Keyword Boosting

Speech-to-Text is excellent on everyday language. Keyword boosting extends that strength to the vocabulary that is specific to your product: player names, brands, products, and channel names. Terms like Mbappé or Vinícius come out exactly the way you expect. You pass a dictionary of the terms you care about in json_config, and the model gives them priority while decoding, in real time, with no retraining.

The idea

Add a keywords object with the terms and a boost weight:

json_config = {
    "language": "en",
    "keywords": {
        "words": ["Mbappé", "Haaland", "Vinícius", "Bellingham", "Lewandowski"],
        "boost": 3,
    },
}

boost is applied in log-probability space, so its effect is exponential: a small value already shifts decoding noticeably. Use the range -6 to 6, with 3 as a strong default. See Transcription Settings for the full parameter reference.

Build a good dictionary

Keywords are matched one token at a time, which means two things.

Use single tokens, with no spaces. Split multi-word names, so "Ferran Torres" becomes "Ferran" and "Torres".
Match case and accents. "Mbappé" and "Mbappe" are different keys, so include the variants you expect.

A small helper that does both:

import unicodedata


def build_keywords(terms):
    strip = lambda s: "".join(
        c for c in unicodedata.normalize("NFKD", s) if not unicodedata.combining(c)
    )
    out = []
    for term in terms:
        for token in term.split():                       # split phrases into tokens
            for variant in (token, token.lower(), strip(token), strip(token).lower()):
                if variant not in out:
                    out.append(variant)
    return out


build_keywords(["Ferran Torres", "Mbappé"])
# ['Ferran', 'ferran', 'Torres', 'torres', 'Mbappé', 'mbappé', 'Mbappe', 'mbappe']

Full example

import asyncio
import gradium

TERMS = ["Kylian Mbappé", "Erling Haaland", "Vinícius Júnior", "Jude Bellingham"]


async def transcribe(audio_source):
    client = gradium.client.GradiumClient(api_key="your-api-key")

    async with client.stt_realtime(
        model_name="default",
        input_format="pcm",
        json_config={
            "language": "en",
            "delay_in_frames": 16,
            "keywords": {"words": build_keywords(TERMS), "boost": 3},
        },
    ) as stt:
        transcript = []

        async def producer():
            async for chunk in audio_source:
                await stt.send_audio(chunk)
            await stt.send_eos()

        async def consumer():
            async for msg in stt:
                if msg["type"] == "text":
                    transcript.append(msg["text"])
                elif msg["type"] == "end_of_stream":
                    return

        await asyncio.gather(producer(), consumer())
        return " ".join(transcript).strip()

Tuning

Boost	Behaviour	Use it when
`1`-`2`	Gentle nudge, no risk	Fairly common vocabulary, where a tiebreak is enough.
`3`	Recommended default; recovers most rare names	Most cases.
`4`	Stronger recovery	Dense or foreign proper nouns.
`5`-`6`	Diminishing returns, and the decoder may start to loop (repeat a keyword)	Rarely, and watch the output.
negative	De-boosts a term	Only to remove a specific word you never want.

Boosting is fully compatible with streaming, and it earns its keep most at low delay_in_frames, where the model has less look-ahead and leans more on the dictionary.

Transcription Settings

The keywords parameter reference and the rest of json_config.

Speech-to-Text WebSocket

STT message types, VAD details, and flushing.

Getting Started

Text-to-Speech

Speech-to-Text

Speech-to-Speech

Shared

Real-time Recipes

Migrations

Voices

Resources

The idea

Build a good dictionary

Full example

Tuning

Transcription Settings

Speech-to-Text WebSocket

​The idea

​Build a good dictionary

​Full example

​Tuning

​Related

Transcription Settings

Speech-to-Text WebSocket

The idea

Build a good dictionary

Full example

Tuning

Related