I am fine tuning 0.5.1 on common voice dataset. The language model used while training
There is no need to re-create a new LM, you should be able to re-use the default one.
I am trying to fine tune the Deep Speech 0.5.1 model with deepspeech-0.5.1-checkpoint.tar.gz downloaded.
The code snippet is as below 
source myenv/bin/activate
cd DeepSpeech-0.5.1/
pip3 install -r requirements.txt
pip3 install tensorflow-gpu==1.13.1
pip3 install $(python util/taskcluster.py --decoder)
python util/taskcluster.py --arch gpu --target native_client
# Creating a LM with kenlm
cd ..
git clone https://github.com/kpu/kenlm.git
cd kenlm/
mkdir build
cd build/
cmake ..
make -j 4
cd ../../my-model/
../kenlm/build/bin/lmplz -o 5 <some.txt >lm.arpa
../kenlm/build/bin/build_binary lm.arpa lm.binary
../DeepSpeech-0.5.1/native_client/generate_trie alphabet.txt lm.binary trie
cd ../DeepSpeech-0.5.1/
nohup python3 -u DeepSpeech.py \
--train_files "/home/dev_ds/deepspeech_dir_1/corpus/corpus-train.csv" \
--dev_files "/home/dev_ds/deepspeech_dir_1/corpus/corpus-dev.csv" \
--test_files "/home/dev_ds/deepspeech_dir_1/corpus/corpus-test.csv" \
--alphabet_config_path "/home/dev_ds/deepspeech_dir/deepspeech-0.5.1-models/alphabet.txt" \
--lm_binary_path "/home/dev_ds/deepspeech_dir/my-model/lm.binary" \
--lm_trie_path "/home/dev_ds/deepspeech_dir/my-model/trie" \
--checkpoint_dir /home/dev_ds/deepspeech_dir/deepspeech-0.5.1-checkpoint/ \
--train_batch_size 48 \
--dev_batch_size 4 \
--test_batch_size 4 \
--learning_rate 0.00005 \
--export_dir "/home/dev_ds/deepspeech_dir_1/my-model/" \
&>> new_spkr.log &
---------------------------------------------------------------------------
But log file has following error at the end 
Error: Trie file version mismatch (4 instead of expected 3). Update your trie file.
terminate called after throwing an instance of 'int'
But when I use the lm.binary and trie files from deepspeech-0.5.1-models.tar.gz , output_graph.pb is successfully getting exported. But I am not satisfied with the model performance using that LM.
This issue is already discussed in other threads, but I could not get proper solution. Let me know what is wrong with native_client and/or generate_trie.
pip3 freeze
absl-py==0.7.1
asn1crypto==0.24.0
astor==0.8.0
attrdict==2.0.1
audioread==2.1.8
bcrypt==3.1.7
beautifulsoup4==4.8.0
bs4==0.0.1
certifi==2019.6.16
cffi==1.12.3
chardet==3.0.4
cryptography==2.7
cycler==0.10.0
decorator==4.4.0
deepspeech==0.5.1
deepspeech-gpu==0.5.1
ds-ctcdecoder==0.5.1
gast==0.2.2
grpcio==1.23.0
h5py==2.9.0
idna==2.8
joblib==0.13.2
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
kiwisolver==1.1.0
librosa==0.7.0
llvmlite==0.29.0
Markdown==3.1.1
matplotlib==3.1.1
mock==3.0.5
numba==0.45.1
numpy==1.15.4
pandas==0.25.1
paramiko==2.6.0
progressbar2==3.43.1
protobuf==3.9.1
pycparser==2.19
PyNaCl==1.3.0
pyparsing==2.4.2
python-dateutil==2.8.0
python-utils==2.3.0
pytz==2019.2
pyxdg==0.26
requests==2.22.0
resampy==0.2.2
scikit-learn==0.21.3
scipy==1.3.1
six==1.12.0
SoundFile==0.10.2
soupsieve==1.9.3
sox==1.3.7
tensorboard==1.13.1
tensorflow-estimator==1.13.0
tensorflow-gpu==1.13.1
termcolor==1.1.0
urllib3==1.25.3
Werkzeug==0.15.5
wget==3.2
– Thank you
There are issues with model’s performance and accuracy. I tried fine tuning it over common voice with lr 1e-6 for 7 epochs with checkpoint lm and still the model is no way near accurate. It keeps throwing random words for phrases super obvious in the audio.
The error already explicit that, you have built a mismatching version for the LM. Since you don’t document how you proceed, I cannot tell you what is wrong.
util/taskcluster.py by default will download the latest (master) version of the artifact, even though you’re running the code from v0.5.1. This means you’re using a newer incompatible version of generate_trie. You have to specify the version with --branch v0.5.1.
Thank you Reuben Morais, it worked finally 