diff --git a/DeepSpeech.py b/DeepSpeech.py
index dd4ef113..efda6a0b 100755
--- a/DeepSpeech.py
+++ b/DeepSpeech.py
@@ -162,16 +162,12 @@ def create_flags():
     tf.app.flags.DEFINE_string  ('lm_trie_path',         'data/lm/trie', 'path to the language model trie file created with native_client/generate_trie')
     tf.app.flags.DEFINE_integer ('beam_width',        1024,       'beam width used in the CTC decoder when building candidate transcriptions')
     tf.app.flags.DEFINE_float   ('lm_weight',         1.50,       'the alpha hyperparameter of the CTC decoder. Language Model weight.')
-    tf.app.flags.DEFINE_float   ('valid_word_count_weight', 2.25, 'valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary.')
+    tf.app.flags.DEFINE_float   ('valid_word_count_weight', 2.10, 'valid word insertion weight. This is used to lessen the word insertion penalty when the inserted word is part of the vocabulary.')
 
     # Inference mode
 
     tf.app.flags.DEFINE_string  ('one_shot_infer',       '',       'one-shot inference mode: specify a wav file and the script will load the checkpoint and perform inference on it. Disables training, testing and exporting.')
 
-    # Initialize from frozen model
-
-    tf.app.flags.DEFINE_string  ('initialize_from_frozen_model', '', 'path to frozen model to initialize from. This behaves like a checkpoint, loading the weights from the frozen model and starting training with those weights. The optimizer parameters aren\'t restored, so remember to adjust the learning rate.')
-
 FLAGS = tf.app.flags.FLAGS
 
 def initialize_globals():
@@ -870,15 +866,15 @@ def new_id():
     return id_counter
 
 class Sample(object):
-    def __init__(self, src, res, loss, mean_edit_distance, sample_wer):
-        '''Represents one item of a WER report.
+    '''Represents one item of a WER report.
 
-        Args:
-            src (str): source text
-            res (str): resulting text
-            loss (float): computed loss of this item
-            mean_edit_distance (float): computed mean edit distance of this item
-        '''
+    Args:
+        src (str): source text
+        res (str): resulting text
+        loss (float): computed loss of this item
+        mean_edit_distance (float): computed mean edit distance of this item
+    '''
+    def __init__(self, src, res, loss, mean_edit_distance, sample_wer):
         self.src = src
         self.res = res
         self.loss = loss
@@ -889,16 +885,16 @@ class Sample(object):
         return 'WER: %f, loss: %f, mean edit distance: %f\n - src: "%s"\n - res: "%s"' % (self.wer, self.loss, self.mean_edit_distance, self.src, self.res)
 
 class WorkerJob(object):
-    def __init__(self, epoch_id, index, set_name, steps, report):
-        '''Represents a job that should be executed by a worker.
+    '''Represents a job that should be executed by a worker.
 
-        Args:
-            epoch_id (int): the ID of the 'parent' epoch
-            index (int): the epoch index of the 'parent' epoch
-            set_name (str): the name of the data-set - one of 'train', 'dev', 'test'
-            steps (int): the number of `session.run` calls
-            report (bool): if this job should produce a WER report
-        '''
+    Args:
+        epoch_id (int): the ID of the 'parent' epoch
+        index (int): the epoch index of the 'parent' epoch
+        set_name (str): the name of the data-set - one of 'train', 'dev', 'test'
+        steps (int): the number of `session.run` calls
+        report (bool): if this job should produce a WER report
+    '''
+    def __init__(self, epoch_id, index, set_name, steps, report):
         self.id = new_id()
         self.epoch_id = epoch_id
         self.index = index
@@ -1070,6 +1066,12 @@ class Epoch(object):
 
 
 class TrainingCoordinator(object):
+    ''' Central training coordination class.
+    Used for distributing jobs among workers of a cluster.
+    Instantiated on all workers, calls of non-chief workers will transparently
+    HTTP-forwarded to the chief worker instance.
+    '''
+
     class TrainingCoordinationHandler(BaseHTTPServer.BaseHTTPRequestHandler):
         '''Handles HTTP requests from remote workers to the Training Coordinator.
         '''
@@ -1114,13 +1116,7 @@ class TrainingCoordinator(object):
             '''
             return
 
-
     def __init__(self):
-        ''' Central training coordination class.
-        Used for distributing jobs among workers of a cluster.
-        Instantiated on all workers, calls of non-chief workers will transparently
-        HTTP-forwarded to the chief worker instance.
-        '''
         self._init()
         self._lock = Lock()
         self.started = False
@@ -1579,26 +1575,6 @@ def train(server=None):
         saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
         hooks.append(tf.train.CheckpointSaverHook(checkpoint_dir=FLAGS.checkpoint_dir, save_secs=FLAGS.checkpoint_secs, saver=saver))
 
-    if len(FLAGS.initialize_from_frozen_model) > 0:
-        with tf.gfile.FastGFile(FLAGS.initialize_from_frozen_model, 'rb') as fin:
-            graph_def = tf.GraphDef()
-            graph_def.ParseFromString(fin.read())
-
-        var_names = [v.name for v in tf.trainable_variables()]
-        var_tensors = tf.import_graph_def(graph_def, return_elements=var_names)
-
-        # build a { var_name: var_tensor } dict
-        var_tensors = dict(zip(var_names, var_tensors))
-
-        training_graph = tf.get_default_graph()
-
-        assign_ops = []
-        for name, restored_tensor in var_tensors.items():
-            training_tensor = training_graph.get_tensor_by_name(name)
-            assign_ops.append(tf.assign(training_tensor, restored_tensor))
-
-        init_from_frozen_model_op = tf.group(*assign_ops)
-
     no_dropout_feed_dict = {
         dropout_rates[0]: 0.,
         dropout_rates[1]: 0.,
@@ -1661,11 +1637,6 @@ def train(server=None):
                                                config=session_config) as session:
             tf.get_default_graph().finalize()
 
