wx44wx/pipecat
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge pull request #3118 from pipecat-ai/mb/deepgram-stt-sagemaker

Browse Source 
Add SageMaker BiDi client and DeepgramSageMakerSTTService
This commit is contained in:
Mark Backman
2025-11-24 16:47:25 -05:00
committed by GitHub
parent 012c2f7dde a357ff0205
commit b52b108932
10 changed files with 937 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
CHANGELOG.md
View File

@@ -9,6 +9,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Added
- Added `DeepgramSageMakerSTTService` which connects to a SageMaker hosted
Deepgram STT model. Added `07c-interruptible-deepgram-sagemaker.py`
foundational example.
- Added `SageMakerBidiClient` to connect to SageMaker hosted BiDi compatible
services.
- Added support for `include_timestamps` and `enable_logging` in
`ElevenLabsRealtimeSTTService`. When `include_timestamps` is enabled,
timestamp data is included in the `TranscriptionFrame`'s `result`

1
env.example
View File

@@ -44,6 +44,7 @@ DAILY_SAMPLE_ROOM_URL=https://...
# Deepgram
DEEPGRAM_API_KEY=...
SAGEMAKER_ENDPOINT_NAME=...
# DeepSeek
DEEPSEEK_API_KEY=...

137
examples/foundational/07c-interruptible-deepgram-sagemaker.py Normal file
View File

@@ -0,0 +1,137 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.aws.llm import AWSBedrockLLMService
from pipecat.services.deepgram.stt_sagemaker import DeepgramSageMakerSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
load_dotenv(override=True)
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
"daily": lambda: DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
),
"twilio": lambda: FastAPIWebsocketParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
),
"webrtc": lambda: TransportParams(
audio_in_enabled=True,
audio_out_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
),
}
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot")
# Initialize Deepgram SageMaker STT Service
# This requires:
# - AWS credentials configured (via environment variables or AWS CLI)
# - A deployed SageMaker endpoint with Deepgram model
stt = DeepgramSageMakerSTTService(
endpoint_name=os.getenv("SAGEMAKER_ENDPOINT_NAME"),
region=os.getenv("AWS_REGION"),
)
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
llm = AWSBedrockLLMService(
aws_region=os.getenv("AWS_REGION"),
model="us.amazon.nova-pro-v1:0",
params=AWSBedrockLLMService.InputParams(temperature=0.8),
)
messages = [
{
"role": "system",
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
},
]
context = LLMContext(messages)
context_aggregator = LLMContextAggregatorPair(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
context_aggregator.assistant(), # Assistant spoken responses
]
)
task = PipelineTask(
pipeline,
params=PipelineParams(
enable_metrics=True,
enable_usage_metrics=True,
),
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
)
@transport.event_handler("on_client_connected")
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected")
await task.cancel()
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
await runner.run(task)
async def bot(runner_args: RunnerArguments):
"""Main bot entry point compatible with Pipecat Cloud."""
transport = await create_transport(runner_args, transport_params)
await run_bot(transport, runner_args)
if __name__ == "__main__":
from pipecat.runner.run import main
main()

