fix: replace import tf.keras to keras, update tiny rnnt model result

Author: nglehuy

.pre-commit-config.yaml

@@ -9,3 +9,12 @@ repos:
         stages: [pre-commit]
         fail_fast: true
         verbose: true
+      - id: pylint-check
+        name: pylint-check
+        entry: pylint --rcfile=.pylintrc -rn -sn
+        language: system
+        types: [python]
+        stages: [pre-commit]
+        fail_fast: true
+        require_serial: true
+        verbose: true

.pylintrc

@@ -114,7 +114,11 @@ disable=too-few-public-methods,
         consider-using-enumerate,
         too-many-statements,
         assignment-from-none,
-        eval-used
+        eval-used,
+        duplicate-code,
+        redefined-outer-name,
+        consider-using-f-string,
+        fixme,
 
 # Enable the message, report, category or checker with the given id(s). You can
 # either give multiple identifier separated by comma (,) or put this option

examples/inferences/main.py

@@ -15,8 +15,10 @@
 import os
 
 import tensorflow as tf
+import keras
 
 from tensorflow_asr import schemas, tokenizers
+from tensorflow_asr.models import base_model
 from tensorflow_asr.configs import Config
 from tensorflow_asr.utils import cli_util, data_util, env_util, file_util
 
@@ -35,7 +37,7 @@ def main(
     config = Config(config_path, training=False, repodir=repodir)
     tokenizer = tokenizers.get(config)
 
-    model: tf.keras.Model = tf.keras.models.model_from_config(config.model_config)
+    model: base_model.BaseModel = keras.models.model_from_config(config.model_config)
     model.make(batch_size=1)
     model.load_weights(h5, by_name=file_util.is_hdf5_filepath(h5), skip_mismatch=False)
     model.summary()
@@ -44,7 +46,15 @@ def main(
     signal = tf.reshape(signal, [1, -1])
     signal_length = tf.reshape(tf.shape(signal)[1], [1])
 
-    outputs = model.recognize(schemas.PredictInput(signal, signal_length))
+    outputs = model.recognize(
+        schemas.PredictInput(
+            inputs=signal,
+            inputs_length=signal_length,
+            previous_tokens=model.get_initial_tokens(),
+            previous_encoder_states=model.get_initial_encoder_states(),
+            previous_decoder_states=model.get_initial_decoder_states(),
+        )
+    )
     print(outputs.tokens)
     transcript = tokenizer.detokenize(outputs.tokens)[0].numpy().decode("utf-8")
 

