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Merge branch 'espnet:master' into master
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roshansh-cmu authored Mar 1, 2022
2 parents 58aec43 + 7999009 commit bd8e400
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4 changes: 2 additions & 2 deletions doc/installation.md
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Expand Up @@ -202,14 +202,14 @@ We also have [prebuilt Kaldi binaries](https://github.com/espnet/espnet/blob/mas
```sh
$ cd <espnet-root>/tools
$ make TH_VERSION=1.3.1
$ make TH_VERSION=1.10.1
```
Note that the CUDA version is derived from `nvcc` command. If you'd like to specify the other CUDA version, you need to give `CUDA_VERSION`.

```sh
$ cd <espnet-root>/tools
$ make TH_VERSION=1.3.1 CUDA_VERSION=10.1
$ make TH_VERSION=1.10.1 CUDA_VERSION=11.3
```

If you don't have `nvcc` command, packages are installed for CPU mode by default.
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2 changes: 2 additions & 0 deletions egs2/TEMPLATE/asr1/db.sh
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Expand Up @@ -125,6 +125,7 @@ TOTONAC=downloads
PRIMEWORDS_CHINESE=downloads
SEAME=
BENGALI=downloads
IWSLT14=

# For only CMU TIR environment
if [[ "$(hostname)" == tir* ]]; then
Expand Down Expand Up @@ -154,6 +155,7 @@ if [[ "$(hostname)" == tir* ]]; then
BABEL_403=/projects/tir5/data/speech_corpora/babel/IARPA-babel403b-v1.0b-build/BABEL_OP3_403/
BABEL_404=/projects/tir5/data/speech_corpora/babel/IARPA_BABEL_OP3_404/
GRABO=/projects/tir5/data/speech_corpora/Grabo
IWSLT14=/projects/tir5/data/iwslt14
IWSLT22_DIALECT=/projects/tir5/data/speech_corpora/LDC2022E01_IWSLT22_Tunisian_Arabic_Shared_Task_Training_Data/
PRIMEWORDS_CHINESE=/projects/tir5/data/speech_corpora/Primewords_Chinese
FISHER_CALLHOME_SPANISH=/projects/tir5/data/speech_corpora/fisher_callhome_spanish
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2 changes: 1 addition & 1 deletion egs2/TEMPLATE/enh1/enh.sh
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Expand Up @@ -72,7 +72,7 @@ init_param=

# Enhancement related
inference_args="--normalize_output_wav true"
inference_model=valid.si_snr.ave.pth
inference_model=valid.loss.ave.pth
download_model=

# Evaluation related
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6 changes: 5 additions & 1 deletion egs2/TEMPLATE/enh1/scripts/utils/show_enh_score.sh
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Expand Up @@ -69,7 +69,11 @@ while IFS= read -r expdir; do
line="|${dset}|"
for ((i=0; i<${#metrics[@]}; i++)); do
type=${metrics[$i]}
score=$(head -n1 "${expdir}"/${dset}/scoring/result_${type}.txt)
if [ -f "${expdir}"/${dset}/scoring/result_${type}.txt ]; then
score=$(head -n1 "${expdir}"/${dset}/scoring/result_${type}.txt)
else
score=""
fi
line+="${score}|"
done
echo $line
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110 changes: 110 additions & 0 deletions egs2/TEMPLATE/mt1/cmd.sh
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@@ -0,0 +1,110 @@
# ====== About run.pl, queue.pl, slurm.pl, and ssh.pl ======
# Usage: <cmd>.pl [options] JOB=1:<nj> <log> <command...>
# e.g.
# run.pl --mem 4G JOB=1:10 echo.JOB.log echo JOB
#
# Options:
# --time <time>: Limit the maximum time to execute.
# --mem <mem>: Limit the maximum memory usage.
# -–max-jobs-run <njob>: Limit the number parallel jobs. This is ignored for non-array jobs.
# --num-threads <ngpu>: Specify the number of CPU core.
# --gpu <ngpu>: Specify the number of GPU devices.
# --config: Change the configuration file from default.
#
# "JOB=1:10" is used for "array jobs" and it can control the number of parallel jobs.
# The left string of "=", i.e. "JOB", is replaced by <N>(Nth job) in the command and the log file name,
# e.g. "echo JOB" is changed to "echo 3" for the 3rd job and "echo 8" for 8th job respectively.
# Note that the number must start with a positive number, so you can't use "JOB=0:10" for example.
#
# run.pl, queue.pl, slurm.pl, and ssh.pl have unified interface, not depending on its backend.
# These options are mapping to specific options for each backend and
# it is configured by "conf/queue.conf" and "conf/slurm.conf" by default.
# If jobs failed, your configuration might be wrong for your environment.
#
#
# The official documentation for run.pl, queue.pl, slurm.pl, and ssh.pl:
# "Parallelization in Kaldi": http://kaldi-asr.org/doc/queue.html
# =========================================================~


