(DeepSpeech2Trainer):
 
     def export(self):
         infer_model = DeepSpeech2InferModel.from_pretrained(
-            self.test_loader.dataset, self.config, self.args.checkpoint_path)
+            self.test_loader, self.config, self.args.checkpoint_path)
         infer_model.eval()
         feat_dim = self.test_loader.collate_fn.feature_size
         static_model = paddle.jit.to_static(

deepspeech/exps/u2/model.py

@@ -506,7 +506,7 @@ 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.collate_fn.feature_size

deepspeech/frontend/augmentor/spec_augment.py

@@ -124,7 +124,7 @@ class SpecAugmentor(AugmentorBase):
     def time_warp(xs, W=40):
         raise NotImplementedError
     
-    def randomize_parameters(self, n_bins, n_frame):
+    def randomize_parameters(self, n_frame, n_bins):
         # n_bins = xs.shape[0]
         # n_frames = xs.shape[1]
 
@@ -156,66 +156,69 @@ class SpecAugmentor(AugmentorBase):
             self.t_0.append(int(self._rng.uniform(low=0, high=n_frames - t)))
 
     def apply(self, xs: np.ndarray):
-        n_bins = xs.shape[0]
-        n_frames = xs.shape[1]
+        '''
+        input xs [T, D]
+        '''
+        n_frames = xs.shape[0]
+        n_bins = xs.shape[1]
         for i in range(0, self.n_freq_masks):
             f = self.f[i]
             f_0 = self.f_0[i]
-            xs[f_0:f_0 + f, :] = 0
+            xs[:, f_0:f_0 + f] = 0
             assert f_0 <= f_0 + f
         
         for i in range(self.n_masks):
             t = self.t[i]
             t_0 = self.t_0[i]
-            xs[:, t_0:t_0 + t] = 0
+            xs[t_0:t_0 + t, :] = 0
             assert t_0 <= t_0 + t
         return xs
 
 
-    def mask_freq(self, xs, replace_with_zero=False):
-        n_bins = xs.shape[0]
-        for i in range(0, self.n_freq_masks):
-            f = int(self._rng.uniform(low=0, high=self.F))
-            f_0 = int(self._rng.uniform(low=0, high=n_bins - f))
-            xs[f_0:f_0 + f, :] = 0
-            assert f_0 <= f_0 + f
-            self._freq_mask = (f_0, f_0 + f)
-        return xs
-
-    def mask_time(self, xs, replace_with_zero=False):
-        n_frames = xs.shape[1]
-
-        if self.adaptive_number_ratio > 0:
-            n_masks = int(n_frames * self.adaptive_number_ratio)
-            n_masks = min(n_masks, self.max_n_time_masks)
-        else:
-            n_masks = self.n_time_masks
-
-        if self.adaptive_size_ratio > 0:
-            T = self.adaptive_size_ratio * n_frames
-        else:
-            T = self.T
+    # def mask_freq(self, xs, replace_with_zero=False):
+    #     n_bins = xs.shape[0]
+    #     for i in range(0, self.n_freq_masks):
+    #         f = int(self._rng.uniform(low=0, high=self.F))
+    #         f_0 = int(self._rng.uniform(low=0, high=n_bins - f))
+    #         xs[f_0:f_0 + f, :] = 0
+    #         assert f_0 <= f_0 + f
+    #         self._freq_mask = (f_0, f_0 + f)
+    #     return xs
 
-        for i in range(n_masks):
-            t = int(self._rng.uniform(low=0, high=T))
-            t = min(t, int(n_frames * self.p))
-            t_0 = int(self._rng.uniform(low=0, high=n_frames - t))
-            xs[:, t_0:t_0 + t] = 0
-            assert t_0 <= t_0 + t
-            self._time_mask = (t_0, t_0 + t)
-        return xs
+    # def mask_time(self, xs, replace_with_zero=False):
+    #     n_frames = xs.shape[1]
+
+    #     if self.adaptive_number_ratio > 0:
+    #         n_masks = int(n_frames * self.adaptive_number_ratio)
+    #         n_masks = min(n_masks, self.max_n_time_masks)
+    #     else:
+    #         n_masks = self.n_time_masks
+
+    #     if self.adaptive_size_ratio > 0:
+    #         T = self.adaptive_size_ratio * n_frames
+    #     else:
+    #         T = self.T
+
+    #     for i in range(n_masks):
+    #         t = int(self._rng.uniform(low=0, high=T))
+    #         t = min(t, int(n_frames * self.p))
+    #         t_0 = int(self._rng.uniform(low=0, high=n_frames - t))
+    #         xs[:, t_0:t_0 + t] = 0
+    #         assert t_0 <= t_0 + t
+    #         self._time_mask = (t_0, t_0 + t)
+    #     return xs
 
