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|
{}
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TAGS
#region-us 

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
-------------



Conformer （train from scartch）
------------------------------




TAGS
#region-us 

passage: TAGS
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