readme: The ch-w2v-conformer model uses following datasets to pretrain:  
  
  ISML datasets (6 languages,70k hours): internal dataset contains 40k hours Chinese, Cantonese, Tibetan, Inner Mongolian, Inner Kazakh, Uighur.
  
  Babel datasets (17 languages, 2k hours): Assamese, Bengali, Cantonese, Cebuano, Georgian, Haitian, Kazakh, Kurmanji, Lao, Pashto, Swahili, Tagalog, Tamil, Tok, Turkish, Vietnamese, Zulu
  
  After pretraining, we build ASR system based on CTC-Attention structure. In very low resource task, we find that if too many initialization network structures are constructed in the upper layer of pre-training conformer encoder, the migration performance of the pre-training model will be destroyed, so we only build a single-layer transformer decoder for joint training.
  
  pretrained model link: 
  
  
  ## constrained-plus Task Performance
  
  * Languages: Cantonese,mongolian,kazakh
  * config: conf/train_conformer_large_10h.yaml
  * Feature info: using mfcc feature, with dither 1.0, without cmvn
  * Training info: lr 0.001, batch size 10, 4 gpus on V100, acc_grad 1, 80 epochs
  * Decoding info: ctc_weight 0.5, average_num 35
  
  dev set results trained only with 10 hours training set 
  
  ## w2v-Conformer
  
  |   decoding_method   | Cantonese(CER)  | mongolian(WER) | 
  |:-------------------:|:----:|:----:|
  |  ctc_greedy_search  | 31.46 | 53.64 |
  |  ctc_prefix_search |  31.47   | 53.50 |
  | attention_rescoring | 31.45 |  52.96 | 
  
  ## Conformer （train from scartch）
  
  
  |   decoding_method   |  Cantonese(CER)  | mongolian(WER) |
  |:-------------------:|----:|:----:|
  |  ctc_greedy_search  | 61.43 | 89.38 |
  |  ctc_prefix_search |  61.37   | 89.53|
  | attention_rescoring | 60.61 | 89.60|