15
pyproject.toml
View File

@@ -50,13 +50,13 @@ anthropic = [ "anthropic~=0.49.0" ]
assemblyai = [ "pipecat-ai[websockets-base]" ]
asyncai = [ "pipecat-ai[websockets-base]" ]
aws = [ "aioboto3~=15.0.0", "pipecat-ai[websockets-base]" ]
aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.1; python_version>='3.12'" ]
aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.2.0; python_version>='3.12'" ]
azure = [ "azure-cognitiveservices-speech~=1.42.0"]
cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
cerebras = []
deepseek = []
daily = [ "daily-python~=0.22.0" ]
deepgram = [ "deepgram-sdk~=4.7.0" ]
deepseek = []
elevenlabs = [ "pipecat-ai[websockets-base]" ]
fal = [ "fal-client~=0.5.9" ]
fireworks = []
@@ -69,19 +69,21 @@ gstreamer = [ "pygobject~=3.50.0" ]
heygen = [ "livekit>=1.0.13", "pipecat-ai[websockets-base]" ]
hume = [ "hume>=0.11.2" ]
inworld = []
krisp = [ "pipecat-ai-krisp~=0.4.0" ]
koala = [ "pvkoala~=2.0.3" ]
krisp = [ "pipecat-ai-krisp~=0.4.0" ]
langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ]
livekit = [ "livekit~=1.0.13", "livekit-api~=1.0.5", "tenacity>=8.2.3,<10.0.0" ]
lmnt = [ "pipecat-ai[websockets-base]" ]
local = [ "pyaudio~=0.2.14" ]
local-smart-turn = [ "coremltools>=8.0", "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
local-smart-turn-v3 = [ "transformers", "onnxruntime>=1.20.1,<2" ]
mcp = [ "mcp[cli]>=1.11.0,<2" ]
mem0 = [ "mem0ai~=0.1.94" ]
mistral = []
mlx-whisper = [ "mlx-whisper~=0.4.2" ]
moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0" ]
nim = []
neuphonic = [ "pipecat-ai[websockets-base]" ]
nim = []
noisereduce = [ "noisereduce~=3.0.3" ]
openai = [ "pipecat-ai[websockets-base]" ]
openpipe = [ "openpipe>=4.50.0,<6" ]
@@ -89,15 +91,14 @@ openrouter = []
perplexity = []
playht = [ "pipecat-ai[websockets-base]" ]
qwen = []
remote-smart-turn = []
rime = [ "pipecat-ai[websockets-base]" ]
riva = [ "nvidia-riva-client~=2.21.1" ]
runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<0.122.0", "pipecat-ai-small-webrtc-prebuilt>=1.0.0"]
sagemaker = ["aws_sdk_sagemaker_runtime_http2; python_version>='3.12'"]
sambanova = []
sarvam = [ "sarvamai==0.1.21", "pipecat-ai[websockets-base]" ]
sentry = [ "sentry-sdk>=2.28.0,<3" ]
local-smart-turn = [ "coremltools>=8.0", "transformers", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
local-smart-turn-v3 = [ "transformers", "onnxruntime>=1.20.1,<2" ]
remote-smart-turn = []
silero = [ "onnxruntime>=1.20.1,<2" ]
simli = [ "simli-ai~=1.0.3"]
soniox = [ "pipecat-ai[websockets-base]" ]

1
src/pipecat/services/aws/__init__.py
View File

@@ -10,6 +10,7 @@ from pipecat.services import DeprecatedModuleProxy
from .llm import *
from .nova_sonic import *
from .sagemaker import *
from .stt import *
from .tts import *

