Yuekai Zhang 5df24c1685
Whisper large fine-tuning on wenetspeech, mutli-hans-zh (#1483)
* add whisper fbank for wenetspeech

* add whisper fbank for other dataset

* add str to bool

* add decode for wenetspeech

* add requirments.txt

* add original model decode with 30s

* test feature extractor speed

* add aishell2 feat

* change compute feature batch

* fix overwrite

* fix executor

* regression

* add kaldifeatwhisper fbank

* fix io issue

* parallel jobs

* use multi machines

* add wenetspeech fine-tune scripts

* add monkey patch codes

* remove useless file

* fix subsampling factor

* fix too long audios

* add remove long short

* fix whisper version to support multi batch beam

* decode all wav files

* remove utterance more than 30s in test_net

* only test net

* using soft links

* add kespeech whisper feats

* fix index error

* add manifests for whisper

* change to licomchunky writer

* add missing option

* decrease cpu usage 

* add speed perturb for kespeech

* fix kespeech speed perturb

* add dataset

* load checkpoint from specific path

* add speechio

* add speechio results

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Co-authored-by: zr_jin <peter.jin.cn@gmail.com>
2024-03-07 19:04:27 +08:00
..
2023-06-26 09:33:18 +08:00

Introduction

This recipe includes some different ASR models trained with WenetSpeech.

./RESULTS.md contains the latest results.

Transducers

There are various folders containing the name transducer in this folder. The following table lists the differences among them.

Encoder Decoder Comment
pruned_transducer_stateless2 Conformer(modified) Embedding + Conv1d Using k2 pruned RNN-T loss
pruned_transducer_stateless5 Conformer(modified) Embedding + Conv1d Using k2 pruned RNN-T loss

The decoder in transducer_stateless is modified from the paper Rnn-Transducer with Stateless Prediction Network. We place an additional Conv1d layer right after the input embedding layer.