didi / athena Goto Github PK

hi friends:
when I run below command:

python athena/main.py examples/translate/spa-eng-example/transformer.json

there is one error as below:

(athena-train-env) parallels@parallels-Parallels-Virtual-Platform:~/Desktop/asr/athena$ python athena/main.py examples/translate/spa-eng-example/transformer.json
There is some problem with your horovod installation. But it wouldn't affect single-gpu training
There is some problem with your horovod installation. But it wouldn't affect single-gpu training
Traceback (most recent call last):
File "athena/main.py", line 24, in
from athena import *
File "/home/parallels/Desktop/asr/athena/athena/init.py", line 56, in
from .solver import BaseSolver
File "/home/parallels/Desktop/asr/athena/athena/solver.py", line 34, in
from pydecoders import WFSTDecoder
ModuleNotFoundError: No module named 'pydecoders'

and I try to install pydecoders, but there is not:

(athena-train-env) parallels@parallels-Parallels-Virtual-Platform:~/Desktop/asr/athena$ pip install pydecoders
ERROR: Could not find a version that satisfies the requirement pydecoders (from versions: none)
ERROR: No matching distribution found for pydecoders

So could you tell me how should I install it please?

Thanks.

Install Environment：
VMware15.0, Ubuntu 18.04, python3.6.9

Error:*
Running setup.py install for horovod ... error
ERROR: Command errored out with exit status 1:
command: /home/luxury/luxy/venv_athena/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-s_f3vxnr/horovod/setup.py'"'"'; file='"'"'/tmp/pip-install-s_f3vxnr/horovod/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record /tmp/pip-record-uelars4k/install-record.txt --single-version-externally-managed --compile --install-headers /home/luxury/luxy/venv_athena/include/site/python3.6/horovod
cwd: /tmp/pip-install-s_f3vxnr/horovod/
Complete output (190 lines):
running install
running build
running build_py
creating build
creating build/lib.linux-x86_64-3.6
creating build/lib.linux-x86_64-3.6/horovod
copying horovod/init.py -> build/lib.linux-x86_64-3.6/horovod
creating build/lib.linux-x86_64-3.6/horovod/keras
copying horovod/keras/callbacks.py -> build/lib.linux-x86_64-3.6/horovod/keras
copying horovod/keras/init.py -> build/lib.linux-x86_64-3.6/horovod/keras
creating build/lib.linux-x86_64-3.6/horovod/tensorflow
copying horovod/tensorflow/util.py -> build/lib.linux-x86_64-3.6/horovod/tensorflow
copying horovod/tensorflow/mpi_ops.py -> build/lib.linux-x86_64-3.6/horovod/tensorflow
copying horovod/tensorflow/init.py -> build/lib.linux-x86_64-3.6/horovod/tensorflow
copying horovod/tensorflow/compression.py -> build/lib.linux-x86_64-3.6/horovod/tensorflow
creating build/lib.linux-x86_64-3.6/horovod/run
copying horovod/run/run_task.py -> build/lib.linux-x86_64-3.6/horovod/run
copying horovod/run/gloo_run.py -> build/lib.linux-x86_64-3.6/horovod/run
copying horovod/run/init.py -> build/lib.linux-x86_64-3.6/horovod/run
copying horovod/run/run.py -> build/lib.linux-x86_64-3.6/horovod/run
copying horovod/run/mpi_run.py -> build/lib.linux-x86_64-3.6/horovod/run
copying horovod/run/task_fn.py -> build/lib.linux-x86_64-3.6/horovod/run
creating build/lib.linux-x86_64-3.6/horovod/spark
copying horovod/spark/init.py -> build/lib.linux-x86_64-3.6/horovod/spark
creating build/lib.linux-x86_64-3.6/horovod/common
copying horovod/common/util.py -> build/lib.linux-x86_64-3.6/horovod/common
copying horovod/common/init.py -> build/lib.linux-x86_64-3.6/horovod/common
copying horovod/common/basics.py -> build/lib.linux-x86_64-3.6/horovod/common
creating build/lib.linux-x86_64-3.6/horovod/mxnet
copying horovod/mxnet/mpi_ops.py -> build/lib.linux-x86_64-3.6/horovod/mxnet
copying horovod/mxnet/init.py -> build/lib.linux-x86_64-3.6/horovod/mxnet
creating build/lib.linux-x86_64-3.6/horovod/_keras
copying horovod/_keras/callbacks.py -> build/lib.linux-x86_64-3.6/horovod/_keras
copying horovod/_keras/init.py -> build/lib.linux-x86_64-3.6/horovod/_keras