-            if len(FLAGS.initialize_from_frozen_model) > 0:
-                log_info('Initializing from frozen model: {}'.format(FLAGS.initialize_from_frozen_model))
-                model_feeder.set_data_set(no_dropout_feed_dict, model_feeder.train)
-                session.run(init_from_frozen_model_op, feed_dict=no_dropout_feed_dict)
-
             try:
                 if is_chief:
                     # Retrieving global_step from the (potentially restored) model
diff --git a/README.md b/README.md
index ee01bf23..1b1ca17d 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ See the output of `deepspeech -h` for more information on the use of `deepspeech
   - [Checkpointing](#checkpointing)
   - [Exporting a model for inference](#exporting-a-model-for-inference)
   - [Distributed computing across more than one machine](#distributed-training-across-more-than-one-machine)
-  - [Continuing training from a frozen graph](#continuing-training-from-a-frozen-graph)
+  - [Continuing training from a release model](#continuing-training-from-a-release-model)
 - [Code documentation](#code-documentation)
 - [Contact/Getting Help](#contactgetting-help)
 
@@ -69,7 +69,7 @@ git clone https://github.com/mozilla/DeepSpeech
 If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the [DeepSpeech releases page](https://github.com/mozilla/DeepSpeech/releases). Alternatively, you can run the following command to download and unzip the files in your current directory:
 
 ```bash
-wget -O - https://github.com/mozilla/DeepSpeech/releases/download/v0.2.0/deepspeech-0.2.0-models.tar.gz | tar xvfz -
+wget -O - https://github.com/mozilla/DeepSpeech/releases/download/v0.3.0/deepspeech-0.3.0-models.tar.gz | tar xvfz -
 ```
 
 ## Using the model
@@ -353,18 +353,18 @@ $ run-cluster.sh 1:2:1 --epoch 10
 Be aware that for the help example to be able to run, you need at least two `CUDA` capable GPUs (2 workers times 1 GPU). The script utilizes environment variable `CUDA_VISIBLE_DEVICES` for `DeepSpeech.py` to see only the provided number of GPUs per worker.
 The script is meant to be a template for your own distributed computing instrumentation. Just modify the startup code for the different servers (workers and parameter servers) accordingly. You could use SSH or something similar for running them on your remote hosts.
 
-### Continuing training from a frozen graph
+### Continuing training from a release model
 
-If you'd like to use one of the pre-trained models released by Mozilla to bootstrap your training process (transfer learning, fine tuning), you can do so by using the `--initialize_from_frozen_model` flag in `DeepSpeech.py`. For best results, make sure you're passing an empty `--checkpoint_dir` when resuming from a frozen model.
+If you'd like to use one of the pre-trained models released by Mozilla to bootstrap your training process (transfer learning, fine tuning), you can do so by using the `--checkpoint_dir` flag in `DeepSpeech.py`. Specify the path where you downloaded the checkpoint from the release, and training will resume from the pre-trained model.
 
 For example, if you want to fine tune the entire graph using your own data in `my-train.csv`, `my-dev.csv` and `my-test.csv`, for three epochs, you can something like the following, tuning the hyperparameters as needed:
 
 ```bash
 mkdir fine_tuning_checkpoints
-python3 DeepSpeech.py --n_hidden 2048 --initialize_from_frozen_model path/to/model/output_graph.pb --checkpoint_dir fine_tuning_checkpoints --epoch 3 --train_files my-train.csv --dev_files my-dev.csv --test_files my_dev.csv --learning_rate 0.0001
+python3 DeepSpeech.py --n_hidden 2048 --checkpoint_dir path/to/checkpoint/folder --epoch -3 --train_files my-train.csv --dev_files my-dev.csv --test_files my_dev.csv --learning_rate 0.0001
 ```
 
-Note: the released models were trained with `--n_hidden 2048`, so you need to use that same value when initializing from the release models.
+Note: the released models were trained with `--n_hidden 2048`, so you need to use that same value when initializing from the release models. Note as well the use of a negative epoch count -3 (meaning 3 more epochs) since the checkpoint you're loading from was already trained for several epochs.
 
 ## Code documentation
 
diff --git a/VERSION b/VERSION
index 07bf7a73..0d91a54c 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-0.3.0-alpha.0
+0.3.0
diff --git a/bin/import_cv.py b/bin/import_cv.py
index 37a87496..4988671b 100755
--- a/bin/import_cv.py
+++ b/bin/import_cv.py
@@ -10,6 +10,7 @@ sys.path.insert(1, os.path.join(sys.path[0], '..'))
 import csv
 import tarfile
 import subprocess
+import progressbar
 
 from glob import glob
 from os import path
diff --git a/bin/import_ts.py b/bin/import_ts.py
index a2d061ca..a6f1ff00 100755
--- a/bin/import_ts.py
+++ b/bin/import_ts.py
@@ -3,6 +3,7 @@ from __future__ import absolute_import, division, print_function
 
 # Make sure we can import stuff from util/
 # This script needs to be run from the root of the DeepSpeech repository
+import argparse
 import os
 import re
 import sys
@@ -11,6 +12,7 @@ import sys
 sys.path.insert(1, os.path.join(sys.path[0], '..'))
 
 import csv
+import unidecode
 import zipfile
 
 from os import path
@@ -25,7 +27,7 @@ ARCHIVE_DIR_NAME = 'ts_' + ARCHIVE_NAME
 ARCHIVE_URL = 'https://s3.eu-west-3.amazonaws.com/audiocorp/releases/' + ARCHIVE_NAME + '.zip'
 
 
-def _download_and_preprocess_data(target_dir):
+def _download_and_preprocess_data(target_dir, english_compatible=False):
     # Making path absolute
     target_dir = path.abspath(target_dir)
     # Conditionally download data
@@ -33,7 +35,8 @@ def _download_and_preprocess_data(target_dir):
     # Conditionally extract archive data
     _maybe_extract(target_dir, ARCHIVE_DIR_NAME, archive_path)
     # Conditionally convert TrainingSpeech data to DeepSpeech CSVs and wav
-    _maybe_convert_sets(target_dir, ARCHIVE_DIR_NAME)
+    _maybe_convert_sets(target_dir, ARCHIVE_DIR_NAME, english_compatible=english_compatible)
+
 
 def _maybe_extract(target_dir, extracted_data, archive_path):
     # If target_dir/extracted_data does not exist, extract archive in target_dir
@@ -47,7 +50,8 @@ def _maybe_extract(target_dir, extracted_data, archive_path):
     else:
         print('Found directory "%s" - not extracting it from archive.' % archive_path)
 