0
src/pipecat/services/aws/sagemaker/__init__.py Normal file
View File

283
src/pipecat/services/aws/sagemaker/bidi_client.py Normal file
View File

@@ -0,0 +1,283 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""AWS SageMaker bidirectional streaming client.
This module provides a client for streaming bidirectional communication with
SageMaker endpoints using the HTTP/2 protocol. Supports sending audio, text,
and JSON data to SageMaker model endpoints and receiving streaming responses.
"""
import os
from typing import Optional
from loguru import logger
try:
from aws_sdk_sagemaker_runtime_http2.client import SageMakerRuntimeHTTP2Client
from aws_sdk_sagemaker_runtime_http2.config import Config, HTTPAuthSchemeResolver
from aws_sdk_sagemaker_runtime_http2.models import (
InvokeEndpointWithBidirectionalStreamInput,
RequestPayloadPart,
RequestStreamEventPayloadPart,
ResponseStreamEvent,
)
from smithy_aws_core.auth.sigv4 import SigV4AuthScheme
from smithy_aws_core.identity import EnvironmentCredentialsResolver
from smithy_core.aio.eventstream import DuplexEventStream
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error(
"In order to use SageMaker BiDi client, you need to `pip install pipecat-ai[sagemaker]`."
)
raise Exception(f"Missing module: {e}")
class SageMakerBidiClient:
"""Client for bidirectional streaming with AWS SageMaker endpoints.
Handles low-level HTTP/2 bidirectional streaming protocol for communicating
with SageMaker model endpoints. Provides methods for sending various data
types (audio, text, JSON) and receiving streaming responses.
This client uses AWS SigV4 authentication and supports credential resolution
from environment variables, AWS CLI configuration, and instance metadata.
Example::
client = SageMakerBidiClient(
endpoint_name="my-deepgram-endpoint",
region="us-east-2",
model_invocation_path="v1/listen",
model_query_string="model=nova-3&language=en"
)
await client.start_session()
await client.send_audio_chunk(audio_bytes)
response = await client.receive_response()
await client.close_session()
"""
def __init__(
self,
endpoint_name: str,
region: str,
model_invocation_path: str = "",
model_query_string: str = "",
):
"""Initialize the SageMaker BiDi client.
Args:
endpoint_name: Name of the SageMaker endpoint to connect to.
region: AWS region where the endpoint is deployed.
model_invocation_path: API path for the model invocation (e.g., "v1/listen").
model_query_string: Query string parameters for the model (e.g., "model=nova-3").
"""
self.endpoint_name = endpoint_name
self.region = region
self.model_invocation_path = model_invocation_path
self.model_query_string = model_query_string
self.bidi_endpoint = f"https://runtime.sagemaker.{region}.amazonaws.com:8443"
self._client: Optional[SageMakerRuntimeHTTP2Client] = None
self._stream: Optional[
DuplexEventStream[RequestStreamEventPayloadPart, ResponseStreamEvent, any]
] = None
self._output_stream = None
self._is_active = False
def _initialize_client(self):
"""Initialize the SageMaker Runtime HTTP2 client with AWS credentials.
Creates and configures the SageMaker Runtime HTTP2 client with SigV4
authentication. Attempts to resolve AWS credentials from environment
variables, AWS CLI configuration, or instance metadata.
"""
logger.debug(f"Initializing SageMaker BiDi client for region: {self.region}")
logger.debug(f"Using endpoint URI: {self.bidi_endpoint}")
# Check for AWS credentials
has_env_creds = bool(os.getenv("AWS_ACCESS_KEY_ID") and os.getenv("AWS_SECRET_ACCESS_KEY"))
if not has_env_creds:
logger.warning(
"AWS credentials not found in environment variables. "
"Attempting to use EnvironmentCredentialsResolver which will check "
"AWS CLI configuration and instance metadata."
)
config = Config(
endpoint_uri=self.bidi_endpoint,
region=self.region,
aws_credentials_identity_resolver=EnvironmentCredentialsResolver(),
auth_scheme_resolver=HTTPAuthSchemeResolver(),
auth_schemes={"aws.auth#sigv4": SigV4AuthScheme(service="sagemaker")},
)
self._client = SageMakerRuntimeHTTP2Client(config=config)
async def start_session(self):
"""Start a bidirectional streaming session with the SageMaker endpoint.
Initializes the client if needed, creates the bidirectional stream, and
establishes the connection to the SageMaker endpoint. Must be called
before sending or receiving data.
Returns:
