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# Text Recognizer
Implementing the text recognizer project from the course ["Full Stack Deep Learning Course"](https://fullstackdeeplearning.com/march2019) (FSDL) in PyTorch in order to learn best practices when building a deep learning project. I have expanded on this project by adding additional feature and ideas given by Claudio Jolowicz in ["Hypermodern Python"](https://cjolowicz.github.io/posts/hypermodern-python-01-setup/).
+## Installation
-## Setup
+Install poetry and pyenv.
-TBC
+```sh
+pyenv local 3.9.1
+make install
+```
+## Generate Datasets
-### Build word piece dataset
+Download and generate datasets by running:
-Extract text from the iam dataset:
-TODO: Fix these!
-```
-python extract-iam-text --use_words --save_text train.txt --save_tokens letters.txt
+```sh
+make download
+make generate
```
-Create word pieces from the extracted training text:
-```
-python make-wordpieces --output_prefix iamdb_1kwp --text_file train.txt --num_pieces 100
-```
-Optionally, build a transition graph for word pieces:
-```
-python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
-```
-(TODO: Not working atm, needed for GTN loss function)
+## TODO
## Todo
- [ ] Local attention for target sequence
@@ -41,8 +37,10 @@ python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kw
- [ ] Train with Smoothloss
- [ ] Train with SWA
- [ ] VqTransformer without the quantization
+- [ ] VqTransformer with extra layer
+
-## Run Sweeps
+## Run Sweeps (old stuff)
Run the following commands to execute hyperparameter search with W&B:
```
@@ -51,3 +49,9 @@ export SWEEP_ID=...
wandb agent $SWEEP_ID
```
+
+(TODO: Not working atm, needed for GTN loss function)
+Optionally, build a transition graph for word pieces:
+```
+python build-transitions --tokens iamdb_1kwp_tokens_1000.txt --lexicon iamdb_1kwp_lex_1000.txt --blank optional --self_loops --save_path 1kwp_prune_0_10_optblank.bin --prune 0 10
+```