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）