-def _maybe_convert_sets(target_dir, extracted_data):
+
+def _maybe_convert_sets(target_dir, extracted_data, english_compatible=False):
     extracted_dir = path.join(target_dir, extracted_data)
     # override existing CSV with normalized one
     target_csv_template = os.path.join(target_dir, 'ts_' + ARCHIVE_NAME + '_{}.csv')
@@ -70,7 +74,7 @@ def _maybe_convert_sets(target_dir, extracted_data):
                 test_writer.writeheader()
 
                 for i, item in enumerate(data):
-                    transcript = validate_label(cleanup_transcript(item['text']))
+                    transcript = validate_label(cleanup_transcript(item['text'], english_compatible=english_compatible))
                     if not transcript:
                         continue
                     wav_filename = os.path.join(target_dir, extracted_data, item['path'])
@@ -92,12 +96,22 @@ PUNCTUATIONS_REG = re.compile(r"[°\-,;!?.()\[\]*…—]")
 MULTIPLE_SPACES_REG = re.compile(r'\s{2,}')
 
 
-def cleanup_transcript(text):
+def cleanup_transcript(text, english_compatible=False):
     text = text.replace('’', "'").replace('\u00A0', ' ')
     text = PUNCTUATIONS_REG.sub(' ', text)
     text = MULTIPLE_SPACES_REG.sub(' ', text)
+    if english_compatible:
+        text = unidecode.unidecode(text)
     return text.strip().lower()
 
 
+def handle_args():
+    parser = argparse.ArgumentParser(description='Importer for TrainingSpeech dataset.')
+    parser.add_argument(dest='target_dir')
+    parser.add_argument('--english-compatible', action='store_true', dest='english_compatible', help='Remove diactrics and other non-ascii chars.')
+    return parser.parse_args()
+
+
 if __name__ == "__main__":
-    _download_and_preprocess_data(sys.argv[1])
+    cli_args = handle_args()
+    _download_and_preprocess_data(cli_args.target_dir, cli_args.english_compatible)
diff --git a/bin/run-tc-ldc93s1_checkpoint.sh b/bin/run-tc-ldc93s1_checkpoint.sh
new file mode 100755
index 00000000..6ebb597d
--- /dev/null
+++ b/bin/run-tc-ldc93s1_checkpoint.sh
@@ -0,0 +1,31 @@
+#!/bin/sh
+
+set -xe
+
+ldc93s1_dir="./data/ldc93s1-tc"
+ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
+
+epoch_count=$1
+
+if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
+    echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
+    python -u bin/import_ldc93s1.py ${ldc93s1_dir}
+fi;
+
+python -u DeepSpeech.py --noshow_progressbar \
+  --train_files ${ldc93s1_csv} --train_batch_size 1 \
+  --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
+  --test_files ${ldc93s1_csv} --test_batch_size 1 \
+  --n_hidden 494 --epoch -1 --random_seed 4567 --default_stddev 0.046875 \
+  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt' \
+  --learning_rate 0.001 --dropout_rate 0.05 \
+  --decoder_library_path '/tmp/ds/libctc_decoder_with_kenlm.so' \
+  --lm_binary_path 'data/smoke_test/vocab.pruned.lm' \
+  --lm_trie_path 'data/smoke_test/vocab.trie' | tee /tmp/resume.log
+
+if ! grep "Training of Epoch $epoch_count" /tmp/resume.log; then
+  echo "Did not resume training from checkpoint"
+  exit 1
+else
+  exit 0
+fi
diff --git a/bin/run-tc-ldc93s1_frozen.sh b/bin/run-tc-ldc93s1_frozen.sh
deleted file mode 100755
index 0ea633b6..00000000
--- a/bin/run-tc-ldc93s1_frozen.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/sh
-
-set -xe
-
-ldc93s1_dir="./data/ldc93s1-tc"
-ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
-
-if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
-    echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
-    python -u bin/import_ldc93s1.py ${ldc93s1_dir}
-fi;
-
-python -u DeepSpeech.py \
-  --train_files ${ldc93s1_csv} --train_batch_size 1 \
-  --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
-  --test_files ${ldc93s1_csv} --test_batch_size 1 \
-  --n_hidden 494 --epoch 1 --random_seed 4567 --default_stddev 0.046875 \
-  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt' --checkpoint_secs 0 \
-  --learning_rate 0.001 --dropout_rate 0.05  --export_dir '/tmp/train' \
-  --nouse_seq_length --decoder_library_path '/tmp/ds/libctc_decoder_with_kenlm.so' \
-  --lm_binary_path 'data/smoke_test/vocab.pruned.lm' \
-  --lm_trie_path 'data/smoke_test/vocab.trie' \
-  --initialize_from_frozen_model '/tmp/frozen_model.pb'
diff --git a/bin/run-tc-ldc93s1_new.sh b/bin/run-tc-ldc93s1_new.sh
index 524c9f06..a5d12cb9 100755
--- a/bin/run-tc-ldc93s1_new.sh
+++ b/bin/run-tc-ldc93s1_new.sh
@@ -5,6 +5,8 @@ set -xe
 ldc93s1_dir="./data/ldc93s1-tc"
 ldc93s1_csv="${ldc93s1_dir}/ldc93s1.csv"
 
