Update README.md

README.md

@@ -80,7 +80,7 @@ With streaming, the results with different chunk sizes on test-clean are the fol
 This ASR system is a Conformer model trained with the RNN-T loss (with an auxiliary CTC loss to stabilize training). The model operates with a unigram tokenizer.
 Architecture details are described in the [training hyperparameters file](https://github.com/speechbrain/speechbrain/blob/develop/recipes/LibriSpeech/ASR/transducer/hparams/conformer_transducer.yaml).
 
-Streaming support makes use of Dynamic Chunk Training. Chunked attention is used for the multi-head attention module, and an implementation of [Dynamic Chunk Convolutions](https://www.amazon.science/publications/dynamic-chunk-convolution-for-unified-streaming-and-non-streaming-conformer-asr) were used.  
+Streaming support makes use of Dynamic Chunk Training. Chunked attention is used for the multi-head attention module, and an implementation of [Dynamic Chunk Convolutions](https://www.amazon.science/publications/dynamic-chunk-convolution-for-unified-streaming-and-non-streaming-conformer-asr) was used.  
 The model was trained with support for different chunk sizes (and even full context), and so is suitable for various chunk sizes and offline transcription.
 
 The system is trained with recordings sampled at 16kHz (single channel).