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# Saarika

> Saarika v2.5 - High-accuracy speech recognition model for Indian languages with superior multi-speaker handling, telephony optimization, and automatic code-mixing support.

Saarika-v2.5 is our flagship speech recognition model, specifically designed for Indian languages and accents. It always transcribes the audio in the same language it was spoken. It excels in handling complex multi-speaker conversations, telephony audio, and code-mixed speech with superior accuracy across 11 languages.

> **Warning**
>
> **Deprecation Notice:** Saarika v2.5 will be deprecated soon. For transcription features, we recommend using [**Saaras v4**](/api/getting-started/models/saaras) with `mode="transcribe"`, which offers improved accuracy and additional output modes.

## Key Features

#### Superior Telephony Performance

Optimized for 8KHz telephony audio with enhanced noise handling and superior multi-speaker recognition capabilities.

#### Intelligent Entity Preservation

Preserves proper nouns and entities accurately across languages, maintaining context and meaning in transcriptions.

#### Automatic Language Detection

Optional automatic language identification with LID output. Use "unknown" when language is not known for automatic detection.

#### Speaker Diarization

Provides diarized outputs with precise timestamps for multi-speaker conversations through batch API processing.

#### Automatic Code Mixing

Intelligently handles mid-sentence language switches in code-mixed speech, perfect for India's multilingual conversations.

#### Multi-Language Support

Comprehensive support for Indian languages with high accuracy in mixed-language environments.

## Language Support

Saarika supports 11 languages with comprehensive dialect and accent coverage, including code-mixed audio support and intelligent proper noun preservation.

| Language  | Language Code |
| --------- | ------------- |
| English   | `en-IN`       |
| Hindi     | `hi-IN`       |
| Bengali   | `bn-IN`       |
| Tamil     | `ta-IN`       |
| Telugu    | `te-IN`       |
| Gujarati  | `gu-IN`       |
| Kannada   | `kn-IN`       |
| Malayalam | `ml-IN`       |
| Marathi   | `mr-IN`       |
| Punjabi   | `pa-IN`       |
| Odia      | `od-IN`       |

> **Note**
>
> For automatic language detection, use `language_code="unknown"`. The model will automatically identify the spoken language and return it in the response.

## Performance Benchmarks

Saarika delivers exceptional accuracy across all supported languages, as measured on the VISTAAR Benchmark.

### CER (Character Error Rate) Scores

*Lower is better - Compared on VISTAAR Benchmark*

* **Across 11 Languages: 4.96%**
* **English: 4.45%**
* **Hindi: 4.42%**
* **9 Other languages: 5.07%**

### WER (Word Error Rate) Scores

*Lower is better - Compared on VISTAAR Benchmark*

* **Across 11 Languages: 18.32%**
* **English: 8.26%**
* **Hindi: 11.81%**
* **9 Other languages: 20.15%**

### Detailed CER Performance by Language

CER (Character Error Rate) measures the percentage of characters that are wrong in a transcription.
Lower scores are better, with 0% being perfect.

## Key Capabilities

> **Warning**
>
> **Deprecated Model:** Saarika v2.5 is a deprecated model and code examples have been removed to avoid new integrations against it. We recommend using **Saaras v3** (`model="saaras:v3"`) with the `mode` parameter for the best accuracy and features. See the [Saaras documentation](/api/getting-started/models/saaras) for up-to-date code examples covering basic transcription, code-mixed speech, and automatic language detection.

Saarika supports the same core transcription capabilities now available in Saaras v3:

* **Basic transcription** with a specified `language_code` for single-language content.
* **Code-mixed speech** with automatic detection of language switches within a sentence.
* **Automatic language detection** via `language_code="unknown"`.

For implementation, use Saaras v3 with `mode="transcribe"`. See the [Saaras documentation](/api/getting-started/models/saaras).

## Limits

| Limit                                             | Value                                                                                            |
| ------------------------------------------------- | ------------------------------------------------------------------------------------------------ |
| Max audio duration (real-time REST)               | 30 seconds                                                                                       |
| Supported formats                                 | WAV, MP3, AAC, AIFF, OGG, OPUS, FLAC, MP4, AMR, WMA, WebM (auto-detected)                        |
| Raw PCM input (`pcm_s16le`, `pcm_l16`, `pcm_raw`) | Requires `input_audio_codec`; must be 16 kHz                                                     |
| Longer audio                                      | Use the [Batch API](/api/api-guides-tutorials/speech-to-text/batch-api) (up to 2 hours per file) |
| Rate limits                                       | See [Rate Limits](/api/getting-started/ratelimits)                                               |

## Known Limitations

| Limitation                          | Detail                                                                            | Workaround                                                                                                             |
| ----------------------------------- | --------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
| **30-second cap on real-time REST** | The real-time `/speech-to-text` endpoint only accepts audio up to 30 seconds long | Use the [Batch API](/api/api-guides-tutorials/speech-to-text/batch-api) for longer recordings (up to 2 hours per file) |

## Next Steps

#### [Developer quickstart](/api/api-guides-tutorials/speech-to-text/overview)

Learn how to integrate the Saarika API within your application.

#### [API Reference](/api-reference/speech-to-text/transcribe)

Complete API documentation for speech to text endpoints.

#### [Cookbook](/api/api-guides-tutorials/speech-to-text/rest-api)

Step-by-step tutorial for speech-to-text transcription.

> Saarika v2.5 - High-accuracy speech recognition model for Indian languages with superior multi-speaker handling, telephony optimization, and automatic code-mixing support.