creating build/lib.linux-x86_64-3.6/horovod/torch
copying horovod/torch/mpi_ops.py -> build/lib.linux-x86_64-3.6/horovod/torch
copying horovod/torch/init.py -> build/lib.linux-x86_64-3.6/horovod/torch
copying horovod/torch/compression.py -> build/lib.linux-x86_64-3.6/horovod/torch
creating build/lib.linux-x86_64-3.6/horovod/tensorflow/keras
copying horovod/tensorflow/keras/callbacks.py -> build/lib.linux-x86_64-3.6/horovod/tensorflow/keras
copying horovod/tensorflow/keras/init.py -> build/lib.linux-x86_64-3.6/horovod/tensorflow/keras
creating build/lib.linux-x86_64-3.6/horovod/run/task
copying horovod/run/task/task_service.py -> build/lib.linux-x86_64-3.6/horovod/run/task
copying horovod/run/task/init.py -> build/lib.linux-x86_64-3.6/horovod/run/task
creating build/lib.linux-x86_64-3.6/horovod/run/http
copying horovod/run/http/http_client.py -> build/lib.linux-x86_64-3.6/horovod/run/http
copying horovod/run/http/init.py -> build/lib.linux-x86_64-3.6/horovod/run/http
copying horovod/run/http/http_server.py -> build/lib.linux-x86_64-3.6/horovod/run/http
creating build/lib.linux-x86_64-3.6/horovod/run/common
copying horovod/run/common/init.py -> build/lib.linux-x86_64-3.6/horovod/run/common
creating build/lib.linux-x86_64-3.6/horovod/run/util
copying horovod/run/util/network.py -> build/lib.linux-x86_64-3.6/horovod/run/util
copying horovod/run/util/init.py -> build/lib.linux-x86_64-3.6/horovod/run/util
copying horovod/run/util/cache.py -> build/lib.linux-x86_64-3.6/horovod/run/util
copying horovod/run/util/threads.py -> build/lib.linux-x86_64-3.6/horovod/run/util
creating build/lib.linux-x86_64-3.6/horovod/run/driver
copying horovod/run/driver/driver_service.py -> build/lib.linux-x86_64-3.6/horovod/run/driver
copying horovod/run/driver/init.py -> build/lib.linux-x86_64-3.6/horovod/run/driver
creating build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/timeout.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/config_parser.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/secret.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/network.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/safe_shell_exec.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/settings.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/codec.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/init.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/host_hash.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
copying horovod/run/common/util/env.py -> build/lib.linux-x86_64-3.6/horovod/run/common/util
creating build/lib.linux-x86_64-3.6/horovod/run/common/service
copying horovod/run/common/service/task_service.py -> build/lib.linux-x86_64-3.6/horovod/run/common/service
copying horovod/run/common/service/driver_service.py -> build/lib.linux-x86_64-3.6/horovod/run/common/service
copying horovod/run/common/service/init.py -> build/lib.linux-x86_64-3.6/horovod/run/common/service
creating build/lib.linux-x86_64-3.6/horovod/spark/task
copying horovod/spark/task/task_info.py -> build/lib.linux-x86_64-3.6/horovod/spark/task
copying horovod/spark/task/task_service.py -> build/lib.linux-x86_64-3.6/horovod/spark/task
copying horovod/spark/task/mpirun_exec_fn.py -> build/lib.linux-x86_64-3.6/horovod/spark/task
copying horovod/spark/task/init.py -> build/lib.linux-x86_64-3.6/horovod/spark/task
creating build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/estimator.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/tensorflow.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/util.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/bare.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/init.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/remote.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
copying horovod/spark/keras/optimizer.py -> build/lib.linux-x86_64-3.6/horovod/spark/keras
