CMU 11751/18781 Fall 2022: ESPnet Tutorial

ESPnet is a widely-used end-to-end speech processing toolkit. It has supported various speech processing tasks. ESPnet uses PyTorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments.

Main references: - ESPnet repository - ESPnet documentation - ESPnet tutorial in Speech Recognition and Understanding (Fall 2021) - Recitation in Multilingual NLP (Spring 2022)

Author: Yifan Peng (yifanpen@andrew.cmu.edu)

❗Important Notes❗

  • We are using Colab to show the demo. However, Colab has some constraints on the total GPU runtime. If you use too much GPU, you may fail to connect to a GPU backend for some time.

  • There are multiple in-class checkpoints ✅ throughout this tutorial. There will also be some after-class excersices 📗 after the tutorial. Your participation points are based on these tasks. Please try your best to follow all the steps! If you encounter issues, please notify the TAs as soon as possible so that we can make an adjustment for you.

  • Please submit PDF files of your completed notebooks to Gradescope. You can print the notebook using File -> Print in the menu bar.

  • This tutorial covers the basics of ESPnet, which will be the foundation of the next tutorial on Wednesday.

Objectives

After this tutorial, you are expected to know: - How to run existing recipes (data prep, training, inference and scoring) in ESPnet2 - How to change the training and decoding configurations - How to create a new recipe from scratch - Where to find resources if you encounter an issue

Install ESPnet

Function to print date and time

We first define a function to print the current date and time, which will be used in multiple places below.

[ ]:
def print_date_and_time():
  from datetime import datetime
  import pytz

  now = datetime.now(pytz.timezone("America/New_York"))
  print("=" * 60)
  print(f' Current date and time: {now.strftime("%m/%d/%Y %H:%M:%S")}')
  print("=" * 60)

# example output
print_date_and_time()
============================================================
 Current date and time: 09/19/2022 16:46:16
============================================================

Check GPU type

Let’s check the GPU type of this allocated environment.

[ ]:
!nvidia-smi
Mon Sep 19 20:46:34 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   47C    P8     9W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

Download ESPnet

We use git clone to download the source code of ESPnet and then go to a specific commit.

Important: In other versions of ESPnet, you may encounter errors related to imcompatible package versions (numba). Please use the same commit to avoid such issues.

[ ]:
# It takes a few seconds
!git clone --depth 5 https://github.com/espnet/espnet

# We use a specific commit just for reproducibility.
%cd /content/espnet
!git checkout 3a22d1584317ae59974aad62feab8719c003ae05
Cloning into 'espnet'...
remote: Enumerating objects: 5719, done.
remote: Counting objects: 100% (5719/5719), done.
remote: Compressing objects: 100% (4091/4091), done.
remote: Total 5719 (delta 2005), reused 3036 (delta 961), pack-reused 0
Receiving objects: 100% (5719/5719), 6.86 MiB | 1.28 MiB/s, done.
Resolving deltas: 100% (2005/2005), done.
/content/espnet
Note: checking out '3a22d1584317ae59974aad62feab8719c003ae05'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -b with the checkout command again. Example:

  git checkout -b <new-branch-name>

HEAD is now at 3a22d15 Merge pull request #4637 from espnet/kamo-naoyuki-patch-2-1

Setup Python environment based on anaconda

There are several other installation methods, but we highly recommend the anaconda-based one.

[ ]:
# It takes 30 seconds
%cd /content/espnet/tools
!./setup_anaconda.sh anaconda espnet 3.9
/content/espnet/tools
--2022-09-19 20:48:01--  https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
Resolving repo.continuum.io (repo.continuum.io)... 104.18.200.79, 104.18.201.79, 2606:4700::6812:c84f, ...
Connecting to repo.continuum.io (repo.continuum.io)|104.18.200.79|:443... connected.
HTTP request sent, awaiting response... 301 Moved Permanently
Location: https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh [following]
--2022-09-19 20:48:01--  https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8203, ...
Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 76607678 (73M) [application/x-sh]
Saving to: ‘miniconda.sh’

miniconda.sh        100%[===================>]  73.06M   352MB/s    in 0.2s

2022-09-19 20:48:01 (352 MB/s) - ‘miniconda.sh’ saved [76607678/76607678]

PREFIX=/content/espnet/tools/anaconda
Unpacking payload ...
Collecting package metadata (current_repodata.json): - \ done
Solving environment: / - done

## Package Plan ##

  environment location: /content/espnet/tools/anaconda

  added / updated specs:
    - _libgcc_mutex==0.1=main
    - _openmp_mutex==4.5=1_gnu
    - brotlipy==0.7.0=py39h27cfd23_1003
    - ca-certificates==2022.3.29=h06a4308_1
    - certifi==2021.10.8=py39h06a4308_2
    - cffi==1.15.0=py39hd667e15_1
    - charset-normalizer==2.0.4=pyhd3eb1b0_0
    - colorama==0.4.4=pyhd3eb1b0_0
    - conda-content-trust==0.1.1=pyhd3eb1b0_0
    - conda-package-handling==1.8.1=py39h7f8727e_0
    - conda==4.12.0=py39h06a4308_0
    - cryptography==36.0.0=py39h9ce1e76_0
    - idna==3.3=pyhd3eb1b0_0
    - ld_impl_linux-64==2.35.1=h7274673_9
    - libffi==3.3=he6710b0_2
    - libgcc-ng==9.3.0=h5101ec6_17
    - libgomp==9.3.0=h5101ec6_17
    - libstdcxx-ng==9.3.0=hd4cf53a_17
    - ncurses==6.3=h7f8727e_2
    - openssl==1.1.1n=h7f8727e_0
    - pip==21.2.4=py39h06a4308_0
    - pycosat==0.6.3=py39h27cfd23_0
    - pycparser==2.21=pyhd3eb1b0_0
    - pyopenssl==22.0.0=pyhd3eb1b0_0
    - pysocks==1.7.1=py39h06a4308_0
    - python==3.9.12=h12debd9_0
    - readline==8.1.2=h7f8727e_1
    - requests==2.27.1=pyhd3eb1b0_0
    - ruamel_yaml==0.15.100=py39h27cfd23_0
    - setuptools==61.2.0=py39h06a4308_0
    - six==1.16.0=pyhd3eb1b0_1
    - sqlite==3.38.2=hc218d9a_0
    - tk==8.6.11=h1ccaba5_0
    - tqdm==4.63.0=pyhd3eb1b0_0
    - tzdata==2022a=hda174b7_0
    - urllib3==1.26.8=pyhd3eb1b0_0
    - wheel==0.37.1=pyhd3eb1b0_0
    - xz==5.2.5=h7b6447c_0
    - yaml==0.2.5=h7b6447c_0
    - zlib==1.2.12=h7f8727e_1


The following NEW packages will be INSTALLED:

  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main
  _openmp_mutex      pkgs/main/linux-64::_openmp_mutex-4.5-1_gnu
  brotlipy           pkgs/main/linux-64::brotlipy-0.7.0-py39h27cfd23_1003
  ca-certificates    pkgs/main/linux-64::ca-certificates-2022.3.29-h06a4308_1
  certifi            pkgs/main/linux-64::certifi-2021.10.8-py39h06a4308_2
  cffi               pkgs/main/linux-64::cffi-1.15.0-py39hd667e15_1
  charset-normalizer pkgs/main/noarch::charset-normalizer-2.0.4-pyhd3eb1b0_0
  colorama           pkgs/main/noarch::colorama-0.4.4-pyhd3eb1b0_0
  conda              pkgs/main/linux-64::conda-4.12.0-py39h06a4308_0
  conda-content-tru~ pkgs/main/noarch::conda-content-trust-0.1.1-pyhd3eb1b0_0
  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.8.1-py39h7f8727e_0
  cryptography       pkgs/main/linux-64::cryptography-36.0.0-py39h9ce1e76_0
  idna               pkgs/main/noarch::idna-3.3-pyhd3eb1b0_0
  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.35.1-h7274673_9
  libffi             pkgs/main/linux-64::libffi-3.3-he6710b0_2
  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.3.0-h5101ec6_17
  libgomp            pkgs/main/linux-64::libgomp-9.3.0-h5101ec6_17
  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.3.0-hd4cf53a_17
  ncurses            pkgs/main/linux-64::ncurses-6.3-h7f8727e_2
  openssl            pkgs/main/linux-64::openssl-1.1.1n-h7f8727e_0
  pip                pkgs/main/linux-64::pip-21.2.4-py39h06a4308_0
  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py39h27cfd23_0
  pycparser          pkgs/main/noarch::pycparser-2.21-pyhd3eb1b0_0
  pyopenssl          pkgs/main/noarch::pyopenssl-22.0.0-pyhd3eb1b0_0
  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py39h06a4308_0
  python             pkgs/main/linux-64::python-3.9.12-h12debd9_0
  readline           pkgs/main/linux-64::readline-8.1.2-h7f8727e_1
  requests           pkgs/main/noarch::requests-2.27.1-pyhd3eb1b0_0
  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.100-py39h27cfd23_0
  setuptools         pkgs/main/linux-64::setuptools-61.2.0-py39h06a4308_0
  six                pkgs/main/noarch::six-1.16.0-pyhd3eb1b0_1
  sqlite             pkgs/main/linux-64::sqlite-3.38.2-hc218d9a_0
  tk                 pkgs/main/linux-64::tk-8.6.11-h1ccaba5_0
  tqdm               pkgs/main/noarch::tqdm-4.63.0-pyhd3eb1b0_0
  tzdata             pkgs/main/noarch::tzdata-2022a-hda174b7_0
  urllib3            pkgs/main/noarch::urllib3-1.26.8-pyhd3eb1b0_0
  wheel              pkgs/main/noarch::wheel-0.37.1-pyhd3eb1b0_0
  xz                 pkgs/main/linux-64::xz-5.2.5-h7b6447c_0
  yaml               pkgs/main/linux-64::yaml-0.2.5-h7b6447c_0
  zlib               pkgs/main/linux-64::zlib-1.2.12-h7f8727e_1


Preparing transaction: | / - done
Executing transaction: | / - \ | / - \ | / - \ | done
installation finished.
WARNING:
    You currently have a PYTHONPATH environment variable set. This may cause
    unexpected behavior when running the Python interpreter in Miniconda3.
    For best results, please verify that your PYTHONPATH only points to
    directories of packages that are compatible with the Python interpreter
    in Miniconda3: /content/espnet/tools/anaconda
Could not find conda environment: espnet
You can list all discoverable environments with `conda info --envs`.

Collecting package metadata (current_repodata.json): - \ done
Solving environment: / done


==> WARNING: A newer version of conda exists. <==
  current version: 4.12.0
  latest version: 4.14.0

Please update conda by running

    $ conda update -n base -c defaults conda



## Package Plan ##

  environment location: /content/espnet/tools/anaconda/envs/espnet



Preparing transaction: \ done
Verifying transaction: / done
Executing transaction: \ done
#
# To activate this environment, use
#
#     $ conda activate espnet
#
# To deactivate an active environment, use
#
#     $ conda deactivate

Collecting package metadata (current_repodata.json): - \ | / done
Solving environment: \ | done


==> WARNING: A newer version of conda exists. <==
  current version: 4.12.0
  latest version: 4.14.0

Please update conda by running

    $ conda update -n base -c defaults conda



## Package Plan ##

  environment location: /content/espnet/tools/anaconda/envs/espnet

  added / updated specs:
    - conda
    - python=3.9


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    _openmp_mutex-5.1          |            1_gnu          21 KB
    ca-certificates-2022.07.19 |       h06a4308_0         124 KB
    certifi-2022.9.14          |   py39h06a4308_0         155 KB
    cffi-1.15.1                |   py39h74dc2b5_0         228 KB
    conda-4.14.0               |   py39h06a4308_0         915 KB
    conda-package-handling-1.9.0|   py39h5eee18b_0         887 KB
    cryptography-37.0.1        |   py39h9ce1e76_0         1.3 MB
    cytoolz-0.11.0             |   py39h27cfd23_0         350 KB
    ld_impl_linux-64-2.38      |       h1181459_1         654 KB
    libgcc-ng-11.2.0           |       h1234567_1         5.3 MB
    libgomp-11.2.0             |       h1234567_1         474 KB
    libstdcxx-ng-11.2.0        |       h1234567_1         4.7 MB
    ncurses-6.3                |       h5eee18b_3         781 KB
    openssl-1.1.1q             |       h7f8727e_0         2.5 MB
    pip-22.1.2                 |   py39h06a4308_0         2.5 MB
    python-3.9.13              |       haa1d7c7_1        20.8 MB
    requests-2.28.1            |   py39h06a4308_0          92 KB
    setuptools-63.4.1          |   py39h06a4308_0         1.1 MB
    sqlite-3.39.2              |       h5082296_0         1.1 MB
    tk-8.6.12                  |       h1ccaba5_0         3.0 MB
    toolz-0.11.2               |     pyhd3eb1b0_0          49 KB
    tqdm-4.64.0                |   py39h06a4308_0         126 KB
    tzdata-2022c               |       h04d1e81_0         107 KB
    urllib3-1.26.11            |   py39h06a4308_0         182 KB
    xz-5.2.5                   |       h7f8727e_1         339 KB
    zlib-1.2.12                |       h5eee18b_3         103 KB
    ------------------------------------------------------------
                                           Total:        47.7 MB

The following NEW packages will be INSTALLED:

  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main
  _openmp_mutex      pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu
  brotlipy           pkgs/main/linux-64::brotlipy-0.7.0-py39h27cfd23_1003
  ca-certificates    pkgs/main/linux-64::ca-certificates-2022.07.19-h06a4308_0
  certifi            pkgs/main/linux-64::certifi-2022.9.14-py39h06a4308_0
  cffi               pkgs/main/linux-64::cffi-1.15.1-py39h74dc2b5_0
  charset-normalizer pkgs/main/noarch::charset-normalizer-2.0.4-pyhd3eb1b0_0
  conda              pkgs/main/linux-64::conda-4.14.0-py39h06a4308_0
  conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.9.0-py39h5eee18b_0
  cryptography       pkgs/main/linux-64::cryptography-37.0.1-py39h9ce1e76_0
  cytoolz            pkgs/main/linux-64::cytoolz-0.11.0-py39h27cfd23_0
  idna               pkgs/main/noarch::idna-3.3-pyhd3eb1b0_0
  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.38-h1181459_1
  libffi             pkgs/main/linux-64::libffi-3.3-he6710b0_2
  libgcc-ng          pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1
  libgomp            pkgs/main/linux-64::libgomp-11.2.0-h1234567_1
  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1
  ncurses            pkgs/main/linux-64::ncurses-6.3-h5eee18b_3
  openssl            pkgs/main/linux-64::openssl-1.1.1q-h7f8727e_0
  pip                pkgs/main/linux-64::pip-22.1.2-py39h06a4308_0
  pycosat            pkgs/main/linux-64::pycosat-0.6.3-py39h27cfd23_0
  pycparser          pkgs/main/noarch::pycparser-2.21-pyhd3eb1b0_0
  pyopenssl          pkgs/main/noarch::pyopenssl-22.0.0-pyhd3eb1b0_0
  pysocks            pkgs/main/linux-64::pysocks-1.7.1-py39h06a4308_0
  python             pkgs/main/linux-64::python-3.9.13-haa1d7c7_1
  readline           pkgs/main/linux-64::readline-8.1.2-h7f8727e_1
  requests           pkgs/main/linux-64::requests-2.28.1-py39h06a4308_0
  ruamel_yaml        pkgs/main/linux-64::ruamel_yaml-0.15.100-py39h27cfd23_0
  setuptools         pkgs/main/linux-64::setuptools-63.4.1-py39h06a4308_0
  sqlite             pkgs/main/linux-64::sqlite-3.39.2-h5082296_0
  tk                 pkgs/main/linux-64::tk-8.6.12-h1ccaba5_0
  toolz              pkgs/main/noarch::toolz-0.11.2-pyhd3eb1b0_0
  tqdm               pkgs/main/linux-64::tqdm-4.64.0-py39h06a4308_0
  tzdata             pkgs/main/noarch::tzdata-2022c-h04d1e81_0
  urllib3            pkgs/main/linux-64::urllib3-1.26.11-py39h06a4308_0
  wheel              pkgs/main/noarch::wheel-0.37.1-pyhd3eb1b0_0
  xz                 pkgs/main/linux-64::xz-5.2.5-h7f8727e_1
  yaml               pkgs/main/linux-64::yaml-0.2.5-h7b6447c_0
  zlib               pkgs/main/linux-64::zlib-1.2.12-h5eee18b_3



Downloading and Extracting Packages
openssl-1.1.1q       | 2.5 MB    | : 100% 1.0/1 [00:00<00:00,  8.35it/s]
tqdm-4.64.0          | 126 KB    | : 100% 1.0/1 [00:00<00:00, 20.14it/s]
zlib-1.2.12          | 103 KB    | : 100% 1.0/1 [00:00<00:00, 28.50it/s]
_openmp_mutex-5.1    | 21 KB     | : 100% 1.0/1 [00:00<00:00, 27.74it/s]
toolz-0.11.2         | 49 KB     | : 100% 1.0/1 [00:00<00:00, 29.38it/s]
certifi-2022.9.14    | 155 KB    | : 100% 1.0/1 [00:00<00:00, 26.75it/s]
libstdcxx-ng-11.2.0  | 4.7 MB    | : 100% 1.0/1 [00:00<00:00,  7.30it/s]
pip-22.1.2           | 2.5 MB    | : 100% 1.0/1 [00:00<00:00,  5.25it/s]
ca-certificates-2022 | 124 KB    | : 100% 1.0/1 [00:00<00:00, 28.60it/s]
libgcc-ng-11.2.0     | 5.3 MB    | : 100% 1.0/1 [00:00<00:00,  5.45it/s]
xz-5.2.5             | 339 KB    | : 100% 1.0/1 [00:00<00:00, 13.32it/s]
python-3.9.13        | 20.8 MB   | : 100% 1.0/1 [00:00<00:00,  1.66it/s]
cryptography-37.0.1  | 1.3 MB    | : 100% 1.0/1 [00:00<00:00,  8.45it/s]
urllib3-1.26.11      | 182 KB    | : 100% 1.0/1 [00:00<00:00, 19.53it/s]
cytoolz-0.11.0       | 350 KB    | : 100% 1.0/1 [00:00<00:00, 19.51it/s]
tk-8.6.12            | 3.0 MB    | : 100% 1.0/1 [00:00<00:00,  7.94it/s]
ncurses-6.3          | 781 KB    | : 100% 1.0/1 [00:00<00:00,  4.41it/s]
cffi-1.15.1          | 228 KB    | : 100% 1.0/1 [00:00<00:00, 16.02it/s]
sqlite-3.39.2        | 1.1 MB    | : 100% 1.0/1 [00:00<00:00, 12.93it/s]
libgomp-11.2.0       | 474 KB    | : 100% 1.0/1 [00:00<00:00, 21.56it/s]
conda-4.14.0         | 915 KB    | : 100% 1.0/1 [00:00<00:00, 11.14it/s]
ld_impl_linux-64-2.3 | 654 KB    | : 100% 1.0/1 [00:00<00:00, 17.19it/s]
tzdata-2022c         | 107 KB    | : 100% 1.0/1 [00:00<00:00, 14.46it/s]
setuptools-63.4.1    | 1.1 MB    | : 100% 1.0/1 [00:00<00:00,  9.95it/s]
requests-2.28.1      | 92 KB     | : 100% 1.0/1 [00:00<00:00, 23.93it/s]
conda-package-handli | 887 KB    | : 100% 1.0/1 [00:00<00:00, 19.36it/s]
Preparing transaction: - \ | done
Verifying transaction: - \ | / - \ | / - \ | / - done
Executing transaction: | / - \ | / - \ | / - \ | / - \ | / done
Collecting package metadata (current_repodata.json): - \ | / - done
Solving environment: | / - done


==> WARNING: A newer version of conda exists. <==
  current version: 4.12.0
  latest version: 4.14.0

Please update conda by running

    $ conda update -n base -c defaults conda



# All requested packages already installed.

Install ESPnet

This step installs PyTorch and other required tools.

We specify CUDA_VERSION=11.6 for PyTorch 1.12.1. We also support many other versions. Please check https://github.com/espnet/espnet/blob/master/tools/installers/install_torch.sh for the detailed version list.