examples/inferences/rnn_transducer.py

@@ -1,89 +1,89 @@
-# Copyright 2020 Huy Le Nguyen (@nglehuy)
-#
-# 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 argparse
-
-from tensorflow_asr.utils import data_util, env_util, math_util
-
-logger = env_util.setup_environment()
-import tensorflow as tf
-
-parser = argparse.ArgumentParser(prog="Rnn Transducer non streaming")
-
-parser.add_argument("filename", metavar="FILENAME", help="audio file to be played back")
-
-parser.add_argument("--config", type=str, default=None, help="Path to rnnt config yaml")
-
-parser.add_argument("--saved", type=str, default=None, help="Path to rnnt saved h5 weights")
-
-parser.add_argument("--beam_width", type=int, default=0, help="Beam width")
-
-parser.add_argument("--timestamp", default=False, action="store_true", help="Return with timestamp")
-
-parser.add_argument("--device", type=int, default=0, help="Device's id to run test on")
-
-parser.add_argument("--cpu", default=False, action="store_true", help="Whether to only use cpu")
-
-parser.add_argument("--subwords", default=False, action="store_true", help="Path to file that stores generated subwords")
-
-parser.add_argument("--sentence_piece", default=False, action="store_true", help="Whether to use `SentencePiece` model")
-
-args = parser.parse_args()
-
-env_util.setup_devices([args.device], cpu=args.cpu)
-
-from tensorflow_asr.configs import Config
-from tensorflow_asr.features.speech_featurizers import SpeechFeaturizer, read_raw_audio
-from tensorflow_asr.models.transducer.rnnt import RnnTransducer
-from tensorflow_asr.tokenizers import CharTokenizer, SentencePieceTokenizer, SubwordFeaturizer
-
-config = Config(args.config)
-speech_featurizer = SpeechFeaturizer(config.speech_config)
-if args.sentence_piece:
-    logger.info("Loading SentencePiece model ...")
-    text_featurizer = SentencePieceTokenizer(config.decoder_config)
-elif args.subwords:
-    logger.info("Loading subwords ...")
-    text_featurizer = SubwordFeaturizer(config.decoder_config)
-else:
-    text_featurizer = CharTokenizer(config.decoder_config)
-text_featurizer.decoder_config.beam_width = args.beam_width
-
-# build model
-rnnt = RnnTransducer(**config.model_config, vocab_size=text_featurizer.num_classes)
-rnnt.make(speech_featurizer.shape)
-rnnt.load_weights(args.saved, by_name=True, skip_mismatch=True)
-rnnt.summary()
-rnnt.add_featurizers(speech_featurizer, text_featurizer)
-
-signal = read_raw_audio(args.filename)
-features = speech_featurizer.tf_extract(signal)
-input_length = math_util.get_reduced_length(tf.shape(features)[0], rnnt.time_reduction_factor)
-
-if args.beam_width:
-    transcript = rnnt.recognize_beam(data_util.create_inputs(inputs=features[None, ...], inputs_length=input_length[None, ...]))
-    logger.info("Transcript:", transcript[0].numpy().decode("UTF-8"))
-elif args.timestamp:
-    transcript, stime, etime, _, _, _ = rnnt.recognize_tflite_with_timestamp(
-        signal=signal,
-        predicted=tf.constant(text_featurizer.blank, dtype=tf.int32),
-        encoder_states=rnnt.encoder.get_initial_state(),
-        prediction_states=rnnt.predict_net.get_initial_state(),
-    )
-    logger.info("Transcript:", transcript)
-    logger.info("Start time:", stime)
-    logger.info("End time:", etime)
-else:
-    transcript = rnnt.recognize(data_util.create_inputs(inputs=features[None, ...], inputs_length=input_length[None, ...]))
-    logger.info("Transcript:", transcript[0].numpy().decode("UTF-8"))
+# # Copyright 2020 Huy Le Nguyen (@nglehuy)
+# #
+# # 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 argparse
+
+# from tensorflow_asr.utils import data_util, env_util, math_util
+
+# logger = env_util.setup_environment()
+# import tensorflow as tf
+
+# parser = argparse.ArgumentParser(prog="Rnn Transducer non streaming")
+
+# parser.add_argument("filename", metavar="FILENAME", help="audio file to be played back")
+
+# parser.add_argument("--config", type=str, default=None, help="Path to rnnt config yaml")
+
+# parser.add_argument("--saved", type=str, default=None, help="Path to rnnt saved h5 weights")
+
+# parser.add_argument("--beam_width", type=int, default=0, help="Beam width")
+
+# parser.add_argument("--timestamp", default=False, action="store_true", help="Return with timestamp")
+
+# parser.add_argument("--device", type=int, default=0, help="Device's id to run test on")
+
+# parser.add_argument("--cpu", default=False, action="store_true", help="Whether to only use cpu")
+
+# parser.add_argument("--subwords", default=False, action="store_true", help="Path to file that stores generated subwords")
+
+# parser.add_argument("--sentence_piece", default=False, action="store_true", help="Whether to use `SentencePiece` model")
+
+# args = parser.parse_args()
+
+# env_util.setup_devices([args.device], cpu=args.cpu)
+
+# from tensorflow_asr.configs import Config
+# from tensorflow_asr.features.speech_featurizers import SpeechFeaturizer, read_raw_audio
+# from tensorflow_asr.models.transducer.rnnt import RnnTransducer
+# from tensorflow_asr.tokenizers import CharTokenizer, SentencePieceTokenizer, SubwordFeaturizer
+
+# config = Config(args.config)
+# speech_featurizer = SpeechFeaturizer(config.speech_config)
+# if args.sentence_piece:
+#     logger.info("Loading SentencePiece model ...")
+#     text_featurizer = SentencePieceTokenizer(config.decoder_config)
+# elif args.subwords:
+#     logger.info("Loading subwords ...")
+#     text_featurizer = SubwordFeaturizer(config.decoder_config)
+# else:
+#     text_featurizer = CharTokenizer(config.decoder_config)
+# text_featurizer.decoder_config.beam_width = args.beam_width
+
+# # build model
+# rnnt = RnnTransducer(**config.model_config, vocab_size=text_featurizer.num_classes)
+# rnnt.make(speech_featurizer.shape)
+# rnnt.load_weights(args.saved, by_name=True, skip_mismatch=True)
+# rnnt.summary()
+# rnnt.add_featurizers(speech_featurizer, text_featurizer)
+
+# signal = read_raw_audio(args.filename)
+# features = speech_featurizer.tf_extract(signal)
+# input_length = math_util.get_reduced_length(tf.shape(features)[0], rnnt.time_reduction_factor)
+
+# if args.beam_width:
+#     transcript = rnnt.recognize_beam(data_util.create_inputs(inputs=features[None, ...], inputs_length=input_length[None, ...]))
+#     logger.info("Transcript:", transcript[0].numpy().decode("UTF-8"))
+# elif args.timestamp:
+#     transcript, stime, etime, _, _, _ = rnnt.recognize_tflite_with_timestamp(
+#         signal=signal,
+#         predicted=tf.constant(text_featurizer.blank, dtype=tf.int32),
+#         encoder_states=rnnt.encoder.get_initial_state(),
+#         prediction_states=rnnt.predict_net.get_initial_state(),
+#     )
+#     logger.info("Transcript:", transcript)
+#     logger.info("Start time:", stime)
+#     logger.info("End time:", etime)
+# else:
+#     transcript = rnnt.recognize(data_util.create_inputs(inputs=features[None, ...], inputs_length=input_length[None, ...]))
+#     logger.info("Transcript:", transcript[0].numpy().decode("UTF-8"))