The output stream for receiving responses.
Raises:
RuntimeError: If client initialization or connection fails.
"""
if not self._client:
self._initialize_client()
logger.debug(f"Starting BiDi session with endpoint: {self.endpoint_name}")
logger.debug(f"Model invocation path: {self.model_invocation_path}")
logger.debug(f"Model query string: {self.model_query_string}")
# Create the bidirectional stream
stream_input = InvokeEndpointWithBidirectionalStreamInput(
endpoint_name=self.endpoint_name,
model_invocation_path=self.model_invocation_path,
model_query_string=self.model_query_string,
)
try:
self._stream = await self._client.invoke_endpoint_with_bidirectional_stream(
stream_input
)
self._is_active = True
# Get output stream
output = await self._stream.await_output()
self._output_stream = output[1]
logger.debug("BiDi session started successfully")
return self._output_stream
except Exception as e:
logger.error(f"Failed to start BiDi session: {e}")
self._is_active = False
raise RuntimeError(f"Failed to start SageMaker BiDi session: {e}")
async def send_data(self, data_bytes: bytes, data_type: Optional[str] = None):
"""Send a chunk of data to the stream.
Generic method for sending any type of data to the SageMaker endpoint.
Use the convenience methods (send_audio_chunk, send_text, send_json)
for common data types.
Args:
data_bytes: Raw bytes to send.
data_type: Optional data type header. Common values are "BINARY" for
audio/binary data and "UTF8" for text/JSON data.
Raises:
RuntimeError: If session is not active or send fails.
"""
if not self._is_active or not self._stream:
raise RuntimeError("BiDi session not active")
try:
payload = RequestPayloadPart(bytes_=data_bytes, data_type=data_type)
event = RequestStreamEventPayloadPart(value=payload)
await self._stream.input_stream.send(event)
except Exception as e:
logger.error(f"Failed to send data: {e}")
raise
async def send_audio_chunk(self, audio_bytes: bytes):
"""Send a chunk of audio data to the stream.
Convenience method for sending audio data. Automatically sets the data
type to "BINARY".
Args:
audio_bytes: Raw audio bytes to send (e.g., PCM audio data).
Raises:
RuntimeError: If session is not active or send fails.
"""
await self.send_data(audio_bytes, data_type="BINARY")
async def send_text(self, text: str):
"""Send text data to the stream.
Convenience method for sending text data. Automatically encodes the text
as UTF-8 and sets the data type to "UTF8".
Args:
text: Text string to send.
Raises:
RuntimeError: If session is not active or send fails.
"""
await self.send_data(text.encode("utf-8"), data_type="UTF8")
async def send_json(self, data: dict):
"""Send JSON data to the stream.
Convenience method for sending JSON-encoded messages. Useful for control
messages like KeepAlive or CloseStream. Automatically serializes the
dictionary to JSON, encodes as UTF-8, and sets the data type to "UTF8".
Args:
data: Dictionary to send as JSON (e.g., {"type": "KeepAlive"}).
Raises:
RuntimeError: If session is not active or send fails.
"""
import json
await self.send_data(json.dumps(data).encode("utf-8"), data_type="UTF8")
async def receive_response(self) -> Optional[ResponseStreamEvent]:
"""Receive a response from the stream.
Blocks until a response is available from the SageMaker endpoint. Returns
None when the stream is closed.
Returns:
The response event containing payload data, or None if stream is closed.
Raises:
RuntimeError: If session is not active.
"""
if not self._is_active or not self._output_stream:
raise RuntimeError("BiDi session not active")
try:
result = await self._output_stream.receive()
return result
except Exception as e:
logger.error(f"Failed to receive response: {e}")
raise
async def close_session(self):
"""Close the bidirectional streaming session.
Gracefully closes the input stream and marks the session as inactive.
Safe to call multiple times.
"""
if not self._is_active:
return
logger.debug("Closing BiDi session...")
self._is_active = False
try:
if self._stream:
await self._stream.input_stream.close()
logger.debug("BiDi session closed successfully")
except Exception as e:
logger.warning(f"Error closing BiDi session: {e}")
@property
def is_active(self) -> bool:
"""Check if the session is currently active.
Returns:
True if session is active, False otherwise.
"""
return self._is_active