[ ]:
# It may take 12 minutes
%cd /content/espnet/tools
!make TH_VERSION=1.12.1 CUDA_VERSION=11.6
/content/espnet/tools
test -f kaldi/egs/wsj/s5/utils/parse_options.sh || git clone --depth 1 https://github.com/kaldi-asr/kaldi
Cloning into 'kaldi'...
remote: Enumerating objects: 7657, done.
remote: Counting objects: 100% (7657/7657), done.
remote: Compressing objects: 100% (5210/5210), done.
remote: Total 7657 (delta 2441), reused 4330 (delta 2020), pack-reused 0
Receiving objects: 100% (7657/7657), 12.68 MiB | 7.82 MiB/s, done.
Resolving deltas: 100% (2441/2441), done.
CUDA_VERSION=11.6
PYTHON=/content/espnet/tools/anaconda/envs/espnet/bin/python3
PYTHON_VERSION=Python 3.9.13
USE_CONDA=1
TH_VERSION=1.12.1
. ./activate_python.sh && python3 -m pip install packaging
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting packaging
  Downloading packaging-21.3-py3-none-any.whl (40 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 40.8/40.8 kB 196.7 kB/s eta 0:00:00
Collecting pyparsing!=3.0.5,>=2.0.2
  Downloading pyparsing-3.0.9-py3-none-any.whl (98 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 98.3/98.3 kB 12.1 MB/s eta 0:00:00
Installing collected packages: pyparsing, packaging
Successfully installed packaging-21.3 pyparsing-3.0.9
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
touch packaging.done
. ./activate_python.sh && python3 -m pip install -U numba
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting numba
  Downloading numba-0.56.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (3.5 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3.5/3.5 MB 92.2 MB/s eta 0:00:00
Collecting numpy<1.24,>=1.18
  Downloading numpy-1.23.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.1 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 17.1/17.1 MB 80.1 MB/s eta 0:00:00
Collecting llvmlite<0.40,>=0.39.0dev0
  Downloading llvmlite-0.39.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.6 MB)
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Collecting setuptools<60
  Downloading setuptools-59.8.0-py3-none-any.whl (952 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 952.8/952.8 kB 59.2 MB/s eta 0:00:00
Installing collected packages: setuptools, numpy, llvmlite, numba
  Attempting uninstall: setuptools
    Found existing installation: setuptools 63.4.1
    Uninstalling setuptools-63.4.1:
      Successfully uninstalled setuptools-63.4.1
Successfully installed llvmlite-0.39.1 numba-0.56.2 numpy-1.23.3 setuptools-59.8.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
touch numba.done
. ./activate_python.sh && ./installers/install_torch.sh "true" "1.12.1" "11.6"
2022-09-19T20:49:23 (install_torch.sh:125:main) [INFO] python_version=3.9.13
2022-09-19T20:49:23 (install_torch.sh:126:main) [INFO] torch_version=1.12.1
2022-09-19T20:49:23 (install_torch.sh:127:main) [INFO] cuda_version=11.6
2022-09-19T20:49:23 (install_torch.sh:57:install_torch) conda install -y pytorch=1.12.1 torchaudio=0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge
Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
Solving environment: / failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
Solving environment: / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done

## Package Plan ##

  environment location: /content/espnet/tools/anaconda/envs/espnet

  added / updated specs:
    - cudatoolkit=11.6
    - pytorch=1.12.1
    - torchaudio=0.12.1


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    blas-1.0                   |              mkl           6 KB
    ca-certificates-2022.9.14  |       ha878542_0         152 KB  conda-forge
    certifi-2022.9.14          |     pyhd8ed1ab_0         156 KB  conda-forge
    conda-4.14.0               |   py39hf3d152e_0        1011 KB  conda-forge
    cudatoolkit-11.6.0         |      hecad31d_10       821.2 MB  conda-forge
    intel-openmp-2021.4.0      |    h06a4308_3561         4.2 MB
    mkl-2021.4.0               |     h06a4308_640       142.6 MB
    mkl-service-2.4.0          |   py39h7e14d7c_0          61 KB  conda-forge
    mkl_fft-1.3.1              |   py39h0c7bc48_1         213 KB  conda-forge
    mkl_random-1.2.2           |   py39hde0f152_0         379 KB  conda-forge
    numpy-1.23.1               |   py39h6c91a56_0          11 KB
    numpy-base-1.23.1          |   py39ha15fc14_0         5.6 MB
    python_abi-3.9             |           2_cp39           4 KB  conda-forge
    pytorch-1.12.1             |py3.9_cuda11.6_cudnn8.3.2_0        1.20 GB  pytorch
    pytorch-mutex-1.0          |             cuda           3 KB  pytorch
    six-1.16.0                 |     pyh6c4a22f_0          14 KB  conda-forge
    torchaudio-0.12.1          |       py39_cu116         6.1 MB  pytorch
    typing_extensions-4.3.0    |     pyha770c72_0          28 KB  conda-forge
    ------------------------------------------------------------
                                           Total:        2.16 GB

The following NEW packages will be INSTALLED:

  blas               pkgs/main/linux-64::blas-1.0-mkl
  cudatoolkit        conda-forge/linux-64::cudatoolkit-11.6.0-hecad31d_10
  intel-openmp       pkgs/main/linux-64::intel-openmp-2021.4.0-h06a4308_3561
  mkl                pkgs/main/linux-64::mkl-2021.4.0-h06a4308_640
  mkl-service        conda-forge/linux-64::mkl-service-2.4.0-py39h7e14d7c_0
  mkl_fft            conda-forge/linux-64::mkl_fft-1.3.1-py39h0c7bc48_1
  mkl_random         conda-forge/linux-64::mkl_random-1.2.2-py39hde0f152_0
  numpy              pkgs/main/linux-64::numpy-1.23.1-py39h6c91a56_0
  numpy-base         pkgs/main/linux-64::numpy-base-1.23.1-py39ha15fc14_0
  python_abi         conda-forge/linux-64::python_abi-3.9-2_cp39
  pytorch            pytorch/linux-64::pytorch-1.12.1-py3.9_cuda11.6_cudnn8.3.2_0
  pytorch-mutex      pytorch/noarch::pytorch-mutex-1.0-cuda
  six                conda-forge/noarch::six-1.16.0-pyh6c4a22f_0
  torchaudio         pytorch/linux-64::torchaudio-0.12.1-py39_cu116
  typing_extensions  conda-forge/noarch::typing_extensions-4.3.0-pyha770c72_0

The following packages will be UPDATED:

  ca-certificates    pkgs/main::ca-certificates-2022.07.19~ --> conda-forge::ca-certificates-2022.9.14-ha878542_0

The following packages will be SUPERSEDED by a higher-priority channel:

  certifi            pkgs/main/linux-64::certifi-2022.9.14~ --> conda-forge/noarch::certifi-2022.9.14-pyhd8ed1ab_0
  conda              pkgs/main::conda-4.14.0-py39h06a4308_0 --> conda-forge::conda-4.14.0-py39hf3d152e_0



Downloading and Extracting Packages
typing_extensions-4. | 28 KB     | : 100% 1.0/1 [00:00<00:00, 19.82it/s]
mkl_random-1.2.2     | 379 KB    | : 100% 1.0/1 [00:00<00:00, 13.07it/s]
certifi-2022.9.14    | 156 KB    | : 100% 1.0/1 [00:00<00:00, 24.78it/s]
mkl-service-2.4.0    | 61 KB     | : 100% 1.0/1 [00:00<00:00, 31.87it/s]
conda-4.14.0         | 1011 KB   | : 100% 1.0/1 [00:00<00:00,  4.36it/s]
python_abi-3.9       | 4 KB      | : 100% 1.0/1 [00:00<00:00, 41.25it/s]
mkl_fft-1.3.1        | 213 KB    | : 100% 1.0/1 [00:00<00:00, 17.65it/s]
six-1.16.0           | 14 KB     | : 100% 1.0/1 [00:00<00:00, 34.67it/s]
blas-1.0             | 6 KB      | : 100% 1.0/1 [00:00<00:00,  6.74it/s]
pytorch-1.12.1       | 1.20 GB   | : 100% 1.0/1 [02:36<00:00, 156.11s/it]
ca-certificates-2022 | 152 KB    | : 100% 1.0/1 [00:00<00:00, 19.16it/s]
intel-openmp-2021.4. | 4.2 MB    | : 100% 1.0/1 [00:00<00:00,  2.07it/s]
numpy-base-1.23.1    | 5.6 MB    | : 100% 1.0/1 [00:00<00:00,  2.54it/s]
cudatoolkit-11.6.0   | 821.2 MB  | : 100% 1.0/1 [01:38<00:00, 98.02s/it]
torchaudio-0.12.1    | 6.1 MB    | : 100% 1.0/1 [00:01<00:00,  1.04s/it]
numpy-1.23.1         | 11 KB     | : 100% 1.0/1 [00:00<00:00,  9.09it/s]
mkl-2021.4.0         | 142.6 MB  | : 100% 1.0/1 [00:06<00:00,  6.14s/it]
pytorch-mutex-1.0    | 3 KB      | : 100% 1.0/1 [00:00<00:00, 36.16it/s]
Preparing transaction: / - \ | / - \ | / done
Verifying transaction: \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
Executing transaction: - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html

\ | / - \ | / - \ | / - \ | / - \ | / done
Retrieving notices: ...working... done
touch pytorch.done
. ./activate_python.sh && { command -v bc || conda install -y bc -c conda-forge; }
Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
Solving environment: | / - \ | / done


==> WARNING: A newer version of conda exists. <==
  current version: 4.12.0
  latest version: 4.14.0

Please update conda by running

    $ conda update -n base -c defaults conda



## Package Plan ##

  environment location: /content/espnet/tools/anaconda/envs/espnet

  added / updated specs:
    - bc


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    bc-1.07.1                  |       h7f98852_0         100 KB  conda-forge
    ------------------------------------------------------------
                                           Total:         100 KB

The following NEW packages will be INSTALLED:

  bc                 conda-forge/linux-64::bc-1.07.1-h7f98852_0



Downloading and Extracting Packages
bc-1.07.1            | 100 KB    | : 100% 1.0/1 [00:00<00:00, 13.57it/s]
Preparing transaction: \ done
Verifying transaction: / done
Executing transaction: \ done
touch bc.done
. ./activate_python.sh && { command -v cmake || conda install -y cmake; }
/usr/local/bin/cmake
touch cmake.done
. ./activate_python.sh && { command -v flac || conda install -y libflac -c conda-forge; }
Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ done
Solving environment: / - \ | / - \ | done


==> WARNING: A newer version of conda exists. <==
  current version: 4.12.0
  latest version: 4.14.0

Please update conda by running

    $ conda update -n base -c defaults conda



## Package Plan ##

  environment location: /content/espnet/tools/anaconda/envs/espnet

  added / updated specs:
    - libflac


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    gettext-0.19.8.1           |    h0b5b191_1005         3.6 MB  conda-forge
    libflac-1.3.4              |       h27087fc_0         474 KB  conda-forge
    libogg-1.3.4               |       h7f98852_1         206 KB  conda-forge
    ------------------------------------------------------------
                                           Total:         4.3 MB

The following NEW packages will be INSTALLED:

  gettext            conda-forge/linux-64::gettext-0.19.8.1-h0b5b191_1005
  libflac            conda-forge/linux-64::libflac-1.3.4-h27087fc_0
  libogg             conda-forge/linux-64::libogg-1.3.4-h7f98852_1



Downloading and Extracting Packages
libogg-1.3.4         | 206 KB    | : 100% 1.0/1 [00:00<00:00, 12.73it/s]
gettext-0.19.8.1     | 3.6 MB    | : 100% 1.0/1 [00:00<00:00,  1.39it/s]
libflac-1.3.4        | 474 KB    | : 100% 1.0/1 [00:00<00:00, 11.85it/s]
Preparing transaction: - done
Verifying transaction: | / done
Executing transaction: \ | done
touch flac.done
. ./activate_python.sh && { command -v ffmpeg || conda install -y ffmpeg -c conda-forge; }
/usr/bin/ffmpeg
touch ffmpeg.done
. ./activate_python.sh && { command -v sox || conda install -y sox -c conda-forge; }
Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | done
Solving environment: - \ | / - \ | / - \ | done


==> WARNING: A newer version of conda exists. <==
  current version: 4.12.0
  latest version: 4.14.0

Please update conda by running

    $ conda update -n base -c defaults conda



## Package Plan ##

  environment location: /content/espnet/tools/anaconda/envs/espnet

  added / updated specs:
    - sox


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    lame-3.100                 |    h7f98852_1001         496 KB  conda-forge
    libpng-1.6.37              |       hbc83047_0         278 KB
    mad-0.15.1b                |       h9c3ff4c_1         113 KB  conda-forge
    sox-14.4.2                 |    h27f72bc_1013         743 KB  conda-forge
    ------------------------------------------------------------
                                           Total:         1.6 MB

The following NEW packages will be INSTALLED:

  lame               conda-forge/linux-64::lame-3.100-h7f98852_1001
  libpng             pkgs/main/linux-64::libpng-1.6.37-hbc83047_0
  mad                conda-forge/linux-64::mad-0.15.1b-h9c3ff4c_1
  sox                conda-forge/linux-64::sox-14.4.2-h27f72bc_1013



Downloading and Extracting Packages
mad-0.15.1b          | 113 KB    | : 100% 1.0/1 [00:00<00:00, 16.10it/s]
libpng-1.6.37        | 278 KB    | : 100% 1.0/1 [00:00<00:00, 11.83it/s]
sox-14.4.2           | 743 KB    | : 100% 1.0/1 [00:00<00:00,  8.12it/s]
lame-3.100           | 496 KB    | : 100% 1.0/1 [00:00<00:00, 10.87it/s]
Preparing transaction: - done
Verifying transaction: | done
Executing transaction: - done
touch sox.done
. ./activate_python.sh && { python3 -c "from ctypes.util import find_library as F; assert F('sndfile') is not None" || conda install -y libsndfile -c conda-forge; }
touch sndfile.done
touch conda_packages.done
NUMPY_VERSION=1.23.1
. ./activate_python.sh && python3 -m pip install -e "..[train, recipe]"  # Install editable mode by default
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Obtaining file:///content/espnet
  Preparing metadata (setup.py) ... done
Requirement already satisfied: setuptools>=38.5.1 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from espnet==202207) (59.8.0)
Requirement already satisfied: packaging in ./anaconda/envs/espnet/lib/python3.9/site-packages (from espnet==202207) (21.3)
Collecting configargparse>=1.2.1
  Downloading ConfigArgParse-1.5.3-py3-none-any.whl (20 kB)
Collecting typeguard>=2.7.0
  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)
Collecting humanfriendly
  Downloading humanfriendly-10.0-py2.py3-none-any.whl (86 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 86.8/86.8 kB 11.4 MB/s eta 0:00:00
Collecting scipy>=1.4.1
  Downloading scipy-1.9.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (43.9 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 43.9/43.9 MB 18.2 MB/s eta 0:00:00
Collecting filelock
  Downloading filelock-3.8.0-py3-none-any.whl (10 kB)
Collecting librosa>=0.8.0
  Downloading librosa-0.9.2-py3-none-any.whl (214 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 214.3/214.3 kB 23.5 MB/s eta 0:00:00
Collecting jamo==0.4.1
  Downloading jamo-0.4.1-py3-none-any.whl (9.5 kB)
Collecting PyYAML>=5.1.2
  Downloading PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (661 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 661.8/661.8 kB 44.9 MB/s eta 0:00:00
Collecting soundfile>=0.10.2
  Downloading SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
Collecting h5py>=2.10.0
  Downloading h5py-3.7.0-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.5/4.5 MB 102.0 MB/s eta 0:00:00
Collecting kaldiio>=2.17.0
  Downloading kaldiio-2.17.2.tar.gz (24 kB)
  Preparing metadata (setup.py) ... done
Requirement already satisfied: torch>=1.3.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from espnet==202207) (1.12.1)
Collecting torch_complex
  Downloading torch_complex-0.4.3-py3-none-any.whl (9.1 kB)
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Building wheels for collected packages: editdistance, fast-bss-eval, ctc-segmentation, kaldiio, mir-eval, pysptk, pystoi, pyworld, ci_sdr, fastdtw, gdown, nnmnkwii, audioread, promise, future, jaconv, pathtools, distance
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  Created wheel for distance: filename=Distance-0.1.3-py3-none-any.whl size=16276 sha256=5e4a65784d422f2cf852e75f3f5edc2352ac76c0cc5a32cf2b2ca2118b0d8f3b
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Successfully built editdistance fast-bss-eval ctc-segmentation kaldiio mir-eval pysptk pystoi pyworld ci_sdr fastdtw gdown nnmnkwii audioread promise future jaconv pathtools distance
Installing collected packages: youtube_dl, tensorboard-plugin-wit, sentencepiece, pytz, pyasn1, pathtools, morfessor, jamo, jaconv, einops, editdistance, distance, appdirs, zipp, unidecode, typeguard, torch_complex, threadpoolctl, tensorboard-data-server, tabulate, soupsieve, smmap, simplejson, shortuuid, setproctitle, sentry-sdk, scipy, rsa, regex, PyYAML, pytorch_wpe, python-dateutil, pyrsistent, pypinyin, pydantic, pyasn1-modules, psutil, protobuf, promise, portalocker, pillow, oauthlib, MarkupSafe, lxml, kiwisolver, kaldiio, joblib, humanfriendly, h5py, grpcio, future, fonttools, filelock, fastdtw, docker-pycreds, decorator, Cython, cycler, contourpy, configargparse, colorama, click, cachetools, bottleneck, audioread, attrs, absl-py, werkzeug, soundfile, scikit-learn, sacrebleu, resampy, requests-oauthlib, pyworld, pystoi, pysptk, pyaml, pooch, pandas, nltk, mir-eval, matplotlib, jsonschema, inflect, importlib-metadata, huggingface-hub, google-auth, gitdb, ffmpeg-python, fast-bss-eval, ctc-segmentation, ci_sdr, beautifulsoup4, stempeg, nnmnkwii, nara_wpe, markdown, librosa, google-auth-oauthlib, GitPython, gdown, g2p-en, wandb, tensorboard, musdb, espnet_tts_frontend, museval, espnet, espnet_model_zoo
  Running setup.py develop for espnet
Successfully installed Cython-0.29.32 GitPython-3.1.27 MarkupSafe-2.1.1 PyYAML-6.0 absl-py-1.2.0 appdirs-1.4.4 attrs-22.1.0 audioread-3.0.0 beautifulsoup4-4.11.1 bottleneck-1.3.5 cachetools-5.2.0 ci_sdr-0.0.2 click-8.1.3 colorama-0.4.5 configargparse-1.5.3 contourpy-1.0.5 ctc-segmentation-1.7.3 cycler-0.11.0 decorator-5.1.1 distance-0.1.3 docker-pycreds-0.4.0 editdistance-0.5.2 einops-0.4.1 espnet-202207 espnet_model_zoo-0.1.7 espnet_tts_frontend-0.0.3 fast-bss-eval-0.1.3 fastdtw-0.3.4 ffmpeg-python-0.2.0 filelock-3.8.0 fonttools-4.37.2 future-0.18.2 g2p-en-2.1.0 gdown-4.5.1 gitdb-4.0.9 google-auth-2.11.0 google-auth-oauthlib-0.4.6 grpcio-1.48.1 h5py-3.7.0 huggingface-hub-0.9.1 humanfriendly-10.0 importlib-metadata-4.12.0 inflect-6.0.0 jaconv-0.3 jamo-0.4.1 joblib-1.2.0 jsonschema-4.16.0 kaldiio-2.17.2 kiwisolver-1.4.4 librosa-0.9.2 lxml-4.9.1 markdown-3.4.1 matplotlib-3.6.0 mir-eval-0.7 morfessor-2.0.6 musdb-0.4.0 museval-0.4.0 nara_wpe-0.0.8 nltk-3.7 nnmnkwii-0.1.1 oauthlib-3.2.1 pandas-1.5.0 pathtools-0.1.2 pillow-9.2.0 pooch-1.6.0 portalocker-2.5.1 promise-2.3 protobuf-3.19.5 psutil-5.9.2 pyaml-21.10.1 pyasn1-0.4.8 pyasn1-modules-0.2.8 pydantic-1.10.2 pypinyin-0.44.0 pyrsistent-0.18.1 pysptk-0.1.21 pystoi-0.3.3 python-dateutil-2.8.2 pytorch_wpe-0.0.1 pytz-2022.2.1 pyworld-0.3.0 regex-2022.9.13 requests-oauthlib-1.3.1 resampy-0.4.2 rsa-4.9 sacrebleu-2.2.1 scikit-learn-1.1.2 scipy-1.9.1 sentencepiece-0.1.97 sentry-sdk-1.9.8 setproctitle-1.3.2 shortuuid-1.0.9 simplejson-3.17.6 smmap-5.0.0 soundfile-0.10.3.post1 soupsieve-2.3.2.post1 stempeg-0.2.3 tabulate-0.8.10 tensorboard-2.10.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 threadpoolctl-3.1.0 torch_complex-0.4.3 typeguard-2.13.3 unidecode-1.3.4 wandb-0.13.3 werkzeug-2.2.2 youtube_dl-2021.12.17 zipp-3.8.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
NUMPY_VERSION=1.23.1
touch espnet.done
. ./activate_python.sh && ./installers/install_chainer.sh "11.6"
Cloning into 'chainer'...
remote: Enumerating objects: 1763, done.
remote: Counting objects: 100% (1763/1763), done.
remote: Compressing objects: 100% (1694/1694), done.
remote: Total 1763 (delta 190), reused 414 (delta 40), pack-reused 0
Receiving objects: 100% (1763/1763), 9.13 MiB | 9.91 MiB/s, done.
Resolving deltas: 100% (190/190), done.
Note: checking out '17796d7cf17f667eb4e7b7cd41778ad9ec56c749'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by performing another checkout.

If you want to create a new branch to retain commits you create, you may
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  git checkout -b <new-branch-name>

patching file chainer/setup.py
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Obtaining file:///content/espnet/tools/chainer
  Preparing metadata (setup.py) ... done
Requirement already satisfied: setuptools in ./anaconda/envs/espnet/lib/python3.9/site-packages (from chainer==6.0.0) (59.8.0)
Requirement already satisfied: typing_extensions in ./anaconda/envs/espnet/lib/python3.9/site-packages (from chainer==6.0.0) (4.3.0)
Requirement already satisfied: filelock in ./anaconda/envs/espnet/lib/python3.9/site-packages (from chainer==6.0.0) (3.8.0)
Requirement already satisfied: numpy>=1.9.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from chainer==6.0.0) (1.23.3)
Requirement already satisfied: protobuf in ./anaconda/envs/espnet/lib/python3.9/site-packages (from chainer==6.0.0) (3.19.5)
Requirement already satisfied: six>=1.9.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from chainer==6.0.0) (1.16.0)
Installing collected packages: chainer
  Running setup.py develop for chainer
Successfully installed chainer-6.0.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
[INFO] Skip cupy installation
touch chainer.done
. ./activate_python.sh && ./installers/install_fairscale.sh
[INFO] torch_version=1.12.1
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting fairscale
  Downloading fairscale-0.4.9.tar.gz (265 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 265.2/265.2 kB 20.1 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Installing backend dependencies ... done
  Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: torch>=1.8.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairscale) (1.12.1)
Requirement already satisfied: numpy>=1.22.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairscale) (1.23.3)
Requirement already satisfied: typing_extensions in ./anaconda/envs/espnet/lib/python3.9/site-packages (from torch>=1.8.0->fairscale) (4.3.0)
Building wheels for collected packages: fairscale
  Building wheel for fairscale (pyproject.toml) ... done
  Created wheel for fairscale: filename=fairscale-0.4.9-py3-none-any.whl size=327402 sha256=978a2cb3bc765524f4296f94d3fb20cac6ab76e06ce23cec1adb7e1820ac8f77
  Stored in directory: /root/.cache/pip/wheels/17/be/af/12d4c34a5f99ccee287a18add9d7eef6b4ca7bf31ffe7ae08a
Successfully built fairscale
Installing collected packages: fairscale
Successfully installed fairscale-0.4.9
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
touch fairscale.done
. ./activate_python.sh && ./installers/install_torch_optimizer.sh
[INFO] torch_version=1.12.1
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting torch_optimizer
  Downloading torch_optimizer-0.3.0-py3-none-any.whl (61 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 61.9/61.9 kB 7.7 MB/s eta 0:00:00
Collecting pytorch-ranger>=0.1.1
  Downloading pytorch_ranger-0.1.1-py3-none-any.whl (14 kB)
Requirement already satisfied: torch>=1.5.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from torch_optimizer) (1.12.1)
Requirement already satisfied: typing_extensions in ./anaconda/envs/espnet/lib/python3.9/site-packages (from torch>=1.5.0->torch_optimizer) (4.3.0)
Installing collected packages: pytorch-ranger, torch_optimizer
Successfully installed pytorch-ranger-0.1.1 torch_optimizer-0.3.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
touch torch_optimizer.done
./installers/install_sctk.sh
--2022-09-19 20:59:42--  https://github.com/espnet/kaldi-bin/releases/download/v0.0.2/sctk-2.4.10-20151007-1312Z.tar.bz2
Resolving github.com (github.com)... 20.205.243.166
Connecting to github.com (github.com)|20.205.243.166|:443... connected.
HTTP request sent, awaiting response... 302 Found
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Saving to: ‘sctk-2.4.10-20151007-1312Z.tar.bz2’

sctk-2.4.10-2015100 100%[===================>]   1.92M  --.-KB/s    in 0.008s

2022-09-19 20:59:42 (243 MB/s) - ‘sctk-2.4.10-20151007-1312Z.tar.bz2’ saved [2008966/2008966]

