Merge pull request #966 from Jackwaterveg/dev

change the lm dataset dir, add the 'LM_BIN_DIR' in s2 path.sh

examples/librispeech/s2/path.sh

@@ -14,6 +14,10 @@ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
 MODEL=u2_kaldi
 export BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/${MODEL}/bin
 
+LM_MODEL=transformer
+export LM_BIN_DIR=${MAIN_ROOT}/paddlespeech/s2t/exps/lm/${LM_MODEL}/bin
+
+
 # srilm
 export LIBLBFGS=${MAIN_ROOT}/tools/liblbfgs-1.10
 export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:${LIBLBFGS}/lib/.libs
@@ -25,4 +29,4 @@ export KALDI_ROOT=${MAIN_ROOT}/tools/kaldi
 [ -f $KALDI_ROOT/tools/env.sh ] && . $KALDI_ROOT/tools/env.sh
 export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PWD:$PATH
 [ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present, can not using Kaldi!"
-[ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh
+[ -f $KALDI_ROOT/tools/config/common_path.sh ] && . $KALDI_ROOT/tools/config/common_path.sh

paddlespeech/s2t/exps/lm/transformer/lm_cacu_perplexity.py

@@ -19,8 +19,8 @@ import paddle
 from paddle.io import DataLoader
 from yacs.config import CfgNode
 
-from paddlespeech.s2t.io.collator import TextCollatorSpm
-from paddlespeech.s2t.io.dataset import TextDataset
+from paddlespeech.s2t.models.lm.dataset import TextCollatorSpm
+from paddlespeech.s2t.models.lm.dataset import TextDataset
 from paddlespeech.s2t.models.lm_interface import dynamic_import_lm
 from paddlespeech.s2t.utils.log import Log
 

paddlespeech/s2t/io/collator.py

@@ -19,7 +19,6 @@ from yacs.config import CfgNode
 
 from paddlespeech.s2t.frontend.augmentor.augmentation import AugmentationPipeline
 from paddlespeech.s2t.frontend.featurizer.speech_featurizer import SpeechFeaturizer
-from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
 from paddlespeech.s2t.frontend.normalizer import FeatureNormalizer
 from paddlespeech.s2t.frontend.speech import SpeechSegment
 from paddlespeech.s2t.frontend.utility import IGNORE_ID
@@ -46,43 +45,6 @@ def _tokenids(text, keep_transcription_text):
     return tokens
 
 
-class TextCollatorSpm():
-    def __init__(self, unit_type, vocab_filepath, spm_model_prefix):
-        assert (vocab_filepath is not None)
-        self.text_featurizer = TextFeaturizer(
-            unit_type=unit_type,
-            vocab_filepath=vocab_filepath,
-            spm_model_prefix=spm_model_prefix)
-        self.eos_id = self.text_featurizer.eos_id
-        self.blank_id = self.text_featurizer.blank_id
-
-    def __call__(self, batch):
-        """
-        return type  [List, np.array [B, T], np.array [B, T], np.array[B]]
-        """
-        keys = []
-        texts = []
-        texts_input = []
-        texts_output = []
-        text_lens = []
-
-        for idx, item in enumerate(batch):
-            key = item.split(" ")[0].strip()
-            text = " ".join(item.split(" ")[1:])
-            keys.append(key)
-            token_ids = self.text_featurizer.featurize(text)
-            texts_input.append(
-                np.array([self.eos_id] + token_ids).astype(np.int64))
-            texts_output.append(
-                np.array(token_ids + [self.eos_id]).astype(np.int64))
-            text_lens.append(len(token_ids) + 1)
-
-        ys_input_pad = pad_list(texts_input, self.blank_id).astype(np.int64)
-        ys_output_pad = pad_list(texts_output, self.blank_id).astype(np.int64)
-        y_lens = np.array(text_lens).astype(np.int64)
-        return keys, ys_input_pad, ys_output_pad, y_lens
-
-
 class SpeechCollatorBase():
     def __init__(
             self,

paddlespeech/s2t/io/dataset.py

@@ -26,25 +26,6 @@ __all__ = ["ManifestDataset", "TransformDataset"]
 logger = Log(__name__).getlog()
 
 
-class TextDataset(Dataset):
-    @classmethod
-    def from_file(cls, file_path):
-        dataset = cls(file_path)
-        return dataset
-
-    def __init__(self, file_path):
-        self._manifest = []
-        with open(file_path) as f:
-            for line in f:
-                self._manifest.append(line.strip())
-
-    def __len__(self):
-        return len(self._manifest)
-
-    def __getitem__(self, idx):
-        return self._manifest[idx]
-
-
 class ManifestDataset(Dataset):
     @classmethod
     def params(cls, config: Optional[CfgNode]=None) -> CfgNode:

paddlespeech/s2t/models/lm/dataset.py

@@ -0,0 +1,74 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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 numpy as np
+from paddle.io import Dataset
+
+from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
+from paddlespeech.s2t.io.utility import pad_list
+
+
+class TextDataset(Dataset):
+    @classmethod
+    def from_file(cls, file_path):
+        dataset = cls(file_path)
+        return dataset
+
+    def __init__(self, file_path):
+        self._manifest = []
+        with open(file_path) as f:
+            for line in f:
+                self._manifest.append(line.strip())
+
+    def __len__(self):
+        return len(self._manifest)
+
+    def __getitem__(self, idx):
+        return self._manifest[idx]
+
+
+class TextCollatorSpm():
+    def __init__(self, unit_type, vocab_filepath, spm_model_prefix):
+        assert (vocab_filepath is not None)
+        self.text_featurizer = TextFeaturizer(
+            unit_type=unit_type,
+            vocab_filepath=vocab_filepath,
+            spm_model_prefix=spm_model_prefix)
+        self.eos_id = self.text_featurizer.eos_id
+        self.blank_id = self.text_featurizer.blank_id
+
+    def __call__(self, batch):
+        """
+        return type  [List, np.array [B, T], np.array [B, T], np.array[B]]
+        """
+        keys = []
+        texts = []
+        texts_input = []
+        texts_output = []
+        text_lens = []
+
+        for idx, item in enumerate(batch):
+            key = item.split(" ")[0].strip()
+            text = " ".join(item.split(" ")[1:])
+            keys.append(key)
+            token_ids = self.text_featurizer.featurize(text)
+            texts_input.append(
+                np.array([self.eos_id] + token_ids).astype(np.int64))
+            texts_output.append(
+                np.array(token_ids + [self.eos_id]).astype(np.int64))
+            text_lens.append(len(token_ids) + 1)
+
+        ys_input_pad = pad_list(texts_input, self.blank_id).astype(np.int64)
+        ys_output_pad = pad_list(texts_output, self.blank_id).astype(np.int64)
+        y_lens = np.array(text_lens).astype(np.int64)
+        return keys, ys_input_pad, ys_output_pad, y_lens