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[Recipe PR] MELD: Multimodal EmotionLines Dataset
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# MELD RESULTS | ||
## Environments | ||
- date: `Thu Nov 10 09:07:40 EST 2022` | ||
- python version: `3.8.6 (default, Dec 17 2020, 16:57:01) [GCC 10.2.0]` | ||
- espnet version: `espnet 202207` | ||
- pytorch version: `pytorch 1.8.1+cu102` | ||
- Git hash: `a7bd6522b32ec6472c13f6a2289dcdff4a846c12` | ||
- Commit date: `Wed Sep 14 08:34:27 2022 -0400` | ||
- Pretrained Model | ||
- Huggingface Hub: [ [model card](https://huggingface.co/espnet/realzza-meld-asr-hubert-transformer) | [model](https://huggingface.co/espnet/realzza-meld-asr-hubert-transformer/blob/main/exp/asr_train_asr_hubert_transformer_adam_specaug_meld_raw_en_bpe850/valid.acc.ave_5best.pth) ] | ||
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## asr_train_asr_hubert_transformer_adam_specaug_meld_raw_en_bpe850 | ||
- ASR config: conf/tuning/train_asr_hubert_transformer_adam_specaug_meld.yaml | ||
- token_type: bpe | ||
- keep_nbest_models: 5 | ||
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|dataset|Snt|Emotion Classification (%)| | ||
|---|---|---| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/test|2608|39.22| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|42.64| | ||
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### ASR results | ||
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#### WER | ||
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| | ||
|---|---|---|---|---|---|---|---|---| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/test|2608|24809|55.5|28.0|16.5|8.4|52.9|96.5| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|10171|55.3|29.4|15.3|7.0|51.7|96.2| | ||
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#### CER | ||
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| | ||
|---|---|---|---|---|---|---|---|---| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/test|2608|120780|71.1|10.7|18.2|10.6|39.5|96.5| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|49323|71.3|11.1|17.6|9.4|38.1|96.2| | ||
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#### TER | ||
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|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| | ||
|---|---|---|---|---|---|---|---|---| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/test|2608|35287|57.6|21.8|20.5|7.8|50.2|96.5| | ||
|decoder_asr_asr_model_valid.acc.ave_5best/valid|1104|14430|57.4|23.2|19.4|6.1|48.6|96.2| | ||
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../../TEMPLATE/asr1/asr.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 | ||
# =========================================================~ | ||
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# Select the backend used by run.sh from "local", "stdout", "sge", "slurm", or "ssh" | ||
cmd_backend='local' | ||
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# Local machine, without any Job scheduling system | ||
if [ "${cmd_backend}" = local ]; then | ||
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# The other usage | ||
export train_cmd="run.pl" | ||
# Used for "*_train.py": "--gpu" is appended optionally by run.sh | ||
export cuda_cmd="run.pl" | ||
# Used for "*_recog.py" | ||
export decode_cmd="run.pl" | ||
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# Local machine logging to stdout and log file, without any Job scheduling system | ||
elif [ "${cmd_backend}" = stdout ]; then | ||
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# 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" | ||
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# "qsub" (Sun Grid Engine, or derivation of it) | ||
elif [ "${cmd_backend}" = sge ]; then | ||
# The default setting is written in conf/queue.conf. | ||
# You must change "-q g.q" for the "queue" for your environment. | ||
# To know the "queue" names, type "qhost -q" | ||
# Note that to use "--gpu *", you have to setup "complex_value" for the system scheduler. | ||
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export train_cmd="queue.pl" | ||
export cuda_cmd="queue.pl" | ||
export decode_cmd="queue.pl" | ||
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# "qsub" (Torque/PBS.) | ||
elif [ "${cmd_backend}" = pbs ]; then | ||
# The default setting is written in conf/pbs.conf. | ||
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export train_cmd="pbs.pl" | ||
export cuda_cmd="pbs.pl" | ||
export decode_cmd="pbs.pl" | ||
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# "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}". | ||
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export train_cmd="slurm.pl" | ||
export cuda_cmd="slurm.pl" | ||
export decode_cmd="slurm.pl" | ||
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elif [ "${cmd_backend}" = ssh ]; then | ||
# You have to create ".queue/machines" to specify the host to execute jobs. | ||
# e.g. .queue/machines | ||
# host1 | ||
# host2 | ||
# host3 | ||
# Assuming you can login them without any password, i.e. You have to set ssh keys. | ||
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export train_cmd="ssh.pl" | ||
export cuda_cmd="ssh.pl" | ||
export decode_cmd="ssh.pl" | ||
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# This is an example of specifying several unique options in the JHU CLSP cluster setup. | ||
# Users can modify/add their own command options according to their cluster environments. | ||
elif [ "${cmd_backend}" = jhu ]; then | ||
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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" | ||