+epoch_count=$1
+
 if [ ! -f "${ldc93s1_dir}/ldc93s1.csv" ]; then
     echo "Downloading and preprocessing LDC93S1 example data, saving in ${ldc93s1_dir}."
     python -u bin/import_ldc93s1.py ${ldc93s1_dir}
@@ -14,8 +16,9 @@ python -u DeepSpeech.py \
   --train_files ${ldc93s1_csv} --train_batch_size 1 \
   --dev_files ${ldc93s1_csv} --dev_batch_size 1 \
   --test_files ${ldc93s1_csv} --test_batch_size 1 \
-  --n_hidden 494 --epoch 85 --random_seed 4567 --default_stddev 0.046875 \
-  --max_to_keep 1 --checkpoint_dir '/tmp/ckpt' --checkpoint_secs 0 \
+  --n_hidden 494 --epoch $epoch_count --random_seed 4567 \
+  --default_stddev 0.046875 --max_to_keep 1 \
+  --checkpoint_dir '/tmp/ckpt' \
   --learning_rate 0.001 --dropout_rate 0.05  --export_dir '/tmp/train' \
   --decoder_library_path '/tmp/ds/libctc_decoder_with_kenlm.so' \
   --lm_binary_path 'data/smoke_test/vocab.pruned.lm' \
diff --git a/evaluate.py b/evaluate.py
index d4842d62..c1a2d45a 100755
--- a/evaluate.py
+++ b/evaluate.py
@@ -99,12 +99,31 @@ def main(_):
 
     # sort examples by length, improves packing of batches and timesteps
     test_data = preprocess(
-        FLAGS.test_files,
+        FLAGS.test_files.split(','),
         FLAGS.test_batch_size,
-        hdf5_dest_path=FLAGS.hdf5_test_set).sort_values(
+        alphabet=alphabet,
+        numcep=N_FEATURES,
+        numcontext=N_CONTEXT,
+        hdf5_cache_path=FLAGS.hdf5_test_set).sort_values(
         by="features_len",
         ascending=False)
 
+    def create_windows(features):
+        num_strides = len(features) - (N_CONTEXT * 2)
+
+        # Create a view into the array with overlapping strides of size
+        # numcontext (past) + 1 (present) + numcontext (future)
+        window_size = 2*N_CONTEXT+1
+        features = np.lib.stride_tricks.as_strided(
+            features,
+            (num_strides, window_size, N_FEATURES),
+            (features.strides[0], features.strides[0], features.strides[1]),
+            writeable=False)
+
+        return features
+
+    test_data['features'] = test_data['features'].apply(create_windows)
+
     with tf.Session() as session:
         inputs, outputs = create_inference_graph(batch_size=FLAGS.test_batch_size, n_steps=N_STEPS)
 
@@ -158,7 +177,7 @@ def main(_):
 
             logits = np.empty([0, FLAGS.test_batch_size, alphabet.size() + 1])
             for i in range(0, batch_features.shape[1], N_STEPS):
-                chunk_features = batch_features[:, i:i + N_STEPS, :]
+                chunk_features = batch_features[:, i:i + N_STEPS, :, :]
                 chunk_features_len = np.minimum(
                     batch_features_len, full_step_len)
 
@@ -168,6 +187,7 @@ def main(_):
                     chunk_features = np.pad(chunk_features,
                                             ((0, 0),
                                              (0, FLAGS.n_steps - steps_in_chunk),
+                                             (0, 0),
                                              (0, 0)),
                                             mode='constant',
                                             constant_values=0)
diff --git a/examples/vad_transcriber/wavTranscriber.py b/examples/vad_transcriber/wavTranscriber.py
index 5fdcb394..b3f0d272 100644
--- a/examples/vad_transcriber/wavTranscriber.py
+++ b/examples/vad_transcriber/wavTranscriber.py
@@ -19,8 +19,8 @@ def load_model(models, alphabet, lm, trie):
     N_FEATURES = 26
     N_CONTEXT = 9
     BEAM_WIDTH = 500
-    LM_WEIGHT = 1.75
-    VALID_WORD_COUNT_WEIGHT = 1.00
+    LM_WEIGHT = 1.50
+    VALID_WORD_COUNT_WEIGHT = 2.10
 
     model_load_start = timer()
     ds = Model(models, N_FEATURES, N_CONTEXT, alphabet, BEAM_WIDTH)
diff --git a/examples/vad_transcriber/wavTranscription.md b/examples/vad_transcriber/wavTranscription.md
index e34c1c75..efa7839b 100644
--- a/examples/vad_transcriber/wavTranscription.md
+++ b/examples/vad_transcriber/wavTranscription.md
@@ -26,7 +26,7 @@ Set the aggressiveness mode, to an integer between 0 and 3.
 
 ```
 (venv) ~/Deepspeech/examples/vad_transcriber
-$ python3 audioTranscript_cmd.py --aggressive 1 --audio ./audio/guido-van-rossum.wav --model ./models/0.2.0/
+$ python3 audioTranscript_cmd.py --aggressive 1 --audio ./audio/guido-van-rossum.wav --model ./models/0.3.0/
 
 
 Filename                       Duration(s)          Inference Time(s)    Model Load Time(s)   LM Load Time(s)
@@ -58,4 +58,4 @@ In such a scenario, the GUI tool will not work. The following steps is known to
 (venv) ~/Deepspeech/examples/vad_transcriber$ export PYTHONPATH=/usr/lib/python3/dist-packages/
 (venv) ~/Deepspeech/examples/vad_transcriber$ python3 audioTranscript_gui.py
 
-```
\ No newline at end of file
+```
diff --git a/native_client/README.md b/native_client/README.md
index 5858a818..4afe0ed7 100644
--- a/native_client/README.md
+++ b/native_client/README.md
@@ -133,4 +133,4 @@ make package
 make npm-pack
 ```
 
