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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.

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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.

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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:

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Example 1: Using Deepgram with a phone call

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=PunctuationEndpointingConfig()
    ),
    ...
)
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.

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Example 2: Using Deepgram in StreamingConversation locally

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=PunctuationEndpointingConfig()
            )
        ),
        ...
    )
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.

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Endpointing

Endpointing is the process of understanding when someone has finished speaking. The EndpointingConfig controls how this is done. There are a couple of different ways to configure endpointing:
  • Time-based endpointing: This method considers the speaker to be finished when there is a certain duration of silence.
  • Punctuation-based endpointing: This method considers the speaker to be finished when there is a certain duration of silence after a punctuation mark.
In the first example, the PunctuationEndpointingConfig is used to configure the Deepgram transcriber for punctuation-based endpointing.