This is our submission for the final graded project for the WS22/23 course "Neural Networks: Theory and Implementation" at Saarland University.
We focus on developing a SDR system in a speaker-independent setting. That is, the speakers in the evaluation set are disjoint from the training set speakers. We do so because we expect real-world ASR systems to generalize to different speakers than those we have data for. Moreover, for many languages that are under-resourced, we have have limited annotated speech data from a single speaker, but we would still want the system to be deployed to work on any speaker of that language. We tackle the problem of spoken digit recognition as a sequence classification task. Concretely, the inputs are short audio clips of a specific digit (in the range 0-9), then the goal is to build deep neural network models to classify a short audio clip and predict the digit that was spoken.
The dataset contains 3000 audio clips of spoken digits (0-9) in English in .wav format it can be found in the folder speech_data.
The total size of the dataset is 26Mb. The file SDR_metadata.tsv contains information such as the labels of the audio clips and to whether they are used for training, evaluation or testing.
The Python version used in our project is 3.9.13. You can use poetry to install the dependencies. To set up the poetry environment, run the following command:
poetry install
poetry run pip3 install torch torchvision torchaudio torchmetrics --extra-index-url https://download.pytorch.org/whl/cu117The last step is necessary to ensure that pytorch is installed with GPU support. Alternatively you can use the requirements.txt file to install the dependencies.
Models are trained based on Mel-spectrograms of the original raw audio files. In total we train four models. The simplest one is a linear model applied to truncated Mel-spectrograms. Along with the utilities necessary to generate its train- validation and test- data it can be found in the folder model_baseline. The folder model_neural contains implementations of two distinct types of neural network architectures. The first is a 1d convolutional neural network and the second is a vision transformer based neural network. The utilities for training the models are contained in the utils subfolder.
Apart from supervised training, this project also has an implementation of unsupervised training based on contrastive loss. The contrastive loss based training is implemented in the file contrastive_training.py contained in the model_neural folder.
For further details about the project feel free to take a look at the project report pdf contained in this repository. Our most important sources for inspiration and code were: