Currently Tested on pytorch  with cuda10 and python3.7.
Branch trainableFrontEnd : contains the code in progress to train the model using the raw audio samples only.
Branch python27 : contains the same code as of master but for python2.7 and pytorch0.4.1
- Train Wav2Letter.
- Language model support using kenlm.
- Noise injection for online training to improve noise robustness.
- Audio augmentation to improve noise robustness.
- Easy start/stop capabilities in the event of crash or hard stop during training.
- Visdom/Tensorboard support for visualizing training graphs.
Several libraries are needed to be installed for training to work. I will assume that everything is being installed in an Anaconda installation on Ubuntu.
Install PyTorch if you haven't already.
Install this fork for Warp-CTC bindings:
git clone https://github.com/SeanNaren/warp-ctc.git cd warp-ctc mkdir build; cd build cmake .. make export CUDA_HOME="/usr/local/cuda" cd ../pytorch_binding python setup.py install
Install pytorch audio:
sudo apt-get install sox libsox-dev libsox-fmt-all git clone https://github.com/pytorch/audio.git cd audio pip install cffi python setup.py install
If you want decoding to support beam search with an optional language model, install ctcdecode:
git clone --recursive https://github.com/parlance/ctcdecode.git cd ctcdecode pip install .
Finally clone this repo and run this within the repo:
pip install -r requirements.txt
To create a custom dataset you must create a CSV file containing the locations of the training data. This has to be in the format of:
/path/to/audio.wav,transcription /path/to/audio2.wav,transcription ...
The first path is to the audio file, and the second is the text containing the transcript on one line. This can then be used as stated below.
python train.py --train-manifest data/train_manifest.csv --val-manifest data/val_manifest.csv
python train.py --help for more parameters and options.
There is also Visdom support to visualize training. Once a server has been started, to use:
python train.py --visdom
There is also Tensorboard support to visualize training. Follow the instructions to set up. To use:
python train.py --tensorboard --logdir log_dir/ # Make sure the Tensorboard instance is made pointing to this log directory
For both visualisation tools, you can add your own name to the run by changing the
--id parameter when training.
There is support for two different types of noise; noise augmentation and noise injection.
Applies small changes to the tempo and gain when loading audio to increase robustness. To use, use the
--augment flag when training.
Dynamically adds noise into the training data to increase robustness. To use, first fill a directory up with all the noise files you want to sample from. The dataloader will randomly pick samples from this directory.
To enable noise injection, use the
--noise-dir /path/to/noise/dir/ to specify where your noise files are. There are a few noise parameters to tweak, such as
--noise_prob to determine the probability that noise is added, and the
--noise-max parameters to determine the minimum and maximum noise to add in training.
Included is a script to inject noise into an audio file to hear what different noise levels/files would sound like. Useful for curating the noise dataset.
python noise_inject.py --input-path /path/to/input.wav --noise-path /path/to/noise.wav --output-path /path/to/input_injected.wav --noise-level 0.5 # higher levels means more noise
Training supports saving checkpoints of the model to continue training from should an error occur or early termination. To enable epoch checkpoints use:
python train.py --checkpoint
To enable checkpoints every N batches through the epoch as well as epoch saving:
python train.py --checkpoint --checkpoint-per-batch N # N is the number of batches to wait till saving a checkpoint at this batch.
Note for the batch checkpointing system to work, you cannot change the batch size when loading a checkpointed model from it's original training run.
To continue from a checkpointed model that has been saved:
python train.py --continue-from models/wav2Letter_checkpoint_epoch_N_iter_N.pth.tar
This continues from the same training state as well as recreates the visdom graph to continue from if enabled.
If you would like to start from a previous checkpoint model but not continue training, add the
--finetune flag to restart training
Choosing batch sizes
Included is a script that can be used to benchmark whether training can occur on your hardware, and the limits on the size of the model/batch sizes you can use. To use:
python benchmark.py --batch-size 32
Use the flag
--help to see other parameters that can be used with the script.
Saved models contain the metadata of their training process. To see the metadata run the below command:
python model.py --model-path models/wav2Letter.pth.tar
To also note, there is no final softmax layer on the model as when trained, warp-ctc does this softmax internally. This will have to also be implemented in complex decoders if anything is built on top of the model, so take this into consideration!
To evaluate a trained model on a test set (has to be in the same format as the training set):
python test.py --model-path models/wav2Letter.pth --test-manifest /path/to/test_manifest.csv --cuda
test.py use a
GreedyDecoder which picks the highest-likelihood output label at each timestep. Repeated and blank symbols are then filtered to give the final output.
A beam search decoder can optionally be used with the installation of the
ctcdecode library as described in the Installation section. The
transcribe scripts have a
--decoder argument. To use the beam decoder, add
--decoder beam. The beam decoder enables additional decoding parameters:
- beam_width how many beams to consider at each timestep
- lm_path optional binary KenLM language model to use for decoding
- alpha weight for language model
- beta bonus weight for words
--offsets flag to get positional information of each character in the transcription when using
transcribe.py script. The offsets are based on the size
of the output tensor, which you need to convert into a format required.
For example, based on default parameters you could multiply the offsets by a scalar (duration of file in seconds / size of output) to get the offsets in seconds.