make[1]: Entering directory '/content/espnet/tools/sctk-2.4.10'
(mkdir -p bin)
(cd src; make config)
make[2]: Entering directory '/content/espnet/tools/sctk-2.4.10/src'
(cd asclite; make config)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite'
(cd core; sh config.sh -prefix=`cd ../../..; pwd`)
checking for g++... g++
checking for C++ compiler default output file name... a.out
checking whether the C++ compiler works... yes
checking whether we are cross compiling... no
checking for suffix of executables...
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checking whether we are using the GNU C++ compiler... yes
checking whether g++ accepts -g... yes
checking whether make sets $(MAKE)... yes
checking for gcc... gcc
checking whether we are using the GNU C compiler... yes
checking whether gcc accepts -g... yes
checking for gcc option to accept ANSI C... none needed
checking for main in -lm... yes
checking how to run the C preprocessor... gcc -E
checking for egrep... grep -E
checking for ANSI C header files... yes
checking for sys/types.h... yes
checking for sys/stat.h... yes
checking for stdlib.h... yes
checking for string.h... yes
checking for memory.h... yes
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checking for inttypes.h... yes
checking for stdint.h... yes
checking for unistd.h... yes
checking for unistd.h... (cached) yes
checking for string.h... (cached) yes
checking iostream usability... no
checking iostream presence... no
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checking math.h usability... yes
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checking time.h usability... yes
checking time.h presence... yes
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checking list usability... no
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checking assert.h usability... yes
checking assert.h presence... yes
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checking limits.h usability... yes
checking limits.h presence... yes
checking for limits.h... yes
checking for an ANSI C-conforming const... yes
checking for size_t... yes
checking for install... /usr/bin/install
config.sh: creating ./config.status
config.status: creating makefile
(cd test; sh config.sh -prefix=`cd ../../..; pwd`)
checking for g++... g++
checking for C++ compiler default output file name... a.out
checking whether the C++ compiler works... yes
checking whether we are cross compiling... no
checking for suffix of executables...
checking for suffix of object files... o
checking whether we are using the GNU C++ compiler... yes
checking whether g++ accepts -g... yes
checking whether make sets $(MAKE)... yes
checking for gcc... gcc
checking whether we are using the GNU C compiler... yes
checking whether gcc accepts -g... yes
checking for gcc option to accept ANSI C... none needed
checking for main in -lm... yes
checking how to run the C preprocessor... gcc -E
checking for egrep... grep -E
checking for ANSI C header files... yes
checking for sys/types.h... yes
checking for sys/stat.h... yes
checking for stdlib.h... yes
checking for string.h... yes
checking for memory.h... yes
checking for strings.h... yes
checking for inttypes.h... yes
checking for stdint.h... yes
checking for unistd.h... yes
checking for unistd.h... (cached) yes
checking for an ANSI C-conforming const... yes
checking for size_t... yes
checking for install... /usr/bin/install
config.sh: creating ./config.status
config.status: creating makefile
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite'
(cd sclite; sh config.sh -prefix=`cd ../..; pwd`)
creating cache ./config.cache
checking for gcc... gcc
checking whether we are using GNU C... yes
checking whether gcc accepts -g... yes
checking whether make sets ${MAKE}... yes
checking for -lm... yes
checking how to run the C preprocessor... gcc -E
checking whether cross-compiling... no
checking for ANSI C header files... yes
checking for unistd.h... yes
checking for working const... yes
checking for size_t... yes
checking for install... /usr/bin/install
Checking installation site
updating cache ./config.cache
creating ./config.status
creating makefile
(cd rfilter1; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd csrfilt; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd chfilt; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd hamzaNorm; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd tanweenFilt; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd acomp; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd def_art; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd hubscr; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd utf_filt; sh config.sh -prefix=`cd ../..; pwd`)
checking for nsgmls... no
checking for onsgmls... no
config.sh: creating ./config.status
config.status: creating makefile
(cd rttmSort; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd rttmSmooth; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd rttm2ctm; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd spkr2sad; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd mergectm2rttm; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd align2html; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd md-eval; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd stm2rttm; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd stmValidator; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd rttmValidator; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd ctmValidator; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
(cd slatreport; sed -e "s:PREFIX=.*:PREFIX=`cd ../..; pwd`:" < makefile.in > makefile)
make[2]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src'
make[1]: Leaving directory '/content/espnet/tools/sctk-2.4.10'
make[1]: Entering directory '/content/espnet/tools/sctk-2.4.10'
(mkdir -p bin)
(cd src; make all)
make[2]: Entering directory '/content/espnet/tools/sctk-2.4.10/src'
(cd asclite; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite'
(cd core; make all)
make[4]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite/core'
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os ctm_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os checker.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os graphalignedtoken.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os timedobject.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os graphalignedsegment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os spkrautooverlap.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os alignedspeechiterator.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os properties.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os filter.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os segmentsgroup.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os ctmstmrttm_segmentor.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os alignedsegment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os token.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os levenshteinmatrix.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os scorer.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os stm_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os linestyle_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os rawsys_reportgenerator.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os graph_coordinate.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os segmentor.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os logger.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os uemfilter.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os trntrn_segmentor.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os id.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os speech.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os tokenalignment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os compressedlevenshteinmatrix.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os speakermatch.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os aligner.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os sgml_generic_reportgenerator.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os recording.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os statistics.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os rttm_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os stt_scorer.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os alignedsegmentiterator.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os levenshtein.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os speechset.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os alignedspeech.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os trn_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os main.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os sgml_reportgenerator.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os reportgenerator.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os graph.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os segment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os alignment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1 -DHAVE_STRING_H=1 -DHAVE_STDIO_H=1 -DHAVE_STDLIB_H=1 -DHAVE_MATH_H=1 -DHAVE_TIME_H=1 -DHAVE_ASSERT_H=1 -DHAVE_FLOAT_H=1 -DHAVE_LIMITS_H=1  -I. -g -Os arraylevenshteinmatrix.cpp
g++ -g -Os   -c -o lzma/LzmaDec.o lzma/LzmaDec.c
g++ -g -Os   -c -o lzma/LzmaLib.o lzma/LzmaLib.c
g++ -g -Os   -c -o lzma/Alloc.o lzma/Alloc.c
g++ -g -Os   -c -o lzma/LzmaEnc.o lzma/LzmaEnc.c
g++ -g -Os   -c -o lzma/LzFind.o lzma/LzFind.c
g++ -o asclite -g -Os  ctm_inputparser.o checker.o graphalignedtoken.o timedobject.o graphalignedsegment.o spkrautooverlap.o alignedspeechiterator.o properties.o filter.o segmentsgroup.o ctmstmrttm_segmentor.o alignedsegment.o token.o levenshteinmatrix.o scorer.o stm_inputparser.o linestyle_inputparser.o rawsys_reportgenerator.o graph_coordinate.o segmentor.o logger.o inputparser.o uemfilter.o trntrn_segmentor.o id.o speech.o tokenalignment.o compressedlevenshteinmatrix.o speakermatch.o aligner.o sgml_generic_reportgenerator.o recording.o statistics.o rttm_inputparser.o stt_scorer.o alignedsegmentiterator.o levenshtein.o speechset.o alignedspeech.o trn_inputparser.o main.o sgml_reportgenerator.o reportgenerator.o graph.o segment.o alignment.o arraylevenshteinmatrix.o lzma/LzmaDec.o lzma/LzmaLib.o lzma/Alloc.o lzma/LzmaEnc.o lzma/LzFind.o -lm
make[4]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite/core'
(cd test; make all)
make[4]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite/test'
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion alignment_test.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_ctm_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_segment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion alignedspeech_test.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_trn_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion asctools.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_ctmstm_segmentor.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_token.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion perf_benchmark.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_stm_inputparser.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_graphalignedsegment.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_graph.cpp
test_graph.cpp: In member function ‘bool TestGraph::TestAll()’:
test_graph.cpp:38:26: warning: conversion to ‘float’ from ‘int’ may alter its value [-Wconversion]
  float size = MemoryUsage();
               ~~~~~~~~~~~^~
test_graph.cpp:40:10: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]
   unit = "KB";
          ^~~~
test_graph.cpp:44:10: warning: ISO C++ forbids converting a string constant to ‘char*’ [-Wwrite-strings]
   unit = "MB";
          ^~~~
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion benchmark.cpp
benchmark.cpp: In member function ‘int Benchmark::GetTestSize()’:
benchmark.cpp:61:20: warning: conversion to ‘int’ from ‘std::vector<SegmentsGroup*>::size_type {aka long unsigned int}’ may alter its value [-Wconversion]
   return tests.size();
          ~~~~~~~~~~^~
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_speech.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion tokenalignment_test.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_speechset.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion std_benchmark.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_graphalignedtoken.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_levenshtein.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_properties.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_unit.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_trntrn_segmentor.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion alignedsegment_test.cpp
g++ -c  -DPACKAGE_NAME=\"\" -DPACKAGE_TARNAME=\"\" -DPACKAGE_VERSION=\"\" -DPACKAGE_STRING=\"\" -DPACKAGE_BUGREPORT=\"\" -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_SYS_TYPES_H=1 -DHAVE_SYS_STAT_H=1 -DHAVE_STDLIB_H=1 -DHAVE_STRING_H=1 -DHAVE_MEMORY_H=1 -DHAVE_STRINGS_H=1 -DHAVE_INTTYPES_H=1 -DHAVE_STDINT_H=1 -DHAVE_UNISTD_H=1 -DHAVE_UNISTD_H=1  -I. -I../core -Os  -Wall -Wconversion test_rttm_inputparser.cpp
g++ -o asclite_test -Os  -Wall -Wconversion  alignment_test.o test_ctm_inputparser.o test_segment.o alignedspeech_test.o test_trn_inputparser.o asctools.o test_ctmstm_segmentor.o test_token.o perf_benchmark.o test_stm_inputparser.o test_graphalignedsegment.o test_graph.o benchmark.o test_speech.o tokenalignment_test.o test_speechset.o std_benchmark.o test_graphalignedtoken.o test_levenshtein.o test_properties.o test_unit.o test_trntrn_segmentor.o alignedsegment_test.o test_rttm_inputparser.o ../core/ctm_inputparser.o ../core/checker.o ../core/timedobject.o ../core/statistics.o ../core/stm_inputparser.o ../core/alignedspeechiterator.o ../core/speech.o ../core/properties.o ../core/filter.o ../core/inputparser.o ../core/ctmstmrttm_segmentor.o ../core/speechset.o ../core/scorer.o ../core/alignedsegment.o ../core/alignment.o ../core/levenshtein.o ../core/speakermatch.o ../core/alignedspeech.o ../core/trn_inputparser.o ../core/graph.o ../core/arraylevenshteinmatrix.o ../core/aligner.o ../core/id.o ../core/graphalignedtoken.o ../core/rawsys_reportgenerator.o ../core/graphalignedsegment.o ../core/spkrautooverlap.o ../core/uemfilter.o ../core/linestyle_inputparser.o ../core/graph_coordinate.o ../core/token.o ../core/levenshteinmatrix.o ../core/trntrn_segmentor.o ../core/segmentor.o ../core/compressedlevenshteinmatrix.o ../core/recording.o ../core/sgml_generic_reportgenerator.o ../core/rttm_inputparser.o ../core/tokenalignment.o ../core/logger.o ../core/segmentsgroup.o ../core/reportgenerator.o ../core/alignedsegmentiterator.o ../core/sgml_reportgenerator.o ../core/segment.o ../core/stt_scorer.o ../core/lzma/LzmaDec.o ../core/lzma/LzmaLib.o ../core/lzma/Alloc.o ../core/lzma/LzmaEnc.o ../core/lzma/LzFind.o -lm
make[4]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite/test'
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite'
(cd sclite; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/sclite'
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os order.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os rsprintf.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os path.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os text.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os scores.c
scores.c: In function ‘load_SCORES_sgml’:
scores.c:505:35: warning: comparison between pointer and zero character constant [-Wpointer-compare]
    if ((tp2 = TEXT_strchr(tp,','))==NULL_TEXT){
                                   ^~
scores.c:505:8: note: did you mean to dereference the pointer?
    if ((tp2 = TEXT_strchr(tp,','))==NULL_TEXT){
        ^
scores.c:667:37: warning: comparison between pointer and zero character constant [-Wpointer-compare]
    if ((p2 = TEXT_strchr(p1+1,',')) == NULL_TEXT)
                                     ^~
scores.c:667:8: note: did you mean to dereference the pointer?
    if ((p2 = TEXT_strchr(p1+1,',')) == NULL_TEXT)
        ^
scores.c:677:39: warning: comparison between pointer and zero character constant [-Wpointer-compare]
         if ((p3 = TEXT_strchr(p1,'+'))==NULL_TEXT){
                                       ^~
scores.c:677:13: note: did you mean to dereference the pointer?
         if ((p3 = TEXT_strchr(p1,'+'))==NULL_TEXT){
             ^
scores.c:691:39: warning: comparison between pointer and zero character constant [-Wpointer-compare]
         if ((p3 = TEXT_strchr(p1,'+'))==NULL_TEXT){
                                       ^~
scores.c:691:13: note: did you mean to dereference the pointer?
         if ((p3 = TEXT_strchr(p1,'+'))==NULL_TEXT){
             ^
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os word.c
word.c: In function ‘release_WORD’:
word.c:330:19: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (tw->value != NULL_TEXT) TEXT_free(tw->value);
                   ^~
word.c:330:9: note: did you mean to dereference the pointer?
     if (tw->value != NULL_TEXT) TEXT_free(tw->value);
         ^
word.c:331:26: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (tw->intern_value != NULL_TEXT) TEXT_free(tw->intern_value);
                          ^~
word.c:331:9: note: did you mean to dereference the pointer?
     if (tw->intern_value != NULL_TEXT) TEXT_free(tw->intern_value);
         ^
word.c:332:18: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (tw->tag1 != NULL_TEXT) TEXT_free(tw->tag1);
                  ^~
word.c:332:9: note: did you mean to dereference the pointer?
     if (tw->tag1 != NULL_TEXT) TEXT_free(tw->tag1);
         ^
word.c:333:18: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (tw->tag2 != NULL_TEXT) TEXT_free(tw->tag2);
                  ^~
word.c:333:9: note: did you mean to dereference the pointer?
     if (tw->tag2 != NULL_TEXT) TEXT_free(tw->tag2);
         ^
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os rpg.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os statdist.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os pad.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os sgml.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os readpipe.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os alex.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os sc_dtl.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os det.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os wwscr_f.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os corresp.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os slm_intf.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os addarc1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os delarc1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os llist.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os marc1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os arcseq1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os mnode1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os boolpr1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os killarc1.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os net_adt.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os knode2.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os net_dp.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os stm.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os align.c
align.c: In function ‘remove_id’:
align.c:29:17: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (R_paren == NULL_TEXT && L_paren == NULL_TEXT)
                 ^~
align.c:29:9: note: did you mean to dereference the pointer?
     if (R_paren == NULL_TEXT && L_paren == NULL_TEXT)
         ^
align.c:29:41: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (R_paren == NULL_TEXT && L_paren == NULL_TEXT)
                                         ^~
align.c:29:33: note: did you mean to dereference the pointer?
     if (R_paren == NULL_TEXT && L_paren == NULL_TEXT)
                                 ^
align.c:32:17: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (R_paren == NULL_TEXT || L_paren == NULL_TEXT){
                 ^~
align.c:32:9: note: did you mean to dereference the pointer?
     if (R_paren == NULL_TEXT || L_paren == NULL_TEXT){
         ^
align.c:32:41: warning: comparison between pointer and zero character constant [-Wpointer-compare]
     if (R_paren == NULL_TEXT || L_paren == NULL_TEXT){
                                         ^~
align.c:32:33: note: did you mean to dereference the pointer?
     if (R_paren == NULL_TEXT || L_paren == NULL_TEXT){
                                 ^
align.c: In function ‘extract_speaker’:
align.c:63:33: warning: comparison between pointer and zero character constant [-Wpointer-compare]
  if (((p = TEXT_strchr(id,'-')) == NULL_TEXT) &&
                                 ^~
align.c:63:7: note: did you mean to dereference the pointer?
  if (((p = TEXT_strchr(id,'-')) == NULL_TEXT) &&
       ^
align.c:64:33: warning: comparison between pointer and zero character constant [-Wpointer-compare]
      ((p = TEXT_strchr(id,'_')) == NULL_TEXT)){
                                 ^~
align.c:64:7: note: did you mean to dereference the pointer?
      ((p = TEXT_strchr(id,'_')) == NULL_TEXT)){
       ^
align.c: In function ‘set_temp_files’:
align.c:933:15: warning: implicit declaration of function ‘getpid’; did you mean ‘set_pad’? [-Wimplicit-function-declaration]
     int pid = getpid();
               ^~~~~~
               set_pad
align.c: In function ‘align_trans_mode_diff’:
align.c:1070:5: warning: implicit declaration of function ‘unlink’; did you mean ‘unix’? [-Wimplicit-function-declaration]
     unlink(refwords);
     ^~~~~~
     unix
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os dwtoke2.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os fillmrks.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os wtokeini.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os lur.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os stm2ctm.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os ctm2ctm.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os mfa.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os cores.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os rank.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os range.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os anovar.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os signtest.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os wilcoxon.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os sentmcn.c
gcc -c  -DHAVE_LIBM=1 -DSTDC_HEADERS=1 -DHAVE_UNISTD_H=1  -I. -Os mtchprs.c
mtchprs.c: In function ‘count_seg’:
mtchprs.c:588:14: warning: too many arguments for format [-Wformat-extra-args]
       printf("Count Segment: segment# %3d:  sys1=[beg=%d,end=%d,ref=%d,err=%d] sys2=[beg=%d,end=%d,ref=%d,err=%d]\n",
              ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
gcc -o sclite -Os  order.o rsprintf.o path.o text.o scores.o word.o rpg.o statdist.o pad.o sgml.o readpipe.o alex.o sc_dtl.o det.o wwscr_f.o corresp.o slm_intf.o addarc1.o delarc1.o llist.o marc1.o arcseq1.o mnode1.o boolpr1.o killarc1.o net_adt.o knode2.o net_dp.o  stm.o align.o dwtoke2.o fillmrks.o wtokeini.o lur.o stm2ctm.o ctm2ctm.o mfa.o cores.o rank.o range.o anovar.o signtest.o wilcoxon.o sentmcn.o mtchprs.o  sclite.c -lm
sclite.c: In function ‘proc_args’:
sclite.c:579:15: warning: implicit declaration of function ‘TEXT_set_lang_prof’; did you mean ‘TEXT_set_encoding’? [-Wimplicit-function-declaration]
          if (!TEXT_set_lang_prof(argv[opt+1]))
               ^~~~~~~~~~~~~~~~~~
               TEXT_set_encoding
gcc -o sc_stats -Os  order.o rsprintf.o path.o text.o scores.o word.o rpg.o statdist.o pad.o sgml.o readpipe.o alex.o sc_dtl.o det.o wwscr_f.o corresp.o slm_intf.o addarc1.o delarc1.o llist.o marc1.o arcseq1.o mnode1.o boolpr1.o killarc1.o net_adt.o knode2.o net_dp.o  stm.o align.o dwtoke2.o fillmrks.o wtokeini.o lur.o stm2ctm.o ctm2ctm.o mfa.o cores.o rank.o range.o anovar.o signtest.o wilcoxon.o sentmcn.o mtchprs.o  sc_stats.c -lm
gcc -o rover -Os  order.o rsprintf.o path.o text.o scores.o word.o rpg.o statdist.o pad.o sgml.o readpipe.o alex.o sc_dtl.o det.o wwscr_f.o corresp.o slm_intf.o addarc1.o delarc1.o llist.o marc1.o arcseq1.o mnode1.o boolpr1.o killarc1.o net_adt.o knode2.o net_dp.o  stm.o align.o dwtoke2.o fillmrks.o wtokeini.o lur.o stm2ctm.o ctm2ctm.o mfa.o cores.o rank.o range.o anovar.o signtest.o wilcoxon.o sentmcn.o mtchprs.o  rover.c -lm
gcc -o sctkUnit -Os  order.o rsprintf.o path.o text.o scores.o word.o rpg.o statdist.o pad.o sgml.o readpipe.o alex.o sc_dtl.o det.o wwscr_f.o corresp.o slm_intf.o addarc1.o delarc1.o llist.o marc1.o arcseq1.o mnode1.o boolpr1.o killarc1.o net_adt.o knode2.o net_dp.o  stm.o align.o dwtoke2.o fillmrks.o wtokeini.o lur.o stm2ctm.o ctm2ctm.o mfa.o cores.o rank.o range.o anovar.o signtest.o wilcoxon.o sentmcn.o mtchprs.o  sctkUnit.c -lm
sctkUnit.c: In function ‘unitTestTEXT’:
sctkUnit.c:1156:6: warning: implicit declaration of function ‘TEXT_set_lang_prof’; did you mean ‘TEXT_set_encoding’? [-Wimplicit-function-declaration]
      TEXT_set_lang_prof("babel_turkish");
      ^~~~~~~~~~~~~~~~~~
      TEXT_set_encoding
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/sclite'
(cd rfilter1; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rfilter1'
On cygwin, it is possible this compilation will fail, please see/adapt the rfilter1/makefile OPTIONS entry
gcc -DNEED_STRCMP=1 -o rfilter1 rfilter1.c
In file included from rfilter1.c:94:0:
include/strdup2.c: In function ‘strdup_safe’:
include/strdup2.c:18:49: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast]
   if (memory_trace) printf("%s MALLOC %x\n",pdb,(int)pd);
                                                 ^
In file included from rfilter1.c:102:0:
include/calloc2.c: In function ‘calloc_safe’:
include/calloc2.c:16:49: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast]
   if (memory_trace) printf("%s CALLOC %x\n",pdb,(int)x);
                                                 ^
In file included from rfilter1.c:112:0:
include/frstr1.c: In function ‘free_str’:
include/frstr1.c:9:49: warning: cast from pointer to integer of different size [-Wpointer-to-int-cast]
      {if (memory_trace) printf("*DB: FREE %x\n",(int)s);
                                                 ^
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rfilter1'
(cd csrfilt; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/csrfilt'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/csrfilt'
(cd chfilt; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/chfilt'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/chfilt'
(cd hamzaNorm; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/hamzaNorm'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/hamzaNorm'
(cd tanweenFilt; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/tanweenFilt'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/tanweenFilt'
(cd acomp; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/acomp'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/acomp'
(cd def_art; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/def_art'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/def_art'
(cd hubscr; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/hubscr'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/hubscr'
(cd utf_filt; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/utf_filt'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/utf_filt'
(cd rttmSort; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttmSort'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttmSort'
(cd rttm2ctm; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttm2ctm'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttm2ctm'
(cd rttmSmooth; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttmSmooth'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttmSmooth'
(cd spkr2sad; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/spkr2sad'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/spkr2sad'
(cd mergectm2rttm; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/mergectm2rttm'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/mergectm2rttm'
(cd align2html; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/align2html'
perl buildInstallVersion.pl > align2html.pl
chmod +x align2html.pl
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/align2html'
(cd md-eval; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/md-eval'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/md-eval'
(cd stm2rttm; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/stm2rttm'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/stm2rttm'
(cd stmValidator; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/stmValidator'
perl buildInstallVersion.pl > stmValidator.pl
chmod +x stmValidator.pl
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/stmValidator'
(cd rttmValidator; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttmValidator'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttmValidator'
(cd ctmValidator; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/ctmValidator'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/ctmValidator'
(cd slatreport; make all)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/slatreport'
make[3]: Nothing to be done for 'all'.
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/slatreport'
make[2]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src'
make[1]: Leaving directory '/content/espnet/tools/sctk-2.4.10'
make[1]: Entering directory '/content/espnet/tools/sctk-2.4.10'
(mkdir -p bin)
(cd src; make install)
make[2]: Entering directory '/content/espnet/tools/sctk-2.4.10/src'
(cd asclite; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite'
(cd core; make install)
make[4]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite/core'
for p in asclite; do \
        /usr/bin/install $p /content/espnet/tools/sctk/bin/$p; \
done
make[4]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite/core'
(cd test; make install)
make[4]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/asclite/test'
make[4]: Nothing to be done for 'install'.
make[4]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite/test'
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/asclite'
(cd sclite; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/sclite'
/bin/sh: 5: Syntax error: end of file unexpected (expecting "done")
makefile:171: recipe for target 'testinstalldirs' failed
make[3]: [testinstalldirs] Error 2 (ignored)
for p in sclite sc_stats rover sctkUnit; do \
  /usr/bin/install $p /content/espnet/tools/sctk/bin/$p; \
done
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/sclite'
(cd rfilter1; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rfilter1'
install rfilter1 /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rfilter1'
(cd csrfilt; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/csrfilt'
cp csrfilt.sh /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/csrfilt'
(cd chfilt; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/chfilt'
cp chfilt.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/chfilt'
(cd hamzaNorm; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/hamzaNorm'
cp hamzaNorm.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/hamzaNorm'
(cd tanweenFilt; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/tanweenFilt'
cp tanweenFilt.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/tanweenFilt'
(cd acomp; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/acomp'
cp acomp.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/acomp'
(cd def_art; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/def_art'
cp def_art.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/def_art'
(cd hubscr; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/hubscr'
cp hubscr.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/hubscr'
(cd utf_filt; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/utf_filt'
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/utf_filt'
(cd rttmSort; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttmSort'
cp rttmSort.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttmSort'
(cd rttm2ctm; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttm2ctm'
cp rttm2ctm.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttm2ctm'
(cd rttmSmooth; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttmSmooth'
cp rttmSmooth.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttmSmooth'
(cd spkr2sad; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/spkr2sad'
cp spkr2sad.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/spkr2sad'
(cd mergectm2rttm; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/mergectm2rttm'
cp mergectm2rttm.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/mergectm2rttm'
(cd align2html; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/align2html'
perl buildInstallVersion.pl > /content/espnet/tools/sctk/bin/align2html.pl
chmod +x /content/espnet/tools/sctk/bin/align2html.pl
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/align2html'
(cd md-eval; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/md-eval'
cp md-eval.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/md-eval'
(cd stm2rttm; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/stm2rttm'
cp stm2rttm.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/stm2rttm'
(cd stmValidator; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/stmValidator'
perl buildInstallVersion.pl > stmValidator.pl
chmod +x stmValidator.pl
perl buildInstallVersion.pl > /content/espnet/tools/sctk/bin/stmValidator.pl
chmod +x /content/espnet/tools/sctk/bin/stmValidator.pl
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/stmValidator'
(cd rttmValidator; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/rttmValidator'
cp rttmValidator.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/rttmValidator'
(cd ctmValidator; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/ctmValidator'
cp ctmValidator.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/ctmValidator'
(cd slatreport; make install)
make[3]: Entering directory '/content/espnet/tools/sctk-2.4.10/src/slatreport'
cp slatreport.pl /content/espnet/tools/sctk/bin
make[3]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src/slatreport'
make[2]: Leaving directory '/content/espnet/tools/sctk-2.4.10/src'
make[1]: Leaving directory '/content/espnet/tools/sctk-2.4.10'
make[1]: Entering directory '/content/espnet/tools/sctk-2.4.10'
(cd doc; make all)
make[2]: Entering directory '/content/espnet/tools/sctk-2.4.10/doc'
for p in align2html asclite ctmValidator mergectm2rttm; do \
        pod2man $p.pod -o $p.1; \
        pod2html $p.pod > $p.html; \
done 2> /dev/null
make[2]: Leaving directory '/content/espnet/tools/sctk-2.4.10/doc'
make[1]: Leaving directory '/content/espnet/tools/sctk-2.4.10'
touch sctk.done
./installers/install_sph2pipe.sh
--2022-09-19 21:00:53--  https://github.com/espnet/kaldi-bin/releases/download/v0.0.2/sph2pipe_v2.5.tar.gz
Resolving github.com (github.com)... 20.205.243.166
Connecting to github.com (github.com)|20.205.243.166|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/198329952/d5cce680-e25e-11ea-92fb-f0a1816f580a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220919%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220919T210036Z&X-Amz-Expires=300&X-Amz-Signature=add8533d6b7c69158159b8ddd4938c8a5bd2ab03e17d5f7ab50aa2f955c33807&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=198329952&response-content-disposition=attachment%3B%20filename%3Dsph2pipe_v2.5.tar.gz&response-content-type=application%2Foctet-stream [following]
--2022-09-19 21:00:53--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/198329952/d5cce680-e25e-11ea-92fb-f0a1816f580a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220919%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220919T210036Z&X-Amz-Expires=300&X-Amz-Signature=add8533d6b7c69158159b8ddd4938c8a5bd2ab03e17d5f7ab50aa2f955c33807&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=198329952&response-content-disposition=attachment%3B%20filename%3Dsph2pipe_v2.5.tar.gz&response-content-type=application%2Foctet-stream
Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 329832 (322K) [application/octet-stream]
Saving to: ‘sph2pipe_v2.5.tar.gz’