-This will create the package `deepspeech-0.2.0.tgz` in `native_client/javascript`.
+This will create the package `deepspeech-0.3.0.tgz` in `native_client/javascript`.
diff --git a/native_client/client.cc b/native_client/client.cc
index b2cda25d..f6220140 100644
--- a/native_client/client.cc
+++ b/native_client/client.cc
@@ -23,7 +23,7 @@
 #define N_CONTEXT 9
 #define BEAM_WIDTH 500
 #define LM_WEIGHT 1.50f
-#define VALID_WORD_COUNT_WEIGHT 2.25f
+#define VALID_WORD_COUNT_WEIGHT 2.10f
 
 typedef struct {
   const char* string;
diff --git a/native_client/definitions.mk b/native_client/definitions.mk
index 7796da9d..4ed675b9 100644
--- a/native_client/definitions.mk
+++ b/native_client/definitions.mk
@@ -65,7 +65,8 @@ LDFLAGS_NEEDED := -Wl,--no-as-needed
 LDFLAGS_RPATH  := -Wl,-rpath,\$$ORIGIN
 endif
 ifeq ($(OS),Darwin)
-LDFLAGS_NEEDED :=
+CXXFLAGS       += -stdlib=libc++ -mmacosx-version-min=10.10
+LDFLAGS_NEEDED := -stdlib=libc++ -mmacosx-version-min=10.10
 LDFLAGS_RPATH  := -Wl,-rpath,@executable_path
 endif
 
diff --git a/native_client/javascript/binding.gyp b/native_client/javascript/binding.gyp
index d85dee28..789e4a91 100644
--- a/native_client/javascript/binding.gyp
+++ b/native_client/javascript/binding.gyp
@@ -8,6 +8,21 @@
       ],
       "include_dirs": [
         "../"
+      ],
+      "conditions": [
+        [ "OS=='mac'", {
+            "xcode_settings": {
+              "OTHER_CXXFLAGS": [
+                "-stdlib=libc++",
+                "-mmacosx-version-min=10.10"
+              ],
+              "OTHER_LDFLAGS": [
+                "-stdlib=libc++",
+                "-mmacosx-version-min=10.10"
+              ]
+            }
+          }
+        ]
       ]
     },
     {
diff --git a/native_client/javascript/client.js b/native_client/javascript/client.js
index 3f6c78ac..1e92379f 100644
--- a/native_client/javascript/client.js
+++ b/native_client/javascript/client.js
@@ -19,7 +19,7 @@ const LM_WEIGHT = 1.50;
 
 // Valid word insertion weight. This is used to lessen the word insertion penalty
 // when the inserted word is part of the vocabulary
-const VALID_WORD_COUNT_WEIGHT = 2.25;
+const VALID_WORD_COUNT_WEIGHT = 2.10;
 
 
 // These constants are tied to the shape of the graph used (changing them changes
diff --git a/native_client/python/client.py b/native_client/python/client.py
index 7becb3e0..60e8097c 100644
--- a/native_client/python/client.py
+++ b/native_client/python/client.py
@@ -27,7 +27,7 @@ LM_WEIGHT = 1.50
 
 # Valid word insertion weight. This is used to lessen the word insertion penalty
 # when the inserted word is part of the vocabulary
-VALID_WORD_COUNT_WEIGHT = 2.25
+VALID_WORD_COUNT_WEIGHT = 2.10
 
 
 # These constants are tied to the shape of the graph used (changing them changes
diff --git a/taskcluster/darwin-amd64-cpu-opt.yml b/taskcluster/darwin-amd64-cpu-opt.yml
index 1bfc3c29..5cd89616 100644
--- a/taskcluster/darwin-amd64-cpu-opt.yml
+++ b/taskcluster/darwin-amd64-cpu-opt.yml
@@ -6,8 +6,8 @@ build:
     - "index.project.deepspeech.deepspeech.native_client.osx.${event.head.sha}"
     - "notify.irc-channel.${notifications.irc}.on-exception"
     - "notify.irc-channel.${notifications.irc}.on-failed"
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.osx/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.osx/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.osx/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.osx/artifacts/public/summarize_graph"
   scripts:
     build: "taskcluster/host-build.sh"
     package: "taskcluster/package.sh"
diff --git a/taskcluster/darwin-opt-base.tyml b/taskcluster/darwin-opt-base.tyml
index cd398974..5157302c 100644
--- a/taskcluster/darwin-opt-base.tyml
+++ b/taskcluster/darwin-opt-base.tyml
@@ -87,6 +87,7 @@ payload:
         export LC_ALL=C &&
         export PKG_CONFIG_PATH="$TASKCLUSTER_TASK_DIR/homebrew/lib/pkgconfig" &&
         export MACOSX_DEPLOYMENT_TARGET=10.10 &&
+        export HOMEBREW_NO_AUTO_UPDATE=1 &&
         env &&
         (wget -O - $TENSORFLOW_BUILD_ARTIFACT | pixz -d | gtar -C $TASKCLUSTER_TASK_DIR -xf - ) &&
         git clone --quiet ${event.head.repo.url} $TASKCLUSTER_TASK_DIR/DeepSpeech/ds/ &&
@@ -97,9 +98,10 @@ payload:
         ${swig.patch_nodejs.osx} &&
         $TASKCLUSTER_TASK_DIR/DeepSpeech/ds/${build.scripts.build} &&
         $TASKCLUSTER_TASK_DIR/DeepSpeech/ds/${build.scripts.package} ;
+        export TASKCLUSTER_TASK_EXIT_CODE=$? &&
         mv $TASKCLUSTER_TASK_DIR/homebrew/ $TASKCLUSTER_ORIG_TASKDIR/homebrew/ &&
         mv $TASKCLUSTER_TASK_DIR/homebrew.cache/ $TASKCLUSTER_ORIG_TASKDIR/homebrew.cache/ &&
-        cd $TASKCLUSTER_TASK_DIR/../ && rm -fr tc-workdir/
+        cd $TASKCLUSTER_TASK_DIR/../ && rm -fr tc-workdir/ && exit $TASKCLUSTER_TASK_EXIT_CODE
 
   artifacts:
     - type: "directory"
diff --git a/taskcluster/docker-build-base.tyml b/taskcluster/docker-build-base.tyml
index 48ec93ee..d9de927d 100644
--- a/taskcluster/docker-build-base.tyml
+++ b/taskcluster/docker-build-base.tyml
@@ -43,8 +43,7 @@ then:
           add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" &&
           apt-get -qq update && apt-get -qq -y install docker-ce && mkdir -p /opt/deepspeech &&
           git clone --quiet ${event.head.repo.url} /opt/deepspeech && cd /opt/deepspeech && git checkout --quiet ${event.head.sha} &&
-          curl -fsSL "https://raw.githubusercontent.com/${event.head.user.login}/${event.head.repo.name}/${event.head.sha}/${dockerfile}" > ${dockerfile} &&
-          docker build .
+          docker build --file ${dockerfile} .
 
