Merge pull request #3 from moconnor725/beta-candidate

Beta candidate

Author: okuchaiev

Diff for: .gitignore

@@ -27,6 +27,7 @@ eggs/
 .eggs/
 lib/
 lib64/
+#parts/
 sdist/
 var/
 wheels/
@@ -143,3 +144,4 @@ examples/*/outputs
 examples/*/wandb
 examples/*/data
 wandb
+dump.py

Diff for: LICENSE

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+Please refer to per-package licenses

Diff for: README.rst

@@ -15,6 +15,10 @@ Neural Modules’ inputs and outputs have Neural Type for semantic checking.
 
 An application built with NeMo application is a Directed Acyclic Graph(DAG) of connected modules enabling researchers to define and build new speech and nlp networks easily through API Compatible modules.
 
+**Documentation and Tutorials**
+
+Please refer to the HTML documentation in the `docs` folder
+
 
 **VIDEO**
 
@@ -31,11 +35,6 @@ An application built with NeMo application is a Directed Acyclic Graph(DAG) of c
 * **Collections** - NeMo comes with collections - related group of modules such as `nemo_asr` (for Speech Recognition) and `nemo_nlp` for NLP
 
 
-**Documentation**
-
-Please refer to the HTML documentation in the `docs` folder
-
-
 **Requirements**
 
 1) Python 3.6 or 3.7
@@ -60,7 +59,7 @@ Run this:
 2) Go to `nemo` folder and do: `python setup.py install`
 3) Install collections:
     a) ASR collection from `collections/nemo_asr` do: `python setup.py install`
-    b) NLP collection coming soon ...
+    b) NLP collection from `collections/nemo_nlp` do: `python setup.py install`
 
 4) For development do: `python setup.py develop` instead of `python setup.py install` in Step (3) above
 5) Go to `examples/start_here` to get started with few simple examples

Diff for: collections/nemo_asr/nemo_asr/__init__.py

@@ -1,4 +1,17 @@
-# Copyright (c) 2019 NVIDIA Corporation
+# Copyright 2019 AI Applications Design Team at NVIDIA. 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.
+# ==============================================================================
 from nemo.core import Backend
 
 from .data_layer import AudioToTextDataLayer, AudioPreprocessing, \
@@ -11,3 +24,4 @@
 
 name = "nemo_asr"
 backend = Backend.PyTorch
+__version__ = "0.1"

Diff for: collections/nemo_asr/nemo_asr/data_layer.py

@@ -6,7 +6,7 @@
 import torch
 from apex import amp
 
-from nemo.backends.pytorch.nm import DataLayerNM, NonTrainableNM
+from nemo.backends.pytorch.nm import DataLayerNM, TrainableNM, NonTrainableNM
 from nemo.core import Optimization, DeviceType
 from nemo.core.neural_types import *
 from .parts.dataset import AudioDataset, seq_collate_fn
@@ -112,13 +112,12 @@ def __init__(
             labels=labels,
             featurizer=self._featurizer, max_duration=max_duration,
             min_duration=min_duration, normalize=normalize_transcripts,
-            trim=trim_silence, verbose=self._master_process,
+            trim=trim_silence, logger=self._logger,
             eos_id=eos_id, load_audio=load_audio
         )
 
         if self._placement == DeviceType.AllGpu:
-            if self._master_process:
-                print('Parallelizing DATALAYER')
+            self._logger.info('Parallelizing DATALAYER')
             sampler = torch.utils.data.distributed.DistributedSampler(
                 self._dataset)
         else:
@@ -146,7 +145,7 @@ def data_iterator(self):
         return self._dataloader
 
 
-class AudioPreprocessing(NonTrainableNM):
+class AudioPreprocessing(TrainableNM):
     """
     Neural Module that does batch processing of audio files and converts them
     to spectrogram representations
@@ -232,7 +231,7 @@ def __init__(
             raise NotImplementedError("AudioPreprocessing currently only "
                                       "accepts 'fbank' or 'logfbank' as "
                                       "feat_type")
-        NonTrainableNM.__init__(self, **kwargs)
+        TrainableNM.__init__(self, **kwargs)
 
         self.featurizer = FilterbankFeatures(
             sample_rate=sample_rate,
@@ -248,14 +247,14 @@ def __init__(
             dither=dither,
             pad_to=pad_to,
             frame_splicing=frame_splicing,
-            stft_conv=stft_conv
+            stft_conv=stft_conv,
+            logger=self._logger
         )
         # _pre_procesing_config = self.local_parameters
         # self.featurizer = FeatureFactory.from_config(_pre_procesing_config)
         self.featurizer.to(self._device)
 
-        stft_conv = kwargs.get("stft_conv", False)
-        self.disable_casts = (self._opt_level != Optimization.nothing and
+        self.disable_casts = (self._opt_level == Optimization.mxprO1 and
                               not stft_conv)
 
     def forward(self, input_signal, length):