sph2pipe_v2.5.tar.g 100%[===================>] 322.10K  --.-KB/s    in 0.003s

2022-09-19 21:00:53 (94.6 MB/s) - ‘sph2pipe_v2.5.tar.gz’ saved [329832/329832]

file_headers.c: In function ‘readSphHeader’:
file_headers.c:148:59: warning: format ‘%d’ expects argument of type ‘int’, but argument 5 has type ‘__off_t {aka long int}’ [-Wformat=]
     "Warning:%s: sample_count reset to %d to match size (%d bytes)\n",
                                                          ~^
                                                          %ld
    inpname, sampcount, statbuf.st_size );
                        ~~~~~~~~~~~~~~~
file_headers.c: In function ‘copyshort’:
file_headers.c:326:2: warning: implicit declaration of function ‘swab’; did you mean ‘scalb’? [-Wimplicit-function-declaration]
  swab((char *) &val, short_order.ch, 2 );
  ^~~~
  scalb
file_headers.c: At top level:
file_headers.c:579:1: warning: return type defaults to ‘int’ [-Wimplicit-int]
 ConvertToIeeeExtended(num, bytes)
 ^~~~~~~~~~~~~~~~~~~~~
shorten_x.c: In function ‘fwrite_type’:
shorten_x.c:325:22: warning: implicit declaration of function ‘pcm2alaw’ [-Wimplicit-function-declaration]
       *writebufp++ = pcm2alaw( ulaw2pcm[data0[i]] );
                      ^~~~~~~~
shorten_x.c:381:24: warning: implicit declaration of function ‘pcm2ulaw’ [-Wimplicit-function-declaration]
         *writebufp++ = pcm2ulaw( data0[i] );
                        ^~~~~~~~
shorten_x.c:464:6: warning: implicit declaration of function ‘swab’; did you mean ‘stat’? [-Wimplicit-function-declaration]
      swab(writebuf, writefub, sizeout * nchanout * nitem);
      ^~~~
      stat
sph2pipe.c: In function ‘getUserOpts’:
sph2pipe.c:191:18: warning: implicit declaration of function ‘getopt’; did you mean ‘getsubopt’? [-Wimplicit-function-declaration]
     while (( i = getopt( ac, av, "daupf:c:t:s:h:" )) != EOF )
                  ^~~~~~
                  getsubopt
sph2pipe.c: In function ‘copySamples’:
sph2pipe.c:537:3: warning: implicit declaration of function ‘swab’; did you mean ‘stat’? [-Wimplicit-function-declaration]
   swab( outbuf, inpbuf, nb );      /* it, do byte swapping too */
   ^~~~
   stat
touch sph2pipe.done
. ./activate_python.sh; . ./extra_path.sh; python3 check_install.py
[x] python=3.9.13 (main, Aug 25 2022, 23:26:10)  [GCC 11.2.0]

Python modules:
[x] torch=1.12.1
[x] torch cuda=11.6
[x] torch cudnn=8302
[x] torch nccl
[x] chainer=6.0.0
[ ] chainer cuda
[ ] chainer cudnn
[ ] cupy
[x] torchaudio=0.12.1
[x] torch_optimizer=0.3.0
[ ] warpctc_pytorch
[ ] warprnnt_pytorch
[ ] chainer_ctc
[ ] pyopenjtalk
[ ] tdmelodic_pyopenjtalk
[ ] kenlm
[ ] mmseg
[x] espnet=202207
[x] numpy=1.23.1
[ ] fairseq
[ ] phonemizer
[ ] gtn
[ ] s3prl
[ ] transformers
[ ] speechbrain
[ ] k2
[ ] longformer
[ ] nlg-eval
[ ] datasets

Executables:
[x] sclite
[x] sph2pipe
[ ] PESQ
[ ] BeamformIt

INFO:
Use 'installers/install_warp-ctc.sh' to install warpctc_pytorch
Use 'installers/install_warp-transducer.sh' to install warprnnt_pytorch
Use 'installers/install_chainer_ctc.sh' to install chainer_ctc
Use 'installers/install_pyopenjtalk.sh' to install pyopenjtalk
Use 'installers/install_tdmelodic_pyopenjtalk.sh' to install tdmelodic_pyopenjtalk
Use 'installers/install_kenlm.sh' to install kenlm
Use 'installers/install_py3mmseg.sh' to install mmseg
Use 'installers/install_fairseq.sh' to install fairseq
Use 'installers/install_phonemizer.sh' to install phonemizer
Use 'installers/install_gtn.sh' to install gtn
Use 'installers/install_s3prl.sh' to install s3prl
Use 'installers/install_transformers.sh' to install transformers
Use 'installers/install_speechbrain.sh' to install speechbrain
Use 'installers/install_k2.sh' to install k2
Use 'installers/install_longformer.sh' to install longformer
Use 'installers/install_longformer.sh' to install nlg-eval
Use 'installers/install_longformer.sh' to install datasets
Use 'installers/install_pesq.sh' to install PESQ
Use 'installers/install_beamformit.sh' to install BeamformIt

If other listed packages are necessary, install any of them using

. ./activation_python.sh && ./installers/install_xxx.sh

We show two examples, although they are not used in this demo.

[ ]:
# s3prl and fairseq are necessary if you want to use self-supervised pre-trained models
# It takes 50s
%cd /content/espnet/tools

!. ./activate_python.sh && ./installers/install_s3prl.sh      # install s3prl to use SSLRs
!. ./activate_python.sh && ./installers/install_fairseq.sh    # install s3prl to use Wav2Vec2 / HuBERT model series
/content/espnet/tools
cuda_version=116
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting s3prl
  Downloading s3prl-0.4.2.tar.gz (614 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 614.8/614.8 kB 27.7 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
Requirement already satisfied: torchaudio>=0.8.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from s3prl) (0.12.1)
Requirement already satisfied: torch>=1.8.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from s3prl) (1.12.1)
Requirement already satisfied: tqdm>=4.56.0 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from s3prl) (4.64.0)
Requirement already satisfied: numpy>=1.21 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from s3prl) (1.23.3)
Requirement already satisfied: PyYAML>=5.4.1 in ./anaconda/envs/espnet/lib/python3.9/site-packages (from s3prl) (6.0)
Requirement already satisfied: filelock in ./anaconda/envs/espnet/lib/python3.9/site-packages (from s3prl) (3.8.0)
Collecting omegaconf>=2.1.1
  Downloading omegaconf-2.2.3-py3-none-any.whl (79 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.3/79.3 kB 10.8 MB/s eta 0:00:00
Collecting setuptools==59.5.0
  Downloading setuptools-59.5.0-py3-none-any.whl (952 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 952.4/952.4 kB 61.8 MB/s eta 0:00:00
Collecting antlr4-python3-runtime==4.9.*
  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 117.0/117.0 kB 15.1 MB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
Requirement already satisfied: typing_extensions in ./anaconda/envs/espnet/lib/python3.9/site-packages (from torch>=1.8.0->s3prl) (4.3.0)
Building wheels for collected packages: s3prl, antlr4-python3-runtime
  Building wheel for s3prl (setup.py) ... done
  Created wheel for s3prl: filename=s3prl-0.4.2-py3-none-any.whl size=765959 sha256=eea4e087ad519782293171ad3d1afec8c003b1d1d1b78ebb6d3be3b1c3eb2d70
  Stored in directory: /root/.cache/pip/wheels/9f/50/63/9a74004e89ed4491ef03e2c1a8b2d70a29ea65fcfa2f02938a
  Building wheel for antlr4-python3-runtime (setup.py) ... done
  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144575 sha256=f60c5b1f2a978628ff5c9ccdab2a0f1731d9e640b6c15a34363cb5a30fd12982
  Stored in directory: /root/.cache/pip/wheels/23/cf/80/f3efa822e6ab23277902ee9165fe772eeb1dfb8014f359020a
Successfully built s3prl antlr4-python3-runtime
Installing collected packages: antlr4-python3-runtime, setuptools, omegaconf, s3prl
  Attempting uninstall: setuptools
    Found existing installation: setuptools 59.8.0
    Uninstalling setuptools-59.8.0:
      Successfully uninstalled setuptools-59.8.0
Successfully installed antlr4-python3-runtime-4.9.3 omegaconf-2.2.3 s3prl-0.4.2 setuptools-59.5.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
[INFO] torch_version=1.12.1
Cloning into 'fairseq'...
remote: Enumerating objects: 32641, done.
remote: Counting objects: 100% (4/4), done.
remote: Compressing objects: 100% (4/4), done.
remote: Total 32641 (delta 0), reused 0 (delta 0), pack-reused 32637
Receiving objects: 100% (32641/32641), 22.55 MiB | 13.39 MiB/s, done.
Resolving deltas: 100% (23925/23925), done.
Switched to a new branch 'sync_commit'
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Obtaining file:///content/espnet/tools/fairseq
  Installing build dependencies ... done
  Checking if build backend supports build_editable ... done
  Getting requirements to build editable ... done
  Installing backend dependencies ... done
  Preparing editable metadata (pyproject.toml) ... done
Collecting hydra-core<1.1
  Downloading hydra_core-1.0.7-py3-none-any.whl (123 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 123.8/123.8 kB 13.4 MB/s eta 0:00:00
Requirement already satisfied: regex in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairseq==1.0.0a0+313ff05) (2022.9.13)
Requirement already satisfied: torch in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairseq==1.0.0a0+313ff05) (1.12.1)
Requirement already satisfied: tqdm in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairseq==1.0.0a0+313ff05) (4.64.0)
Requirement already satisfied: cython in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairseq==1.0.0a0+313ff05) (0.29.32)
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Requirement already satisfied: numpy in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairseq==1.0.0a0+313ff05) (1.23.3)
Collecting omegaconf<2.1
  Downloading omegaconf-2.0.6-py3-none-any.whl (36 kB)
Requirement already satisfied: cffi in ./anaconda/envs/espnet/lib/python3.9/site-packages (from fairseq==1.0.0a0+313ff05) (1.15.1)
Collecting antlr4-python3-runtime==4.8
  Downloading antlr4-python3-runtime-4.8.tar.gz (112 kB)
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  Preparing metadata (setup.py) ... done
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Building wheels for collected packages: fairseq, antlr4-python3-runtime
  Building editable for fairseq (pyproject.toml) ... done
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Successfully built fairseq antlr4-python3-runtime
Installing collected packages: antlr4-python3-runtime, omegaconf, hydra-core, fairseq
  Attempting uninstall: antlr4-python3-runtime
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    Uninstalling antlr4-python3-runtime-4.9.3:
      Successfully uninstalled antlr4-python3-runtime-4.9.3
  Attempting uninstall: omegaconf
    Found existing installation: omegaconf 2.2.3
    Uninstalling omegaconf-2.2.3:
      Successfully uninstalled omegaconf-2.2.3
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
s3prl 0.4.2 requires omegaconf>=2.1.1, but you have omegaconf 2.0.6 which is incompatible.
Successfully installed antlr4-python3-runtime-4.8 fairseq-1.0.0a0+313ff05 hydra-core-1.0.7 omegaconf-2.0.6
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: filelock in ./anaconda/envs/espnet/lib/python3.9/site-packages (3.8.0)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv

Check installation

Now let’s make sure torch, torch cuda, and espnet are successfully installed.

...
[x] torch=1.12.1
[x] torch cuda=11.6
[x] torch cudnn=8302
...
[x] espnet=202207
...

✅ Checkpoint 1 (1 point)

Print the output of check_install.py.

[ ]:
%cd /content/espnet/tools
!. ./activate_python.sh && python3 check_install.py | head -n 40

# NOTE: Checkpoint 1
print_date_and_time()
/content/espnet/tools
[x] python=3.9.13 (main, Aug 25 2022, 23:26:10)  [GCC 11.2.0]

Python modules:
[x] torch=1.12.1
[x] torch cuda=11.6
[x] torch cudnn=8302
[x] torch nccl
[x] chainer=6.0.0
[ ] chainer cuda
[ ] chainer cudnn
[ ] cupy
[x] torchaudio=0.12.1
[x] torch_optimizer=0.3.0
[ ] warpctc_pytorch
[ ] warprnnt_pytorch
[ ] chainer_ctc
[ ] pyopenjtalk
[ ] tdmelodic_pyopenjtalk
[ ] kenlm
[ ] mmseg
[x] espnet=202207
[x] numpy=1.23.1
[x] fairseq
[ ] phonemizer
[ ] gtn
[ ] s3prl
[ ] transformers
[ ] speechbrain
[ ] k2
[ ] longformer
[ ] nlg-eval
[ ] datasets

Executables:
[ ] sclite
[ ] sph2pipe
[ ] PESQ
[ ] BeamformIt

INFO:
============================================================
 Current date and time: 09/19/2022 17:02:52
============================================================

Run an existing recipe

ESPnet has a number of recipes (130 recipes on Sep. 11, 2022). Please refer to https://github.com/espnet/espnet/blob/master/egs2/README.md for a complete list.

Please also check the general usage of the recipe in https://espnet.github.io/espnet/espnet2_tutorial.html#recipes-using-espnet2

CMU AN4 recipe

In this tutorial, we will use the CMU an4 recipe. This is a small-scale speech recognition task mainly used for testing.

First, let’s go to the recipe directory.

[ ]:
%cd /content/espnet/egs2/an4/asr1
!ls
/content/espnet/egs2/an4/asr1
asr.sh  conf   local    pyscripts  run.sh   steps
cmd.sh  db.sh  path.sh  README.md  scripts  utils

egs2/an4/asr1/  - conf/      # Configuration files for training, inference, etc.  - scripts/   # Bash utilities of espnet2  - pyscripts/ # Python utilities of espnet2  - steps/     # From Kaldi utilities  - utils/     # From Kaldi utilities  - db.sh      # The directory path of each corpora  - path.sh    # Setup script for environment variables  - cmd.sh     # Configuration for your backend of job scheduler  - run.sh     # Entry point  - asr.sh     # Invoked by run.sh

ESPnet is designed for various use cases (local machines or cluster machines) based on Kaldi tools. If you use it in the cluster machines, please also check https://kaldi-asr.org/doc/queue.html

The main stages can be parallelized by various jobs.

[ ]:
!cat run.sh
#!/usr/bin/env bash
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail

./asr.sh \
    --lang en \
    --asr_config conf/train_asr_transformer.yaml \
    --inference_config conf/decode_asr.yaml \
    --lm_config conf/train_lm.yaml \
    --speed_perturb_factors "0.9 1.0 1.1" \
    --train_set train_nodev \
    --valid_set train_dev \
    --test_sets "train_dev test" \
    --bpe_train_text "dump/raw/train_nodev_sp/text" \
    --lm_train_text "data/train_nodev_sp/text" "$@"

run.sh calls asr.sh, which completes the entire speech recognition experiments, including data preparation, training, inference, and scoring. They are separated into multiple stages (totally 16).

Instead of executing the entire pipeline by run.sh, let’s run it stage-by-stage to understand the process in each stage.

Data preparation

Stage 1: Data preparation: download raw data, split the entire set into train/dev/test, and prepare them in the Kaldi format

Note that --stage <N> is to start from this stage and --stop_stage <N> is to stop after this stage. We also need to specify the train, dev and test sets.

[ ]:
# a few seconds
!./asr.sh --stage 1 --stop_stage 1 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test"
2022-09-19T21:08:39 (asr.sh:254:main) ./asr.sh --stage 1 --stop_stage 1 --train_set train_nodev --valid_set train_dev --test_sets train_dev test
2022-09-19T21:08:40 (asr.sh:445:main) Stage 1: Data preparation for data/train_nodev, data/train_dev, etc.
2022-09-19T21:08:40 (data.sh:23:main) local/data.sh
2022-09-19T21:08:40 (data.sh:39:main) stage 1: Data Download
local/download_and_untar.sh: downloading data (64 MB) from http://www.speech.cs.cmu.edu/databases/an4//an4_sphere.tar.gz.
--2022-09-19 21:08:40--  http://www.speech.cs.cmu.edu/databases/an4//an4_sphere.tar.gz
Resolving www.speech.cs.cmu.edu (www.speech.cs.cmu.edu)... 128.2.220.147
Connecting to www.speech.cs.cmu.edu (www.speech.cs.cmu.edu)|128.2.220.147|:80... connected.
HTTP request sent, awaiting response... 404 Not Found
2022-09-19 21:08:42 ERROR 404: Not Found.

local/download_and_untar.sh: error executing wget http://www.speech.cs.cmu.edu/databases/an4//an4_sphere.tar.gz
2022-09-19T21:08:42 (data.sh:42:main) Failed to download from the original site, try a backup site.
local/download_and_untar.sh: downloading data (64 MB) from https://huggingface.co/datasets/espnet/an4/resolve/main/an4_sphere.tar.gz.
--2022-09-19 21:08:42--  https://huggingface.co/datasets/espnet/an4/resolve/main/an4_sphere.tar.gz
Resolving huggingface.co (huggingface.co)... 52.6.16.131, 52.202.207.64, 2600:1f18:147f:e800:afa4:a769:1b42:e343, ...
Connecting to huggingface.co (huggingface.co)|52.6.16.131|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://cdn-lfs.huggingface.co/repos/d6/93/d6930d4851cd7069cd6a09d251e19ce4c9824fa124b29e3f0784444d19a13419/a0525579493735d32b60fb6fa5975dd0eaacde087b0a4eeb7f13b7ef33077b12?response-content-disposition=attachment%3B%20filename%3D%22an4_sphere.tar.gz%22 [following]
--2022-09-19 21:08:43--  https://cdn-lfs.huggingface.co/repos/d6/93/d6930d4851cd7069cd6a09d251e19ce4c9824fa124b29e3f0784444d19a13419/a0525579493735d32b60fb6fa5975dd0eaacde087b0a4eeb7f13b7ef33077b12?response-content-disposition=attachment%3B%20filename%3D%22an4_sphere.tar.gz%22
Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.155.68.94, 18.155.68.128, 18.155.68.73, ...
Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.155.68.94|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 64327561 (61M) [application/x-gzip]
Saving to: 'an4_sphere.tar.gz'

an4_sphere.tar.gz   100%[===================>]  61.35M   370MB/s    in 0.2s

2022-09-19 21:08:43 (370 MB/s) - 'an4_sphere.tar.gz' saved [64327561/64327561]

