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Multilingual Support

The vocode project can be configured to support multiple languages for speech synthesis and automatic speech recognition (ASR).

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Speech Synthesis

The speech synthesizer used in vocode is configurable. By default, the AzureSynthesizer is used which supports over 75 voices across over 45 languages.

To configure a different language, modify the SynthesizerConfig when initializing the conversation:

from vocode.streaming.models.synthesizer import AzureSynthesizerConfig

synthesizer_config = AzureSynthesizerConfig(
    voice_name="de-DE-KatjaNeural" # German voice
    language_code="de-DE" # Language of text to be synthesized
)

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Transcription

The transcriber used in vocode is also configurable. By default, DeepgramTranscriber is used which supports over 35 languages.

To configure a different language model, modify the language code passed to TranscriberConfig when initializing the config object (en-US is the default):

from vocode.streaming.models.transcriber import DeepgramTranscriberConfig

transcriber_config = DeepgramTranscriberConfig(
    language="es" # Spanish
    model="nova-2" # Most languages are supported on the Nova 2 model
)

Note: the default model for Deepgram is Nova, so you must pass model="nova-2" to use that model.

Other transcription services like Google Cloud Speech or Assembly AI could also be used by configuring the appropriate TranscriberConfig.

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Language Configuration

It is recommended to load the speech synthesizer voice and transcription model from environment variables or configuration to avoid hard-coding language choices:

import os
from vocode.streaming.models.synthesizer import AzureSynthesizerConfig
from vocode.streaming.models.transcriber import DeepgramTranscriberConfig

SYNTHESIZER_VOICE = os.getenv("SYNTHESIZER_VOICE", default="en-US-AriaNeural")
ASR_MODEL = os.getenv("DEEPGRAM_MODEL", default="en-US")

synthesizer_config = AzureSynthesizerConfig(voice=SYNTHESIZER_VOICE)
transcriber_config = DeepgramTranscriberConfig(model=DEEPGRAM_MODEL)

This allows dynamically configuring the speech language without code changes.