ishine/css_with_tstransformer

Code for the INTERSPEECH-2021 paper: Ultra Fast Speech Separation Model with Teacher Student Learning.

Ultra Fast Speech Separation Model with Teacher Student Learning

Introduction

Because of the ultra fast inference speed, the small speech separation Transformer model is preferred for the deployment on devices.
In this work, we elaborate Teacher Student learning for better training of the ultra fast speech separation model. The small student model is trained to reproduce the separation results of a large pretrained teacher model.

For a detailed description and experimental results, please refer to our paper: Ultra Fast Speech Separation Model with Teacher Student Learning (Accepted by INTERSPEECH 2021).

Environment

python 3.6.9, torch 1.7.1

Get Started

1. Download the overlapped speech of LibriCSS dataset.

   wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1PdloA-V8HGxkRu9MnT35_civpc3YXJsT' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1PdloA-V8HGxkRu9MnT35_civpc3YXJsT" -O overlapped_speech.zip && rm -rf /tmp/cookies.txt && unzip overlapped_speech.zip && rm overlapped_speech.zip

2. Download the TSTransformer separation models.

   wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1yFTJb0AeyHfTE75BH_8Di4DUC2RDix3T' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1yFTJb0AeyHfTE75BH_8Di4DUC2RDix3T" -O checkpoints.zip && rm -rf /tmp/cookies.txt && unzip checkpoints.zip && rm checkpoints.zip

3. Run the separation.

   3.1 single channel separation

   export MODEL_NAME=1ch_TSTransformer
   python3 separate.py \
       --checkpoint checkpoints/$MODEL_NAME \
       --wav_list utils/overlapped_speech_1ch.scp \
       --sep_dir separated_speech/1ch/utterances_with_${MODEL_NAME} \
       --device-id 0 \
       --num_spks 2 \
       --mvdr false 

   The separated speech can be found in the directory 'separated_speech/1ch/utterances_with_${MODEL_NAME}'

   3.2 seven channel separation

   export MODEL_NAME=TSTransformer
   python3 separate.py \
       --checkpoint checkpoints/$MODEL_NAME \
       --wav_list utils/overlapped_speech_7ch.scp \
       --sep_dir separated_speech/7ch/utterances_with_${MODEL_NAME} \
       --device-id 0 \
       --num_spks 2 \
       --mvdr true 

   The separated speech can be found in the directory 'separated_speech/7ch/utterances_with_${MODEL_NAME}'

Citation

If you find our work useful, please cite our paper:

@inproceedings{CSS_with_TSTransformer,
  author={Sanyuan Chen and Yu Wu and Zhuo Chen and Jian Wu and Takuya Yoshioka and Shujie Liu and Jinyu Li and Xiangzhan Yu},
  title={{Ultra Fast Speech Separation Model with Teacher Student Learning}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={3026--3030},
  doi={10.21437/Interspeech.2021-142}
}