creating build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/estimator.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/util.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/store.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/_namedtuple_fix.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/constants.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/params.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/init.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/cache.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/serialization.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
copying horovod/spark/common/backend.py -> build/lib.linux-x86_64-3.6/horovod/spark/common
creating build/lib.linux-x86_64-3.6/horovod/spark/driver
copying horovod/spark/driver/driver_service.py -> build/lib.linux-x86_64-3.6/horovod/spark/driver
copying horovod/spark/driver/job_id.py -> build/lib.linux-x86_64-3.6/horovod/spark/driver
copying horovod/spark/driver/init.py -> build/lib.linux-x86_64-3.6/horovod/spark/driver
copying horovod/spark/driver/mpirun_rsh.py -> build/lib.linux-x86_64-3.6/horovod/spark/driver
creating build/lib.linux-x86_64-3.6/horovod/spark/torch
copying horovod/spark/torch/estimator.py -> build/lib.linux-x86_64-3.6/horovod/spark/torch
copying horovod/spark/torch/util.py -> build/lib.linux-x86_64-3.6/horovod/spark/torch
copying horovod/spark/torch/init.py -> build/lib.linux-x86_64-3.6/horovod/spark/torch
copying horovod/spark/torch/remote.py -> build/lib.linux-x86_64-3.6/horovod/spark/torch
creating build/lib.linux-x86_64-3.6/horovod/torch/mpi_lib
copying horovod/torch/mpi_lib/init.py -> build/lib.linux-x86_64-3.6/horovod/torch/mpi_lib
creating build/lib.linux-x86_64-3.6/horovod/torch/mpi_lib_impl
copying horovod/torch/mpi_lib_impl/init.py -> build/lib.linux-x86_64-3.6/horovod/torch/mpi_lib_impl
running build_ext
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -std=c++11 -fPIC -O2 -Wall -fassociative-math -ffast-math -ftree-vectorize -funsafe-math-optimizations -mf16c -mavx -mfma -I/home/luxury/luxy/venv_athena/include -I/usr/include/python3.6m -c build/temp.linux-x86_64-3.6/test_compile/test_cpp_flags.cc -o build/temp.linux-x86_64-3.6/test_compile/test_cpp_flags.o
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/test_compile/test_cpp_flags.o -o build/temp.linux-x86_64-3.6/test_compile/test_cpp_flags.so
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/home/luxury/luxy/venv_athena/include -I/usr/include/python3.6m -c build/temp.linux-x86_64-3.6/test_compile/test_link_flags.cc -o build/temp.linux-x86_64-3.6/test_compile/test_link_flags.o
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -Wl,--version-script=horovod.lds build/temp.linux-x86_64-3.6/test_compile/test_link_flags.o -o build/temp.linux-x86_64-3.6/test_compile/test_link_flags.so
INFO: Cannot find CMake, will skip compiling Horovod with Gloo.
Traceback (most recent call last):
File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 341, in get_mpi_flags
shlex.split(show_command), universal_newlines=True).strip()
File "/usr/lib/python3.6/subprocess.py", line 356, in check_output
**kwargs).stdout
File "/usr/lib/python3.6/subprocess.py", line 423, in run
with Popen(*popenargs, **kwargs) as process:
File "/usr/lib/python3.6/subprocess.py", line 729, in init
restore_signals, start_new_session)
File "/usr/lib/python3.6/subprocess.py", line 1364, in _execute_child
raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'mpicxx': 'mpicxx'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 622, in get_common_options
    mpi_flags = get_mpi_flags()
  File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 354, in get_mpi_flags
    '%s' % (show_command, traceback.format_exc()))
distutils.errors.DistutilsPlatformError: mpicxx -show failed (see error below), is MPI in $PATH?
Note: If your version of MPI has a custom command to show compilation flags, please specify it with the HOROVOD_MPICXX_SHOW environment variable.