     artifacts:
       "public":
diff --git a/taskcluster/linux-amd64-cpu-opt.yml b/taskcluster/linux-amd64-cpu-opt.yml
index d7c4f11c..1fede7e0 100644
--- a/taskcluster/linux-amd64-cpu-opt.yml
+++ b/taskcluster/linux-amd64-cpu-opt.yml
@@ -14,8 +14,8 @@ build:
   system_config:
     >
       ${swig.patch_nodejs.linux}
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/summarize_graph"
   scripts:
     build: "taskcluster/host-build.sh"
     package: "taskcluster/package.sh"
diff --git a/taskcluster/linux-amd64-ctc-opt.yml b/taskcluster/linux-amd64-ctc-opt.yml
index 26aa4d1c..9a25a115 100644
--- a/taskcluster/linux-amd64-ctc-opt.yml
+++ b/taskcluster/linux-amd64-ctc-opt.yml
@@ -4,8 +4,8 @@ build:
     - "pull_request.synchronize"
     - "pull_request.reopened"
   template_file: linux-opt-base.tyml
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/summarize_graph"
   scripts:
     build: 'taskcluster/decoder-build.sh'
     package: 'taskcluster/decoder-package.sh'
diff --git a/taskcluster/linux-amd64-cuda-opt.yml b/taskcluster/linux-amd64-cuda-opt.yml
index bda53483..616ba526 100644
--- a/taskcluster/linux-amd64-cuda-opt.yml
+++ b/taskcluster/linux-amd64-cuda-opt.yml
@@ -12,8 +12,8 @@ build:
   system_config:
     >
       ${swig.patch_nodejs.linux}
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cuda/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cuda/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cuda/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cuda/artifacts/public/summarize_graph"
   maxRunTime: 14400
   scripts:
     build: "taskcluster/cuda-build.sh"
diff --git a/taskcluster/linux-arm64-cpu-opt.yml b/taskcluster/linux-arm64-cpu-opt.yml
index 1fe1ad63..8a57fa55 100644
--- a/taskcluster/linux-arm64-cpu-opt.yml
+++ b/taskcluster/linux-arm64-cpu-opt.yml
@@ -4,8 +4,8 @@ build:
     - "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.arm64"
     - "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.${event.head.sha}.arm64"
     - "index.project.deepspeech.deepspeech.native_client.arm64.${event.head.sha}"
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.arm64/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.arm64/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/summarize_graph"
   ## multistrap 2.2.0-ubuntu1 is broken in 14.04: https://bugs.launchpad.net/ubuntu/+source/multistrap/+bug/1313787
   system_setup:
     >
diff --git a/taskcluster/linux-rpi3-cpu-opt.yml b/taskcluster/linux-rpi3-cpu-opt.yml
index 25990dd2..fd174be3 100644
--- a/taskcluster/linux-rpi3-cpu-opt.yml
+++ b/taskcluster/linux-rpi3-cpu-opt.yml
@@ -4,8 +4,8 @@ build:
     - "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.arm"
     - "index.project.deepspeech.deepspeech.native_client.${event.head.branchortag}.${event.head.sha}.arm"
     - "index.project.deepspeech.deepspeech.native_client.arm.${event.head.sha}"
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.arm/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.arm/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/summarize_graph"
   ## multistrap 2.2.0-ubuntu1 is broken in 14.04: https://bugs.launchpad.net/ubuntu/+source/multistrap/+bug/1313787
   system_setup:
     >
diff --git a/taskcluster/node-package.yml b/taskcluster/node-package.yml
index bd94d011..b724821c 100644
--- a/taskcluster/node-package.yml
+++ b/taskcluster/node-package.yml
@@ -16,8 +16,8 @@ build:
   system_config:
     >
       ${swig.patch_nodejs.linux}
-  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/home.tar.xz"
-  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/summarize_graph"
+  tensorflow: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/home.tar.xz"
+  summarize_graph: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/summarize_graph"
   scripts:
     build: "taskcluster/node-build.sh"
     package: "taskcluster/node-package.sh"
diff --git a/taskcluster/test-armbian-opt-base.tyml b/taskcluster/test-armbian-opt-base.tyml
index 8e80eb5e..1c674b8d 100644
--- a/taskcluster/test-armbian-opt-base.tyml
+++ b/taskcluster/test-armbian-opt-base.tyml
@@ -44,7 +44,7 @@ then:
         PIP_DEFAULT_TIMEOUT: "60"
         PIP_EXTRA_INDEX_URL: "https://lissyx.github.io/deepspeech-python-wheels/"
         EXTRA_PYTHON_CONFIGURE_OPTS: "--with-fpectl" # Required by Debian Stretch
-        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5464-geb72bde"
+        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5466-g1b14736"
 
     command:
       - "/bin/bash"
diff --git a/taskcluster/test-darwin-opt-base.tyml b/taskcluster/test-darwin-opt-base.tyml
index 431259e9..ae263397 100644
--- a/taskcluster/test-darwin-opt-base.tyml
+++ b/taskcluster/test-darwin-opt-base.tyml
@@ -41,7 +41,7 @@ then:
         DEEPSPEECH_TEST_MODEL: https://queue.taskcluster.net/v1/task/${training}/artifacts/public/output_graph.pb
         DEEPSPEECH_PROD_MODEL: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pb
         DEEPSPEECH_PROD_MODEL_MMAP: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pbmm
-        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5464-geb72bde"
+        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5466-g1b14736"
 