1
src/pipecat/services/deepgram/__init__.py
View File

@@ -10,6 +10,7 @@ from pipecat.services import DeprecatedModuleProxy
from .flux import *
from .stt import *
from .stt_sagemaker import *
from .tts import *
sys.modules[__name__] = DeprecatedModuleProxy(globals(), "deepgram", "deepgram.[stt,tts]")

447
src/pipecat/services/deepgram/stt_sagemaker.py Normal file
View File

@@ -0,0 +1,447 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
"""Deepgram speech-to-text service for AWS SageMaker.
This module provides a Pipecat STT service that connects to Deepgram models
deployed on AWS SageMaker endpoints. Uses HTTP/2 bidirectional streaming for
low-latency real-time transcription with support for interim results, multiple
languages, and various Deepgram features.
"""
import asyncio
import json
from typing import AsyncGenerator, Optional
from loguru import logger
from pipecat.frames.frames import (
CancelFrame,
EndFrame,
ErrorFrame,
Frame,
InterimTranscriptionFrame,
StartFrame,
TranscriptionFrame,
UserStartedSpeakingFrame,
UserStoppedSpeakingFrame,
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.aws.sagemaker.bidi_client import SageMakerBidiClient
from pipecat.services.stt_service import STTService
from pipecat.transcriptions.language import Language
from pipecat.utils.time import time_now_iso8601
from pipecat.utils.tracing.service_decorators import traced_stt
try:
from deepgram import LiveOptions
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error(
"In order to use DeepgramSageMakerSTTService, you need to `pip install pipecat-ai[deepgram,sagemaker]`."
)
raise Exception(f"Missing module: {e}")
class DeepgramSageMakerSTTService(STTService):
"""Deepgram speech-to-text service for AWS SageMaker.
Provides real-time speech recognition using Deepgram models deployed on
AWS SageMaker endpoints. Uses HTTP/2 bidirectional streaming for low-latency
transcription with support for interim results, speaker diarization, and
multiple languages.
Requirements:
- AWS credentials configured (via environment variables, AWS CLI, or instance metadata)
- A deployed SageMaker endpoint with Deepgram model: https://developers.deepgram.com/docs/deploy-amazon-sagemaker
- Deepgram SDK for LiveOptions configuration
Example::
stt = DeepgramSageMakerSTTService(
endpoint_name="my-deepgram-endpoint",
region="us-east-2",
live_options=LiveOptions(
model="nova-3",
language="en",
interim_results=True,
punctuate=True,
),
)
"""
def __init__(
self,
*,
endpoint_name: str,
region: str,
sample_rate: Optional[int] = None,
live_options: Optional[LiveOptions] = None,
**kwargs,
):
"""Initialize the Deepgram SageMaker STT service.
Args:
endpoint_name: Name of the SageMaker endpoint with Deepgram model
deployed (e.g., "my-deepgram-nova-3-endpoint").
region: AWS region where the endpoint is deployed (e.g., "us-east-2").
sample_rate: Audio sample rate in Hz. If None, uses value from
live_options or defaults to the value from StartFrame.
live_options: Deepgram LiveOptions for detailed configuration. If None,
uses sensible defaults (nova-3 model, English, interim results enabled).
**kwargs: Additional arguments passed to the parent STTService.
"""
sample_rate = sample_rate or (live_options.sample_rate if live_options else None)
super().__init__(sample_rate=sample_rate, **kwargs)
self._endpoint_name = endpoint_name
self._region = region
# Create default options similar to DeepgramSTTService
default_options = LiveOptions(
encoding="linear16",
language=Language.EN,
model="nova-3",
channels=1,
interim_results=True,
punctuate=True,
)
# Merge with provided options
merged_options = default_options.to_dict()
if live_options:
default_model = default_options.model
merged_options.update(live_options.to_dict())
# Handle the "None" string bug from deepgram-sdk
if "model" in merged_options and merged_options["model"] == "None":
merged_options["model"] = default_model
# Convert Language enum to string if needed
if "language" in merged_options and isinstance(merged_options["language"], Language):
merged_options["language"] = merged_options["language"].value
self.set_model_name(merged_options["model"])
self._settings = merged_options
self._client: Optional[SageMakerBidiClient] = None
self._response_task: Optional[asyncio.Task] = None
self._keepalive_task: Optional[asyncio.Task] = None
def can_generate_metrics(self) -> bool:
"""Check if this service can generate processing metrics.
Returns:
True, as Deepgram SageMaker service supports metrics generation.
"""
return True