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an4/etc/an4_train.transcription
an4/etc/an4_test.transcription
an4/etc/an4.dic
an4/etc/an4.phone
an4/etc/an4.filler
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an4/wav/an4_clstk/fkdo/an134-fkdo-b.sph
an4/wav/an4_clstk/fkdo/an135-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen1-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen2-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen3-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen4-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen5-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen6-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen7-fkdo-b.sph
an4/wav/an4_clstk/fkdo/cen8-fkdo-b.sph
an4/wav/an4_clstk/flmm2/
an4/wav/an4_clstk/flmm2/an61-flmm2-b.sph
an4/wav/an4_clstk/flmm2/an62-flmm2-b.sph
an4/wav/an4_clstk/flmm2/an63-flmm2-b.sph
an4/wav/an4_clstk/flmm2/an64-flmm2-b.sph
an4/wav/an4_clstk/flmm2/an65-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen1-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen2-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen3-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen4-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen6-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen7-flmm2-b.sph
an4/wav/an4_clstk/flmm2/cen8-flmm2-b.sph
an4/wav/an4_clstk/flrp/
an4/wav/an4_clstk/flrp/an21-flrp-b.sph
an4/wav/an4_clstk/flrp/an2121-flrp-b.sph
an4/wav/an4_clstk/flrp/an22-flrp-b.sph
an4/wav/an4_clstk/flrp/an23-flrp-b.sph
an4/wav/an4_clstk/flrp/an24-flrp-b.sph
an4/wav/an4_clstk/flrp/an25-flrp-b.sph
an4/wav/an4_clstk/flrp/cen1-flrp-b.sph
an4/wav/an4_clstk/flrp/cen2-flrp-b.sph
an4/wav/an4_clstk/flrp/cen3-flrp-b.sph
an4/wav/an4_clstk/flrp/cen4-flrp-b.sph
an4/wav/an4_clstk/flrp/cen5-flrp-b.sph
an4/wav/an4_clstk/flrp/cen6-flrp-b.sph
an4/wav/an4_clstk/flrp/cen7-flrp-b.sph
an4/wav/an4_clstk/flrp/cen8-flrp-b.sph
an4/wav/an4_clstk/fmjc/
an4/wav/an4_clstk/fmjc/an116-fmjc-b.sph
an4/wav/an4_clstk/fmjc/an117-fmjc-b.sph
an4/wav/an4_clstk/fmjc/an118-fmjc-b.sph
an4/wav/an4_clstk/fmjc/an119-fmjc-b.sph
an4/wav/an4_clstk/fmjc/an120-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen1-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen2-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen3-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen4-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen5-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen6-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen7-fmjc-b.sph
an4/wav/an4_clstk/fmjc/cen8-fmjc-b.sph
an4/wav/an4_clstk/fmjd/
an4/wav/an4_clstk/fmjd/an191-fmjd-b.sph
an4/wav/an4_clstk/fmjd/an192-fmjd-b.sph
an4/wav/an4_clstk/fmjd/an193-fmjd-b.sph
an4/wav/an4_clstk/fmjd/an194-fmjd-b.sph
an4/wav/an4_clstk/fmjd/an195-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen1-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen2-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen3-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen4-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen5-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen6-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen7-fmjd-b.sph
an4/wav/an4_clstk/fmjd/cen8-fmjd-b.sph
an4/wav/an4_clstk/fnsv/
an4/wav/an4_clstk/fnsv/an181-fnsv-b.sph
an4/wav/an4_clstk/fnsv/an182-fnsv-b.sph
an4/wav/an4_clstk/fnsv/an183-fnsv-b.sph
an4/wav/an4_clstk/fnsv/an184-fnsv-b.sph
an4/wav/an4_clstk/fnsv/an185-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen1-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen2-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen3-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen4-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen5-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen6-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen7-fnsv-b.sph
an4/wav/an4_clstk/fnsv/cen8-fnsv-b.sph
an4/wav/an4_clstk/fplp/
an4/wav/an4_clstk/fplp/an91-fplp-b.sph
an4/wav/an4_clstk/fplp/an92-fplp-b.sph
an4/wav/an4_clstk/fplp/an93-fplp-b.sph
an4/wav/an4_clstk/fplp/an94-fplp-b.sph
an4/wav/an4_clstk/fplp/an95-fplp-b.sph
an4/wav/an4_clstk/fplp/cen1-fplp-b.sph
an4/wav/an4_clstk/fplp/cen2-fplp-b.sph
an4/wav/an4_clstk/fplp/cen3-fplp-b.sph
an4/wav/an4_clstk/fplp/cen4-fplp-b.sph
an4/wav/an4_clstk/fplp/cen5-fplp-b.sph
an4/wav/an4_clstk/fplp/cen6-fplp-b.sph
an4/wav/an4_clstk/fplp/cen7-fplp-b.sph
an4/wav/an4_clstk/fplp/cen8-fplp-b.sph
an4/wav/an4_clstk/fsaf2/
an4/wav/an4_clstk/fsaf2/an296-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/an297-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/an298-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/an299-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/an300-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen1-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen2-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen3-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen4-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen5-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen6-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen7-fsaf2-b.sph
an4/wav/an4_clstk/fsaf2/cen8-fsaf2-b.sph
an4/wav/an4_clstk/fsrb/
an4/wav/an4_clstk/fsrb/an166-fsrb-b.sph
an4/wav/an4_clstk/fsrb/an167-fsrb-b.sph
an4/wav/an4_clstk/fsrb/an168-fsrb-b.sph
an4/wav/an4_clstk/fsrb/an169-fsrb-b.sph
an4/wav/an4_clstk/fsrb/an170-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen1-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen2-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen3-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen4-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen5-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen6-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen7-fsrb-b.sph
an4/wav/an4_clstk/fsrb/cen8-fsrb-b.sph
an4/wav/an4_clstk/ftal/
an4/wav/an4_clstk/ftal/an326-ftal-b.sph
an4/wav/an4_clstk/ftal/an327-ftal-b.sph
an4/wav/an4_clstk/ftal/an328-ftal-b.sph
an4/wav/an4_clstk/ftal/an329-ftal-b.sph
an4/wav/an4_clstk/ftal/an330-ftal-b.sph
an4/wav/an4_clstk/ftal/cen1-ftal-b.sph
an4/wav/an4_clstk/ftal/cen2-ftal-b.sph
an4/wav/an4_clstk/ftal/cen3-ftal-b.sph
an4/wav/an4_clstk/ftal/cen4-ftal-b.sph
an4/wav/an4_clstk/ftal/cen5-ftal-b.sph
an4/wav/an4_clstk/ftal/cen6-ftal-b.sph
an4/wav/an4_clstk/ftal/cen7-ftal-b.sph
an4/wav/an4_clstk/ftal/cen8-ftal-b.sph
an4/wav/an4_clstk/ftmj/
an4/wav/an4_clstk/ftmj/an211-ftmj-b.sph
an4/wav/an4_clstk/ftmj/an212-ftmj-b.sph
an4/wav/an4_clstk/ftmj/an213-ftmj-b.sph
an4/wav/an4_clstk/ftmj/an214-ftmj-b.sph
an4/wav/an4_clstk/ftmj/an215-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen1-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen2-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen3-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen4-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen5-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen6-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen7-ftmj-b.sph
an4/wav/an4_clstk/ftmj/cen8-ftmj-b.sph
an4/wav/an4_clstk/fwxs/
an4/wav/an4_clstk/fwxs/an221-fwxs-b.sph
an4/wav/an4_clstk/fwxs/an222-fwxs-b.sph
an4/wav/an4_clstk/fwxs/an223-fwxs-b.sph
an4/wav/an4_clstk/fwxs/an224-fwxs-b.sph
an4/wav/an4_clstk/fwxs/an225-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen1-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen2-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen3-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen4-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen5-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen6-fwxs-b.sph
an4/wav/an4_clstk/fwxs/cen7-fwxs-b.sph
an4/wav/an4_clstk/mblb/
an4/wav/an4_clstk/mblb/an371-mblb-b.sph
an4/wav/an4_clstk/mblb/an372-mblb-b.sph
an4/wav/an4_clstk/mblb/an373-mblb-b.sph
an4/wav/an4_clstk/mblb/an374-mblb-b.sph
an4/wav/an4_clstk/mblb/an375-mblb-b.sph
an4/wav/an4_clstk/mblb/cen1-mblb-b.sph
an4/wav/an4_clstk/mblb/cen2-mblb-b.sph
an4/wav/an4_clstk/mblb/cen3-mblb-b.sph
an4/wav/an4_clstk/mblb/cen4-mblb-b.sph
an4/wav/an4_clstk/mblb/cen5-mblb-b.sph
an4/wav/an4_clstk/mblb/cen6-mblb-b.sph
an4/wav/an4_clstk/mblb/cen7-mblb-b.sph
an4/wav/an4_clstk/mblb/cen8-mblb-b.sph
an4/wav/an4_clstk/mblw/
an4/wav/an4_clstk/mblw/an1-mblw-b.sph
an4/wav/an4_clstk/mblw/an2-mblw-b.sph
an4/wav/an4_clstk/mblw/an3-mblw-b.sph
an4/wav/an4_clstk/mblw/an4-mblw-b.sph
an4/wav/an4_clstk/mblw/an5-mblw-b.sph
an4/wav/an4_clstk/mblw/cen1-mblw-b.sph
an4/wav/an4_clstk/mblw/cen2-mblw-b.sph
an4/wav/an4_clstk/mblw/cen3-mblw-b.sph
an4/wav/an4_clstk/mblw/cen4-mblw-b.sph
an4/wav/an4_clstk/mblw/cen5-mblw-b.sph
an4/wav/an4_clstk/mblw/cen6-mblw-b.sph
an4/wav/an4_clstk/mblw/cen7-mblw-b.sph
an4/wav/an4_clstk/mblw/cen8-mblw-b.sph
an4/wav/an4_clstk/mbmg/
an4/wav/an4_clstk/mbmg/an266-mbmg-b.sph
an4/wav/an4_clstk/mbmg/an267-mbmg-b.sph
an4/wav/an4_clstk/mbmg/an268-mbmg-b.sph
an4/wav/an4_clstk/mbmg/an269-mbmg-b.sph
an4/wav/an4_clstk/mbmg/an270-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen1-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen2-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen3-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen4-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen5-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen6-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen7-mbmg-b.sph
an4/wav/an4_clstk/mbmg/cen8-mbmg-b.sph
an4/wav/an4_clstk/mcel/
an4/wav/an4_clstk/mcel/an201-mcel-b.sph
an4/wav/an4_clstk/mcel/an202-mcel-b.sph
an4/wav/an4_clstk/mcel/an203-mcel-b.sph
an4/wav/an4_clstk/mcel/an204-mcel-b.sph
an4/wav/an4_clstk/mcel/an205-mcel-b.sph
an4/wav/an4_clstk/mcel/cen1-mcel-b.sph
an4/wav/an4_clstk/mcel/cen2-mcel-b.sph
an4/wav/an4_clstk/mcel/cen3-mcel-b.sph
an4/wav/an4_clstk/mcel/cen4-mcel-b.sph
an4/wav/an4_clstk/mcel/cen5-mcel-b.sph
an4/wav/an4_clstk/mcel/cen6-mcel-b.sph
an4/wav/an4_clstk/mcel/cen8-mcel-b.sph
an4/wav/an4_clstk/mcen/
an4/wav/an4_clstk/mcen/an126-mcen-b.sph
an4/wav/an4_clstk/mcen/an127-mcen-b.sph
an4/wav/an4_clstk/mcen/an128-mcen-b.sph
an4/wav/an4_clstk/mcen/an129-mcen-b.sph
an4/wav/an4_clstk/mcen/an130-mcen-b.sph
an4/wav/an4_clstk/mcen/cen1-mcen-b.sph
an4/wav/an4_clstk/mcen/cen2-mcen-b.sph
an4/wav/an4_clstk/mcen/cen3-mcen-b.sph
an4/wav/an4_clstk/mcen/cen4-mcen-b.sph
an4/wav/an4_clstk/mcen/cen5-mcen-b.sph
an4/wav/an4_clstk/mcen/cen6-mcen-b.sph
an4/wav/an4_clstk/mcen/cen7-mcen-b.sph
an4/wav/an4_clstk/mcen/cen8-mcen-b.sph
an4/wav/an4_clstk/mcfl/
an4/wav/an4_clstk/mcfl/an261-mcfl-b.sph
an4/wav/an4_clstk/mcfl/an262-mcfl-b.sph
an4/wav/an4_clstk/mcfl/an263-mcfl-b.sph
an4/wav/an4_clstk/mcfl/an264-mcfl-b.sph
an4/wav/an4_clstk/mcfl/an265-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen1-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen2-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen3-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen4-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen5-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen6-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen7-mcfl-b.sph
an4/wav/an4_clstk/mcfl/cen8-mcfl-b.sph
an4/wav/an4_clstk/mcrt/
an4/wav/an4_clstk/mcrt/an141-mcrt-b.sph
an4/wav/an4_clstk/mcrt/an142-mcrt-b.sph
an4/wav/an4_clstk/mcrt/an143-mcrt-b.sph
an4/wav/an4_clstk/mcrt/an144-mcrt-b.sph
an4/wav/an4_clstk/mcrt/an145-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen1-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen2-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen3-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen4-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen5-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen6-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen7-mcrt-b.sph
an4/wav/an4_clstk/mcrt/cen8-mcrt-b.sph
an4/wav/an4_clstk/mcsc/
an4/wav/an4_clstk/mcsc/an231-mcsc-b.sph
an4/wav/an4_clstk/mcsc/an232-mcsc-b.sph
an4/wav/an4_clstk/mcsc/an233-mcsc-b.sph
an4/wav/an4_clstk/mcsc/an234-mcsc-b.sph
an4/wav/an4_clstk/mcsc/an235-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen1-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen2-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen3-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen4-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen5-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen6-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen7-mcsc-b.sph
an4/wav/an4_clstk/mcsc/cen8-mcsc-b.sph
an4/wav/an4_clstk/mdcs/
an4/wav/an4_clstk/mdcs/an241-mdcs-b.sph
an4/wav/an4_clstk/mdcs/an242-mdcs-b.sph
an4/wav/an4_clstk/mdcs/an243-mdcs-b.sph
an4/wav/an4_clstk/mdcs/an244-mdcs-b.sph
an4/wav/an4_clstk/mdcs/an245-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen1-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen2-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen3-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen4-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen5-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen6-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen7-mdcs-b.sph
an4/wav/an4_clstk/mdcs/cen8-mdcs-b.sph
an4/wav/an4_clstk/mdcs2/
an4/wav/an4_clstk/mdcs2/an111-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/an112-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/an113-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/an114-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/an115-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen1-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen2-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen3-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen4-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen5-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen6-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen7-mdcs2-b.sph
an4/wav/an4_clstk/mdcs2/cen8-mdcs2-b.sph
an4/wav/an4_clstk/mdmc/
an4/wav/an4_clstk/mdmc/an206-mdmc-b.sph
an4/wav/an4_clstk/mdmc/an207-mdmc-b.sph
an4/wav/an4_clstk/mdmc/an208-mdmc-b.sph
an4/wav/an4_clstk/mdmc/an209-mdmc-b.sph
an4/wav/an4_clstk/mdmc/an210-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen1-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen2-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen3-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen4-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen5-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen6-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen7-mdmc-b.sph
an4/wav/an4_clstk/mdmc/cen8-mdmc-b.sph
an4/wav/an4_clstk/mdxn/
an4/wav/an4_clstk/mdxn/an106-mdxn-b.sph
an4/wav/an4_clstk/mdxn/an107-mdxn-b.sph
an4/wav/an4_clstk/mdxn/an108-mdxn-b.sph
an4/wav/an4_clstk/mdxn/an109-mdxn-b.sph
an4/wav/an4_clstk/mdxn/an110-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen1-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen2-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen3-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen4-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen5-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen6-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen7-mdxn-b.sph
an4/wav/an4_clstk/mdxn/cen8-mdxn-b.sph
an4/wav/an4_clstk/mdxs/
an4/wav/an4_clstk/mdxs/an26-mdxs-b.sph
an4/wav/an4_clstk/mdxs/an27-mdxs-b.sph
an4/wav/an4_clstk/mdxs/an28-mdxs-b.sph
an4/wav/an4_clstk/mdxs/an29-mdxs-b.sph
an4/wav/an4_clstk/mdxs/an30-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen1-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen2-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen3-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen4-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen5-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen6-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen7-mdxs-b.sph
an4/wav/an4_clstk/mdxs/cen8-mdxs-b.sph
an4/wav/an4_clstk/meab/
an4/wav/an4_clstk/meab/an136-meab-b.sph
an4/wav/an4_clstk/meab/an137-meab-b.sph
an4/wav/an4_clstk/meab/an138-meab-b.sph
an4/wav/an4_clstk/meab/an139-meab-b.sph
an4/wav/an4_clstk/meab/an140-meab-b.sph
an4/wav/an4_clstk/meab/cen1-meab-b.sph
an4/wav/an4_clstk/meab/cen2-meab-b.sph
an4/wav/an4_clstk/meab/cen3-meab-b.sph
an4/wav/an4_clstk/meab/cen4-meab-b.sph
an4/wav/an4_clstk/meab/cen5-meab-b.sph
an4/wav/an4_clstk/meab/cen6-meab-b.sph
an4/wav/an4_clstk/meab/cen7-meab-b.sph
an4/wav/an4_clstk/meab/cen8-meab-b.sph
an4/wav/an4_clstk/meht/
an4/wav/an4_clstk/meht/an66-meht-b.sph
an4/wav/an4_clstk/meht/an67-meht-b.sph
an4/wav/an4_clstk/meht/an68-meht-b.sph
an4/wav/an4_clstk/meht/an69-meht-b.sph
an4/wav/an4_clstk/meht/an70-meht-b.sph
an4/wav/an4_clstk/meht/cen1-meht-b.sph
an4/wav/an4_clstk/meht/cen2-meht-b.sph
an4/wav/an4_clstk/meht/cen3-meht-b.sph
an4/wav/an4_clstk/meht/cen4-meht-b.sph
an4/wav/an4_clstk/meht/cen5-meht-b.sph
an4/wav/an4_clstk/meht/cen6-meht-b.sph
an4/wav/an4_clstk/meht/cen7-meht-b.sph
an4/wav/an4_clstk/meht/cen8-meht-b.sph
an4/wav/an4_clstk/mema/
an4/wav/an4_clstk/mema/an286-mema-b.sph
an4/wav/an4_clstk/mema/an287-mema-b.sph
an4/wav/an4_clstk/mema/an288-mema-b.sph
an4/wav/an4_clstk/mema/an289-mema-b.sph
an4/wav/an4_clstk/mema/an290-mema-b.sph
an4/wav/an4_clstk/mema/cen1-mema-b.sph
an4/wav/an4_clstk/mema/cen2-mema-b.sph
an4/wav/an4_clstk/mema/cen3-mema-b.sph
an4/wav/an4_clstk/mema/cen4-mema-b.sph
an4/wav/an4_clstk/mema/cen5-mema-b.sph
an4/wav/an4_clstk/mema/cen6-mema-b.sph
an4/wav/an4_clstk/mema/cen7-mema-b.sph
an4/wav/an4_clstk/mema/cen8-mema-b.sph
an4/wav/an4_clstk/mewl/
an4/wav/an4_clstk/mewl/an256-mewl-b.sph
an4/wav/an4_clstk/mewl/an257-mewl-b.sph
an4/wav/an4_clstk/mewl/an258-mewl-b.sph
an4/wav/an4_clstk/mewl/an259-mewl-b.sph
an4/wav/an4_clstk/mewl/an260-mewl-b.sph
an4/wav/an4_clstk/mewl/cen1-mewl-b.sph
an4/wav/an4_clstk/mewl/cen2-mewl-b.sph
an4/wav/an4_clstk/mewl/cen3-mewl-b.sph
an4/wav/an4_clstk/mewl/cen4-mewl-b.sph
an4/wav/an4_clstk/mewl/cen5-mewl-b.sph
an4/wav/an4_clstk/mewl/cen6-mewl-b.sph
an4/wav/an4_clstk/mewl/cen7-mewl-b.sph
an4/wav/an4_clstk/mewl/cen8-mewl-b.sph
an4/wav/an4_clstk/mfaa/
an4/wav/an4_clstk/mfaa/an161-mfaa-b.sph
an4/wav/an4_clstk/mfaa/an162-mfaa-b.sph
an4/wav/an4_clstk/mfaa/an163-mfaa-b.sph
an4/wav/an4_clstk/mfaa/an164-mfaa-b.sph
an4/wav/an4_clstk/mfaa/an165-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen1-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen2-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen3-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen4-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen5-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen6-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen7-mfaa-b.sph
an4/wav/an4_clstk/mfaa/cen8-mfaa-b.sph
an4/wav/an4_clstk/mgah/
an4/wav/an4_clstk/mgah/an196-mgah-b.sph
an4/wav/an4_clstk/mgah/an197-mgah-b.sph
an4/wav/an4_clstk/mgah/an198-mgah-b.sph
an4/wav/an4_clstk/mgah/an199-mgah-b.sph
an4/wav/an4_clstk/mgah/an200-mgah-b.sph
an4/wav/an4_clstk/mgah/cen1-mgah-b.sph
an4/wav/an4_clstk/mgah/cen2-mgah-b.sph
an4/wav/an4_clstk/mgah/cen3-mgah-b.sph
an4/wav/an4_clstk/mgah/cen4-mgah-b.sph
an4/wav/an4_clstk/mgah/cen5-mgah-b.sph
an4/wav/an4_clstk/mgah/cen6-mgah-b.sph
an4/wav/an4_clstk/mgah/cen7-mgah-b.sph
an4/wav/an4_clstk/mgah/cen8-mgah-b.sph
an4/wav/an4_clstk/mjbh/
an4/wav/an4_clstk/mjbh/an246-mjbh-b.sph
an4/wav/an4_clstk/mjbh/an247-mjbh-b.sph
an4/wav/an4_clstk/mjbh/an248-mjbh-b.sph
an4/wav/an4_clstk/mjbh/an249-mjbh-b.sph
an4/wav/an4_clstk/mjbh/an250-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen1-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen2-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen3-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen4-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen5-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen6-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen7-mjbh-b.sph
an4/wav/an4_clstk/mjbh/cen8-mjbh-b.sph
an4/wav/an4_clstk/mjda/
an4/wav/an4_clstk/mjda/an171-mjda-b.sph
an4/wav/an4_clstk/mjda/an172-mjda-b.sph
an4/wav/an4_clstk/mjda/an173-mjda-b.sph
an4/wav/an4_clstk/mjda/an174-mjda-b.sph
an4/wav/an4_clstk/mjda/an175-mjda-b.sph
an4/wav/an4_clstk/mjda/cen1-mjda-b.sph
an4/wav/an4_clstk/mjda/cen2-mjda-b.sph
an4/wav/an4_clstk/mjda/cen3-mjda-b.sph
an4/wav/an4_clstk/mjda/cen4-mjda-b.sph
an4/wav/an4_clstk/mjda/cen5-mjda-b.sph
an4/wav/an4_clstk/mjda/cen6-mjda-b.sph
an4/wav/an4_clstk/mjda/cen7-mjda-b.sph
an4/wav/an4_clstk/mjda/cen8-mjda-b.sph
an4/wav/an4_clstk/mjdr/
an4/wav/an4_clstk/mjdr/an16-mjdr-b.sph
an4/wav/an4_clstk/mjdr/an18-mjdr-b.sph
an4/wav/an4_clstk/mjdr/an19-mjdr-b.sph
an4/wav/an4_clstk/mjdr/an20-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen1-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen2-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen3-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen4-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen5-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen6-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen7-mjdr-b.sph
an4/wav/an4_clstk/mjdr/cen8-mjdr-b.sph
an4/wav/an4_clstk/mjes/
an4/wav/an4_clstk/mjes/an156-mjes-b.sph
an4/wav/an4_clstk/mjes/an157-mjes-b.sph
an4/wav/an4_clstk/mjes/an158-mjes-b.sph
an4/wav/an4_clstk/mjes/an159-mjes-b.sph
an4/wav/an4_clstk/mjes/an160-mjes-b.sph
an4/wav/an4_clstk/mjes/cen1-mjes-b.sph
an4/wav/an4_clstk/mjes/cen2-mjes-b.sph
an4/wav/an4_clstk/mjes/cen3-mjes-b.sph
an4/wav/an4_clstk/mjes/cen4-mjes-b.sph
an4/wav/an4_clstk/mjes/cen5-mjes-b.sph
an4/wav/an4_clstk/mjes/cen6-mjes-b.sph
an4/wav/an4_clstk/mjes/cen7-mjes-b.sph
an4/wav/an4_clstk/mjes/cen8-mjes-b.sph
an4/wav/an4_clstk/mjgk/
an4/wav/an4_clstk/mjgk/an216-mjgk-b.sph
an4/wav/an4_clstk/mjgk/an217-mjgk-b.sph
an4/wav/an4_clstk/mjgk/an218-mjgk-b.sph
an4/wav/an4_clstk/mjgk/an219-mjgk-b.sph
an4/wav/an4_clstk/mjgk/an220-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen1-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen2-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen4-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen5-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen6-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen7-mjgk-b.sph
an4/wav/an4_clstk/mjgk/cen8-mjgk-b.sph
an4/wav/an4_clstk/mjhp/
an4/wav/an4_clstk/mjhp/an56-mjhp-b.sph
an4/wav/an4_clstk/mjhp/an57-mjhp-b.sph
an4/wav/an4_clstk/mjhp/an58-mjhp-b.sph
an4/wav/an4_clstk/mjhp/an59-mjhp-b.sph
an4/wav/an4_clstk/mjhp/an60-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen1-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen2-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen3-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen4-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen5-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen6-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen7-mjhp-b.sph
an4/wav/an4_clstk/mjhp/cen8-mjhp-b.sph
an4/wav/an4_clstk/mjjs2/
an4/wav/an4_clstk/mjjs2/an176-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/an177-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/an178-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/an179-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/an180-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen1-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen2-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen4-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen5-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen6-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen7-mjjs2-b.sph
an4/wav/an4_clstk/mjjs2/cen8-mjjs2-b.sph
an4/wav/an4_clstk/mkdb/
an4/wav/an4_clstk/mkdb/an316-mkdb-b.sph
an4/wav/an4_clstk/mkdb/an317-mkdb-b.sph
an4/wav/an4_clstk/mkdb/an318-mkdb-b.sph
an4/wav/an4_clstk/mkdb/an319-mkdb-b.sph
an4/wav/an4_clstk/mkdb/an320-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen1-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen2-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen3-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen4-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen5-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen6-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen7-mkdb-b.sph
an4/wav/an4_clstk/mkdb/cen8-mkdb-b.sph
an4/wav/an4_clstk/mkem/
an4/wav/an4_clstk/mkem/an186-mkem-b.sph
an4/wav/an4_clstk/mkem/an187-mkem-b.sph
an4/wav/an4_clstk/mkem/an188-mkem-b.sph
an4/wav/an4_clstk/mkem/an189-mkem-b.sph
an4/wav/an4_clstk/mkem/an190-mkem-b.sph
an4/wav/an4_clstk/mkem/cen1-mkem-b.sph
an4/wav/an4_clstk/mkem/cen2-mkem-b.sph
an4/wav/an4_clstk/mkem/cen4-mkem-b.sph
an4/wav/an4_clstk/mkem/cen5-mkem-b.sph
an4/wav/an4_clstk/mkem/cen6-mkem-b.sph
an4/wav/an4_clstk/mkem/cen7-mkem-b.sph
an4/wav/an4_clstk/mkem/cen8-mkem-b.sph
an4/wav/an4_clstk/mmaf/
an4/wav/an4_clstk/mmaf/an51-mmaf-b.sph
an4/wav/an4_clstk/mmaf/an52-mmaf-b.sph
an4/wav/an4_clstk/mmaf/an53-mmaf-b.sph
an4/wav/an4_clstk/mmaf/an54-mmaf-b.sph
an4/wav/an4_clstk/mmaf/an55-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen1-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen2-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen3-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen4-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen5-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen6-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen7-mmaf-b.sph
an4/wav/an4_clstk/mmaf/cen8-mmaf-b.sph
an4/wav/an4_clstk/mmal/
an4/wav/an4_clstk/mmal/an321-mmal-b.sph
an4/wav/an4_clstk/mmal/an322-mmal-b.sph
an4/wav/an4_clstk/mmal/an323-mmal-b.sph
an4/wav/an4_clstk/mmal/an324-mmal-b.sph
an4/wav/an4_clstk/mmal/an325-mmal-b.sph
an4/wav/an4_clstk/mmal/cen1-mmal-b.sph
an4/wav/an4_clstk/mmal/cen2-mmal-b.sph
an4/wav/an4_clstk/mmal/cen3-mmal-b.sph
an4/wav/an4_clstk/mmal/cen4-mmal-b.sph
an4/wav/an4_clstk/mmal/cen5-mmal-b.sph
an4/wav/an4_clstk/mmal/cen6-mmal-b.sph
an4/wav/an4_clstk/mmal/cen7-mmal-b.sph
an4/wav/an4_clstk/mmal/cen8-mmal-b.sph
an4/wav/an4_clstk/mmap/
an4/wav/an4_clstk/mmap/an46-mmap-b.sph
an4/wav/an4_clstk/mmap/an47-mmap-b.sph
an4/wav/an4_clstk/mmap/an48-mmap-b.sph
an4/wav/an4_clstk/mmap/an49-mmap-b.sph
an4/wav/an4_clstk/mmap/an50-mmap-b.sph
an4/wav/an4_clstk/mmap/cen1-mmap-b.sph
an4/wav/an4_clstk/mmap/cen2-mmap-b.sph
an4/wav/an4_clstk/mmap/cen3-mmap-b.sph
an4/wav/an4_clstk/mmap/cen4-mmap-b.sph
an4/wav/an4_clstk/mmap/cen5-mmap-b.sph
an4/wav/an4_clstk/mmap/cen6-mmap-b.sph
an4/wav/an4_clstk/mmap/cen7-mmap-b.sph
an4/wav/an4_clstk/mmap/cen8-mmap-b.sph
an4/wav/an4_clstk/mmdg/
an4/wav/an4_clstk/mmdg/an336-mmdg-b.sph
an4/wav/an4_clstk/mmdg/an337-mmdg-b.sph
an4/wav/an4_clstk/mmdg/an338-mmdg-b.sph
an4/wav/an4_clstk/mmdg/an339-mmdg-b.sph
an4/wav/an4_clstk/mmdg/an340-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen1-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen2-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen3-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen4-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen5-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen6-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen7-mmdg-b.sph
an4/wav/an4_clstk/mmdg/cen8-mmdg-b.sph
an4/wav/an4_clstk/mmkw/
an4/wav/an4_clstk/mmkw/an361-mmkw-b.sph
an4/wav/an4_clstk/mmkw/an362-mmkw-b.sph
an4/wav/an4_clstk/mmkw/an363-mmkw-b.sph
an4/wav/an4_clstk/mmkw/an364-mmkw-b.sph
an4/wav/an4_clstk/mmkw/an365-mmkw-b.sph
an4/wav/an4_clstk/mmkw/an59-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen1-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen2-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen3-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen4-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen5-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen6-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen7-mmkw-b.sph
an4/wav/an4_clstk/mmkw/cen8-mmkw-b.sph
an4/wav/an4_clstk/mmsh/
an4/wav/an4_clstk/mmsh/an381-mmsh-b.sph
an4/wav/an4_clstk/mmsh/an382-mmsh-b.sph
an4/wav/an4_clstk/mmsh/an383-mmsh-b.sph
an4/wav/an4_clstk/mmsh/an384-mmsh-b.sph
an4/wav/an4_clstk/mmsh/an385-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen1-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen2-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen3-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen4-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen5-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen6-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen7-mmsh-b.sph
an4/wav/an4_clstk/mmsh/cen8-mmsh-b.sph
an4/wav/an4_clstk/mmtm/
an4/wav/an4_clstk/mmtm/an386-mmtm-b.sph
an4/wav/an4_clstk/mmtm/an387-mmtm-b.sph
an4/wav/an4_clstk/mmtm/an388-mmtm-b.sph
an4/wav/an4_clstk/mmtm/an389-mmtm-b.sph
an4/wav/an4_clstk/mmtm/an390-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen1-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen2-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen3-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen4-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen5-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen6-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen7-mmtm-b.sph
an4/wav/an4_clstk/mmtm/cen8-mmtm-b.sph
an4/wav/an4_clstk/mnfe/
an4/wav/an4_clstk/mnfe/an346-mnfe-b.sph
an4/wav/an4_clstk/mnfe/an347-mnfe-b.sph
an4/wav/an4_clstk/mnfe/an348-mnfe-b.sph
an4/wav/an4_clstk/mnfe/an349-mnfe-b.sph
an4/wav/an4_clstk/mnfe/an350-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen1-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen2-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen3-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen4-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen5-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen6-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen7-mnfe-b.sph
an4/wav/an4_clstk/mnfe/cen8-mnfe-b.sph
an4/wav/an4_clstk/mnjl/
an4/wav/an4_clstk/mnjl/an81-mnjl-b.sph
an4/wav/an4_clstk/mnjl/an82-mnjl-b.sph
an4/wav/an4_clstk/mnjl/an83-mnjl-b.sph
an4/wav/an4_clstk/mnjl/an84-mnjl-b.sph
an4/wav/an4_clstk/mnjl/an85-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen1-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen2-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen3-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen5-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen6-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen7-mnjl-b.sph
an4/wav/an4_clstk/mnjl/cen8-mnjl-b.sph
an4/wav/an4_clstk/mrab/
an4/wav/an4_clstk/mrab/an71-mrab-b.sph
an4/wav/an4_clstk/mrab/an72-mrab-b.sph
an4/wav/an4_clstk/mrab/an73-mrab-b.sph
an4/wav/an4_clstk/mrab/an74-mrab-b.sph
an4/wav/an4_clstk/mrab/an75-mrab-b.sph
an4/wav/an4_clstk/mrab/cen1-mrab-b.sph
an4/wav/an4_clstk/mrab/cen2-mrab-b.sph
an4/wav/an4_clstk/mrab/cen3-mrab-b.sph
an4/wav/an4_clstk/mrab/cen4-mrab-b.sph
an4/wav/an4_clstk/mrab/cen5-mrab-b.sph
an4/wav/an4_clstk/mrab/cen6-mrab-b.sph
an4/wav/an4_clstk/mrab/cen7-mrab-b.sph
an4/wav/an4_clstk/mrab/cen8-mrab-b.sph
an4/wav/an4_clstk/mrcb/
an4/wav/an4_clstk/mrcb/an101-mrcb-b.sph
an4/wav/an4_clstk/mrcb/an102-mrcb-b.sph
an4/wav/an4_clstk/mrcb/an103-mrcb-b.sph
an4/wav/an4_clstk/mrcb/an104-mrcb-b.sph
an4/wav/an4_clstk/mrcb/an105-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen1-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen2-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen3-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen4-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen5-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen6-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen7-mrcb-b.sph
an4/wav/an4_clstk/mrcb/cen8-mrcb-b.sph
an4/wav/an4_clstk/mrjc2/
an4/wav/an4_clstk/mrjc2/an236-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/an237-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/an238-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/an239-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/an240-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen1-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen2-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen3-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen4-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen5-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen6-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen7-mrjc2-b.sph
an4/wav/an4_clstk/mrjc2/cen8-mrjc2-b.sph
an4/wav/an4_clstk/mrmg/
an4/wav/an4_clstk/mrmg/an281-mrmg-b.sph
an4/wav/an4_clstk/mrmg/an282-mrmg-b.sph
an4/wav/an4_clstk/mrmg/an283-mrmg-b.sph
an4/wav/an4_clstk/mrmg/an284-mrmg-b.sph
an4/wav/an4_clstk/mrmg/an285-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen1-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen2-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen3-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen4-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen5-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen6-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen7-mrmg-b.sph
an4/wav/an4_clstk/mrmg/cen8-mrmg-b.sph
an4/wav/an4_clstk/mscg2/
an4/wav/an4_clstk/mscg2/an356-mscg2-b.sph
an4/wav/an4_clstk/mscg2/an357-mscg2-b.sph
an4/wav/an4_clstk/mscg2/an358-mscg2-b.sph
an4/wav/an4_clstk/mscg2/an359-mscg2-b.sph
an4/wav/an4_clstk/mscg2/an360-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen1-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen2-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen3-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen4-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen5-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen6-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen7-mscg2-b.sph
an4/wav/an4_clstk/mscg2/cen8-mscg2-b.sph
an4/wav/an4_clstk/msct/
an4/wav/an4_clstk/msct/an341-msct-b.sph
an4/wav/an4_clstk/msct/an342-msct-b.sph
an4/wav/an4_clstk/msct/an343-msct-b.sph
an4/wav/an4_clstk/msct/an344-msct-b.sph
an4/wav/an4_clstk/msct/an345-msct-b.sph
an4/wav/an4_clstk/msct/cen1-msct-b.sph
an4/wav/an4_clstk/msct/cen2-msct-b.sph
an4/wav/an4_clstk/msct/cen3-msct-b.sph
an4/wav/an4_clstk/msct/cen4-msct-b.sph
an4/wav/an4_clstk/msct/cen5-msct-b.sph
an4/wav/an4_clstk/msct/cen6-msct-b.sph
an4/wav/an4_clstk/msct/cen7-msct-b.sph
an4/wav/an4_clstk/msct/cen8-msct-b.sph
an4/wav/an4_clstk/msjm/
an4/wav/an4_clstk/msjm/an226-msjm-b.sph
an4/wav/an4_clstk/msjm/an227-msjm-b.sph
an4/wav/an4_clstk/msjm/an228-msjm-b.sph
an4/wav/an4_clstk/msjm/an229-msjm-b.sph
an4/wav/an4_clstk/msjm/an230-msjm-b.sph
an4/wav/an4_clstk/msjm/cen1-msjm-b.sph
an4/wav/an4_clstk/msjm/cen2-msjm-b.sph
an4/wav/an4_clstk/msjm/cen3-msjm-b.sph
an4/wav/an4_clstk/msjm/cen4-msjm-b.sph
an4/wav/an4_clstk/msjm/cen5-msjm-b.sph
an4/wav/an4_clstk/msjm/cen6-msjm-b.sph
an4/wav/an4_clstk/msjm/cen7-msjm-b.sph
an4/wav/an4_clstk/msjm/cen8-msjm-b.sph
an4/wav/an4_clstk/msjr/
an4/wav/an4_clstk/msjr/an351-msjr-b.sph
an4/wav/an4_clstk/msjr/an352-msjr-b.sph
an4/wav/an4_clstk/msjr/an353-msjr-b.sph
an4/wav/an4_clstk/msjr/an354-msjr-b.sph
an4/wav/an4_clstk/msjr/an355-msjr-b.sph
an4/wav/an4_clstk/msjr/cen1-msjr-b.sph
an4/wav/an4_clstk/msjr/cen2-msjr-b.sph
an4/wav/an4_clstk/msjr/cen3-msjr-b.sph
an4/wav/an4_clstk/msjr/cen4-msjr-b.sph
an4/wav/an4_clstk/msjr/cen5-msjr-b.sph
an4/wav/an4_clstk/msjr/cen6-msjr-b.sph
an4/wav/an4_clstk/msjr/cen7-msjr-b.sph
an4/wav/an4_clstk/msjr/cen8-msjr-b.sph
an4/wav/an4_clstk/mskh/
an4/wav/an4_clstk/mskh/an100-mskh-b.sph
an4/wav/an4_clstk/mskh/an96-mskh-b.sph
an4/wav/an4_clstk/mskh/an97-mskh-b.sph
an4/wav/an4_clstk/mskh/an98-mskh-b.sph
an4/wav/an4_clstk/mskh/an99-mskh-b.sph
an4/wav/an4_clstk/mskh/cen1-mskh-b.sph
an4/wav/an4_clstk/mskh/cen2-mskh-b.sph
an4/wav/an4_clstk/mskh/cen3-mskh-b.sph
an4/wav/an4_clstk/mskh/cen4-mskh-b.sph
an4/wav/an4_clstk/mskh/cen5-mskh-b.sph
an4/wav/an4_clstk/mskh/cen6-mskh-b.sph
an4/wav/an4_clstk/mskh/cen7-mskh-b.sph
an4/wav/an4_clstk/mskh/cen8-mskh-b.sph
an4/wav/an4_clstk/msmn/
an4/wav/an4_clstk/msmn/an276-msmn-b.sph
an4/wav/an4_clstk/msmn/an277-msmn-b.sph
an4/wav/an4_clstk/msmn/an278-msmn-b.sph
an4/wav/an4_clstk/msmn/an279-msmn-b.sph
an4/wav/an4_clstk/msmn/cen4-msmn-b.sph
an4/wav/an4_clstk/msmn/cen5-msmn-b.sph
an4/wav/an4_clstk/msmn/cen6-msmn-b.sph
an4/wav/an4_clstk/msmn/cen7-msmn-b.sph
an4/wav/an4_clstk/msmn/cen8-msmn-b.sph
an4/wav/an4_clstk/msrb/
an4/wav/an4_clstk/msrb/an331-msrb-b.sph
an4/wav/an4_clstk/msrb/an332-msrb-b.sph
an4/wav/an4_clstk/msrb/an333-msrb-b.sph
an4/wav/an4_clstk/msrb/an334-msrb-b.sph
an4/wav/an4_clstk/msrb/an335-msrb-b.sph
an4/wav/an4_clstk/msrb/cen1-msrb-b.sph
an4/wav/an4_clstk/msrb/cen2-msrb-b.sph
an4/wav/an4_clstk/msrb/cen3-msrb-b.sph
an4/wav/an4_clstk/msrb/cen4-msrb-b.sph
an4/wav/an4_clstk/msrb/cen5-msrb-b.sph
an4/wav/an4_clstk/msrb/cen6-msrb-b.sph
an4/wav/an4_clstk/msrb/cen7-msrb-b.sph
an4/wav/an4_clstk/msrb/cen8-msrb-b.sph
an4/wav/an4_clstk/mtcv/
an4/wav/an4_clstk/mtcv/an301-mtcv-b.sph
an4/wav/an4_clstk/mtcv/an302-mtcv-b.sph
an4/wav/an4_clstk/mtcv/an303-mtcv-b.sph
an4/wav/an4_clstk/mtcv/an304-mtcv-b.sph
an4/wav/an4_clstk/mtcv/an305-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen1-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen2-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen3-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen4-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen5-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen6-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen7-mtcv-b.sph
an4/wav/an4_clstk/mtcv/cen8-mtcv-b.sph
an4/wav/an4_clstk/mtje/
an4/wav/an4_clstk/mtje/an31-mtje-b.sph
an4/wav/an4_clstk/mtje/an32-mtje-b.sph
an4/wav/an4_clstk/mtje/an33-mtje-b.sph
an4/wav/an4_clstk/mtje/an34-mtje-b.sph
an4/wav/an4_clstk/mtje/an35-mtje-b.sph
an4/wav/an4_clstk/mtje/cen1-mtje-b.sph
an4/wav/an4_clstk/mtje/cen2-mtje-b.sph
an4/wav/an4_clstk/mtje/cen3-mtje-b.sph
an4/wav/an4_clstk/mtje/cen4-mtje-b.sph
an4/wav/an4_clstk/mtje/cen5-mtje-b.sph
an4/wav/an4_clstk/mtje/cen6-mtje-b.sph
an4/wav/an4_clstk/mtje/cen7-mtje-b.sph
an4/wav/an4_clstk/mtje/cen8-mtje-b.sph
an4/wav/an4_clstk/mtos/
an4/wav/an4_clstk/mtos/an366-mtos-b.sph
an4/wav/an4_clstk/mtos/an367-mtos-b.sph
an4/wav/an4_clstk/mtos/an368-mtos-b.sph
an4/wav/an4_clstk/mtos/an369-mtos-b.sph
an4/wav/an4_clstk/mtos/an370-mtos-b.sph
an4/wav/an4_clstk/mtos/cen1-mtos-b.sph
an4/wav/an4_clstk/mtos/cen2-mtos-b.sph
an4/wav/an4_clstk/mtos/cen3-mtos-b.sph
an4/wav/an4_clstk/mtos/cen4-mtos-b.sph
an4/wav/an4_clstk/mtos/cen5-mtos-b.sph
an4/wav/an4_clstk/mtos/cen6-mtos-b.sph
an4/wav/an4_clstk/mtos/cen7-mtos-b.sph
an4/wav/an4_clstk/mtos/cen8-mtos-b.sph
an4/wav/an4_clstk/mtxj/
an4/wav/an4_clstk/mtxj/an376-mtxj-b.sph
an4/wav/an4_clstk/mtxj/an377-mtxj-b.sph
an4/wav/an4_clstk/mtxj/an378-mtxj-b.sph
an4/wav/an4_clstk/mtxj/an379-mtxj-b.sph
an4/wav/an4_clstk/mtxj/an380-mtxj-b.sph
an4/wav/an4_clstk/mtxj/cen1-mtxj-b.sph
an4/wav/an4_clstk/mtxj/cen2-mtxj-b.sph
an4/wav/an4_clstk/mtxj/cen3-mtxj-b.sph
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an4/LICENSE
local/download_and_untar.sh: Successfully downloaded and un-tarred ./downloads/an4_sphere.tar.gz
2022-09-19T21:08:44 (data.sh:49:main) stage 2: Data preparation
utils/subset_data_dir.sh: reducing #utt from 948 to 100
utils/subset_data_dir.sh: reducing #utt from 948 to 848
2022-09-19T21:08:44 (data.sh:72:main) Successfully finished. [elapsed=4s]
2022-09-19T21:08:44 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:08:44 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:08:44 (asr.sh:1558:main) Successfully finished. [elapsed=5s]

