> ## Documentation Index
> Fetch the complete documentation index at: https://sambanova-systems.mintlify.site/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Implement audio‑input features

For developers requiring audio support, SambaNova provides OpenAI’s Whisper large-v3 model, which enables real-time transcriptions and translations.

<Warning>
  This feature is available for SambaStack users only. Whisper-Large-v3 is not available on SambaCloud.
</Warning>

## Whisper-Large-v3

* **Model**: Whisper-Large-v3
* **Description**: State-of-the-art automatic speech recognition (ASR) and translation model. Developed by OpenAI and trained on 5M+ hours of labeled audio. Excels in multilingual and zero-shot speech tasks across diverse domains.
* **Model ID**: `Whisper-Large-v3`
* **Supported languages**: Multilingual

### Core capabilities

* Transcribes and translates extended audio inputs (up to 25 MB).
* Demonstrates high accuracy in speech recognition and translation tasks.
* Provides OpenAI-compatible endpoints for transcriptions and translations.

### Request parameters

| Parameter         | Type    | Description                                                                                                                      | Default  | Endpoints                        |
| :---------------- | :------ | :------------------------------------------------------------------------------------------------------------------------------- | :------- | :------------------------------- |
| `model`           | String  | The ID of the model to use.                                                                                                      | Required | `transcriptions`, `translations` |
| `file`            | File    | Audio file in FLAC, MP3, MP4, MPEG, MPGA, M4A, Ogg, WAV, or WebM format. File size limit: 25MB.                                  | Required | `transcriptions`, `translations` |
| `prompt`          | String  | Prompt to influence transcription style or vocabulary. Example: "Please transcribe carefully, including pauses and hesitations." | Optional | `transcriptions`, `translations` |
| `response_format` | String  | Output format: either `json` or `text`.                                                                                          | `json`   | `transcriptions`, `translations` |
| `language`        | String  | The language of the input audio. Using ISO-639-1 format (e.g., `en`) improves accuracy and latency.                              | Optional | `transcriptions`, `translations` |
| `stream`          | Boolean | Enables streaming responses.                                                                                                     | `false`  | `transcriptions`, `translations` |
| `stream_options`  | Object  | Additional streaming configuration (e.g., `{"include_usage": true}`).                                                            | Optional | `transcriptions`, `translations` |

### Example usage

<CodeGroup>
  ```python Python (SambaNova) theme={null}
  from sambanova import SambaNova
  import base64

  client = SambaNova(
      base_url="your-sambanova-base-url",
      api_key="your-sambanova-api-key",
  )

  audio_path="audio_path"
  with open(audio_path, "rb") as audio_file:
     bin_audio = audio_file.read()

  response = client.audio.transcriptions.create(
      model="Whisper-Large-v3",
      file=(audio_path,bin_audio),
  )
  print(str(response))
  ```

  ```python Python (OpenAI) theme={null}
  from openai import OpenAI
  import base64

  client = OpenAI(
      base_url="your-sambanova-base-url",
      api_key="your-sambanova-api-key",
  )

  audio_path="audio_path"
  with open(audio_path, "rb") as audio_file:
     bin_audio = audio_file.read()

  response = client.audio.transcriptions.create(
      model="Whisper-Large-v3",
      file=(audio_path,bin_audio),
  )
  print(str(response))
  ```

  ```javascript Javascript (SambaNova) theme={null}
  import SambaNova from "sambanova";

  const client = new SambaNova({
    baseURL: 'your-sambanova-base-url',
    apiKey: 'your-sambanova-api-key'
  });

  async function run() {
    const audioPath = 'audio_path';
    const binAudio = fs.createReadStream(audioPath);

    const response = await client.audio.transcriptions.create({
      model: 'Whisper-Large-v3',
      file: binAudio,
    });

    console.log('Full Response:');
    console.dir(response, { depth: null });
  }

  run().catch(console.error);
  ```
</CodeGroup>

## Translations

The translations endpoint transcribes audio in any supported language and returns the output in English. Use the `language` parameter to specify the language of the input audio in ISO 639-1 format (for example, `"es"` for Spanish) to improve accuracy and reduce latency.

### Example usage

<CodeGroup>
  ```python Python (SambaNova) theme={null}
  from sambanova import SambaNova

  client = SambaNova(
      base_url="your-sambanova-base-url",
      api_key="your-sambanova-api-key",
  )

  audio_path = "audio_path"
  with open(audio_path, "rb") as audio_file:
      bin_audio = audio_file.read()

  response = client.audio.translations.create(
      model="Whisper-Large-v3",
      file=(audio_path, bin_audio),
      language="es",
  )
  print(str(response))
  ```

  ```python Python (OpenAI) theme={null}
  from openai import OpenAI

  client = OpenAI(
      base_url="your-sambanova-base-url",
      api_key="your-sambanova-api-key",
  )

  audio_path = "audio_path"
  with open(audio_path, "rb") as audio_file:
      bin_audio = audio_file.read()

  response = client.audio.translations.create(
      model="Whisper-Large-v3",
      file=(audio_path, bin_audio),
      language="es",
  )
  print(str(response))
  ```

  ```javascript Javascript (SambaNova) theme={null}
  import SambaNova from "sambanova";

  const client = new SambaNova({
    baseURL: 'your-sambanova-base-url',
    apiKey: 'your-sambanova-api-key'
  });

  async function run() {
    const audioPath = 'audio_path';
    const binAudio = fs.createReadStream(audioPath);

    const response = await client.audio.translations.create({
      model: 'Whisper-Large-v3',
      file: binAudio,
      language: 'es',
    });

    console.log('Full Response:');
    console.dir(response, { depth: null });
  }

  run().catch(console.error);
  ```
</CodeGroup>

### Example response

```json theme={null}
{
  "text": "It is the sound effect of a bell ringing, specifically a church bell."
}
```