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Support CTC/AED option for Zipformer recipe #1389
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Nice results ! Seems that (Zipformer-M-ctc/aed, 90.0M, 2.22 / 4.97) is comparable to (Zipformer-rnnt, 65.55 M, 2.21, 4.82), and (Zipformer-L-ctc/aed, 174.3M, 2.09 / 4.59) surpasses all prior benchmarks. Because we know that in the RNNT model, the majority of the parameters are in the encoder, while in the CTC/AED model, the decoder parameters account for a significant portion. This leads to the appearance that the Zipformer-CTC/AED model has a much larger number of parameters compared to the Zipformer-RNNT, yet the number of encoder parameters in both models might actually be quite similar. Thus I'm particularly intrigued by the parameters utilized in Zipformer-L-ctc/aed, specifically those related to the encoder and decoder components. Could you provide more details on these? |
For these Zipformer CTC/AED models, we keep the attention-decoder model configurations almost same. (Different encoder output dimensions in Zipformer-S/M/L would cause slightly inconsistent number of parameters used in the attention-decoder.)
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* add attention-decoder loss option for zipformer recipe * add attention-decoder-rescoring * update export.py and pretrained_ctc.py * update RESULTS.md
This PR supports CTC/AED system for
zipformer
recipe.