When using the voice agent in typed code, it is suboptimal and error
prone to type the TTS voice variables in your code independently.
With this commit we are making the type exportable so that developers
can just use that and be future-proof.
Example of usage in code:
```
DEFAULT_TTS_VOICE: TTSModelSettings.TTSVoice = "alloy"
...
tts_voice: TTSModelSettings.TTSVoice = DEFAULT_TTS_VOICE
...
output = await VoicePipeline(
workflow=workflow,
config=VoicePipelineConfig(
tts_settings=TTSModelSettings(
buffer_size=512,
transform_data=transform_data,
voice=tts_voice,
instructions=tts_instructions,
))
).run(audio_input)
```
---------
Co-authored-by: Rohan Mehta <rm@openai.com>
192 lines
5.8 KiB
Python
192 lines
5.8 KiB
Python
from __future__ import annotations
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import abc
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from collections.abc import AsyncIterator
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from dataclasses import dataclass
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from typing import Any, Callable, Literal
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from .imports import np, npt
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from .input import AudioInput, StreamedAudioInput
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from .utils import get_sentence_based_splitter
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DEFAULT_TTS_INSTRUCTIONS = (
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"You will receive partial sentences. Do not complete the sentence, just read out the text."
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)
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DEFAULT_TTS_BUFFER_SIZE = 120
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TTSVoice = Literal["alloy", "ash", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer"]
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"""Exportable type for the TTSModelSettings voice enum"""
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@dataclass
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class TTSModelSettings:
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"""Settings for a TTS model."""
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voice: TTSVoice | None = None
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"""
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The voice to use for the TTS model. If not provided, the default voice for the respective model
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will be used.
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"""
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buffer_size: int = 120
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"""The minimal size of the chunks of audio data that are being streamed out."""
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dtype: npt.DTypeLike = np.int16
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"""The data type for the audio data to be returned in."""
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transform_data: (
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Callable[[npt.NDArray[np.int16 | np.float32]], npt.NDArray[np.int16 | np.float32]] | None
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) = None
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"""
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A function to transform the data from the TTS model. This is useful if you want the resulting
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audio stream to have the data in a specific shape already.
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"""
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instructions: str = (
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"You will receive partial sentences. Do not complete the sentence just read out the text."
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)
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"""
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The instructions to use for the TTS model. This is useful if you want to control the tone of the
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audio output.
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"""
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text_splitter: Callable[[str], tuple[str, str]] = get_sentence_based_splitter()
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"""
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A function to split the text into chunks. This is useful if you want to split the text into
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chunks before sending it to the TTS model rather than waiting for the whole text to be
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processed.
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"""
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speed: float | None = None
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"""The speed with which the TTS model will read the text. Between 0.25 and 4.0."""
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class TTSModel(abc.ABC):
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"""A text-to-speech model that can convert text into audio output."""
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@property
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@abc.abstractmethod
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def model_name(self) -> str:
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"""The name of the TTS model."""
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pass
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@abc.abstractmethod
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def run(self, text: str, settings: TTSModelSettings) -> AsyncIterator[bytes]:
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"""Given a text string, produces a stream of audio bytes, in PCM format.
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Args:
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text: The text to convert to audio.
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Returns:
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An async iterator of audio bytes, in PCM format.
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"""
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pass
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class StreamedTranscriptionSession(abc.ABC):
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"""A streamed transcription of audio input."""
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@abc.abstractmethod
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def transcribe_turns(self) -> AsyncIterator[str]:
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"""Yields a stream of text transcriptions. Each transcription is a turn in the conversation.
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This method is expected to return only after `close()` is called.
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"""
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pass
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@abc.abstractmethod
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async def close(self) -> None:
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"""Closes the session."""
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pass
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@dataclass
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class STTModelSettings:
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"""Settings for a speech-to-text model."""
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prompt: str | None = None
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"""Instructions for the model to follow."""
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language: str | None = None
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"""The language of the audio input."""
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temperature: float | None = None
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"""The temperature of the model."""
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turn_detection: dict[str, Any] | None = None
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"""The turn detection settings for the model when using streamed audio input."""
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class STTModel(abc.ABC):
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"""A speech-to-text model that can convert audio input into text."""
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@property
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@abc.abstractmethod
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def model_name(self) -> str:
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"""The name of the STT model."""
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pass
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@abc.abstractmethod
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async def transcribe(
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self,
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input: AudioInput,
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settings: STTModelSettings,
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trace_include_sensitive_data: bool,
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trace_include_sensitive_audio_data: bool,
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) -> str:
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"""Given an audio input, produces a text transcription.
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Args:
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input: The audio input to transcribe.
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settings: The settings to use for the transcription.
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trace_include_sensitive_data: Whether to include sensitive data in traces.
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trace_include_sensitive_audio_data: Whether to include sensitive audio data in traces.
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Returns:
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The text transcription of the audio input.
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"""
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pass
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@abc.abstractmethod
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async def create_session(
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self,
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input: StreamedAudioInput,
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settings: STTModelSettings,
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trace_include_sensitive_data: bool,
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trace_include_sensitive_audio_data: bool,
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) -> StreamedTranscriptionSession:
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"""Creates a new transcription session, which you can push audio to, and receive a stream
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of text transcriptions.
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Args:
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input: The audio input to transcribe.
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settings: The settings to use for the transcription.
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trace_include_sensitive_data: Whether to include sensitive data in traces.
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trace_include_sensitive_audio_data: Whether to include sensitive audio data in traces.
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Returns:
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A new transcription session.
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"""
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pass
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class VoiceModelProvider(abc.ABC):
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"""The base interface for a voice model provider.
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A model provider is responsible for creating speech-to-text and text-to-speech models, given a
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name.
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"""
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@abc.abstractmethod
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def get_stt_model(self, model_name: str | None) -> STTModel:
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"""Get a speech-to-text model by name.
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Args:
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model_name: The name of the model to get.
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Returns:
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The speech-to-text model.
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"""
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pass
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@abc.abstractmethod
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def get_tts_model(self, model_name: str | None) -> TTSModel:
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"""Get a text-to-speech model by name."""
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