     command:
       - - "/bin/bash"
@@ -59,6 +59,7 @@ then:
             export TASKCLUSTER_TMP_DIR="$TASKCLUSTER_TASK_DIR/tmp" &&
             export LC_ALL=C &&
             export MACOSX_DEPLOYMENT_TARGET=10.10 &&
+            export HOMEBREW_NO_AUTO_UPDATE=1 &&
             export PIP_DEFAULT_TIMEOUT=60 &&
             env &&
             git clone --quiet ${event.head.repo.url} $TASKCLUSTER_TASK_DIR/DeepSpeech/ds/ &&
@@ -66,9 +67,10 @@ then:
             cd $TASKCLUSTER_TASK_DIR &&
             source $TASKCLUSTER_TASK_DIR/DeepSpeech/ds/tc-brew-tests.sh && ${extraSystemSetup} &&
             /bin/bash ${build.args.tests_cmdline} ;
+            export TASKCLUSTER_TASK_EXIT_CODE=$? &&
             mv $TASKCLUSTER_TASK_DIR/homebrew/ $TASKCLUSTER_ORIG_TASKDIR/homebrew/ &&
             mv $TASKCLUSTER_TASK_DIR/homebrew.cache/ $TASKCLUSTER_ORIG_TASKDIR/homebrew.cache/ &&
-            cd $TASKCLUSTER_TASK_DIR/../ && rm -fr tc-workdir/
+            cd $TASKCLUSTER_TASK_DIR/../ && rm -fr tc-workdir/ && exit $TASKCLUSTER_TASK_EXIT_CODE
 
     mounts:
       - cacheName: deepspeech-homebrew-bin
diff --git a/taskcluster/test-linux-opt-base.tyml b/taskcluster/test-linux-opt-base.tyml
index 8f9259a2..262b293a 100644
--- a/taskcluster/test-linux-opt-base.tyml
+++ b/taskcluster/test-linux-opt-base.tyml
@@ -44,7 +44,7 @@ then:
         DEEPSPEECH_PROD_MODEL: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pb
         DEEPSPEECH_PROD_MODEL_MMAP: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pbmm
         PIP_DEFAULT_TIMEOUT: "60"
-        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5464-geb72bde"
+        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5466-g1b14736"
 
     command:
       - "/bin/bash"
diff --git a/taskcluster/test-raspbian-opt-base.tyml b/taskcluster/test-raspbian-opt-base.tyml
index ca3a08a7..7c6062f8 100644
--- a/taskcluster/test-raspbian-opt-base.tyml
+++ b/taskcluster/test-raspbian-opt-base.tyml
@@ -44,7 +44,7 @@ then:
         PIP_DEFAULT_TIMEOUT: "60"
         PIP_EXTRA_INDEX_URL: "https://www.piwheels.org/simple"
         EXTRA_PYTHON_CONFIGURE_OPTS: "--with-fpectl" # Required by Raspbian Stretch / PiWheels
-        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5464-geb72bde"
+        EXPECTED_TENSORFLOW_VERSION: "TensorFlow: v1.9.0-rc2-5466-g1b14736"
 
     command:
       - "/bin/bash"
diff --git a/taskcluster/test-training_upstream-linux-amd64-py27mu-opt.yml b/taskcluster/test-training_upstream-linux-amd64-py27mu-opt.yml
index 4c422c6e..1f2bc3a0 100644
--- a/taskcluster/test-training_upstream-linux-amd64-py27mu-opt.yml
+++ b/taskcluster/test-training_upstream-linux-amd64-py27mu-opt.yml
@@ -7,7 +7,7 @@ build:
       apt-get -qq -y install ${python.packages_trusty.apt}
   args:
     tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/tc-train-tests.sh 2.7.14:mu"
-  convert_graphdef: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.eb72bde18fd7abdba07c1476ef6f41b78929403d.cpu/artifacts/public/convert_graphdef_memmapped_format"
+  convert_graphdef: "https://index.taskcluster.net/v1/task/project.deepspeech.tensorflow.pip.master.1b14736dd41f2a54339e59793679546981d9ae2c.cpu/artifacts/public/convert_graphdef_memmapped_format"
   metadata:
     name: "DeepSpeech Linux AMD64 CPU upstream training Py2.7 mu"
     description: "Training a DeepSpeech LDC93S1 model for Linux/AMD64 using upstream TensorFlow Python 2.7 mu, CPU only, optimized version"
diff --git a/taskcluster/test-training_upstream_frozen-linux-amd64-py36m-opt.yml b/taskcluster/test-training_upstream_frozen-linux-amd64-py36m-opt.yml
deleted file mode 100644
index ae81cc3d..00000000
--- a/taskcluster/test-training_upstream_frozen-linux-amd64-py36m-opt.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-build:
-  template_file: test-linux-opt-base.tyml
-  dependencies:
-    - "linux-amd64-ctc-opt"
-    - "test-training_upstream-linux-amd64-py27mu-opt"
-  system_setup:
-    >
-      apt-get -qq -y install ${python.packages_trusty.apt}
-  args:
-    tests_cmdline: "${system.homedir.linux}/DeepSpeech/ds/tc-train-tests.sh 3.6.4:m frozen"
-  metadata:
-    name: "DeepSpeech Linux AMD64 CPU upstream training frozen Py3.6"
-    description: "Training a DeepSpeech LDC93S1 model from frozen model file for Linux/AMD64 using upstream TensorFlow Python 3.6, CPU only, optimized version"
diff --git a/tc-brew-tests.sh b/tc-brew-tests.sh
index fa51d5cb..1a861da5 100644
--- a/tc-brew-tests.sh
+++ b/tc-brew-tests.sh
@@ -37,7 +37,7 @@ install_local_homebrew()
     mkdir -p "${LOCAL_HOMEBREW_DIRECTORY}"
     mkdir -p "${HOMEBREW_CACHE}"
 
