Update InputAudioTranscription to use gpt-4o-transcribe model, update 19 examples to use FunctionSchema
This commit is contained in:
10
CHANGELOG.md
10
CHANGELOG.md
@@ -11,6 +11,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
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- Added `default_headers` parameter to `BaseOpenAILLMService` constructor.
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### Changed
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- Changed the default `InputAudioTranscription` model to `gpt-4o-transcribe`
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for `OpenAIRealtimeBetaLLMService`.
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### Other
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- Update the `19-openai-realtime-beta.py` and `19a-azure-realtime-beta.py`
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examples to use the FunctionSchema format.
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## [0.0.59] - 2025-03-20
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### Added
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@@ -14,6 +14,8 @@ from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.pipeline.pipeline import Pipeline
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@@ -46,28 +48,25 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
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)
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tools = [
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{
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"type": "function",
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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}
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]
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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required=["location", "format"],
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)
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# Create tools schema
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tools = ToolsSchema(standard_tools=[weather_function])
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async def main():
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@@ -10,11 +10,12 @@ import sys
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from datetime import datetime
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import aiohttp
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import websockets
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from dotenv import load_dotenv
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from loguru import logger
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from runner import configure
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from pipecat.adapters.schemas.function_schema import FunctionSchema
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from pipecat.adapters.schemas.tools_schema import ToolsSchema
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from pipecat.audio.vad.silero import SileroVADAnalyzer
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from pipecat.audio.vad.vad_analyzer import VADParams
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from pipecat.pipeline.pipeline import Pipeline
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@@ -47,28 +48,26 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context
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)
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tools = [
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{
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"type": "function",
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"name": "get_current_weather",
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"description": "Get the current weather",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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"required": ["location", "format"],
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# Define weather function using standardized schema
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weather_function = FunctionSchema(
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name="get_current_weather",
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description="Get the current weather",
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properties={
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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}
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]
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"format": {
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"type": "string",
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"enum": ["celsius", "fahrenheit"],
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"description": "The temperature unit to use. Infer this from the users location.",
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},
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},
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required=["location", "format"],
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)
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# Create tools schema
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tools = ToolsSchema(standard_tools=[weather_function])
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async def main():
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@@ -14,17 +14,24 @@ from pydantic import BaseModel, Field
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#
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# session properties
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#
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InputAudioTranscriptionModel = Literal["whisper-1", "gpt-4o-transcribe"]
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class InputAudioTranscription(BaseModel):
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model: InputAudioTranscriptionModel
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"""Configuration for audio transcription settings.
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Attributes:
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model: Transcription model to use (e.g., "gpt-4o-transcribe", "whisper-1").
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language: Optional language code for transcription.
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prompt: Optional transcription hint text.
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"""
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model: str = "gpt-4o-transcribe"
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language: Optional[str]
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prompt: Optional[str]
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def __init__(
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self,
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model: Optional[InputAudioTranscriptionModel] = "whisper-1",
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model: Optional[str] = "gpt-4o-transcribe",
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language: Optional[str] = None,
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prompt: Optional[str] = None,
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):
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