Added attention visualization and jsalt18e2e recipe, and refined Librispeech recipe

Support attention visualization.

• Added PlotAttentionReport which save attention weight as figure for each epoch.
• Refactored test script test_e2e_model to check various attention functions

Added JSALT18 end-to-end ASR recipe

Refined the Librispeech recipe

• Removed long utterances during training
• Added RNNLM

First (test) release

First release.

• CTC, attention-based encoder-decoder, and hybrid CTC/attention based end-to-end ASR
  • Fast/accurate training with CTC/attention multitask training
  • CTC/attention joint decoding to boost monotonic alignment decoding
• Encoder: VGG-like CNN + BLSTM or pyramid BLSTM
• Attention: Dot product, location-aware attention, variants of multihead (pytorch only)
• Incorporate RNNLM/LSTMLM trained only with text data
• Flexible network architecture thanks to chainer and pytorch
• Kaldi style complete recipe
  • Support numbers of ASR benchmarks (WSJ, Switchboard, CHiME-4, Librispeech, TED, CSJ, AMI, HKUST, Voxforge, etc.)
• State-of-the-art performance in Japanese/Chinese benchmarks (comparable/superior to hybrid DNN/HMM and CTC+FST)
• Moderate performance in standard English benchmarks
• Support multiple GPU training