-    curl -L https://github.com/Homebrew/brew/tarball/1.7.7 | tar xz --strip 1 -C "${LOCAL_HOMEBREW_DIRECTORY}"
+    curl -L https://github.com/Homebrew/brew/tarball/1.8.0 | tar xz --strip 1 -C "${LOCAL_HOMEBREW_DIRECTORY}"
     export PATH=${LOCAL_HOMEBREW_DIRECTORY}/bin:$PATH
 
     if [ ! -x "${LOCAL_HOMEBREW_DIRECTORY}/bin/brew" ]; then
diff --git a/tc-tests-utils.sh b/tc-tests-utils.sh
index 5c06bbcc..7ce579d9 100755
--- a/tc-tests-utils.sh
+++ b/tc-tests-utils.sh
@@ -135,26 +135,30 @@ assert_shows_something()
 
 assert_correct_ldc93s1()
 {
-  # FIXME: RE-ENABLE AFTER FIXING LM HYPERPARAMETERS
-  # assert_correct_inference "$1" "she had your dark suit in greasy wash water all year"
-  assert_working_inference "$1" "she had"
+  assert_correct_inference "$1" "she had your dark suit in greasy wash water all year"
+}
+
+assert_correct_ldc93s1_lm()
+{
+  assert_correct_inference "$1" "she had your dark suit in greasy wash water all year"
 }
 
 assert_correct_multi_ldc93s1()
 {
-  # FIXME: RE-ENABLE AFTER FIXING LM HYPERPARAMETERS
-  #assert_shows_something "$1" "/LDC93S1.wav%she had your dark suit in greasy wash water all year%"
-  #assert_shows_something "$1" "/LDC93S1_pcms16le_2_44100.wav%she had your dark suit in greasy wash water all year%"
-  return 0
+  assert_shows_something "$1" "/LDC93S1.wav%she had your dark suit in greasy wash water all year%"
+  assert_shows_something "$1" "/LDC93S1_pcms16le_2_44100.wav%she had your dark suit in greasy wash water all year%"
   ## 8k will output garbage anyway ...
   # assert_shows_something "$1" "/LDC93S1_pcms16le_1_8000.wav%she hayorasryrtl lyreasy asr watal w water all year%"
 }
 
 assert_correct_ldc93s1_prodmodel()
 {
-  # FIXME: RE-ENABLE AFTER FIXING LM HYPERPARAMETERS
-  #assert_correct_inference "$1" "she had tired or so and greasy wash war or year"
-  assert_working_inference "$1" "she had"
+  assert_correct_inference "$1" "she hadered or so and greasy wash war or year"
+}
+
+assert_correct_ldc93s1_prodmodel_stereo_44k()
+{
+  assert_correct_inference "$1" "she had tered or so and greasy wash war or year"
 }
 
 assert_correct_warning_upsampling()
@@ -185,13 +189,13 @@ run_all_inference_tests()
   assert_correct_ldc93s1 "${phrase_pbmodel_nolm}"
 
   phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1.wav)
-  assert_correct_ldc93s1 "${phrase_pbmodel_withlm}"
+  assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm}"
 
   phrase_pbmodel_nolm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
   assert_correct_ldc93s1 "${phrase_pbmodel_nolm_stereo_44k}"
 
   phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
-  assert_correct_ldc93s1 "${phrase_pbmodel_withlm_stereo_44k}"
+  assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm_stereo_44k}"
 
   phrase_pbmodel_nolm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
   assert_correct_warning_upsampling "${phrase_pbmodel_nolm_mono_8k}"
@@ -209,7 +213,7 @@ run_prod_inference_tests()
   assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm}"
 
   phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
-  assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm_stereo_44k}"
+  assert_correct_ldc93s1_prodmodel_stereo_44k "${phrase_pbmodel_withlm_stereo_44k}"
 
   phrase_pbmodel_withlm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
   assert_correct_warning_upsampling "${phrase_pbmodel_withlm_mono_8k}"
@@ -261,11 +265,6 @@ download_data()
   cp ${DS_ROOT_TASK}/DeepSpeech/ds/data/smoke_test/vocab.trie ${TASKCLUSTER_TMP_DIR}/trie
 }
 
-download_for_frozen()
-{
-  wget -O "${TASKCLUSTER_TMP_DIR}/frozen_model.pb" "${DEEPSPEECH_TEST_MODEL}"
-}
-
 download_material()
 {
   target_dir=$1
diff --git a/tc-train-tests.sh b/tc-train-tests.sh
index 121cca10..b6de6d9c 100644
--- a/tc-train-tests.sh
+++ b/tc-train-tests.sh
@@ -6,7 +6,6 @@ source $(dirname "$0")/tc-tests-utils.sh
 
 pyver_full=$1
 ds=$2
-frozen=$2
 
 if [ -z "${pyver_full}" ]; then
     echo "No python version given, aborting."
@@ -62,17 +61,9 @@ else
 fi;
 
 pushd ${HOME}/DeepSpeech/ds/
-    if [ "${frozen}" = "frozen" ]; then
-        download_for_frozen
-        time ./bin/run-tc-ldc93s1_frozen.sh
-    else
-        time ./bin/run-tc-ldc93s1_new.sh
-    fi;
+    time ./bin/run-tc-ldc93s1_new.sh 105
 popd
 
-deactivate
-pyenv uninstall --force ${PYENV_NAME}
-
 cp /tmp/train/output_graph.pb ${TASKCLUSTER_ARTIFACTS}
 
 if [ ! -z "${CONVERT_GRAPHDEF_MEMMAPPED}" ]; then
@@ -82,3 +73,10 @@ if [ ! -z "${CONVERT_GRAPHDEF_MEMMAPPED}" ]; then
   /tmp/${convert_graphdef} --in_graph=/tmp/train/output_graph.pb --out_graph=/tmp/train/output_graph.pbmm
   cp /tmp/train/output_graph.pbmm ${TASKCLUSTER_ARTIFACTS}
 fi;
+
+pushd ${HOME}/DeepSpeech/ds/
+    time ./bin/run-tc-ldc93s1_checkpoint.sh 105
+popd
+
+deactivate
+pyenv uninstall --force ${PYENV_NAME}