# Select the backend used by run.sh from "local", "stdout", "sge", "slurm", or "ssh"
cmd_backend='local'

# Local machine, without any Job scheduling system
if [ "${cmd_backend}" = local ]; then

# The other usage
export train_cmd="run.pl"
# Used for "*_train.py": "--gpu" is appended optionally by run.sh
export cuda_cmd="run.pl"
# Used for "*_recog.py"
export decode_cmd="run.pl"

# Local machine logging to stdout and log file, without any Job scheduling system
elif [ "${cmd_backend}" = stdout ]; then

# The other usage
export train_cmd="stdout.pl"
# Used for "*_train.py": "--gpu" is appended optionally by run.sh
export cuda_cmd="stdout.pl"
# Used for "*_recog.py"
export decode_cmd="stdout.pl"


# "qsub" (Sun Grid Engine, or derivation of it)
elif [ "${cmd_backend}" = sge ]; then
# The default setting is written in conf/queue.conf.
# You must change "-q g.q" for the "queue" for your environment.
# To know the "queue" names, type "qhost -q"
# Note that to use "--gpu *", you have to setup "complex_value" for the system scheduler.

export train_cmd="queue.pl"
export cuda_cmd="queue.pl"
export decode_cmd="queue.pl"


# "qsub" (Torque/PBS.)
elif [ "${cmd_backend}" = pbs ]; then
# The default setting is written in conf/pbs.conf.

export train_cmd="pbs.pl"
export cuda_cmd="pbs.pl"
export decode_cmd="pbs.pl"


# "sbatch" (Slurm)
elif [ "${cmd_backend}" = slurm ]; then
# The default setting is written in conf/slurm.conf.
# You must change "-p cpu" and "-p gpu" for the "partition" for your environment.
# To know the "partion" names, type "sinfo".
# You can use "--gpu * " by default for slurm and it is interpreted as "--gres gpu:*"
# The devices are allocated exclusively using "${CUDA_VISIBLE_DEVICES}".

export train_cmd="slurm.pl"
export cuda_cmd="slurm.pl"
export decode_cmd="slurm.pl"

elif [ "${cmd_backend}" = ssh ]; then
# You have to create ".queue/machines" to specify the host to execute jobs.
# e.g. .queue/machines
# host1
# host2
# host3
# Assuming you can login them without any password, i.e. You have to set ssh keys.

export train_cmd="ssh.pl"
export cuda_cmd="ssh.pl"
export decode_cmd="ssh.pl"

# This is an example of specifying several unique options in the JHU CLSP cluster setup.
# Users can modify/add their own command options according to their cluster environments.
elif [ "${cmd_backend}" = jhu ]; then

export train_cmd="queue.pl --mem 2G"
export cuda_cmd="queue-freegpu.pl --mem 2G --gpu 1 --config conf/queue.conf"
export decode_cmd="queue.pl --mem 4G"

else
echo "$0: Error: Unknown cmd_backend=${cmd_backend}" 1>&2
return 1
fi
11 changes: 11 additions & 0 deletions egs2/TEMPLATE/mt1/conf/pbs.conf
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# Default configuration
command qsub -V -v PATH -S /bin/bash
option name=* -N $0
option mem=* -l mem=$0
option mem=0 # Do not add anything to qsub_opts
option num_threads=* -l ncpus=$0
option num_threads=1 # Do not add anything to qsub_opts
option num_nodes=* -l nodes=$0:ppn=1
default gpu=0
option gpu=0
option gpu=* -l ngpus=$0
12 changes: 12 additions & 0 deletions egs2/TEMPLATE/mt1/conf/queue.conf
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# Default configuration
command qsub -v PATH -cwd -S /bin/bash -j y -l arch=*64*
option name=* -N $0
option mem=* -l mem_free=$0,ram_free=$0
option mem=0 # Do not add anything to qsub_opts
option num_threads=* -pe smp $0
option num_threads=1 # Do not add anything to qsub_opts
option max_jobs_run=* -tc $0
option num_nodes=* -pe mpi $0 # You must set this PE as allocation_rule=1
default gpu=0
option gpu=0
option gpu=* -l gpu=$0 -q g.q
14 changes: 14 additions & 0 deletions egs2/TEMPLATE/mt1/conf/slurm.conf
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# Default configuration
command sbatch --export=PATH
option name=* --job-name $0
option time=* --time $0
option mem=* --mem-per-cpu $0
option mem=0
option num_threads=* --cpus-per-task $0
option num_threads=1 --cpus-per-task 1
option num_nodes=* --nodes $0
default gpu=0
option gpu=0 -p cpu
option gpu=* -p gpu --gres=gpu:$0 -c $0 # Recommend allocating more CPU than, or equal to the number of GPU
# note: the --max-jobs-run option is supported as a special case
# by slurm.pl and you don't have to handle it in the config file.
1 change: 1 addition & 0 deletions egs2/TEMPLATE/mt1/db.sh
Empty file added egs2/TEMPLATE/mt1/local/path.sh
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