async def set_model(self, model: str):
"""Set the Deepgram model and reconnect.
Disconnects from the current session, updates the model setting, and
establishes a new connection with the updated model.
Args:
model: The Deepgram model name to use (e.g., "nova-3").
"""
await super().set_model(model)
logger.info(f"Switching STT model to: [{model}]")
self._settings["model"] = model
await self._disconnect()
await self._connect()
async def set_language(self, language: Language):
"""Set the recognition language and reconnect.
Disconnects from the current session, updates the language setting, and
establishes a new connection with the updated language.
Args:
language: The language to use for speech recognition (e.g., Language.EN,
Language.ES).
"""
logger.info(f"Switching STT language to: [{language}]")
self._settings["language"] = language
await self._disconnect()
await self._connect()
async def start(self, frame: StartFrame):
"""Start the Deepgram SageMaker STT service.
Args:
frame: The start frame containing initialization parameters.
"""
await super().start(frame)
self._settings["sample_rate"] = self.sample_rate
await self._connect()
async def stop(self, frame: EndFrame):
"""Stop the Deepgram SageMaker STT service.
Args:
frame: The end frame.
"""
await super().stop(frame)
await self._disconnect()
async def cancel(self, frame: CancelFrame):
"""Cancel the Deepgram SageMaker STT service.
Args:
frame: The cancel frame.
"""
await super().cancel(frame)
await self._disconnect()
async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
"""Send audio data to Deepgram for transcription.
Args:
audio: Raw audio bytes to transcribe.
Yields:
Frame: None (transcription results come via BiDi stream callbacks).
"""
if self._client and self._client.is_active:
try:
await self._client.send_audio_chunk(audio)
except Exception as e:
logger.error(f"Error sending audio to SageMaker: {e}")
await self.push_error(ErrorFrame(error=f"SageMaker STT error: {e}"))
yield None
async def _connect(self):
"""Connect to the SageMaker endpoint and start the BiDi session.
Builds the Deepgram query string from settings, creates the BiDi client,
starts the streaming session, and launches background tasks for processing
responses and sending KeepAlive messages.
"""
logger.debug("Connecting to Deepgram on SageMaker...")
# Update sample rate in settings
self._settings["sample_rate"] = self.sample_rate
# Build query string from settings, converting booleans to strings
query_params = {}
for key, value in self._settings.items():
if value is not None:
# Convert boolean values to lowercase strings for Deepgram API
if isinstance(value, bool):
query_params[key] = str(value).lower()
else:
query_params[key] = str(value)
query_string = "&".join(f"{k}={v}" for k, v in query_params.items())
# Create BiDi client
self._client = SageMakerBidiClient(
endpoint_name=self._endpoint_name,
region=self._region,
model_invocation_path="v1/listen",
model_query_string=query_string,
)
try:
# Start the session
await self._client.start_session()
# Start processing responses in the background
self._response_task = self.create_task(self._process_responses())
# Start keepalive task to maintain connection
self._keepalive_task = self.create_task(self._send_keepalive())
logger.debug("Connected to Deepgram on SageMaker")
await self._call_event_handler("on_connected")
except Exception as e:
logger.error(f"Failed to connect to SageMaker: {e}")
await self.push_error(ErrorFrame(error=f"SageMaker connection error: {e}"))
await self._call_event_handler("on_connection_error", str(e))
async def _disconnect(self):
"""Disconnect from the SageMaker endpoint.
Sends a CloseStream message to Deepgram, cancels background tasks
(KeepAlive and response processing), and closes the BiDi session.
Safe to call multiple times.
"""
if self._client and self._client.is_active:
logger.debug("Disconnecting from Deepgram on SageMaker...")
# Send CloseStream message to Deepgram
try:
await self._client.send_json({"type": "CloseStream"})
except Exception as e:
logger.warning(f"Failed to send CloseStream message: {e}")
# Cancel keepalive task
if self._keepalive_task and not self._keepalive_task.done():
await self.cancel_task(self._keepalive_task)
# Cancel response processing task
if self._response_task and not self._response_task.done():
await self.cancel_task(self._response_task)
# Close the BiDi session
await self._client.close_session()
logger.debug("Disconnected from Deepgram on SageMaker")
await self._call_event_handler("on_disconnected")
