metadata

title: README
emoji: 💻
colorFrom: purple
colorTo: red
sdk: static
pinned: false

Q & A Using VectorDB FAISS GPT Queries:

Eight key features of a robust AI speech recognition pipeline:

Scaling: The pipeline should be capable of scaling compute, models, and datasets to improve performance. This includes leveraging GPU acceleration and increasing the size of the training dataset.
Deep Learning Approaches: The pipeline should utilize deep learning approaches, such as deep neural networks, to improve speech recognition performance.
Weak Supervision: The pipeline should be able to leverage weakly supervised learning to increase the size of the training dataset. This involves using large amounts of transcripts of audio from the internet.
Zero-shot Transfer Learning: The resulting models from the pipeline should be able to generalize well to standard benchmarks without the need for any fine-tuning in a zero-shot transfer setting.
Accuracy and Robustness: The models generated by the pipeline should approach the accuracy and robustness of human speech recognition.
Pre-training Techniques: The pipeline should incorporate unsupervised pre-training techniques, such as Wav2Vec 2.0, which enable learning directly from raw audio without the need for handcrafted features.
Broad Range of Environments: The goal of the pipeline should be to work reliably "out of the box" in a broad range of environments without requiring supervised fine-tuning for every deployment distribution.
Combining Multiple Datasets: The pipeline should combine multiple existing high-quality speech recognition datasets to improve robustness and effectiveness of the models.