> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gladia.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Recommended Parameters by Use Case
> Best parameter configurations for pre-recorded transcription depending on your application (Meeting Recorders, Call Centers, Podcasts, Subtitles, Multilingual Content).
The right parameter configuration can significantly impact transcription quality for pre-recorded audio. This guide covers recommended starting points for common scenarios and highlights pitfalls that frequently trip up new integrations.
These recommendations apply to the **[Pre-recorded
API](/chapters/pre-recorded-stt/quickstart)** and are passed in the `POST
/v2/pre-recorded` request body. They are starting points — tune them to match
your specific needs.
***
## Language Configuration
One of the most common configuration mistakes is misunderstanding how `language_config` works. Choosing the right setup avoids unnecessary detection overhead and improves accuracy.
**When to set an explicit language:**
* You **know** the language of the audio ahead of time.
* The audio is **monolingual** (single language throughout).
* You want the **fastest, most accurate** results.
```json theme={"system"}
{
"language_config": {
"languages": ["en"],
"code_switching": false
}
}
```
**When to use auto-detection:**
* You process audio in **many different languages** and don't know which one beforehand.
* You want Gladia to pick the language automatically.
```json theme={"system"}
{
"language_config": {
"languages": [],
"code_switching": false
}
}
```
When `code_switching` is `false` and no language is set, the language is
detected on the **first utterance** and reused for the rest of the session or
file. If the beginning of your audio contains silence, music, or a different
language than the main content, this can lead to incorrect detection for the
whole transcription.
Even when using auto-detection, pass a **small list of likely languages** in
`languages` to constrain the search. This improves both accuracy and
processing time.
***
## Code Switching
Code switching (`language_config.code_switching: true`) lets Gladia detect and transcribe **multiple languages** within the same audio, re-evaluating the language on each utterance.
**When to enable it:**
* Speakers **switch languages** mid-conversation (e.g. bilingual meetings, multilingual customer support).
* You need the detected `language` returned **per utterance**.
**When NOT to enable it:**
* The audio is in a **single language** — code switching adds unnecessary processing and can introduce misdetections.
* You've set **exactly one language** in `languages` — in that case `code_switching` is ignored anyway.
```json theme={"system"}
{
"language_config": {
"languages": ["en", "fr", "es"],
"code_switching": true
}
}
```
**Do not enable `code_switching` with an empty `languages` list.** When no
languages are specified, the language detector evaluates every utterance
against 100+ supported languages, which leads to frequent misdetections —
especially between similar-sounding languages. Always provide a short list of
languages you **actually expect** in the audio.
***
## Custom Vocabulary
[Custom vocabulary](/chapters/audio-intelligence/custom-vocabulary) is a post-transcription replacement based on **phoneme similarity**. It's essential for domain-specific terms that speech models frequently mis-transcribe.
**Best practices:**
* **Always provide both** the `custom_vocabulary` flag and a `custom_vocabulary_config`.
* **Add pronunciations** for words that can be said in different ways (accents, foreign speakers). This is more reliable than raising `intensity`.
* **Keep `intensity` moderate** (0.4-0.6). High values increase false positives where unrelated words get replaced.
* **Set `language`** on individual vocabulary entries when your audio is multilingual and a term is pronounced differently depending on the language.
For simple terms that are already close to their phonetic spelling (e.g. brand
names), you can pass them as plain strings instead of objects — Gladia will
use the default intensity.
```json Pre-recorded theme={"system"}
{
"audio_url": "YOUR_AUDIO_URL",
"custom_vocabulary": true,
"custom_vocabulary_config": {
"vocabulary": [
"Kubernetes",
{
"value": "Gladia",
"pronunciations": ["Gladya", "Gladiah"],
"intensity": 0.5
},
{
"value": "PostgreSQL",
"pronunciations": ["Postgres Q L", "Post gress"],
"intensity": 0.4
}
],
"default_intensity": 0.5
}
}
```
```json Live theme={"system"}
{
"realtime_processing": {
"custom_vocabulary": true,
"custom_vocabulary_config": {
"vocabulary": [
"Kubernetes",
{
"value": "Gladia",
"pronunciations": ["Gladya", "Gladiah"],
"intensity": 0.5
},
{
"value": "PostgreSQL",
"pronunciations": ["Postgres Q L", "Post gress"],
"intensity": 0.4
}
],
"default_intensity": 0.5
}
}
}
```
***
## Meeting Recorders
For apps that record and process meetings — team stand-ups, board sessions, 1-on-1s — the goal is to produce **structured, actionable meeting notes** with clear speaker attribution. Meetings typically have a known set of participants and benefit heavily from post-processing features like summarization.
