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=====
To evaluate the simultaneous translation system,
the users need to implement agent class which operate the system logics.
This section will introduce how to implement an agent.
Source-Target Types
-------------------
First of all,
we must declare the source and target types of the agent class.
It can be done by inheriting from
- One of the following four built-in agent types
- :class:`simuleval.agents.TextToTextAgent`
- :class:`simuleval.agents.SpeechToTextAgent`
- :class:`simuleval.agents.TextToSpeechAgent`
- :class:`simuleval.agents.SpeechToSpeechAgent`
- Or :class:`simuleval.agents.GenericAgent`, with explicit declaration of :code:`source_type` and :code:`target_type`.
The follow two examples are equivalent.
.. code-block:: python
from simuleval import simuleval
from simuleval.agents import GenericAgent
class MySpeechToTextAgent(GenericAgent):
source_type = "Speech"
target_type = "Text"
....
.. code-block:: python
from simuleval.agents import SpeechToSpeechAgent
class MySpeechToTextAgent(SpeechToSpeechAgent):
....
.. _agent_policy:
Policy
------
The agent must have a :code:`policy` method which must return one of two actions, :code:`ReadAction` and :code:`WriteAction`.
For example, an agent with a :code:`policy` method should look like this
.. code-block:: python
class MySpeechToTextAgent(SpeechToSpeechAgent):
def policy(self):
if do_we_need_more_input(self.states):
return ReadAction()
else:
prediction = generate_a_token(self.states)
finished = is_sentence_finished(self.states)
return WriteAction(prediction, finished=finished)
..
.. autoclass:: simuleval.agents.actions.WriteAction
..
.. autoclass:: simuleval.agents.actions.ReadAction
States
------------
Each agent has the attribute the :code:`states` to keep track of the progress of decoding.
The :code:`states` attribute will be reset at the beginning of each sentence.
SimulEval provide an built-in states :class:`simuleval.agents.states.AgentStates`,
which has some basic attributes such source and target sequences.
The users can also define customized states with :code:`Agent.build_states` method:
.. code-block:: python
from simuleval.agents.states import AgentStates
from dataclasses import dataclass
@dataclass
class MyComplicatedStates(AgentStates)
some_very_useful_variable: int
def reset(self):
super().reset()
# also remember to reset the value
some_very_useful_variable = 0
class MySpeechToTextAgent(SpeechToSpeechAgent):
def build_states(self):
return MyComplicatedStates(0)
def policy(self):
some_very_useful_variable = self.states.some_very_useful_variable
...
self.states.some_very_useful_variable = new_value
...
..
.. autoclass:: simuleval.agents.states.AgentStates
:members:
Pipeline
--------
The simultaneous system can consist several different components.
For instance, a simultaneous speech-to-text translation can have a streaming automatic speech recognition system and simultaneous text-to-text translation system.
SimulEval introduces the agent pipeline to support this function.
The following is a minimal example.
We concatenate two wait-k systems with different rates (:code:`k=2` and :code:`k=3`)
Note that if there are more than one agent class define,
the :code:`@entrypoint` decorator has to be used to determine the entry point
.. literalinclude:: ../../examples/quick_start/agent_pipeline.py
:language: python
:lines: 7-
Customized Arguments
-----------------------
It is often the case that we need to pass some customized arguments for the system to configure different settings.
The agent class has a built-in static method :code:`add_args` for this purpose.
The following is an updated version of the dummy agent from :ref:`first-agent`.
.. literalinclude:: ../../examples/quick_start/agent_with_configs.py
:language: python
:lines: 6-
Then just simply pass the arguments through command line as follow.
.. code-block:: bash
simuleval \
--source source.txt --source target.txt \ # data arguments
--agent dummy_waitk_text_agent_v2.py \
--waitk 3 --vocab data/dict.txt # agent arguments
Load Agents from Python Class
-----------------------------
If you have the agent class in the python environment, for instance
.. literalinclude:: ../../examples/quick_start/agent_with_configs.py
:language: python
:lines: 6-
You can also start the evaluation with following command
.. code-block:: bash
simuleval \
--source source.txt --source target.txt \ # data arguments
--agent-class DummyWaitkTextAgent \
--waitk 3 --vocab data/dict.txt # agent arguments
Load Agents from Directory
--------------------------
Agent can also be loaded from a directory, which will be referred to as system directory.
The system directory should have everything required to start the agent. Again use the following agent as example
.. literalinclude:: ../../examples/quick_start/agent_with_configs.py
:language: python
:lines: 6-
and the system directory has
.. code-block:: bash
> ls ${system_dir}
main.yaml dict.txt
Where the `main.yaml` has all the command line options. The path will be the relative path to the `${system_dir}`.
.. code-block:: yaml
waitk: 3
vocab: dict.txt
The agent can then be started as following
.. code-block:: bash
simuleval \
--source source.txt --source target.txt \ # data arguments
--system-dir ${system_dir}
By default, the `main.yaml` will be read. You can also have multiple YAML files in the system directory and pass them through command line arguments
.. code-block:: bash
> ls ${system_dir}
main.yaml dict.txt v1.yaml
> simuleval \
--source source.txt --source target.txt \ # data arguments
--system-dir ${system_dir} --system-config v1.yaml |