Model | mean WER [%] | std WER | mean epoch time [s] | std epoch |
---|---|---|---|---|
dot | 20.1 | 0.2 | 1683.5 | 41.0944 |
tanh-add.conv.bias | 20.05 | 0.15 | 1640.25 | 63.9233 |
tanh-add.conv | 20.375 | 0.192029 | 1689 | 70.601 |
tanh-add.conv.wfb | 20.125 | 0.258602 | 1621.5 | 28.9266 |
tanh-add.bias | 20.125 | 0.108972 | 1600.25 | 10.0343 |
tanh-add.bias.wfb | 20.275 | 0.216506 | 1626.5 | 12.2577 |
tanh-add | 20.1 | 0.163299 | 1602.25 | 15.8015 |
tanh-add.wfb | 20.075 | 0.311247 | 1605.5 | 12.5 |
Arch Linux 64-bit | |
Processor Information: | |
Vendor: GenuineIntel | |
CPU Family: 0x6 | |
CPU Model: 0x3c | |
CPU Stepping: 0x3 | |
CPU Type: 0x0 | |
Speed: 3800 Mhz | |
4 logical processors |
#!/usr/bin/python3 | |
# -*- coding: utf-8 -*- | |
import requests | |
from bs4 import BeautifulSoup | |
from os.path import isfile | |
SP_KEY = '' # change to SimplePush.io key | |
SP_URL = 'https://api.simplepush.io/send' | |
def send_sp(title, msg): |
#include <iostream> | |
#include <GLFW/glfw3.h> | |
int main() { | |
if (!glfwInit()) { | |
std::cerr << "Unable to initialize GLFW" << std::endl; | |
return -1; | |
} |
Reviewer #2 comments ===========
This work experimentally compares different hyper-parameters of a seq2seq model for Automatic Speech Recognition (ASR) with attention mechanism and performs some analysis on the trained models. The title is not only too broad as it considers attention models only in ASR but also the analysis is not comprehensive enough. Among others, it looks at the errors of the beam-search, the effect of beam size, effect of number of LSTM layers in encoder, number of hidden units and the effect of pre-training. It would be much appreciated if you focused more on deeper analysis of the models rather than the structure of the networks, as you did in later sections.
The paper is easy to read and the introduction is well-written citing relevant works. However, going through the paper and only judging by the story-telling for the experiments, it seems that the author(s) are describing their effort at manually tuning a ASR model (which is not a bad thing by itself if presented right). If that i
GNU gdb (Ubuntu 7.11.1-0ubuntu1~16.5) 7.11.1 | |
Copyright (C) 2016 Free Software Foundation, Inc. | |
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html> | |
This is free software: you are free to change and redistribute it. | |
There is NO WARRANTY, to the extent permitted by law. Type "show copying" | |
and "show warranty" for details. | |
This GDB was configured as "x86_64-linux-gnu". | |
Type "show configuration" for configuration details. | |
For bug reporting instructions, please see: | |
<http://www.gnu.org/software/gdb/bugs/>. |
GNU gdb (Ubuntu 7.11.1-0ubuntu1~16.5) 7.11.1 | |
Copyright (C) 2016 Free Software Foundation, Inc. | |
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html> | |
This is free software: you are free to change and redistribute it. | |
There is NO WARRANTY, to the extent permitted by law. Type "show copying" | |
and "show warranty" for details. | |
This GDB was configured as "x86_64-linux-gnu". | |
Type "show configuration" for configuration details. | |
For bug reporting instructions, please see: | |
<http://www.gnu.org/software/gdb/bugs/>. |
New maximum RSS usage: 12.5 GB | |
Thread 0x0000150b5fe21700 (most recent call first): | |
File "<__array_function__ internals>", line 5 in dot | |
File "/work/asr3/zeyer/merboldt/py-envs/py3.8-tf2.3/lib/python3.8/site-packages/librosa/feature/spectral.py", line 2011 in melspectrogram | |
File "/work/asr3/zeyer/merboldt/py-envs/py3.8-tf2.3/lib/python3.8/site-packages/librosa/feature/spectral.py", line 1852 in mfcc | |
File "/u/merboldt/setups/librispeech/2020-09-04--librispeech-rnnt-rna/returnn/returnn/datasets/generating.py", line 1297 in _get_audio_features_mfcc | |
File "/u/merboldt/setups/librispeech/2020-09-04--librispeech-rnnt-rna/returnn/returnn/datasets/generating.py", line 1178 in get_audio_features | |
File "/u/merboldt/setups/librispeech/2020-09-04--librispeech-rnnt-rna/returnn/returnn/datasets/generating.py", line 1120 in get_audio_features_from_raw_bytes | |
File "/u/merboldt/setups/librispeech/2020-09-04--librispeech-rnnt-rna/returnn/returnn/datasets/generating.py", line 2613 in _collect_single_seq | |
File "/u/mer |
#!/usr/local_rwth/bin/zsh | |
#SBATCH --mail-user=andre.merboldt@rwth-aachen.de | |
#SBATCH --mail-type=ALL | |
#SBATCH --nodes=1 | |
#SBATCH --ntasks-per-node=8 | |
#SBATCH --cpus-per-task=2 | |
#SBATCH --time=120:00:00 # 5 days | |
#SBATCH --output=log/train-multi-gpu-job.%J.log # stdout/stderr file | |
#SBATCH --partition=dgx2 # DGX2 (has 16 GPUs) | |
#SBATCH --account=supp0003 # substitute appropriate group here |