After this stage is finished, please check the newly created data directory:

[ ]:
!ls data
test  train  train_dev  train_nodev

In this recipe, we use train_nodev as a training set, train_dev as a validation set (monitor the training progress by checking the validation score). We also use test and train_dev sets for the final speech recognition evaluation.

Let’s check one of the training data directories:

[ ]:
!ls -1 data/train_nodev/
spk2utt
text
utt2spk
wav.scp

These are the speech and corresponding text and speaker information in the Kaldi format. To understand their meanings, please check https://github.com/espnet/espnet/tree/master/egs2/TEMPLATE#about-kaldi-style-data-directory.

Please also check the official documentation of Kaldi: https://kaldi-asr.org/doc/data_prep.html

spk2utt # Speaker information
text    # Transcription file
utt2spk # Speaker information
wav.scp # Audio file

Stage 2: Speed perturbation (one of the data augmentation methods)

We do not use speed perturbation for this demo. But you can turn it on by adding an argument --speed_perturb_factors "0.9 1.0 1.1" to the shell script.

Note that we perform speed perturbation and save the augmented data in the disk before training. Another approach is to perform data augmentation during training, such as SpecAug.

[ ]:
!./asr.sh --stage 2 --stop_stage 2 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test"
2022-09-19T21:12:33 (asr.sh:254:main) ./asr.sh --stage 2 --stop_stage 2 --train_set train_nodev --valid_set train_dev --test_sets train_dev test
2022-09-19T21:12:34 (asr.sh:464:main) Skip stage 2: Speed perturbation
2022-09-19T21:12:34 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:12:34 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:12:34 (asr.sh:1558:main) Successfully finished. [elapsed=1s]

Stage 3: Format wav.scp: data/ -> dump/raw

We dump the data with specified format (flac in this case) for the efficient use of the data.

# ====== Recreating "wav.scp" ======
# Kaldi-wav.scp, which can describe the file path with unix-pipe, like "cat /some/path |",
# shouldn't be used in training process.
# "format_wav_scp.sh" dumps such pipe-style-wav to real audio file
# and it can also change the audio-format and sampling rate.
# If nothing is need, then format_wav_scp.sh does nothing:
# i.e. the input file format and rate is same as the output.

Note that --nj <N> means the number of CPU jobs. Please set it appropriately by considering your CPU resources and disk access.