 
-    def transform_feature(self, xs: np.ndarray, single=True):
-        """
-        Args:
-            xs (FloatTensor): `[F, T]`
-        Returns:
-            xs (FloatTensor): `[F, T]`
-        """
-        if(single):
-            self.randomize_parameters(xs)
-        return self.apply(xs)
+    # def transform_feature(self, xs: np.ndarray, single=True):
+    #     """
+    #     Args:
+    #         xs (FloatTensor): `[F, T]`
+    #     Returns:
+    #         xs (FloatTensor): `[F, T]`
+    #     """
+    #     if(single):
+    #         self.randomize_parameters(xs)
+    #     return self.apply(xs)
 
     # def transform_feature(self, xs: np.ndarray):
     #     """

deepspeech/frontend/featurizer/audio_featurizer.py

@@ -221,17 +221,19 @@ class AudioFeaturizer(object):
         """append delat, delta-delta feature.
 
         Args:
-            feat (np.ndarray): (D, T)
+            feat (np.ndarray): (T, D)
 
         Returns:
-            np.ndarray: feat with delta-delta, (3*D, T)
+            np.ndarray: feat with delta-delta, (T, 3*D)
         """
+        # transpose (T, D) --> (D, T)
         feat = np.transpose(feat)
         # Deltas
         d_feat = delta(feat, 2)
         # Deltas-Deltas
         dd_feat = delta(feat, 2)
         # transpose
+        # transpose (D, T) --> (T, D)
         feat = np.transpose(feat)
         d_feat = np.transpose(d_feat)
         dd_feat = np.transpose(dd_feat)
@@ -264,7 +266,7 @@ class AudioFeaturizer(object):
             ValueError: stride_ms > window_ms
 
         Returns:
-            np.ndarray: mfcc feature, (D, T).
+            np.ndarray: mfcc feature, (T, D).
         """
         if max_freq is None:
             max_freq = sample_rate / 2
@@ -322,7 +324,7 @@ class AudioFeaturizer(object):
             ValueError: stride_ms > window_ms
 
         Returns:
-            np.ndarray: mfcc feature, (D, T).
+            np.ndarray: mfcc feature, (T, D).
         """
         if max_freq is None:
             max_freq = sample_rate / 2

deepspeech/io/collator.py

@@ -229,7 +229,7 @@ class SpeechCollator():
     def randomize_feature_parameters(self, n_bins, n_frames):
         self._augmentation_pipeline.andomize_parameters_feature_transform(n_bins, n_frames)
 
-    def process_utterance(self, audio_file, transcript):
+    def process_feature_and_transform(self, audio_file, transcript):
         """Load, augment, featurize and normalize for speech data.
 
         :param audio_file: Filepath or file object of audio file.
@@ -254,6 +254,7 @@ class SpeechCollator():
 
         # # apply specgram augment
         # specgram = self._augmentation_pipeline.apply_feature_transform(specgram)
+
         return specgram, transcript_part
 
 
@@ -318,12 +319,12 @@ class SpeechCollator():
         for utt, audio, text in batch:
             if not self.config.randomize_each_batch:
                 self.randomize_audio_parameters()
-            audio, text = self.process_utterance(audio, text)
+            audio, text = self.process_feature_and_transform(audio, text)
             #utt
             utts.append(utt)
             # audio
-            audios.append(audio.T)  # [T, D]
-            audio_lens.append(audio.shape[1])
+            audios.append(audio)  # [T, D]
+            audio_lens.append(audio.shape[0])
             # text
             # for training, text is token ids
             # else text is string, convert to unicode ord
@@ -346,8 +347,8 @@ class SpeechCollator():
         text_lens = np.array(text_lens).astype(np.int64)
         
         #spec augment
-        n_bins=padded_audios[0]
-        self.randomize_feature_parameters(n_bins, min(audio_lens))
+        n_bins=padded_audios.shape[2]
+        self.randomize_feature_parameters(min(audio_lens), n_bins)
         for i in range(len(padded_audios)):
             if not self.config.randomize_each_batch: 
                 self.randomize_feature_parameters(n_bins, audio_lens[i])

deepspeech/models/deepspeech2.py

@@ -198,11 +198,11 @@ class DeepSpeech2Model(nn.Layer):
             cutoff_top_n, num_processes)
 
