---
title: "Models and languages"
description: "The recommended transcription model for each of 10 Indian languages, plus a Hinglish compatibility code."
---

> Documentation Index
> Fetch the complete documentation index at: https://docs.navana.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Models and languages

Pass a model name to `model` in the form field for non-streaming, in the config
frame for streaming. There is no auto-detection: the model you name determines
the language.

## Available models

| Language | Code | Model | Notes |
| --- | --- | --- | --- |
| Bengali | `bn` | `bn-banking-v2-8khz` | Bilingual with English (code-switching) |
| English | `en` | `en-banking-v2-8khz` | English-only (dedicated model) |
| Gujarati | `gu` | `gu-banking-v2-8khz` | Monolingual |
| Hindi | `hi` | `hi-banking-v2-8khz` | Bilingual with English (code-switching) |
| Hinglish | `hi-en` | `hi-en-banking-v2-8khz` | Bilingual with English, retained for compatibility |
| Kannada | `kn` | `kn-banking-v2-8khz` | Bilingual with English (code-switching) |
| Malayalam | `ml` | `ml-banking-v2-8khz` | Bilingual with English (code-switching) |
| Marathi | `mr` | `mr-banking-v2-8khz` | Bilingual with English (code-switching) |
| Odia | `or` | `or-general-v3-8khz` | Monolingual |
| Tamil | `ta` | `ta-banking-v2-8khz` | Bilingual with English (code-switching) |
| Telugu | `te` | `te-banking-v2-8khz` | Bilingual with English (code-switching) |

Odia has no `banking` model yet, so `or-general-v3-8khz` is its only option
today.

## Reading a model name

```
hi-banking-v2-8khz
│   │       │  └── training sample rate
│   │       └───── model version
│   └───────────── domain
└───────────────── language
```

Odia is the one exception to the version number: its model is
`or-general-v3-8khz`, not `v2`.

**Domain.** These models are tuned for financial vocabulary: account
numbers, balances, transaction terms, the code-switched English that shows up
in Indian banking calls.

**Sample rate.** Every model is trained at 8 kHz, the rate telephony actually
delivers. Feeding higher-rate audio doesn't improve accuracy on a model trained
at 8 kHz, and downsampling a 16 kHz recording to 8 kHz before transcribing
usually matches the training distribution better.

## Choosing a model

1. **Start from the language**

   There's no detection and no fallback. A Hindi model on Tamil audio returns
   a poor transcript rather than an error, so pick the model from your own
   metadata about the call.
2. **Validate on your own audio**

   Run a sample set through the model and check per-segment confidence from
   `aux_info`. That's a cheap, objective way to spot-check without labelled
   ground truth.

## Related

- [Non-streaming transcription](/speech-to-text/non-streaming) — Transcribe a file with one of these models.
- [Streaming transcription](/speech-to-text/streaming) — Declare a model in the config frame.
- [API reference](/api-reference/transcribe) — Where the model name goes in each request.

Source: https://docs.navana.ai/speech-to-text/models/index.mdx