| Parameter | Recommended value | Why |
| -------------------------------------------------- | --------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `diarization` | `true` | Attributes speech to each participant. See [Speaker diarization](/chapters/audio-intelligence/speaker-diarization). |
| `diarization_config.min_speakers` / `max_speakers` | Set a range (e.g. `2`-`10`) | Meeting size varies — a range lets the model adapt without over- or under-splitting speakers. |
| `summarization` | `true` | Generates a summary for quick review. Use `bullet_points` type for action-item style output. See [Summarization](/chapters/audio-intelligence/summarization). |
| `named_entity_recognition` | `true` | Surfaces people, organizations, dates, and other key entities mentioned during the meeting. See [NER](/chapters/audio-intelligence/named-entity-recognition). |
| `sentences` | `true` | Produces well-segmented, readable output suitable for meeting minutes. See [Sentences](/chapters/pre-recorded-stt/features/sentences). |
| `language_config.languages` | Set explicitly | Meeting language is almost always known in advance — setting it avoids detection overhead. |
| `custom_vocabulary` | `true` | Add company-specific terms, project names, and participant names for better accuracy. |
**Diarization vs. multi-channel:** if each speaker is on a **separate audio channel** (e.g. a, use the `channel` field on each utterance to identify who is speaking — diarization is not needed. See [Multiple channels](/chapters/limits-and-specifications/multiple-channels)
If all speakers share a **single audio channel**, enable `diarization` to separate the speakers. See [Speaker diarization](/chapters/audio-intelligence/speaker-diarization).
***
## Call Centers
For recorded phone calls the priorities are **speaker identification** and **accurate transcription** despite variable audio quality (telephony codecs, background noise, cross-talk).
| Parameter | Recommended value | Why |
| --------------------------------------- | ------------------------------ | --------------------------------------------------------------------------------------------------------------------------- |
| `language_config.languages` | Set explicitly (e.g. `["en"]`) | Call center audio typically has a known language. Setting it avoids detection errors on noisy recordings. |
| `diarization` | `true` | Separates agent and customer speech. See [Speaker diarization](/chapters/audio-intelligence/speaker-diarization). |
| `diarization_config.number_of_speakers` | `2` | Most calls have exactly two participants — giving this hint improves speaker assignment accuracy. |
| `custom_vocabulary` | `true` | Add product names, plan names, and internal terminology. |
| `summarization` | `true` | Automatically generates a summary for agent wrap-up notes. See [Summarization](/chapters/audio-intelligence/summarization). |
**Diarization vs. multi-channel:** if each speaker is on a **separate audio channel** (e.g. a, use the `channel` field on each utterance to identify who is speaking — diarization is not needed. See [Multiple channels](/chapters/limits-and-specifications/multiple-channels)
If all speakers share a **single audio channel**, enable `diarization` to separate the speakers. See [Speaker diarization](/chapters/audio-intelligence/speaker-diarization).
***
## Podcasts & Interviews
For long-form audio with multiple speakers the focus is on **readability** and **correct speaker attribution**. Transcripts are often repurposed as articles or show notes, so segment quality matters.
| Parameter | Recommended value | Why |
| -------------------------------------------------- | -------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
| `diarization` | `true` | Essential for multi-speaker content. |
| `diarization_config.min_speakers` / `max_speakers` | Set a range (e.g. `2`-`4`) | Provides a flexible hint when the exact count varies across episodes. |
| `sentences` | `true` | Produces well-segmented, readable output suitable for publishing. See [Sentences](/chapters/pre-recorded-stt/features/sentences). |
| `custom_vocabulary` | `true` | Add recurring guest names, show-specific terms, and brand names. |
| `language_config.languages` | Set explicitly | Podcast language is almost always known in advance. |
***
## Subtitles & Captioning
When generating subtitle files from pre-recorded content, tune the formatting parameters for the best viewing experience. Gladia produces SRT and VTT files directly — no post-processing needed. See [Subtitles](/chapters/audio-intelligence/subtitles) for the full parameter reference.
| Parameter | Recommended value | Why |
| --------------------------------------------- | ------------------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `subtitles` | `true` | Enables subtitle generation. |
| `subtitles_config.formats` | `["srt", "vtt"]` | Generate both formats to cover different players and platforms. |
| `subtitles_config.maximum_characters_per_row` | `42` | Standard broadcast limit for readability. |
| `subtitles_config.maximum_rows_per_caption` | `2` | Keeps captions compact on screen. |
| `subtitles_config.style` | `"compliance"` | Uses stricter formatting rules suited for broadcast or accessibility requirements. |
| `translation` | `true` (if needed) | When enabled, subtitles are automatically generated for each target language. See [Translation](/chapters/audio-intelligence/translation). |
For live captions streamed in real time, use the [Realtime
API](/chapters/live-stt/quickstart) with partial transcripts instead — see the
[Live recommended
parameters](/chapters/live-stt/recommended-parameters#subtitles--captioning)
guide.
***
## Multilingual Content
For content with mixed languages — conferences, multilingual media, interviews with speakers from different countries — combine language detection with code switching.
| Parameter | Recommended value | Why |
| -------------------------------- | ------------------------------------------------------ | --------------------------------------------------------------------------------------------------- |
| `language_config.languages` | List of expected languages (e.g. `["en", "fr", "de"]`) | Constrain to 3-5 expected languages for best accuracy. |
| `language_config.code_switching` | `true` | Detects language shifts across utterances. See [Code switching](/chapters/language/code-switching). |
| `custom_vocabulary` | `true` | Add terms for each language with appropriate `language` tags on each entry. |
Do not enable `code_switching` with an empty `languages` list. The detector
would evaluate every utterance against 100+ languages, leading to frequent
misdetections — especially between similar-sounding languages.