Rhasspy Natural Language Understanding

Library for parsing Rhasspy sentence templates, doing intent recognition, and generating ARPA language models.

Parsing Sentence Templates

Rhasspy voice commands are stored in text files formatted like this:

[Intent1]
this is a sentence
this is another sentence

[Intent2]
a sentence in a different intent

You can parse these into a structured representation with rhasspynlu.parse_ini and then convert them to a graph using rhasspynlu.intents_to_graph:

import rhasspynlu

# Load and parse
intents = rhasspynlu.parse_ini(
"""
[LightOn]
turn on [the] (living room lamp | kitchen light){name}
"""
)

graph = rhasspynlu.intents_to_graph(intents)

The result is a directed graph whose states are words and edges are input/output labels.

You can pass an intent_filter function to parse_ini to return True for only the intent names you want to parse. Additionally, a function can be provided for the sentence_transform argument that each sentence will be passed through (e.g., to lower case).

Template Syntax

Sentence templates are based on the JSGF standard. The following constructs are available:

• Optional words
  • this is [a] test - the word "a" may or may not be present
• Alternatives
  • set color to (red | green | blue) - either "red", "green", or "blue" is possible
• Tags
  • turn on the [den | playroom]{location} light - named entity location will be either "den" or "playroom"
• Substitutions
  • make ten:10 coffees - output will be "make 10 coffees"
  • turn off the: (television | tele):tv - output will be "turn off tv"
  • set brightness to (medium | half){brightness:50} - named entity brightness will be "50"
• Rules
  • rule_name = rule body can be referenced as <rule_name>
• Slots
  • $slot will be replaced by a list of sentences in the replacements argument of intents_to_graph

Rules

Named rules can be added to your template file using the syntax:

rule_name = rule body

and then reference using <rule_name>. The body of a rule is a regular sentence, which may itself contain references to other rules.

You can refrence rules from different intents by prefixing the rule name with the intent name and a dot:

[Intent1]
rule = a test
this is <rule>

[Intent2]
rule = this is
<rule> <Intent1.rule>

In the example above, Intent2 uses its local <rule> as well as the <rule> from Intent1.

Slots

Slot names are prefixed with a dollar sign ($). When calling intents_to_graph, the replacements argument is a dictionary whose keys are slot names (with $) and whose values are lists of (parsed) Sentence objects. Each $slot will be replaced by the corresponding list of sentences, which may contain optional words, tags, rules, and other slots.

For example:

import rhasspynlu

# Load and parse
intents = rhasspynlu.parse_ini(
"""
[SetColor]
set color to $color
"""
)

graph = rhasspynlu.intents_to_graph(
    intents, replacements = {
        "$color": [rhasspynlu.Sentence.parse("red | green | blue")]
    }
)

will replace $color with "red", "green", or "blue".

Intent Recognition

After converting your sentence templates to a graph, you can recognize sentences. Assuming you have a .ini file like this:

[LightOn]
turn on [the] (living room lamp | kitchen light){name}

You can recognize sentences with:

from pathlib import Path
import rhasspynlu

# Load and parse
intents = rhasspynlu.parse_ini(Path("sentences.ini"))
graph = rhasspynlu.intents_to_graph(intents)

rhasspynlu.recognize("turn on living room lamp", graph)

will return a list of Recognition objects like:

[
    Recognition(
        intent=Intent(name='LightOn', confidence=1.0),
        entities=[
            Entity(
                entity='name',
                value='living room lamp',
                raw_value='living room lamp',
                start=8,
                raw_start=8,
                end=24,
                raw_end=24,
                tokens=['living', 'room', 'lamp'],
                raw_tokens=['living', 'room', 'lamp']
            )
        ],
        text='turn on living room lamp',
        raw_text='turn on living room lamp',
        recognize_seconds=0.00010710899914556649,
        tokens=['turn', 'on', 'living', 'room', 'lamp'],
        raw_tokens=['turn', 'on', 'living', 'room', 'lamp']
    )
]

An empty list means that recognition has failed. You can easily convert Recognition objects to JSON:

...

import json

recognitions = rhasspynlu.recognize("turn on living room lamp", graph)
if recognitions:
    recognition_dict = recognitions[0].asdict()
    print(json.dumps(recognition_dict))

You can also pass an intent_filter function to recognize to return True only for intent names you want to include in the search.

Tokens

If your sentence is tokenized by something other than whitespace, pass the list of tokens into recognize instead of a string.

Recognition Fields

The rhasspynlu.Recognition object has the following fields:

• intent - a rhasspynlu.Intent instance
  • name - name of recognized intent
  • confidence - number for 0-1, 1 being sure
• text - substituted input text
• raw_text - input text
• entities - list of rhasspynlu.Entity objects
  • entity - name of recognized entity ("name" in (input:output){name})
  • value - substituted value of recognized entity ("output" in (input:output){name})
  • tokens - list of words in value
  • start - start index of value in text
  • end - end index of value in text (exclusive)
  • raw_value - value of recognized entity ("input" in (input:output){name})
  • raw_tokens - list of words in raw_value
  • raw_start - start index of raw_value in raw_text
  • raw_end - end index of raw_value in raw_text (exclusive)
• recognize_seconds - seconds taken for recognize

Stop Words

You can pass a set of stop_words to recognize:

rhasspynlu.recognize("turn on that living room lamp", graph, stop_words=set(["that"]))

Stop words in the input sentence will be skipped over if they don't match the graph.

Strict Recognition

For faster, but less flexible recognition, set fuzzy to False:

rhasspynlu.recognize("turn on the living room lamp", graph, fuzzy=False)

This is at least twice as fast, but will fail if the sentence is not precisely present in the graph.

Strict recognition also supports stop_words for a little added flexibility. If recognition without stop_words fails, a second attempt will be made using stop_words.

ARPA Language Models

If you have the Opengrm command-line tools in your PATH, you can use rhasspynlu to generate language models in the ARPA format. These models can be used by speech recognition systems, such as Pocketsphinx, Kaldi, and Julius.

The graph_to_fst and fst_to_arpa functions are used to convert between formats. Calling fst_to_arpa requires the following binaries to be present in your PATH:

• fstcompile (from OpenFST)
• ngramcount
• ngrammake
• ngrammerge
• ngramprint
• ngramread

Example:

...

# Convert to FST
graph_fst = rhasspynlu.graph_to_fst(graph)

# Write FST and symbol text files
graph_fst.write("my_fst.txt", "input_symbols.txt", "output_symbols.txt")

# Compile and convert to ARPA language model
rhasspynlu.fst_to_arpa(
    "my_fst.txt",
    "input_symbols.txt", 
    "output_symbols.txt",
    "my_arpa.lm"
)

You can now use my_arpa.lm in any speech recognizer that accepts ARPA-formatted language models.

Language Model Mixing

If you have an existing language model that you'd like to mix with Rhasspy voice commands, you will first need to convert it to an FST:

rhasspynlu.fst_to_arpa("existing_arpa.lm", "existing_arpa.fst")

Now when you call fst_to_arpa, make sure to provide the base_fst_weight argument. This is a tuple with the path to your existing ARPA FST and a mixture weight between 0 and 1. A weight of 0.05 means that the base language model will receive 5% of the overall probability mass in the language model. The rest of the mass will be given to your custom voice commands.

Example:

...
rhasspynlu.fst_to_arpa(
    "my_fst.txt",
    "input_symbols.txt",
    "output_symbols.txt",
    "my_arpa.lm",
    base_fst_weight=("existing_arpa.fst", 0.05)
)