Wyoming Vosk

Wyoming protocol server for the vosk speech to text system, with optional sentence correction using rapidfuzz.

This speech-to-text system can run well, even on a Raspberry Pi 3. Using the corrected or limited modes (described below), you can achieve very high accuracy by restricting the sentences that can be spoken.

Models are automatically downloaded from HuggingFace, but they are originally from Alpha Cephei. Please review the license of each model that you use (model list).

Modes

There are three operating modes:

1. Open-ended - any sentence can be spoken, but recognition is very poor compared to Whisper
2. Corrected - sentences similar to templates are forced to match
3. Limited - only sentences from templates can be spoken

Open-ended

This is the default mode: transcripts from vosk are used directly.

Recognition is very poor compared to Whisper unless you use one of the larger models. To use a specific model, such as vosk-model-en-us-0.21 (1.6GB):

1. Download and extract the model to a directory (<DATA_DIR>) so that you have <DATA_DIR>/vosk-model-en-us-0.21
2. Run wyoming_vosk with --data-dir <DATA_DIR> and --model-for-language en vosk-model-en-us-0.21

Note that wyoming_vosk will only automatically download models listed in download.py.

Corrected

By specifying which sentences will be spoken ahead of time, transcripts from vosk can be corrected using rapidfuzz.

Create your sentence templates and save them to a file named <SENTENCES_DIR>/<LANGUAGE>.yaml where <LANGUAGE> is one of the supported language codes. For example, English sentences should be saved in <SENTENCES_DIR>/en.yaml.

Then, run wyoming_vosk like:

script/run ... --sentences-dir <SENTENCES_DIR> --correct-sentences <CUTOFF>

where <CUTOFF> is:

• empty or 0 - force transcript to be one of the template sentences
• greater than 0 - allow more sentences that are not similar to templates to pass through

When <CUTOFF> is large, speech recognition is effectively open-ended again. Experiment with different values to find one that lets you speak sentences outside your templates without sacrificing accuracy too much.

If you have a set of sentences with a specific pattern that you'd like to skip correction, add them to your no-correct patterns.

Limited

Follow the instructions for corrected mode, then run wyoming_vosk like:

script/run ... --sentences-dir <SENTENCES_DIR> --correct-sentences --limit-sentences

This will tell vosk that only the sentences from you templates can ever be spoken. Sentence correction is still needed (due to how vosk works internally), but it will ensure that sentences outside the templates cannot be sent.

This mode will get you the highest possible accuracy, with the trade-off being that you cannot speak sentences outside the templates.

Sentence Templates

Each language may have a YAML file with sentence templates. Most syntax is supported, including:

• Optional words, surrounded with [square brackets]
• Alternative words, (surrounded|with|parens)
• Lists of values, referenced by {name}
• Expansion rules, inserted by <name>

The general format of a language's YAML file is:

sentences:
  - this is a plain sentence
  - this is a sentence with a {list} and a <rule>
lists:
  list:
    values:
      - value 1
      - value 2
expansion_rules:
  rule: "body of the rule"

Make sure to put quotes around anything in YAML that starts with [ or { characters, since YAML will interpret those as the start of a list or dictionary. Additionally, words like on/off/yes/no have to be quoted to stop them from being turned into booleans.

Sentences have a special in/out form as well, which lets you say one thing (in) but put something else in the transcript (out).

For example:

sentences:
  - in: lou mo ss  # lumos
    out: turn on all the lights
  - in: knocks   # nox
    out: turn off all the lights

lets you say "lumos" to send "turn on all the lights", and "nox" to send "turn off all the lights". Notice that we used words that sound like "lumos" and "nox" because the vocabulary of the default English model is limited (vosk-model-small-en-us-0.15).

The in key can also take a list of sentences, all of them outputting the same out string.

Lists

Lists are useful when you many possible words/phrases in a sentence.

For example:

sentences:
  - set light to {color}
lists:
  color:
    values:
      - red
      - green
      - blue
      - orange
      - yellow
      - purple

lets you set a light to one of six colors.

This could also be written as set light to (red|green|blue|orange|yellow|purple), but the list is more manageable and can be shared between sentences.

List values have a special in/out form that lets you say one thing (in) but put something else in the transcript (out).

For example:

sentences:
  - turn (on|off) {device}
lists:
  device:
    values:
      - in: tv
        out: living room tv
      - in: light
        out: bedroom room light

lets you say "turn on tv" to turn on the living room TV, and "turn off light" to turn off the bedroom light.

Expansion Rules

Repeated parts of a sentence template can be abstracted into an expansion rule.

For example:

sentences:
  - turn on <the> light
  - turn off <the> light
expansion_rules:
  the: [the|my]

lets you say "turn on light" or "turn off my light" without having to repeat the optional part.

No Correct Patterns

When you correct sentences, you want to keep the score cutoff as low as possible to avoid letting invalid sentences though. But what if you just want some open-ended sentences, such as "draw me a picture of ..." which you can then forward to an image generator?

Add the following to your sentences YAML file:

sentences:
  ...
no_correct_patterns:
  - <regular expression>
  - <regular expression>
  ...

You can add as many regular expressions to no_correct_patterns as you'd like. If the transcript matches any of these patterns, it will be sent with no further corrections. This effectively lets you "punch holes" in the sentence templates to allow some sentences through.

Allow Unknown

With --allow-unknown, you can enable the detection of "unknown" words/phrases outside of the model's vocabulary. Transcripts that are "unknown" will be set to empty strings, indicating that nothing was recognized. When combined with limited sentences, this lets you differentiate between in and out of domain sentences.

NOTE: Some models do not support unknown words/phrases. See supported languages.

Supported Languages

• Arabic (ar)
• Catalan (ca)
• Czech (cz)
  • Does not work with allow unknown
• German (de)
  • Does not work with allow unknown
• English (en)
• Spanish (es)
  • Does not work with allow unknown
• Persian (fa)
  • Does not work with allow unknown
• French (fr)
• Hindi (hi)
  • Does not work with allow unknown
• Italian (it)
  • Does not work with allow unknown
• Korean (ko)
  • Does not work with allow unknown
• Dutch (nl)
• Polish (pl)
  • Does not work with allow unknown
• Portuguese (pt)
  • Does not work with allow unknown
• Russian (ru)
  • Does not work with allow unknown
• Swedish (sv)
  • Does not work with limited sentences and allow unknown
• Ukrainian (uk)
• Vietnamese (vn)
• Chinese (zh)

Not tested (no intent support yet in Home Assistant):

• Breton (br)
• Esperanto (eo)
• Japanese (ja)
• Kazakh (kz)
• Tagalog (tl)
• Uzbek (uz)