In this work, we build, train, and evaluate several speech recognition models. The task of Automatic Speech Recognition (ASR) consists of automatically generating a text transcription starting from a raw speech audio file. To tackle this task, we either build and train deep neural models from scratch, or import pre-trained model and fine-tune them on small labelled dataset.
This project was made for the NLP exam of the Master's Degree in Artificial Intelligence, Università di Bologna.
You can read our presentation and paper report to get more detailed information.
- Marco Solime
- Alessandro Folloni
- Daniele Napolitano
- Gabriele Fossi
- Deep Speech Base (ResNetInv + GRUs)
- Deep Speech Attention (ResNetInv + Encoders)
- Jasper Base (5x3)
- Jasper DR (Dense Residual) (5x3)
- Conformer Small
- Wav2Vec2 Base (pre-trained from Hugging Face, then fine-tuned on a fraction of LIBRISPEECH and FLEURS datasets)
We evaluate our models on the LibriSpeech benchmark, and provide results both in Word Error Rate (WER) and Character Error Rate (CER). We did not implement a language model to correct speling mistakes.
Despite this and training with limited hardware, we are able to achieve decent performances compared to SoTA models.
The table below shows our results (top three rows) compared to the baseline results, taken from the original papers, in Word Error Rate (WER) metrics:
Refer to the ufficial PyTorch documentation to install the correct version of CUDA: https://pytorch.org/get-started/locally/. Here we used an Anaconda environment (https://www.anaconda.com/).
conda create -n myenv python=3.12.2
conda activate myenv
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda install -c pytorch torchtext
conda install numpy cffi
pip install pysoundfile
pip install wandb