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else | ||
echo "$0: Error: Unknown cmd_backend=${cmd_backend}" 1>&2 | ||
return 1 | ||
fi |
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beam_size: 10 | ||
ctc_weight: 0.3 | ||
lm_weight: 0.1 | ||
penalty: 0.0 | ||
maxlenratio: 0.0 | ||
minlenratio: 0.0 |
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--sample-frequency=16000 | ||
--num-mel-bins=80 |
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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 |
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--sample-frequency=16000 |
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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 |
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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. |
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tuning/train_asr_hubert_transformer_adam_specaug_meld.yaml |
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egs2/meld/asr1/conf/tuning/train_asr_hubert_transformer_adam_specaug_meld.yaml
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# network architecture | ||
# encoder related | ||
encoder: conformer | ||
encoder_conf: | ||
output_size: 512 # dimension of attention | ||
attention_heads: 8 | ||
linear_units: 2048 # the number of units of position-wise feed forward | ||
num_blocks: 12 # the number of encoder blocks | ||
dropout_rate: 0.2 | ||
positional_dropout_rate: 0.1 | ||
attention_dropout_rate: 0.0 | ||
input_layer: conv2d2 # encoder architecture type | ||
normalize_before: true | ||
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# decoder related | ||
decoder: transformer | ||
decoder_conf: | ||
attention_heads: 8 | ||
linear_units: 2048 | ||
num_blocks: 6 | ||
dropout_rate: 0.2 | ||
positional_dropout_rate: 0.1 | ||
self_attention_dropout_rate: 0.0 | ||
src_attention_dropout_rate: 0.0 | ||
optim: adam | ||
optim_conf: | ||
lr: 0.00015 | ||
scheduler: warmuplr # pytorch v1.1.0+ required | ||
scheduler_conf: | ||
warmup_steps: 16000 | ||
max_epoch: 2 | ||
keep_nbest_models: 5 | ||
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freeze_param: [ | ||
"frontend.upstream" | ||
] | ||
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frontend_conf: | ||
n_fft: 512 | ||
hop_length: 256 | ||
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frontend: s3prl | ||
frontend_conf: | ||
frontend_conf: | ||
upstream: hubert_large_ll60k # Note: If the upstream is changed, please change the input_size in the preencoder. | ||
download_dir: ./hub | ||
multilayer_feature: True | ||
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preencoder: linear | ||
preencoder_conf: | ||
input_size: 1024 # Note: If the upstream is changed, please change this value accordingly. | ||
output_size: 80 | ||
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model_conf: | ||
ctc_weight: 0.3 | ||
lsm_weight: 0.1 | ||
length_normalized_loss: false | ||
extract_feats_in_collect_stats: false # Note: "False" means during collect stats (stage 10), generating dummy stats files rather than extract_feats by forward frontend. | ||
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specaug: specaug | ||
specaug_conf: | ||
apply_time_warp: true | ||
time_warp_window: 5 | ||
time_warp_mode: bicubic | ||
apply_freq_mask: true | ||
freq_mask_width_range: | ||
- 0 | ||
- 30 | ||
num_freq_mask: 2 | ||
apply_time_mask: true | ||
time_mask_width_range: | ||
- 0 | ||
- 40 | ||
num_time_mask: 2 |
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../../TEMPLATE/asr1/db.sh |
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#!/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 | ||
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log() { | ||
local fname=${BASH_SOURCE[1]##*/} | ||
echo -e "$(date '+%Y-%m-%dT%H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*" | ||
} | ||
SECONDS=0 | ||
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stage=1 | ||
stop_stage=100 | ||
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datadir=./downloads | ||
data_url=https://web.eecs.umich.edu/~mihalcea/downloads/ | ||
data_url2=https://huggingface.co/datasets/declare-lab/MELD/resolve/main/ | ||
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log "$0 $*" | ||
. utils/parse_options.sh | ||
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. ./db.sh | ||
. ./path.sh | ||
. ./cmd.sh | ||
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if [ $# -ne 0 ]; then | ||
log "Error: No positional arguments are required." | ||
exit 2 | ||
fi | ||
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if [ -z "${MELD}" ]; then | ||
log "Fill the value of 'MELD' of db.sh" | ||
exit 1 | ||
fi | ||
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then | ||
log "stage 1: Data Download" | ||
mkdir -p ${datadir} | ||
if ! local/download_and_untar.sh --remove-archive ${datadir} ${data_url}; then | ||
log "Failed to download from the original site, try a backup site." | ||
local/download_and_untar.sh --remove-archive ${datadir} ${data_url2} | ||
fi | ||
fi | ||
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then | ||
log "stage 2: Data Preparation" | ||
mkdir -p data/{train,valid,test} | ||
python3 local/data_prep.py ${MELD} | ||
for x in test valid train; do | ||
for f in text wav.scp utt2spk; do | ||
sort data/${x}/${f} -o data/${x}/${f} | ||
done | ||
utils/utt2spk_to_spk2utt.pl data/${x}/utt2spk > "data/${x}/spk2utt" | ||
utils/validate_data_dir.sh --no-feats data/${x} || exit 1 | ||
done | ||
fi | ||
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log "Successfully finished. [elapsed=${SECONDS}s]" |
Oops, something went wrong.