Segment a given audio into utterances using a trained end-to-end ASR model.
- This repository contains the source code used in the paper.
- Maintained code of the CTC segmentation python package: https://github.com/lumaku/ctc-segmentation
- CTC segmentation has been integrated into the ESPnet toolkit as
espnet/bin/asr_align.py, including an example recipe for TEDlium 2 and a small example demo.
Install ESPnet (tested v0.7.0)
https://espnet.github.io/espnet/installation.html
Clone this repo into ESPnets egs folder:
cd <espnet_folder>/egs
git clone https://github.com/cornerfarmer/ctc_segmentation
cd ctc_segmentation
Activate ESPnets virtual environment
source ../../tools/venv/bin/activate
In this example it is explained how to align the utterances in the TED-lium dev and test dataset, but one can easily align any custom data by providing .wav files inside data/<dataset_name> together with equally named .txt files which contain the corresponding transcriptions (one utterance per line).
wget https://projets-lium.univ-lemans.fr/wp-content/uploads/corpus/TED-LIUM/TEDLIUM_release1.tar.gz
tar xvf TEDLIUM_release1.tar.gz
Convert TED-lium data format into .wav + .txt file:
python convert_ted.py TEDLIUM_release1/dev data/tedlium_dev
python convert_ted.py TEDLIUM_release1/test data/tedlium_test
Prepare trained ESPnet model. In this example we're gonna use a RNN trained on TED-lium v2 (tedlium2.rnn.v2) provided by ESPNet.
Download the weights from https://drive.google.com/open?id=1cac5Uc09lJrCYfWkLQsF8eapQcxZnYdf and place the .tar.gz file into the ctc_segmentation folder.
Extract data and move the relavent parts into exp/tedlium2_rnn/.
tar xvf model.streaming.v2.tar.gz --one-top-level
mkdir -p exp/tedlium2_rnn
mv model.streaming.v2/exp/train_trim_sp_pytorch_train2/results/* exp/tedlium2_rnn/
mv model.streaming.v2/data/train_trim_sp/cmvn.ark exp/tedlium2_rnn/
In this step we apply the model to the prepared data. By using the ctc part of the network this gives us a prob distr across all possible symbols per audio frame which are stored in a .npz file.
If you want to speed up this step by using the GPU use the flag --gpu.
If you are using a transformer model it is recommended to use the split mode where the audio is split up into different parts which are decoded individually.
Use therefore the --split <part_size> flag. We found 2000 to work good as a part size.
python decode.py exp/tedlium2_rnn/model.acc.best exp/tedlium2_rnn/cmvn.ark data/tedlium_dev eval/tedlium_dev_tedlium2_rnn
Finally align / segment the audio. This step will apply our ctc segmentation algorithm and store the results in eval/tedlium_dev_tedlium2_rnn/*.txt.
python align.py exp/tedlium2_rnn/model.acc.best data/tedlium_dev eval/tedlium_dev_tedlium2_rnn
CTC-segmentation is described in CTC-Segmentation of Large Corpora for German End-to-end Speech Recognition at https://doi.org/10.1007/978-3-030-60276-5_27 or in pre-print at https://arxiv.org/abs/2007.09127
@InProceedings{ctcsegmentation,
author="K{\"u}rzinger, Ludwig
and Winkelbauer, Dominik
and Li, Lujun
and Watzel, Tobias
and Rigoll, Gerhard",
editor="Karpov, Alexey
and Potapova, Rodmonga",
title="CTC-Segmentation of Large Corpora for German End-to-End Speech Recognition",
booktitle="Speech and Computer",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="267--278",
abstract="Recent end-to-end Automatic Speech Recognition (ASR) systems demonstrated the ability to outperform conventional hybrid DNN/HMM ASR. Aside from architectural improvements in those systems, those models grew in terms of depth, parameters and model capacity. However, these models also require more training data to achieve comparable performance.",
isbn="978-3-030-60276-5"
}