ASR

Introduction

Please refer to https://k2-fsa.github.io/icefall/recipes/Non-streaming-ASR/aishell/index.html for how to run models in this recipe.

Aishell is an open-source Chinese Mandarin speech corpus published by Beijing Shell Shell Technology Co., Ltd. 400 people from different accent areas in China are invited to participate in the recording, which is conducted in a quiet indoor environment using high fidelity microphone and downsampled to 16kHz. The manual transcription accuracy is above 95%, through professional speech annotation and strict quality inspection. The data is free for academic use. We hope to provide moderate amount of data for new researchers in the field of speech recognition.

(From Open Speech and Language Resources)

Transducers

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

	Encoder	Decoder	Comment
transducer_stateless	Conformer	Embedding + Conv1d	with k2.rnnt_loss
transducer_stateless_modified	Conformer	Embedding + Conv1d	with modified transducer from optimized_transducer
transducer_stateless_modified-2	Conformer	Embedding + Conv1d	with modified transducer from optimized_transducer + extra data
pruned_transducer_stateless3	Conformer (reworked)	Embedding + Conv1d	pruned RNN-T + reworked model with random combiner + using aidatatang_20zh as extra data
pruned_transducer_stateless7	Zipformer	Embedding	pruned RNN-T + zipformer encoder + stateless decoder with context-size set to 1
zipformer	Upgraded Zipformer	Embedding + Conv1d	The latest recipe with context-size set to 1

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.

Whisper

Recipe to finetune large pretrained models

	Encoder	Decoder	Comment
whisper	Transformer	Transformer	support fine-tuning using deepspeed