Traceback (most recent call last):
  File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 341, in get_mpi_flags
    shlex.split(show_command), universal_newlines=True).strip()
  File "/usr/lib/python3.6/subprocess.py", line 356, in check_output
    **kwargs).stdout
  File "/usr/lib/python3.6/subprocess.py", line 423, in run
    with Popen(*popenargs, **kwargs) as process:
  File "/usr/lib/python3.6/subprocess.py", line 729, in __init__
    restore_signals, start_new_session)
  File "/usr/lib/python3.6/subprocess.py", line 1364, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'mpicxx': 'mpicxx'


INFO: Cannot find MPI compilation flags, will skip compiling with MPI.
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 1566, in <module>
    scripts=['bin/horovodrun'])
  File "/home/luxury/luxy/venv_athena/lib/python3.6/site-packages/setuptools/__init__.py", line 144, in setup
    return distutils.core.setup(**attrs)
  File "/usr/lib/python3.6/distutils/core.py", line 148, in setup
    dist.run_commands()
  File "/usr/lib/python3.6/distutils/dist.py", line 955, in run_commands
    self.run_command(cmd)
  File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
    cmd_obj.run()
  File "/home/luxury/luxy/venv_athena/lib/python3.6/site-packages/setuptools/command/install.py", line 61, in run
    return orig.install.run(self)
  File "/usr/lib/python3.6/distutils/command/install.py", line 589, in run
    self.run_command('build')
  File "/usr/lib/python3.6/distutils/cmd.py", line 313, in run_command
    self.distribution.run_command(command)
  File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
    cmd_obj.run()
  File "/usr/lib/python3.6/distutils/command/build.py", line 135, in run
    self.run_command(cmd_name)
  File "/usr/lib/python3.6/distutils/cmd.py", line 313, in run_command
    self.distribution.run_command(command)
  File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
    cmd_obj.run()
  File "/home/luxury/luxy/venv_athena/lib/python3.6/site-packages/setuptools/command/build_ext.py", line 87, in run
    _build_ext.run(self)
  File "/usr/lib/python3.6/distutils/command/build_ext.py", line 339, in run
    self.build_extensions()
  File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 1457, in build_extensions
    options = get_common_options(self)
  File "/tmp/pip-install-s_f3vxnr/horovod/setup.py", line 635, in get_common_options
    raise RuntimeError('One of Gloo or MPI are required for Horovod to run. Check the logs above for more info.')
RuntimeError: One of Gloo or MPI are required for Horovod to run. Check the logs above for more info.
----------------------------------------

ERROR: Command errored out with exit status 1: /home/luxury/luxy/venv_athena/bin/python3 -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-s_f3vxnr/horovod/setup.py'"'"'; file='"'"'/tmp/pip-install-s_f3vxnr/horovod/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, file, '"'"'exec'"'"'))' install --record /tmp/pip-record-uelars4k/install-record.txt --single-version-externally-managed --compile --install-headers /home/luxury/luxy/venv_athena/include/site/python3.6/horovod Check the logs for full command output.

How to solve it ?

i follow the step with asr/aishell run.sh modify as hkust ,the finetune stage when load pretrain model mpc，it report the following error , the tensorflow verison is 2.3.1 cuda 10.1 cudnn7 ，is somebody met this kind of case， how to solve it ？thanks
[1]<stderr>:INFO:absl:trying to restore from : examples/asr/aishell/ckpts/mpc [0]<stderr>:INFO:absl:trying to restore from : examples/asr/aishell/ckpts/mpc [0]<stderr>:INFO:absl:Loading data from examples/asr/aishell/data/dev.csv [1]<stderr>:INFO:absl:Loading data from examples/asr/aishell/data/dev.csv [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1 [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1 [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2 [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2 [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay [0]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay [0]<stderr>:WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. [0]<stderr>:WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. [0]<stderr>:INFO:absl:loading from pretrained mpc model [0]<stderr>:INFO:absl:Loading data from examples/asr/aishell/data/train.csv [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1 [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1 [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2 [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2 [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay [1]<stderr>:WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay [1]<stderr>:WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. [1]<stderr>:WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details. [1]<stderr>:INFO:absl:loading from pretrained mpc model [1]<stderr>:INFO:absl:Loading data from examples/asr/aishell/data/train.csv

Thanks for your contribution firstly. It's hard to imagine that you not only finished the paper 'IMPROVING TRANSFORMER-BASED SPEECH RECOGNITION USING UNSUPERVISED
PRE-TRAINING' but also shared the code. Thank you very much.

