Update README.md

README.md

@@ -15,18 +15,19 @@ https://arxiv.org/abs/2308.08155
 Whisper:
 https://arxiv.org/abs/2212.04356
 
-ChatDev:
-https://arxiv.org/pdf/2307.07924.pdf
+# Q & A Using VectorDB FAISS GPT Queries:
+
+## Eight key features of a robust AI speech recognition pipeline:
+1. 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.
+2. Deep Learning Approaches: The pipeline should utilize deep learning approaches, such as deep neural networks, to improve speech recognition performance.
+3. 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.
+4. 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.
+5. Accuracy and Robustness: The models generated by the pipeline should approach the accuracy and robustness of human speech recognition.
+6. 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.
+7. 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.
+8. Combining Multiple Datasets: The pipeline should combine multiple existing high-quality speech recognition datasets to improve robustness and effectiveness of the models.
 
-Ten Teaching Examples:
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+ChatDev:
+https://arxiv.org/pdf/2307.07924.pdf