[Long Review] Conformer: Convolution-augmented Transformer for Speech Recognition скачать в хорошем качестве

[Long Review] Conformer: Convolution-augmented Transformer for Speech Recognition

Eager to train your own #Whisper or #GPT-4o model but running out of data? We are proud to offer this unique large-scale conversational speech dataset in different languages and topics for #ASR, #TTS, #NLP, and other conversational AI R&D. It has speaker labels and high quality transcriptions. The duration of the dataset depends on the customer's needs and can extend up to 1 million hours. See the description and samples in the following post:   / olewave-large-scaled-convesational-speech-...   send an email to [email protected] for more details. Join 'Speech and Language Technologies' Meetup group https://www.meetup.com/speech-and-lan... to see weekly paper reading schedules and discussions. Conformer: Convolution-augmented Transformer for Speech Recognition Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, Ruoming Pang Download PDF Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters. #ml #course #coursera #computervision #cv #asr #convolution #cnn #dnn #ai #metaai #googleai #openai #microsoft #conformer #transformer