async def _send_keepalive(self):
"""Send periodic KeepAlive messages to maintain the connection.
Sends a KeepAlive JSON message to Deepgram every 5 seconds while the
connection is active. This prevents the connection from timing out during
periods of silence.
"""
while self._client and self._client.is_active:
await asyncio.sleep(5)
if self._client and self._client.is_active:
try:
await self._client.send_json({"type": "KeepAlive"})
except Exception as e:
logger.warning(f"Failed to send KeepAlive: {e}")
async def _process_responses(self):
"""Process streaming responses from Deepgram on SageMaker.
Continuously receives responses from the BiDi stream, decodes the payload,
parses JSON responses from Deepgram, and processes transcription results.
Runs as a background task until the connection is closed or cancelled.
"""
try:
while self._client and self._client.is_active:
result = await self._client.receive_response()
if result is None:
break
# Check if this is a PayloadPart with bytes
if hasattr(result, "value") and hasattr(result.value, "bytes_"):
if result.value.bytes_:
response_data = result.value.bytes_.decode("utf-8")
try:
# Parse JSON response from Deepgram
parsed = json.loads(response_data)
# Extract and process transcript if available
if "channel" in parsed:
await self._handle_transcript_response(parsed)
except json.JSONDecodeError:
logger.warning(f"Non-JSON response: {response_data}")
except asyncio.CancelledError:
logger.debug("Response processor cancelled")
except Exception as e:
logger.error(f"Error processing responses: {e}", exc_info=True)
await self.push_error(ErrorFrame(error=f"SageMaker response error: {e}"))
finally:
logger.debug("Response processor stopped")
async def _handle_transcript_response(self, parsed: dict):
"""Handle a transcript response from Deepgram.
Extracts the transcript text, determines if it's final or interim, extracts
language information, and pushes the appropriate frame (TranscriptionFrame
or InterimTranscriptionFrame) downstream.
Args:
parsed: The parsed JSON response from Deepgram containing channel,
alternatives, transcript, and metadata.
"""
alternatives = parsed.get("channel", {}).get("alternatives", [])
if not alternatives or not alternatives[0].get("transcript"):
return
transcript = alternatives[0]["transcript"]
if not transcript.strip():
return
# Stop TTFB metrics on first transcript
await self.stop_ttfb_metrics()
is_final = parsed.get("is_final", False)
speech_final = parsed.get("speech_final", False)
# Extract language if available
language = None
if alternatives[0].get("languages"):
language = alternatives[0]["languages"][0]
language = Language(language)
if is_final and speech_final:
# Final transcription
await self.push_frame(
TranscriptionFrame(
transcript,
self._user_id,
time_now_iso8601(),
language,
result=parsed,
)
)
await self._handle_transcription(transcript, is_final, language)
await self.stop_processing_metrics()
else:
# Interim transcription
await self.push_frame(
InterimTranscriptionFrame(
transcript,
self._user_id,
time_now_iso8601(),
language,
result=parsed,
)
)
@traced_stt
async def _handle_transcription(
self, transcript: str, is_final: bool, language: Optional[Language] = None
):
"""Handle a transcription result with tracing.
This method is decorated with @traced_stt for observability and tracing
integration. The actual transcription processing is handled by the parent
class and observers.
Args:
transcript: The transcribed text.
is_final: Whether this is a final transcription result.
language: The detected language of the transcription, if available.
"""
pass
async def start_metrics(self):
"""Start TTFB and processing metrics collection."""
await self.start_ttfb_metrics()
await self.start_processing_metrics()
async def process_frame(self, frame: Frame, direction: FrameDirection):
"""Process frames with Deepgram SageMaker-specific handling.
Args:
frame: The frame to process.
direction: The direction of frame processing.
"""
await super().process_frame(frame, direction)
# Start metrics when user starts speaking (if VAD is not provided by Deepgram)
if isinstance(frame, UserStartedSpeakingFrame):
await self.start_metrics()
elif isinstance(frame, UserStoppedSpeakingFrame):
# Send finalize message to Deepgram when user stops speaking
# This tells Deepgram to flush any remaining audio and return final results
if self._client and self._client.is_active:
try:
await self._client.send_json({"type": "Finalize"})
except Exception as e:
logger.warning(f"Error sending Finalize message: {e}")