     @classmethod
-    def from_pretrained(cls, dataset, config, checkpoint_path):
+    def from_pretrained(cls, dataloader, config, checkpoint_path):
         """Build a DeepSpeech2Model model from a pretrained model.
         Parameters
         ----------
-        dataset: paddle.io.Dataset
+        dataloader: paddle.io.DataLoader
 
         config: yacs.config.CfgNode
             model configs
@@ -215,8 +215,8 @@ class DeepSpeech2Model(nn.Layer):
         DeepSpeech2Model
             The model built from pretrained result.
         """
-        model = cls(feat_size=dataset.feature_size,
-                    dict_size=dataset.vocab_size,
+        model = cls(feat_size=dataloader.collate_fn.feature_size,
+                    dict_size=dataloader.collate_fn.vocab_size,
                     num_conv_layers=config.model.num_conv_layers,
                     num_rnn_layers=config.model.num_rnn_layers,
                     rnn_size=config.model.rnn_layer_size,

deepspeech/models/u2.py

@@ -876,11 +876,11 @@ class U2Model(U2BaseModel):
         return model
 
     @classmethod
-    def from_pretrained(cls, dataset, config, checkpoint_path):
+    def from_pretrained(cls, dataloader, config, checkpoint_path):
         """Build a DeepSpeech2Model model from a pretrained model.
 
         Args:
-            dataset (paddle.io.Dataset): not used.
+            dataloader (paddle.io.DataLoader): not used.
             config (yacs.config.CfgNode):  model configs
             checkpoint_path (Path or str): the path of pretrained model checkpoint, without extension name
 
@@ -888,8 +888,8 @@ class U2Model(U2BaseModel):
             DeepSpeech2Model: The model built from pretrained result.
         """
         config.defrost()
-        config.input_dim = dataset.feature_size
-        config.output_dim = dataset.vocab_size
+        config.input_dim = dataloader.collate_fn.feature_size
+        config.output_dim = dataloader.collate_fn.vocab_size
         config.freeze()
         model = cls.from_config(config)
 

deepspeech/utils/socket_server.py

@@ -48,9 +48,9 @@ def warm_up_test(audio_process_handler,
     rng = random.Random(random_seed)
     samples = rng.sample(manifest, num_test_cases)
     for idx, sample in enumerate(samples):
-        print("Warm-up Test Case %d: %s", idx, sample['audio_filepath'])
+        print("Warm-up Test Case %d: %s" % (idx, sample['feat']))
         start_time = time.time()
-        transcript = audio_process_handler(sample['audio_filepath'])
+        transcript = audio_process_handler(sample['feat'])
         finish_time = time.time()
         print("Response Time: %f, Transcript: %s" %
               (finish_time - start_time, transcript))

examples/aishell/s0/README.md

@@ -2,9 +2,10 @@
 
 ## Deepspeech2
 
-| Model | release | Config | Test set | Loss | CER |  
-| --- | --- | --- | --- | --- | --- |  
-| DeepSpeech2 | 2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 |  
-| DeepSpeech2 | 2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 |
-| DeepSpeech2 | 2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 |  
-| DeepSpeech2 | 1.8.5 | - | test | - | 0.080447 |  
+| Model | Params | Release | Config | Test set | Loss | CER |  
+| --- | --- | --- | --- | --- | --- | --- |  
+| DeepSpeech2 | 58.4M | 2.2.0 | conf/deepspeech2.yaml + spec aug + new datapipe | test | 6.396368026733398 | 0.068382 |  
+| DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 |  
+| DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 |
+| DeepSpeech2 | 58.4M | 2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 |  
+| DeepSpeech2 | 58.4M | 1.8.5 | - | test | - | 0.080447 |  

examples/aishell/s0/conf/deepspeech2.yaml

@@ -10,8 +10,8 @@ data:
   min_output_input_ratio: 0.00
   max_output_input_ratio: .inf
 
-
 collator:
+  batch_size: 64 # one gpu
   mean_std_filepath: data/mean_std.json
   unit_type: char
   vocab_filepath: data/vocab.txt 
@@ -33,7 +33,6 @@ collator:
   sortagrad: True
   shuffle_method: batch_shuffle
   num_workers: 0
-  batch_size: 64 # one gpu
 
 model:
   num_conv_layers: 2

examples/aishell/s0/run.sh

@@ -2,7 +2,7 @@
 set -e
 source path.sh
 
-gpus=0
+gpus=0,1,2,3
 stage=0
 stop_stage=100
 conf_path=conf/deepspeech2.yaml
@@ -31,10 +31,10 @@ fi
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
+    CUDA_VISIBLE_DEVICES=0 ./local/test.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} || exit -1
 fi
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     # export ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
+    CUDA_VISIBLE_DEVICES=0 ./local/export.sh ${conf_path} exp/${ckpt}/checkpoints/${avg_ckpt} exp/${ckpt}/checkpoints/${avg_ckpt}.jit
 fi

