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

# Using transcribers

> How to configure and use transcribers for speech recognition in your application.

# Overview

Transcribers are used to configure the speech recognition of your application. In this guide, we will show you how to configure and use transcribers in Vocode.

## Supported Transcribers

Vocode currently supports the following transcribers:

1. Deepgram
2. Google
3. Assembly AI
4. Whisper CPP
5. Rev AI
6. Azure

These transcribers are defined using their respective configuration classes, which are subclasses of the `TranscriberConfig` class.

# Configuring Transcribers

To use a transcriber, you need to create a configuration object for the transcriber you want to use. Here are some examples of how to create configuration objects for different transcribers:

### Example 1: Using Deepgram with a phone call

```python theme={null}
from vocode.streaming.telephony.hosted.inbound_call_server import InboundCallServer
from vocode.streaming.models.transcriber import DeepgramTranscriberConfig, PunctuationEndpointingConfig

server = InboundCallServer(
    ...
    transcriber_config=DeepgramTranscriberConfig.from_telephone_input_device(
      endpointing_config=DeepgramEndpointingConfig()
    ),
    ...
)
```

In this example, the `DeepgramTranscriberConfig.from_telephone_input_device()` method is used to create a configuration object for the Deepgram transcriber. The method hardcodes some values like the `sampling_rate`, `audio_encoding`, and `chunk_size` for compatibility with telephone input devices.

### Example 2: Using Deepgram in `StreamingConversation` locally

```python theme={null}
from vocode.streaming.models.transcriber import DeepgramTranscriberConfig, PunctuationEndpointingConfig
from vocode.streaming import StreamingConversation

async def main():
    microphone_input, speaker_output = create_microphone_input_and_speaker_output(
        streaming=True, use_default_devices=False
    )

    conversation = StreamingConversation(
        output_device=speaker_output,
        transcriber=DeepgramTranscriber(
            DeepgramTranscriberConfig.from_input_device(
                microphone_input, endpointing_config=DeepgramEndpointingConfig()
            )
        ),
        ...
    )
```

In this example, the `DeepgramTranscriberConfig.from_input_device()` method is used to create a configuration object for the Deepgram transcriber for use in a local `StreamingConversation`.
The method takes a `microphone_input` object as an argument and extracts the `sampling_rate`, `audio_encoding`, and `chunk_size` from the input device.

See [Conversation Mechanics](/open-source/conversation-mechanics) for more information about endpointing.