Fix issues with ASR notebooks (#2698)

Signed-off-by: smajumdar <[email protected]>

tutorials/asr/Intro_to_Transducers.ipynb

@@ -553,7 +553,7 @@
         "\n",
         "The Joint model config has several essential components which we discuss below :\n",
         "\n",
-        "1) `log_softmax`: Due to the cost of computing softmax on such large tensors, the Numba CUDA implementation of RNNT loss will implicitly compute the log softmax when called (so its inputs should be logits). The CPU version of the loss doesn't face such memory issues so it requires log-probabilities instead. Since the behaviour is different for CPU-GPU, the `null` value will automatically switch behaviour dependent on whether the input tensor is on a CPU or GPU device.\n",
+        "1) `log_softmax`: Due to the cost of computing softmax on such large tensors, the Numba CUDA implementation of RNNT loss will implicitly compute the log softmax when called (so its inputs should be logits). The CPU version of the loss doesn't face such memory issues so it requires log-probabilities instead. Since the behaviour is different for CPU-GPU, the `None` value will automatically switch behaviour dependent on whether the input tensor is on a CPU or GPU device.\n",
         "\n",
         "2) `preserve_memory`: This flag will call `torch.cuda.empty_cache()` at certain critical sections when computing the Joint tensor. While this operation might allow us to preserve some memory, the empty_cache() operation is tremendously slow and will slow down training by an order of magnitude or more. It is available to use but not recommended.\n",
         "\n",