[ ]:
# 25 seconds
!./asr.sh --stage 3 --stop_stage 3 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test" --nj 4
2022-09-19T21:13:14 (asr.sh:254:main) ./asr.sh --stage 3 --stop_stage 3 --train_set train_nodev --valid_set train_dev --test_sets train_dev test --nj 4
2022-09-19T21:13:15 (asr.sh:474:main) Stage 3: Format wav.scp: data/ -> dump/raw
utils/copy_data_dir.sh: copied data from data/train_nodev to dump/raw/org/train_nodev
utils/validate_data_dir.sh: Successfully validated data-directory dump/raw/org/train_nodev
2022-09-19T21:13:15 (format_wav_scp.sh:42:main) scripts/audio/format_wav_scp.sh --nj 4 --cmd run.pl --audio-format flac --fs 16k data/train_nodev/wav.scp dump/raw/org/train_nodev
2022-09-19T21:13:15 (format_wav_scp.sh:110:main) [info]: without segments
2022-09-19T21:13:24 (format_wav_scp.sh:142:main) Successfully finished. [elapsed=9s]
utils/copy_data_dir.sh: copied data from data/train_dev to dump/raw/org/train_dev
utils/validate_data_dir.sh: Successfully validated data-directory dump/raw/org/train_dev
2022-09-19T21:13:24 (format_wav_scp.sh:42:main) scripts/audio/format_wav_scp.sh --nj 4 --cmd run.pl --audio-format flac --fs 16k data/train_dev/wav.scp dump/raw/org/train_dev
2022-09-19T21:13:25 (format_wav_scp.sh:110:main) [info]: without segments
2022-09-19T21:13:27 (format_wav_scp.sh:142:main) Successfully finished. [elapsed=3s]
utils/copy_data_dir.sh: copied data from data/train_dev to dump/raw/org/train_dev
utils/validate_data_dir.sh: Successfully validated data-directory dump/raw/org/train_dev
2022-09-19T21:13:27 (format_wav_scp.sh:42:main) scripts/audio/format_wav_scp.sh --nj 4 --cmd run.pl --audio-format flac --fs 16k data/train_dev/wav.scp dump/raw/org/train_dev
2022-09-19T21:13:28 (format_wav_scp.sh:110:main) [info]: without segments
2022-09-19T21:13:30 (format_wav_scp.sh:142:main) Successfully finished. [elapsed=3s]
utils/copy_data_dir.sh: copied data from data/test to dump/raw/test
utils/validate_data_dir.sh: Successfully validated data-directory dump/raw/test
2022-09-19T21:13:30 (format_wav_scp.sh:42:main) scripts/audio/format_wav_scp.sh --nj 4 --cmd run.pl --audio-format flac --fs 16k data/test/wav.scp dump/raw/test
2022-09-19T21:13:31 (format_wav_scp.sh:110:main) [info]: without segments
2022-09-19T21:13:33 (format_wav_scp.sh:142:main) Successfully finished. [elapsed=3s]
2022-09-19T21:13:33 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:13:33 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:13:33 (asr.sh:1558:main) Successfully finished. [elapsed=19s]

Stage 4: Remove long/short data: dump/raw/org -> dump/raw

Too long and too short audio data are harmful for efficient training. Those utterances are removed for training. But for inference and scoring, we still use the full data, which is important for fair comparison.

[ ]:
!./asr.sh --stage 4 --stop_stage 4 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test"
2022-09-19T21:14:42 (asr.sh:254:main) ./asr.sh --stage 4 --stop_stage 4 --train_set train_nodev --valid_set train_dev --test_sets train_dev test
2022-09-19T21:14:42 (asr.sh:576:main) Stage 4: Remove long/short data: dump/raw/org -> dump/raw
utils/copy_data_dir.sh: copied data from dump/raw/org/train_nodev to dump/raw/train_nodev
utils/validate_data_dir.sh: Successfully validated data-directory dump/raw/train_nodev
fix_data_dir.sh: kept all 848 utterances.
fix_data_dir.sh: old files are kept in dump/raw/train_nodev/.backup
utils/copy_data_dir.sh: copied data from dump/raw/org/train_dev to dump/raw/train_dev
utils/validate_data_dir.sh: Successfully validated data-directory dump/raw/train_dev
fix_data_dir.sh: kept all 100 utterances.
fix_data_dir.sh: old files are kept in dump/raw/train_dev/.backup
2022-09-19T21:14:44 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:14:44 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:14:44 (asr.sh:1558:main) Successfully finished. [elapsed=2s]

Stage 5: Generate token_list from dump/raw/train_nodev/text using BPE.

This is important for text processing. Here, we make a dictionary simply using the English characters. We use the sentencepiece toolkit developed by Google.

[ ]:
!./asr.sh --stage 5 --stop_stage 5 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test"
2022-09-19T21:15:15 (asr.sh:254:main) ./asr.sh --stage 5 --stop_stage 5 --train_set train_nodev --valid_set train_dev --test_sets train_dev test
2022-09-19T21:15:15 (asr.sh:641:main) Stage 5: Generate token_list from dump/raw/train_nodev/text using BPE
sentencepiece_trainer.cc(177) LOG(INFO) Running command: --input=data/token_list/bpe_unigram30/train.txt --vocab_size=30 --model_type=unigram --model_prefix=data/token_list/bpe_unigram30/bpe --character_coverage=1.0 --input_sentence_size=100000000
sentencepiece_trainer.cc(77) LOG(INFO) Starts training with :
trainer_spec {
  input: data/token_list/bpe_unigram30/train.txt
  input_format:
  model_prefix: data/token_list/bpe_unigram30/bpe
  model_type: UNIGRAM
  vocab_size: 30
  self_test_sample_size: 0
  character_coverage: 1
  input_sentence_size: 100000000
  shuffle_input_sentence: 1
  seed_sentencepiece_size: 1000000
  shrinking_factor: 0.75
  max_sentence_length: 4192
  num_threads: 16
  num_sub_iterations: 2
  max_sentencepiece_length: 16
  split_by_unicode_script: 1
  split_by_number: 1
  split_by_whitespace: 1
  split_digits: 0
  treat_whitespace_as_suffix: 0
  allow_whitespace_only_pieces: 0
  required_chars:
  byte_fallback: 0
  vocabulary_output_piece_score: 1
  train_extremely_large_corpus: 0
  hard_vocab_limit: 1
  use_all_vocab: 0
  unk_id: 0
  bos_id: 1
  eos_id: 2
  pad_id: -1
  unk_piece: <unk>
  bos_piece: <s>
  eos_piece: </s>
  pad_piece: <pad>
  unk_surface:  ⁇
  enable_differential_privacy: 0
  differential_privacy_noise_level: 0
  differential_privacy_clipping_threshold: 0
}
normalizer_spec {
  name: nmt_nfkc
  add_dummy_prefix: 1
  remove_extra_whitespaces: 1
  escape_whitespaces: 1
  normalization_rule_tsv:
}
denormalizer_spec {}
trainer_interface.cc(350) LOG(INFO) SentenceIterator is not specified. Using MultiFileSentenceIterator.
trainer_interface.cc(181) LOG(INFO) Loading corpus: data/token_list/bpe_unigram30/train.txt
trainer_interface.cc(406) LOG(INFO) Loaded all 848 sentences
trainer_interface.cc(422) LOG(INFO) Adding meta_piece: <unk>
trainer_interface.cc(422) LOG(INFO) Adding meta_piece: <s>
trainer_interface.cc(422) LOG(INFO) Adding meta_piece: </s>
trainer_interface.cc(427) LOG(INFO) Normalizing sentences...
trainer_interface.cc(536) LOG(INFO) all chars count=16787
trainer_interface.cc(557) LOG(INFO) Alphabet size=27
trainer_interface.cc(558) LOG(INFO) Final character coverage=1
trainer_interface.cc(590) LOG(INFO) Done! preprocessed 848 sentences.
unigram_model_trainer.cc(146) LOG(INFO) Making suffix array...
unigram_model_trainer.cc(150) LOG(INFO) Extracting frequent sub strings...
unigram_model_trainer.cc(201) LOG(INFO) Initialized 244 seed sentencepieces
trainer_interface.cc(596) LOG(INFO) Tokenizing input sentences with whitespace: 848
trainer_interface.cc(607) LOG(INFO) Done! 97
unigram_model_trainer.cc(491) LOG(INFO) Using 97 sentences for EM training
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=0 size=155 obj=5.99767 num_tokens=196 num_tokens/piece=1.26452
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=1 size=125 obj=4.96066 num_tokens=198 num_tokens/piece=1.584
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=0 size=93 obj=4.94096 num_tokens=221 num_tokens/piece=2.37634
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=1 size=93 obj=4.92289 num_tokens=221 num_tokens/piece=2.37634
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=0 size=69 obj=5.44566 num_tokens=284 num_tokens/piece=4.11594
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=1 size=69 obj=5.35755 num_tokens=284 num_tokens/piece=4.11594
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=0 size=51 obj=6.53936 num_tokens=331 num_tokens/piece=6.4902
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=1 size=51 obj=6.18849 num_tokens=331 num_tokens/piece=6.4902
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=0 size=38 obj=8.06918 num_tokens=385 num_tokens/piece=10.1316
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=1 size=38 obj=7.45295 num_tokens=385 num_tokens/piece=10.1316
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=0 size=33 obj=7.76108 num_tokens=412 num_tokens/piece=12.4848
unigram_model_trainer.cc(507) LOG(INFO) EM sub_iter=1 size=33 obj=7.58438 num_tokens=412 num_tokens/piece=12.4848
trainer_interface.cc(685) LOG(INFO) Saving model: data/token_list/bpe_unigram30/bpe.model
trainer_interface.cc(697) LOG(INFO) Saving vocabs: data/token_list/bpe_unigram30/bpe.vocab
2022-09-19T21:15:15 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:15:15 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:15:15 (asr.sh:1558:main) Successfully finished. [elapsed=0s]

Let’s check the content of the dictionary. There are several special symbols, e.g.,

<blank>   used for CTC
<unk>     unknown symbols do not appear in the training data
<sos/eos> start and end sentence symbols

✅ Checkpint 2 (1 point)

Print the generated token list.

[ ]:
!cat data/token_list/bpe_unigram30/tokens.txt

# NOTE: Checkpoint 2
print_date_and_time()
<blank>
<unk>
▁
T
E
O
R
Y
A
H
U
S
I
F
B
L
P
D
G
M
C
V
X
J
K
Z
W
N
Q
<sos/eos>
============================================================
 Current date and time: 09/19/2022 17:15:34
============================================================

Language modeling (skipped in this tutorial)

Stages 6–9: Stages related to language modeling.

We skip the language modeling part in the recipe (stages 6 – 9) in this tutorial.

End-to-end ASR

Before training, we need to set the training configs including the front-end, encoder and decoder, optimizer, scheduler, specaug, etc. These configs are usually specified in .yaml files: /content/espnet/egs2/an4/asr1/conf/train_asr_xxx.yaml, but you can also overwrite them in the command line.

In this example, we will train a small Transformer model. ESPnet also supports other encoder types, such as RNN (LSTM/GRU), Conformer, and Branchformer.

Please do the following: - Create a new config file train_asr_demo_transformer.yaml in the directory /content/espnet/egs2/an4/asr1/conf/ - Copy the following lines to the new config

batch_type: folded
batch_size: 64
accum_grad: 1    # gradient accumulation steps
max_epoch: 100
patience: none
init: xavier_uniform
best_model_criterion:  # criterion to save best models
-   - valid
    - acc
    - max
keep_nbest_models: 10  # save nbest models and average these checkpoints
use_amp: true    # whether to use automatic mixed precision
num_att_plot: 0  # do not save attention plots to save time in the demo
num_workers: 2   # number of workers in dataloader

encoder: transformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    linear_units: 1024
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    normalize_before: true

decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 1024
    num_blocks: 3
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1

model_conf:
    ctc_weight: 0.3  # joint CTC/attention training
    lsm_weight: 0.1  # label smoothing weight
    length_normalized_loss: false

optim: adam
optim_conf:
    lr: 0.001
scheduler: warmuplr  # linearly increase and exponentially decrease
scheduler_conf:
    warmup_steps: 800

Stage 10: ASR collect stats: train_set=dump/raw/train_nodev, valid_set=dump/raw/train_dev

  • We estimate the mean and variance of the data to normalize the data.

  • We also collect the information of input and output lengths for the efficient mini batch creation.

ESPnet supports various methods to create batches, please refer to https://espnet.github.io/espnet/espnet2_training_option.html#change-mini-batch-type.

[ ]:
# 15 seconds
!./asr.sh --stage 10 --stop_stage 10 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test" --nj 4 --asr_config conf/train_asr_demo_transformer.yaml
2022-09-19T21:22:17 (asr.sh:254:main) ./asr.sh --stage 10 --stop_stage 10 --train_set train_nodev --valid_set train_dev --test_sets train_dev test --nj 4 --asr_config conf/train_asr_demo_transformer.yaml
2022-09-19T21:22:18 (asr.sh:918:main) Stage 10: ASR collect stats: train_set=dump/raw/train_nodev, valid_set=dump/raw/train_dev
2022-09-19T21:22:18 (asr.sh:968:main) Generate 'exp/asr_stats_raw_bpe30/run.sh'. You can resume the process from stage 10 using this script
2022-09-19T21:22:18 (asr.sh:972:main) ASR collect-stats started... log: 'exp/asr_stats_raw_bpe30/logdir/stats.*.log'
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/aggregate_stats_dirs.py --input_dir exp/asr_stats_raw_bpe30/logdir/stats.1 --input_dir exp/asr_stats_raw_bpe30/logdir/stats.2 --input_dir exp/asr_stats_raw_bpe30/logdir/stats.3 --input_dir exp/asr_stats_raw_bpe30/logdir/stats.4 --output_dir exp/asr_stats_raw_bpe30
2022-09-19T21:22:32 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:22:32 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:22:32 (asr.sh:1558:main) Successfully finished. [elapsed=15s]

Stage 11: ASR Training: train_set=dump/raw/train_nodev, valid_set=dump/raw/train_dev

This is the main training loop.

During training, please monitor the following files - log file /content/espnet/egs2/an4/asr1/exp/asr_train_asr_demo_transformer_raw_bpe30/train.log - loss /content/espnet/egs2/an4/asr1/exp/asr_train_asr_demo_transformer_raw_bpe30/images/loss.png - accuracy /content/espnet/egs2/an4/asr1/exp/asr_train_asr_demo_transformer_raw_bpe30/images/acc.png

Good examples look like this:

db79ad03e4cd42a6a47c0bf7f080553d

However, bad examples (with too large lr) are like:

dcca11bf29ad44b386252c4b3863c4e7

ESPnet supports tensorboard, you can monitor the training status using it.

[ ]:
# Load the TensorBoard notebook extension
%load_ext tensorboard

# Launch tensorboard before training
%tensorboard --logdir /content/espnet/egs2/an4/asr1/exp
[ ]:
# It takes 12 minutes with a single T4 GPU.
!./asr.sh --stage 11 --stop_stage 11 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test" --ngpu 1 --asr_config conf/train_asr_demo_transformer.yaml
2022-09-19T21:23:38 (asr.sh:254:main) ./asr.sh --stage 11 --stop_stage 11 --train_set train_nodev --valid_set train_dev --test_sets train_dev test --ngpu 1 --asr_config conf/train_asr_demo_transformer.yaml
2022-09-19T21:23:39 (asr.sh:1019:main) Stage 11: ASR Training: train_set=dump/raw/train_nodev, valid_set=dump/raw/train_dev
2022-09-19T21:23:39 (asr.sh:1086:main) Generate 'exp/asr_train_asr_demo_transformer_raw_bpe30/run.sh'. You can resume the process from stage 11 using this script
2022-09-19T21:23:39 (asr.sh:1090:main) ASR training started... log: 'exp/asr_train_asr_demo_transformer_raw_bpe30/train.log'
2022-09-19 21:23:39,263 (launch:94) INFO: /content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/launch.py --cmd 'run.pl --name exp/asr_train_asr_demo_transformer_raw_bpe30/train.log' --log exp/asr_train_asr_demo_transformer_raw_bpe30/train.log --ngpu 1 --num_nodes 1 --init_file_prefix exp/asr_train_asr_demo_transformer_raw_bpe30/.dist_init_ --multiprocessing_distributed true -- python3 -m espnet2.bin.asr_train --use_preprocessor true --bpemodel data/token_list/bpe_unigram30/bpe.model --token_type bpe --token_list data/token_list/bpe_unigram30/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_shape_file exp/asr_stats_raw_bpe30/valid/speech_shape --valid_shape_file exp/asr_stats_raw_bpe30/valid/text_shape.bpe --resume true --init_param --ignore_init_mismatch false --fold_length 80000 --fold_length 150 --output_dir exp/asr_train_asr_demo_transformer_raw_bpe30 --config conf/train_asr_demo_transformer.yaml --frontend_conf fs=16k --normalize=global_mvn --normalize_conf stats_file=exp/asr_stats_raw_bpe30/train/feats_stats.npz --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_shape_file exp/asr_stats_raw_bpe30/train/speech_shape --train_shape_file exp/asr_stats_raw_bpe30/train/text_shape.bpe
2022-09-19 21:23:39,288 (launch:348) INFO: log file: exp/asr_train_asr_demo_transformer_raw_bpe30/train.log
2022-09-19T21:35:40 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:35:40 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:35:40 (asr.sh:1558:main) Successfully finished. [elapsed=722s]

The training log contains all information of the current experiment. Please check it to understand the training status. If your training job fails, this file will show the error messages.

✅ Checkpoint 3 (1 point)

Print the training log.

[ ]:
# NOTE: Checkpoint 3
!tail -20 exp/asr_train_asr_demo_transformer_raw_bpe30/train.log

print_date_and_time()
[b83a93649a12] 2022-09-19 21:32:49,314 (trainer:376) INFO: There are no improvements in this epoch
[b83a93649a12] 2022-09-19 21:32:49,322 (trainer:432) INFO: The model files were removed: exp/asr_train_asr_demo_transformer_raw_bpe30/75epoch.pth
[b83a93649a12] 2022-09-19 21:32:49,322 (trainer:264) INFO: 77/100epoch started. Estimated time to finish: 2 minutes and 51.75 seconds
[b83a93649a12] 2022-09-19 21:32:52,027 (trainer:710) INFO: 77epoch:train:1-10batch: iter_time=0.028, forward_time=0.093, loss_ctc=1.829, loss_att=6.366, acc=0.905, loss=5.005, backward_time=0.053, optim_step_time=0.033, optim0_lr0=8.677e-04, train_time=0.270
[b83a93649a12] 2022-09-19 21:32:53,495 (trainer:330) INFO: 77epoch results: [train] iter_time=0.020, forward_time=0.085, loss_ctc=1.840, loss_att=6.293, acc=0.904, loss=4.957, backward_time=0.047, optim_step_time=0.030, optim0_lr0=8.669e-04, train_time=0.239, time=3.42 seconds, total_count=1078, gpu_max_cached_mem_GB=7.453, [valid] loss_ctc=44.623, cer_ctc=0.477, loss_att=17.203, acc=0.733, cer=0.433, wer=0.830, loss=25.429, time=0.75 seconds, total_count=154, gpu_max_cached_mem_GB=7.453
[b83a93649a12] 2022-09-19 21:32:56,010 (trainer:376) INFO: There are no improvements in this epoch
[b83a93649a12] 2022-09-19 21:32:56,018 (trainer:432) INFO: The model files were removed: exp/asr_train_asr_demo_transformer_raw_bpe30/64epoch.pth
[b83a93649a12] 2022-09-19 21:32:56,019 (trainer:264) INFO: 78/100epoch started. Estimated time to finish: 2 minutes and 44.45 seconds
[b83a93649a12] 2022-09-19 21:32:58,730 (trainer:710) INFO: 78epoch:train:1-10batch: iter_time=0.031, forward_time=0.097, loss_ctc=1.788, loss_att=5.869, acc=0.915, loss=4.645, backward_time=0.053, optim_step_time=0.028, optim0_lr0=8.621e-04, train_time=0.270
[b83a93649a12] 2022-09-19 21:33:00,201 (trainer:330) INFO: 78epoch results: [train] iter_time=0.022, forward_time=0.088, loss_ctc=1.766, loss_att=5.907, acc=0.911, loss=4.665, backward_time=0.047, optim_step_time=0.026, optim0_lr0=8.613e-04, train_time=0.240, time=3.43 seconds, total_count=1092, gpu_max_cached_mem_GB=7.453, [valid] loss_ctc=42.608, cer_ctc=0.416, loss_att=16.573, acc=0.737, cer=0.451, wer=0.870, loss=24.383, time=0.75 seconds, total_count=156, gpu_max_cached_mem_GB=7.453
[b83a93649a12] 2022-09-19 21:33:02,692 (trainer:376) INFO: There are no improvements in this epoch
[b83a93649a12] 2022-09-19 21:33:02,701 (trainer:432) INFO: The model files were removed: exp/asr_train_asr_demo_transformer_raw_bpe30/74epoch.pth
[b83a93649a12] 2022-09-19 21:33:02,701 (trainer:264) INFO: 79/100epoch started. Estimated time to finish: 2 minutes and 37.17 seconds
[b83a93649a12] 2022-09-19 21:33:05,303 (trainer:710) INFO: 79epoch:train:1-10batch: iter_time=0.032, forward_time=0.095, loss_ctc=1.385, loss_att=5.230, acc=0.921, loss=4.077, backward_time=0.049, optim_step_time=0.027, optim0_lr0=8.565e-04, train_time=0.259
[b83a93649a12] 2022-09-19 21:33:06,864 (trainer:330) INFO: 79epoch results: [train] iter_time=0.023, forward_time=0.089, loss_ctc=1.534, loss_att=5.650, acc=0.916, loss=4.415, backward_time=0.044, optim_step_time=0.026, optim0_lr0=8.557e-04, train_time=0.239, time=3.41 seconds, total_count=1106, gpu_max_cached_mem_GB=7.453, [valid] loss_ctc=41.357, cer_ctc=0.457, loss_att=16.425, acc=0.732, cer=0.462, wer=0.840, loss=23.905, time=0.75 seconds, total_count=158, gpu_max_cached_mem_GB=7.453
[b83a93649a12] 2022-09-19 21:33:09,372 (trainer:376) INFO: There are no improvements in this epoch
[b83a93649a12] 2022-09-19 21:33:09,382 (trainer:432) INFO: The model files were removed: exp/asr_train_asr_demo_transformer_raw_bpe30/69epoch.pth
[b83a93649a12] 2022-09-19 21:33:09,382 (trainer:264) INFO: 80/100epoch started. Estimated time to finish: 2 minutes and 29.9 seconds
[b83a93649a12] 2022-09-19 21:33:12,028 (trainer:710) INFO: 80epoch:train:1-10batch: iter_time=0.038, forward_time=0.090, loss_ctc=1.466, loss_att=5.990, acc=0.912, loss=4.633, backward_time=0.052, optim_step_time=0.027, optim0_lr0=8.511e-04, train_time=0.264
[b83a93649a12] 2022-09-19 21:33:13,530 (trainer:330) INFO: 80epoch results: [train] iter_time=0.027, forward_time=0.083, loss_ctc=1.526, loss_att=5.748, acc=0.915, loss=4.481, backward_time=0.047, optim_step_time=0.025, optim0_lr0=8.503e-04, train_time=0.237, time=3.39 seconds, total_count=1120, gpu_max_cached_mem_GB=7.453, [valid] loss_ctc=46.932, cer_ctc=0.434, loss_att=17.528, acc=0.726, cer=0.503, wer=0.850, loss=26.349, time=0.76 seconds, total_count=160, gpu_max_cached_mem_GB=7.453
============================================================
 Current date and time: 09/19/2022 17:37:50
============================================================

Stage 12: Decoding

  • We need to add --use_lm false since we skip the language model.