examples/aishell/s1/README.md

@@ -2,21 +2,21 @@
 
 ## Conformer
 
-| Model | Config | Augmentation| Test set | Decode method | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- |  
-| conformer | conf/conformer.yaml | spec_aug + shift | test | attention | - | 0.059858 |  
-| conformer | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | - | 0.062311 |  
-| conformer | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | - | 0.062196 |  
-| conformer | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | - | 0.054694 |  
+| Model | Params | Config | Augmentation| Test set | Decode method | Loss | WER |  
+| --- | --- |  --- | --- | --- | --- | --- | --- |  
+| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | attention | - | 0.059858 |  
+| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | ctc_greedy_search | - | 0.062311 |  
+| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | - | 0.062196 |  
+| conformer | 47.06M | conf/conformer.yaml | spec_aug + shift | test | attention_rescoring | - | 0.054694 |  
 
 ## Chunk Conformer
 
-| Model | Config | Augmentation| Test set | Decode method | Chunk | Loss | WER |  
-| --- | --- | --- | --- | --- | --- | --- | --- |  
-| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16 | - | 0.061939 |  
-| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16 | - | 0.070806 |  
-| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16 | - | 0.070739 |  
-| conformer | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16 |  - | 0.059400 |  
+| Model | Params | Config | Augmentation| Test set | Decode method | Chunk | Loss | WER |  
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |  
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention | 16 | - | 0.061939 |  
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_greedy_search | 16 | - | 0.070806 |  
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | ctc_prefix_beam_search | 16 | - | 0.070739 |  
+| conformer | 47.06M | conf/chunk_conformer.yaml | spec_aug + shift | test | attention_rescoring | 16 |  - | 0.059400 |  
 
 
 ## Transformer

examples/librispeech/s0/README.md

@@ -2,8 +2,8 @@
 
 ## Deepspeech2
 
-| Model | release |  Config | Test set | Loss | WER |  
-| --- | --- |  --- | --- | --- | --- |  
-| DeepSpeech2 | 2.1.0 | conf/deepspeech2.yaml | 15.184467315673828 | test-clean | 0.072154 |  
-| DeepSpeech2 | 2.0.0 | conf/deepspeech2.yaml | - | test-clean | 0.073973 |  
-| DeepSpeech2 | 1.8.5 | - | test-clean | - | 0.074939 |  
+| Model | Params |  Release |  Config | Test set | Loss | WER |  
+| --- | --- | --- |  --- | --- | --- | --- |  
+| DeepSpeech2 | 42.96M |  2.1.0 | conf/deepspeech2.yaml | 15.184467315673828 | test-clean | 0.072154 |  
+| DeepSpeech2 | 42.96M |  2.0.0 | conf/deepspeech2.yaml | - | test-clean | 0.073973 |  
+| DeepSpeech2 | 42.96M |  1.8.5 | - | test-clean | - | 0.074939 |  

examples/librispeech/s0/conf/deepspeech2.yaml

@@ -3,16 +3,21 @@ data:
   train_manifest: data/manifest.train
   dev_manifest: data/manifest.dev-clean
   test_manifest: data/manifest.test-clean
-  mean_std_filepath: data/mean_std.json
-  vocab_filepath: data/vocab.txt 
-  augmentation_config: conf/augmentation.json
-  batch_size: 20
   min_input_len: 0.0
   max_input_len: 27.0 # second
   min_output_len: 0.0
   max_output_len: .inf
   min_output_input_ratio: 0.00
   max_output_input_ratio: .inf
+
+collator:
+  batch_size: 20
+  mean_std_filepath: data/mean_std.json
+  unit_type: char
+  vocab_filepath: data/vocab.txt 
+  augmentation_config: conf/augmentation.json
+  random_seed: 0
+  spm_model_prefix: 
   specgram_type: linear
   target_sample_rate: 16000
   max_freq: None

tools/Makefile

@@ -31,5 +31,5 @@ sox.done:
 
 soxbindings.done:
 	test -d soxbindings || git clone https://github.com/pseeth/soxbindings.git
-	source venv/bin/activate; cd soxbindings && python3 setup.py install
-	touch soxbindings.done
+	source venv/bin/activate; cd soxbindings && python setup.py install
+	touch soxbindings.done
\ No newline at end of file