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Merge pull request #5338 from Emrys365/tse
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Add musdb18 recipe for music source separation
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sw005320 committed Jul 23, 2023
2 parents 479b8d4 + 255570b commit 3ae1280
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1 change: 1 addition & 0 deletions egs2/README.md
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Expand Up @@ -106,6 +106,7 @@ See: https://espnet.github.io/espnet/espnet2_tutorial.html#recipes-using-espnet2
| ml_superb | Multilingual SUPERB benchamrk | ASR | 145 languages | Not Released | |
| mucs21_subtask1 | MUltilingual and Code-Switching ASR Challenges for Low Resource Indian Languages | ASR | 6 indian languages | https://navana-tech.github.io/MUCS2021/challenge_details.html | |
| mucs21_subtask2 | MUltilingual and Code-Switching ASR Challenges for Low Resource Indian Languages | ASR | 2 codeswitching data | https://navana-tech.github.io/MUCS2021/challenge_details.html | |
| musdb18 | Music source separation corpus | ENH | ENG | https://sigsep.github.io/datasets/musdb.htmlmust-c/ | |
| must_c | https://ict.fbk.eu/must-c/ | ASR/MT/ST | ENG->14langs | https://ict.fbk.eu/must-c/ | |
| must_c_v2 | https://ict.fbk.eu/must-c/ | ASR/MT/ST | ENG->DEU | https://ict.fbk.eu/must-c/ | |
| nit_song070 | The NITech Japanese speech database | SVS | JPN | http://hts.sp.nitech.ac.jp/archives/2.3/HTS-demo_NIT-SONG070-F001.tar.bz2
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1 change: 1 addition & 0 deletions egs2/TEMPLATE/asr1/db.sh
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Expand Up @@ -69,6 +69,7 @@ LIBRIMIX=downloads
LIBRITTS=
LJSPEECH=downloads
MUSAN=
MUSDB18=downloads
MUST_C=downloads
NSC=
NIT_SONG070=
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110 changes: 110 additions & 0 deletions egs2/musdb18/enh1/cmd.sh
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# ====== 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/musdb18/enh1/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/musdb18/enh1/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/musdb18/enh1/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.
59 changes: 59 additions & 0 deletions egs2/musdb18/enh1/conf/tuning/train_enh_conv_tasnet.yaml
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optim: adam
init: xavier_uniform
max_epoch: 100
batch_type: folded
batch_size: 4
iterator_type: chunk
chunk_length: 176400 # 4s in 44100 Hz
num_workers: 4
optim_conf:
lr: 1.0e-03
eps: 1.0e-08
weight_decay: 1.0e-05
patience: 4
val_scheduler_criterion:
- valid
- loss
best_model_criterion:
- - valid
- loss
- min
keep_nbest_models: 1
scheduler: reducelronplateau
scheduler_conf:
mode: min
factor: 0.5
patience: 1
encoder: conv
encoder_conf:
channel: 256
kernel_size: 20
stride: 10
decoder: conv
decoder_conf:
channel: 256
kernel_size: 20
stride: 10
separator: tcn
separator_conf:
num_spk: 2
layer: 8
stack: 4
bottleneck_dim: 256
hidden_dim: 512
kernel: 3
causal: False
norm_type: "gLN"
nonlinear: relu

criterions:
# The first criterion
- name: mr_l1_tfd
conf:
window_sz: [512, 1024, 1536, 2048]
hop_sz: null
eps: 1.0e-8
time_domain_weight: 0.5
wrapper: fixed_order
wrapper_conf:
weight: 1.0
1 change: 1 addition & 0 deletions egs2/musdb18/enh1/db.sh
1 change: 1 addition & 0 deletions egs2/musdb18/enh1/enh.sh

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