There is a problem when I using athena project. It looks fine in "Preparing data"、"Pretraining"、"Fine-tuning" step, develop dataset has high accuracy. But when I use athena/decode_main.py with test dataset in "Decoding" step, the "avg_acc" is always zero in log messages, like this:

INFO:absl:predictions: tf.Tensor([[4233]], shape=(1, 1), dtype=int64) labels: [[ 424 2477 3491 1238 850 1284 1269]] errs: 7 avg_acc: 0.0000 sec/iter: 0.3383

My script file is modified from "hkust" exmaple, here is my script：

# coding=utf-8
# Copyright (C) ATHENA AUTHORS
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

if [ "athena" != $(basename "$PWD") ]; then
    echo "You should run this script in athena directory!!"
    exit 1
fi

source tools/env.sh

stage=3
stop_stage=3

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    # prepare data
    echo "Preparing data"
    python examples/asr/aishell/local/prepare_data.py /nfs/project/datasets/opensource_data/aishell
    mkdir -p examples/asr/aishell/data
    cp /nfs/project/datasets/opensource_data/aishell/{train,dev}.csv examples/asr/aishell/data/

    # cal cmvn
    cat examples/asr/aishell/data/train.csv > examples/asr/aishell/data/all.csv
    tail -n +2 examples/asr/aishell/data/dev.csv >> examples/asr/aishell/data/all.csv
    python athena/cmvn_main.py examples/asr/aishell/mpc.json examples/asr/aishell/data/all.csv
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    # pretrain stage
    echo "Pretraining"
    # we recommend training with multi-gpu, for single gpu, run "python athena/main.py examples/asr/aishell/mpc.json" instead
    horovodrun -np 4 -H localhost:4 python athena/horovod_main.py examples/asr/aishell/mpc.json
fi

if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    # finetuning stage
    echo "Fine-tuning"
    # we recommend training with multi-gpu, for single gpu, run "python athena/main.py examples/asr/aishell/mtl_transformer.json" instead
    horovodrun -np 4 -H localhost:4 python athena/horovod_main.py examples/asr/aishell/mtl_transformer.json
fi

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    # decoding stage
    echo "Decoding"
    # prepare language model
    tail -n +2 examples/asr/aishell/data/train.csv | cut -f 3 > examples/asr/aishell/data/text
    python examples/asr/aishell/local/segment_word.py examples/asr/aishell/data/vocab \
       examples/asr/aishell/data/text > examples/asr/aishell/data/text.seg
    tools/kenlm/build/bin/lmplz -o 4 < examples/asr/aishell/data/text.seg > examples/asr/aishell/data/lm.bin

    python athena/decode_main.py examples/asr/aishell/mtl_transformer.json
fi

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    echo "training rnnlm"
    tail -n +2 examples/asr/aishell/data/train.csv | awk '{print $3"\t"$3}' > examples/asr/aishell/data/train.trans.csv
    tail -n +2 examples/asr/aishell/data/dev.csv | awk '{print $3"\t"$3}' > examples/asr/aishell/data/dev.trans.csv
    python athena/main.py examples/asr/aishell/rnnlm.json
fi

I'm not sure where is the problem.

Hi, is there any published speech emotion recognition pipeline?

Hi, there are some problems when I try to run the librispeech examples
KeyError: 'subword'.
Can you update the right config file? Thanks!

缺少 examples/asr/aishell/run.sh 文件

Hi, is there any published speech emotion recognition pipeline?

We need scripts to validate things such as data directory structure. Otherwise we won't know if certain step fails. I'll assign it to myself for now but may take some time to get back to this.

i read the positon encoding code found that
def call(self, x): """ call function """ seq_len = tf.shape(x)[1] if self.scale: x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32)) x += self.pos_encoding[:, :seq_len, :] return x

my question is when to use the scale ,when not use ? is there any experimental result or theory to direct the seleciton?