  • --asr_exp exp/asr_train_asr_demo_transformer_raw_bpe30 specifies the experiment directory name.

  • --inference_nj <N> specifies the number of inference jobs.

  • We can enable GPU decoding by setting --gpu_inference true. Otherwise CPU will be used for decoding.

  • --inference_config conf/decode_asr.yaml specifies the decoding config file.

Let’s monitor the log /content/espnet/egs2/an4/asr1/exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/logdir/asr_inference.1.log

[ ]:
# It would take 3 minutes with a single T4 GPU.
!./asr.sh --use_lm false --gpu_inference true --inference_nj 1 --stage 12 --stop_stage 12 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test" --asr_exp exp/asr_train_asr_demo_transformer_raw_bpe30 --inference_config conf/decode_asr.yaml
2022-09-19T21:39:23 (asr.sh:254:main) ./asr.sh --use_lm false --gpu_inference true --inference_nj 1 --stage 12 --stop_stage 12 --train_set train_nodev --valid_set train_dev --test_sets train_dev test --asr_exp exp/asr_train_asr_demo_transformer_raw_bpe30 --inference_config conf/decode_asr.yaml
2022-09-19T21:39:25 (asr.sh:897:main) Stage 6-8: Skip lm-related stages: use_lm=false
2022-09-19T21:39:25 (asr.sh:1166:main) Stage 12: Decoding: training_dir=exp/asr_train_asr_demo_transformer_raw_bpe30
2022-09-19T21:39:25 (asr.sh:1194:main) Generate 'exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/run.sh'. You can resume the process from stage 12 using this script
2022-09-19T21:39:25 (asr.sh:1251:main) Decoding started... log: 'exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/logdir/asr_inference.*.log'
2022-09-19T21:40:37 (asr.sh:1267:main) Calculating RTF & latency... log: 'exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/logdir/calculate_rtf.log'
2022-09-19T21:40:38 (asr.sh:1251:main) Decoding started... log: 'exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/test/logdir/asr_inference.*.log'
2022-09-19T21:41:57 (asr.sh:1267:main) Calculating RTF & latency... log: 'exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/test/logdir/calculate_rtf.log'
2022-09-19T21:41:57 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:41:57 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:41:57 (asr.sh:1558:main) Successfully finished. [elapsed=154s]

Stage 13: Scoring

You can find word error rate (WER), character error rate (CER), etc. for each test set.

[ ]:
!./asr.sh --stage 13 --stop_stage 13 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test" --use_lm false --asr_exp exp/asr_train_asr_demo_transformer_raw_bpe30 --inference_config conf/decode_asr.yaml
2022-09-19T21:42:36 (asr.sh:254:main) ./asr.sh --stage 13 --stop_stage 13 --train_set train_nodev --valid_set train_dev --test_sets train_dev test --use_lm false --asr_exp exp/asr_train_asr_demo_transformer_raw_bpe30 --inference_config conf/decode_asr.yaml
2022-09-19T21:42:37 (asr.sh:897:main) Stage 6-8: Skip lm-related stages: use_lm=false
2022-09-19T21:42:37 (asr.sh:1293:main) Stage 13: Scoring
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type char --non_linguistic_symbols none --remove_non_linguistic_symbols true --cleaner none
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type char --non_linguistic_symbols none --remove_non_linguistic_symbols true
2022-09-19T21:42:40 (asr.sh:1394:main) Write cer result in exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/score_cer/result.txt
|   SPKR      |   # Snt      # Wrd   |   Corr         Sub         Del         Ins        Err       S.Err    |
|   Sum/Avg   |    100        1915   |   79.7         7.1        13.2         1.7       22.0        76.0    |
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type word --non_linguistic_symbols none --remove_non_linguistic_symbols true --cleaner none
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type word --non_linguistic_symbols none --remove_non_linguistic_symbols true
2022-09-19T21:42:42 (asr.sh:1394:main) Write wer result in exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/score_wer/result.txt
|   SPKR      |   # Snt      # Wrd   |   Corr         Sub         Del         Ins        Err       S.Err    |
|   Sum/Avg   |    100         591   |   64.3        25.9         9.8         2.5       38.2        76.0    |
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type bpe --bpemodel data/token_list/bpe_unigram30/bpe.model --cleaner none
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type bpe --bpemodel data/token_list/bpe_unigram30/bpe.model
2022-09-19T21:42:43 (asr.sh:1394:main) Write ter result in exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/score_ter/result.txt
|   SPKR      |   # Snt      # Wrd   |   Corr         Sub         Del         Ins        Err       S.Err    |
|   Sum/Avg   |    100        2015   |   80.6         6.7        12.6         1.6       20.9        76.0    |
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type char --non_linguistic_symbols none --remove_non_linguistic_symbols true --cleaner none
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type char --non_linguistic_symbols none --remove_non_linguistic_symbols true
2022-09-19T21:42:45 (asr.sh:1394:main) Write cer result in exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/test/score_cer/result.txt
|   SPKR     |   # Snt     # Wrd    |   Corr        Sub        Del        Ins         Err      S.Err   |
|   Sum/Avg  |    130       2565    |   86.4        4.8        8.8        1.4        15.0       66.9   |
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type word --non_linguistic_symbols none --remove_non_linguistic_symbols true --cleaner none
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type word --non_linguistic_symbols none --remove_non_linguistic_symbols true
2022-09-19T21:42:47 (asr.sh:1394:main) Write wer result in exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/test/score_wer/result.txt
|   SPKR     |   # Snt     # Wrd    |   Corr        Sub        Del        Ins         Err      S.Err   |
|   Sum/Avg  |    130        773    |   73.0       20.2        6.9        1.6        28.6       66.9   |
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type bpe --bpemodel data/token_list/bpe_unigram30/bpe.model --cleaner none
/content/espnet/tools/anaconda/envs/espnet/bin/python3 /content/espnet/espnet2/bin/tokenize_text.py -f 2- --input - --output - --token_type bpe --bpemodel data/token_list/bpe_unigram30/bpe.model
2022-09-19T21:42:50 (asr.sh:1394:main) Write ter result in exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/test/score_ter/result.txt
|   SPKR     |   # Snt     # Wrd    |   Corr        Sub        Del        Ins         Err      S.Err   |
|   Sum/Avg  |    130       2695    |   87.1        4.5        8.4        1.4        14.3       66.9   |
ls: cannot access 'exp/asr_train_asr_demo_transformer_raw_bpe30/*/*/score_wer/scoring/*.filt.sys': No such file or directory
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Sep 19 21:42:50 UTC 2022`
- python version: `3.9.13 (main, Aug 25 2022, 23:26:10)  [GCC 11.2.0]`
- espnet version: `espnet 202207`
- pytorch version: `pytorch 1.12.1`
- Git hash: `3a22d1584317ae59974aad62feab8719c003ae05`
  - Commit date: `Tue Sep 13 13:33:07 2022 +0000`

## asr_train_asr_demo_transformer_raw_bpe30
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|773|73.0|20.2|6.9|1.6|28.6|66.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|591|64.3|25.9|9.8|2.5|38.2|76.0|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|2565|86.4|4.8|8.8|1.4|15.0|66.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|1915|79.7|7.1|13.2|1.7|22.0|76.0|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|2695|87.1|4.5|8.4|1.4|14.3|66.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|2015|80.6|6.7|12.6|1.6|20.9|76.0|

2022-09-19T21:42:52 (asr.sh:1503:main) Skip the uploading stage
2022-09-19T21:42:52 (asr.sh:1555:main) Skip the uploading to HuggingFace stage
2022-09-19T21:42:52 (asr.sh:1558:main) Successfully finished. [elapsed=16s]

You can also check the break down of the word error rate in /content/espnet/egs2/an4/asr1/exp/asr_train_asr_demo_transformer_raw_bpe30/decode_asr_asr_model_valid.acc.ave/train_dev/score_wer/result.txt

✅ Checkpoint 4 (1 point)

Print the scoring results.

[ ]:
# NOTE: Checkpoint 4
!cat exp/asr_train_asr_demo_transformer_raw_bpe30/RESULTS.md

print_date_and_time()
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Sep 19 21:42:50 UTC 2022`
- python version: `3.9.13 (main, Aug 25 2022, 23:26:10)  [GCC 11.2.0]`
- espnet version: `espnet 202207`
- pytorch version: `pytorch 1.12.1`
- Git hash: `3a22d1584317ae59974aad62feab8719c003ae05`
  - Commit date: `Tue Sep 13 13:33:07 2022 +0000`

## asr_train_asr_demo_transformer_raw_bpe30
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|773|73.0|20.2|6.9|1.6|28.6|66.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|591|64.3|25.9|9.8|2.5|38.2|76.0|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|2565|86.4|4.8|8.8|1.4|15.0|66.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|1915|79.7|7.1|13.2|1.7|22.0|76.0|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|2695|87.1|4.5|8.4|1.4|14.3|66.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|2015|80.6|6.7|12.6|1.6|20.9|76.0|

============================================================
 Current date and time: 09/19/2022 17:44:38
============================================================

Checkpoint Submission: If you have completed all the four checkpoints, please print your notebook and submit it to Gradescope.

How to change the configs?

Let’s revisit the configs, since this is probably the most important part to improve the performance.

All training options are changed in the config file.

Pleae check https://espnet.github.io/espnet/espnet2_training_option.html

Let’s first check config files prepared in the an4 recipe

  • LSTM-based E2E ASR /content/espnet/egs2/an4/asr1/conf/train_asr_rnn.yaml

  • Transformer based E2E ASR /content/espnet/egs2/an4/asr1/conf/train_asr_transformer.yaml

You can run

RNN

./asr.sh --stage 10 \
   --train_set train_nodev \
   --valid_set train_dev \
   --test_sets "train_dev test" \
   --nj 4 \
   --inference_nj 4 \
   --use_lm false \
   --asr_config conf/train_asr_rnn.yaml

Transformer

./asr.sh --stage 10 \
   --train_set train_nodev \
   --valid_set train_dev \
   --test_sets "train_dev test" \
   --nj 4 \
   --inference_nj 4 \
   --use_lm false \
   --asr_config conf/train_asr_transformer.yaml

You can also find various configs in other recipes espnet/egs2/*/asr1/conf/, including - Conformer egs2/librispeech/asr1/conf/tuning/train_asr_conformer10_hop_length160.yaml - Branchformer egs2/librispeech/asr1/conf/tuning/train_asr_branchformer_hop_length160_e18_linear3072.yaml

You can also customize it by passing the command line arguments, e.g.,

./run.sh --stage 10 --asr_args "--model_conf ctc_weight=0.3"
./run.sh --stage 10 --asr_args "--optim_conf lr=0.1"

This approach has a highest priority. Thus, the arguments passed in the command line will overwrite those defined in the config file. This is convenient if you only want to change a few arguments.

Please refer to https://espnet.github.io/espnet/espnet2_tutorial.html#change-the-configuration-for-training for more details.

📗 Exercise 1 (1 point bonus)

Run training, inference and scoring on AN4 using a new config. If you achieve a better character error rate (CER) than this following one, you can get a bonus point. I suggest tuning the total number of epochs, learning rate, warmup steps or data augmentation (speed perturbation, SpecAug) to improve the result.

This AN4 dataset is very small, so the result can be unstable even for the same configs. Generally, we should compare different methods using a large dataset such as LibriSpeech 960h.


Here is an example config using Branchformer (Peng et al, ICML 2022). Only the encoder is changed and the others are identical.

Similarly, we create a config file named train_asr_demo_branchformer.yaml and start training.

encoder: branchformer
encoder_conf:
    output_size: 256
    use_attn: true
    attention_heads: 4
    attention_layer_type: rel_selfattn
    pos_enc_layer_type: rel_pos
    rel_pos_type: latest
    use_cgmlp: true
    cgmlp_linear_units: 1024
    cgmlp_conv_kernel: 31
    use_linear_after_conv: false
    gate_activation: identity
    merge_method: concat
    cgmlp_weight: 0.5               # used only if merge_method is "fixed_ave"
    attn_branch_drop_rate: 0.0      # used only if merge_method is "learned_ave"
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    stochastic_depth_rate: 0.0

My result is shown below:

## asr_train_asr_demo_branchformer_raw_bpe30
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|773|85.4|11.3|3.4|0.4|15.0|46.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|591|77.3|15.7|6.9|0.7|23.4|62.0|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|2565|93.5|2.9|3.6|1.2|7.8|46.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|1915|87.8|5.0|7.2|1.8|14.0|62.0|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_asr_model_valid.acc.ave/test|130|2695|93.8|2.8|3.4|1.2|7.4|46.9|
|decode_asr_asr_model_valid.acc.ave/train_dev|100|2015|88.4|4.8|6.8|1.7|13.3|62.0|

image.png

[ ]:
# Run multiple stages
!./asr.sh --stage 10 --stop_stage 13 --train_set train_nodev --valid_set train_dev --test_sets "train_dev test" --nj 4 --ngpu 1 --use_lm false --gpu_inference true --inference_nj 1 --asr_config conf/train_asr_demo_branchformer.yaml --inference_config conf/decode_asr.yaml
[ ]:
# NOTE: Exercise 1 Result 1
!scripts/utils/show_asr_result.sh exp

print_date_and_time()
[ ]:
# NOTE: Exercise 1 Result 2
from IPython.display import Image, display
display(
    Image('exp/asr_train_asr_demo_transformer_raw_bpe30/images/acc.png', width=400),
    Image('exp/asr_train_asr_demo_branchformer_raw_bpe30/images/acc.png', width=400),
)

print_date_and_time()

Make a new recipe

Please carefully read the document: https://github.com/espnet/espnet/tree/master/egs2/TEMPLATE to understand how to create a new recipe. The major part is to prepare the data directory, which organizes the processed data in the Kaldi format. The other parts can be accomplished by executing some shared scripts.

We use the TIDIGITS dataset and we select part of it which contains around 2000 utterances for training and another 2000 for testing. Note that this dataset is provided only for private use. Please do not share it elsewhere.

You need to finish the entire pipeline from data preparation to scoring.

We need to create a new directory in egs2/ for the new dataset. This will automatically create several other files and directories.

asr.sh  cmd.sh  conf  db.sh  local  path.sh  pyscripts  scripts  steps  utils
[ ]:
%cd /content/espnet
!egs2/TEMPLATE/asr1/setup.sh egs2/tidigits/asr1
%cd egs2/tidigits/asr1
!ls
/content/espnet
2022-09-19T21:52:07 (setup.sh:58:main) Created: egs2/tidigits/asr1/egs2/tidigits/asr1/../../TEMPLATE/asr1/cmd.sh egs2/tidigits/asr1/egs2/tidigits/asr1/../../TEMPLATE/asr1/conf egs2/tidigits/asr1/egs2/tidigits/asr1/../../TEMPLATE/asr1/local egs2/tidigits/asr1/../../TEMPLATE/asr1/asr.sh egs2/tidigits/asr1/../../TEMPLATE/asr1/path.sh egs2/tidigits/asr1/../../TEMPLATE/asr1/db.sh egs2/tidigits/asr1/../../TEMPLATE/asr1/scripts egs2/tidigits/asr1/../../TEMPLATE/asr1/pyscripts egs2/tidigits/asr1/../../../tools/kaldi/egs/wsj/s5/steps egs2/tidigits/asr1/../../../tools/kaldi/egs/wsj/s5/utils
/content/espnet/egs2/tidigits/asr1
asr.sh  cmd.sh  conf  db.sh  local  path.sh  pyscripts  scripts  steps  utils

Please download the compressed dataset from Google Drive (removed in this public version) and decompress it. Then, specify the absolute path to the dataset in db.sh as follows:

...
TIDIGITS=/content/espnet/egs2/tidigits/asr1/downloads/TIDIGITS_children_boy  # our newly added path
...
[ ]:
!mkdir downloads
%cd downloads
!gdown <FILE_ID>
!tar -xzf TIDIGITS_children_boy.tar.gz && ls TIDIGITS_children_boy
[ ]:
%cd /content/espnet/egs2/tidigits/asr1
!echo "" >> db.sh
!echo "TIDIGITS=/content/espnet/egs2/tidigits/asr1/downloads/TIDIGITS_children_boy" >> db.sh

In Stage 1, asr.sh calls local/data.sh to prepare the dataset in the Kaldi format. So you need to do the following things.

  1. Create a file local/data.sh with the following content. This is task specific, but here we have prepared it for you.

#!/usr/bin/env bash
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail

log() {
    local fname=${BASH_SOURCE[1]##*/}
    echo -e "$(date '+%Y-%m-%dT%H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
SECONDS=0

stage=1
stop_stage=100

log "$0 $*"
. utils/parse_options.sh

. ./db.sh
. ./path.sh
. ./cmd.sh

if [ $# -ne 0 ]; then
    log "Error: No positional arguments are required."
    exit 2
fi

if [ -z "${TIDIGITS}" ]; then
    log "Fill the value of 'TIDIGITS' of db.sh"
    exit 1
fi

train_set="train_nodev"
train_dev="train_dev"
ndev_utt=200


if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    log "stage 1: Data preparation"
    mkdir -p data/{train,test}

    if [ ! -f ${TIDIGITS}/readme.1st ]; then
        echo Cannot find TIDIGITS root! Exiting...
        exit 1
    fi

    # Prepare data in the Kaldi format, including three files:
    # text, wav.scp, utt2spk
    python3 local/data_prep.py ${TIDIGITS} sph2pipe

    for x in test train; do
        for f in text wav.scp utt2spk; do
            sort data/${x}/${f} -o data/${x}/${f}
        done
        utils/utt2spk_to_spk2utt.pl data/${x}/utt2spk > "data/${x}/spk2utt"
    done

    # make a dev set
    utils/subset_data_dir.sh --first data/train "${ndev_utt}" "data/${train_dev}"
    n=$(($(wc -l < data/train/text) - ndev_utt))
    utils/subset_data_dir.sh --last data/train "${n}" "data/${train_set}"
fi

log "Successfully finished. [elapsed=${SECONDS}s]"
  1. Create a python script local/data_prep.py which is used by the previous shell script. Similarly, we have provided most of the code for you. You only need to finish a few lines of code (see the TODO tag).

import os
import glob
import sys


if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python data_prep.py [root] [sph2pipe]")
        sys.exit(1)
    root = sys.argv[1]
    sph2pipe = sys.argv[2]

    for x in ["train", "test"]:
        # We only use the data from boy children
        all_audio_list = glob.glob(
            os.path.join(root, "data", "children", x, "boy", "*", "*.wav")
        )

        with open(os.path.join("data", x, "text"), "w") as text_f, open(
            os.path.join("data", x, "wav.scp"), "w"
        ) as wav_scp_f, open(
            os.path.join("data", x, "utt2spk"), "w"
        ) as utt2spk_f:

            for audio_path in all_audio_list:
                filename = os.path.basename(audio_path)     # "o73a.wav" etc
                speaker = os.path.basename(os.path.dirname(audio_path))     # "lc", "sk", etc

                transcript = " ".join(list(filename[:-5]))  # "o73" -> "o 7 3"
                uttid = f"{speaker}-{filename[:-4]}"    # "sk-o73a"

                wav_scp_f.write(
                    f"{uttid} {sph2pipe} -f wav -p -c 1 {audio_path} |\n"
                )

                ### TODO: write the other files in the Kaldi format
[ ]:
!touch local/data.sh && chmod +x local/data.sh
!touch local/data_prep.py
## TODO: copy the script and finish it

Now let’s create a shell script run.sh as the entry point. You can directly use the following one.

What are the differences compared to the AN4 recipe?

  • We use words as modeling units.

  • We pass decoding arguments in the command line, see inference_args.

  • We perform data prep, training, decoding and scoring in this single job.

#!/usr/bin/env bash
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
set -u
set -o pipefail

./asr.sh \
    --stage 1 \
    --stop_stage 13 \
    --nj 4 \
    --ngpu 1 \
    --gpu_inference true \
    --inference_nj 1 \
    --use_lm false \
    --lang en \
    --token_type word \
    --asr_config conf/train_asr_demo_branchformer.yaml \
    --inference_args "--beam_size 10 --ctc_weight 0.3" \
    --train_set train_nodev \
    --valid_set train_dev \
    --test_sets "train_dev test" \
    --bpe_train_text "data/train_nodev/text" \
    --lm_train_text "data/train_nodev/text" "$@"
[ ]:
!touch run.sh && chmod +x run.sh
## TODO: copy and paste the code

We can use the previous Branchformer config train_asr_demo_branchformer.yaml. It should work well.

Note that we pass the decoding configs in the command line instead of using a separate file.

[ ]:
!touch conf/train_asr_demo_branchformer.yaml
## TODO: copy and paste the config from an4

Now we are ready to launch the job. We start from Stage 1 to Stage 13.

Again, we can monitor the status using Tensorboard.

[ ]:
# Load the TensorBoard notebook extension
%load_ext tensorboard

# Launch tensorboard before training
%tensorboard --logdir /content/espnet/egs2/tidigits/asr1/exp
[ ]:
# It takes 30 minutes with a single T4 GPU
!./run.sh

We print the results and display the figure. Please modify the path based on your situation.

The example output looks like the following:

image.png

## asr_train_asr_demo_branchformer_raw_en_word
### WER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_beam_size10_ctc_weight0.3_asr_model_valid.acc.ave/test|1925|6325|98.4|1.2|0.3|0.3|1.9|5.9|
|inference_beam_size10_ctc_weight0.3_asr_model_valid.acc.ave/train_dev|200|665|99.8|0.2|0.0|0.3|0.5|1.5|

### CER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_beam_size10_ctc_weight0.3_asr_model_valid.acc.ave/test|1925|10725|98.9|0.7|0.4|0.4|1.5|5.9|
|inference_beam_size10_ctc_weight0.3_asr_model_valid.acc.ave/train_dev|200|1130|99.9|0.1|0.0|0.4|0.4|1.5|

### TER

|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
[ ]:
# NOTE: Exercise 2
# Remember to modify the path before running this cell!
!cat exp/asr_train_asr_demo_branchformer_raw_en_word/RESULTS.md

from IPython.display import Image, display
display(Image('exp/asr_train_asr_demo_branchformer_raw_en_word/images/acc.png', width=400))

print_date_and_time()

Additional resources

Finally, we list some resources for more advanced usage of ESPnet. Please check them after the tutorial.

Fine-tune a pre-trained model

We can load pre-trained weights into a new model and then fine-tune it on our target dataset. There is a separate tutorial about fine-tuning: https://espnet.github.io/espnet/notebook/espnet2_asr_transfer_learning_demo.html

Notes:

  • Here, I do not use the phrase “transfer learning”, because transfer learning is really a large research area. Fine-tuning is just one type of transfer learning. However, these concepts are usually not distinguished in the current documentation. So, do not get confused about the terminology.

  • There are other ways to pass the pre-trained model path, which require modifications of the scripts asr.sh. For example, you can comment out the pretrained_model and ignore_init_mismatch in asr.sh and set them in the training config instead.

Use self-supervised pre-trained models as the front-end

In addition to the log Mel filterbank features computed by signal processing algorithms, ESPnet also supports self-supervised speech representations extracted from large pre-trained models such as wav2vec 2.0 and HuBERT. These models are pre-trained on large amounts of unlabeled audio data, so the representations are very powerful. This approach is especially useful for low-resource applications.

Please check recipes that use self-supervised pre-trained models, e.g., https://github.com/espnet/espnet/tree/master/egs2/librispeech/asr1#self-supervised-learning-features-wavlm_large-conformer-utt_mvn-with-transformer-lm

Contribute to ESPnet

Please follow https://github.com/espnet/espnet/blob/master/CONTRIBUTING.md to upload your pre-trained model to Hugging Face and make a pull request in the ESPnet repository.