Update result for full libri + GigaSpeech using transducer_stateless.

[csukuangfj]

This PR provides the WER for full libri + GigaSpeech. See #213 for more details.

The following tables compare the WERs with and without using multiple datasets:

Baseline (without using multiple dataset)

Time per epoch (~2 hours 46 minutes, using 4 GPUs)

	test-clean	test-other	comment
greedy search (max sym per frame 1)	2.67	6.67	--epoch 63, --avg 19, --max-duration 100
modified beam search (beam size 4)	2.67	6.57	--epoch 63, --avg 19, --max-duration 100

(tensorboard log: https://tensorboard.dev/experiment/qgvWkbF2R46FYA6ZMNmOjA/#scalars)

With multiple dataset (--giga-prob 0.2)

	test-clean	test-other	comment
greedy search (max sym per frame 1)	2.64	6.55	--epoch 39, --avg 15, --max-duration 100
modified beam search (beam size 4)	2.61	6.46	--epoch 39, --avg 15, --max-duration 100

(tensorboard log: https://tensorboard.dev/experiment/xmo5oCgrRVelH9dCeOkYBg/)

Time per epoch (~4 hours 15 minutes, using 4 GPUs)

The training time per epoch is increased as it is using more data in the training. However, it converges faster (39 epochs vs 63 epochs). If we decrease the probability to select data from GigaSpeech, it will definitely decrease the training time, but it needs more experiments to see how it affects the WER.

[danpovey]

Cool!
Hopefully it will give more improvement in situations where we have less training data available (or where
the model is larger).