86
uv.lock generated
View File

@@ -419,16 +419,30 @@ wheels = [
[[package]]
name = "aws-sdk-bedrock-runtime"
version = "0.1.1"
version = "0.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "smithy-aws-core", extra = ["eventstream", "json"], marker = "python_full_version >= '3.12'" },
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
{ name = "smithy-http", extra = ["awscrt"], marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/1d/78/48574454b3cac869df67665e4a403ebfc3abfcfba2c2ff01ccfd67d55f8f/aws_sdk_bedrock_runtime-0.1.1.tar.gz", hash = "sha256:c896f99e675c3a1ab600633a07b785f3dc9fe8ab94f640b1f992b63da2dfc784", size = 82446, upload-time = "2025-10-21T20:25:25.845Z" }
sdist = { url = "https://files.pythonhosted.org/packages/db/94/f2451bb09c106e5690bbb88fc366637cdcec942b352ed9bb788804c877e0/aws_sdk_bedrock_runtime-0.2.0.tar.gz", hash = "sha256:8de52dd4492e74c73244d4b41a52304e1db368814a10e49dbbf8f4e8e412cd0e", size = 88156, upload-time = "2025-11-22T00:35:44.978Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/83/07/62c0b70223d178c138f29124ac2f7973a6ba803abc7735b6a01a85217f3d/aws_sdk_bedrock_runtime-0.1.1-py3-none-any.whl", hash = "sha256:c0336b377b2112cf88197d3d44302fbeb3efb1101989fa49ae55e78f49cfe345", size = 74954, upload-time = "2025-10-21T20:25:24.973Z" },
{ url = "https://files.pythonhosted.org/packages/eb/6b/07fbddd31dd6e38c967fe088b5e91a7cc3a2bc0f645f18b4e5d45bc03f1f/aws_sdk_bedrock_runtime-0.2.0-py3-none-any.whl", hash = "sha256:19594de50a52d199d73efca153c0a2328bd781827715a6e012d50b11085236cc", size = 79875, upload-time = "2025-11-22T00:35:44.092Z" },
]
[[package]]
name = "aws-sdk-sagemaker-runtime-http2"
version = "0.1.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "smithy-aws-core", extra = ["eventstream", "json"], marker = "python_full_version >= '3.12'" },
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
{ name = "smithy-http", extra = ["awscrt"], marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/6e/ca/00f9c55887fc0f3fa345995dd871d40ff81473ab1591e56b4b4483d99d00/aws_sdk_sagemaker_runtime_http2-0.1.0.tar.gz", hash = "sha256:5077ec0c4440495b15004bbf04e27bc0bc137f1f8950d32195c6b45d7788d837", size = 20863, upload-time = "2025-11-22T00:20:56.358Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/9c/24/2e2f727c51c20f4625cd19364d9421dbd7c893fe2b53a46eb0caaf6263a2/aws_sdk_sagemaker_runtime_http2-0.1.0-py3-none-any.whl", hash = "sha256:1aebb728ba6c6d14e58e29ecf89b51f7abbe8786d34144f8a7d59a419e80bd2f", size = 21911, upload-time = "2025-11-22T00:20:55.054Z" },
]
[[package]]
@@ -4569,6 +4583,9 @@ runner = [
{ name = "python-dotenv" },
{ name = "uvicorn" },
]
sagemaker = [
{ name = "aws-sdk-sagemaker-runtime-http2", marker = "python_full_version >= '3.12'" },
]
sarvam = [
{ name = "sarvamai" },
{ name = "websockets" },
@@ -4654,7 +4671,8 @@ requires-dist = [
{ name = "aiortc", marker = "extra == 'webrtc'", specifier = ">=1.13.0,<2" },
{ name = "anthropic", marker = "extra == 'anthropic'", specifier = "~=0.49.0" },
{ name = "audioop-lts", marker = "python_full_version >= '3.13'", specifier = "~=0.2.1" },
{ name = "aws-sdk-bedrock-runtime", marker = "python_full_version >= '3.12' and extra == 'aws-nova-sonic'", specifier = "~=0.1.1" },
{ name = "aws-sdk-bedrock-runtime", marker = "python_full_version >= '3.12' and extra == 'aws-nova-sonic'", specifier = "~=0.2.0" },
{ name = "aws-sdk-sagemaker-runtime-http2", marker = "python_full_version >= '3.12' and extra == 'sagemaker'" },
{ name = "azure-cognitiveservices-speech", marker = "extra == 'azure'", specifier = "~=1.42.0" },
{ name = "cartesia", marker = "extra == 'cartesia'", specifier = "~=2.0.3" },
{ name = "coremltools", marker = "extra == 'local-smart-turn'", specifier = ">=8.0" },
@@ -4745,7 +4763,7 @@ requires-dist = [
{ name = "wait-for2", marker = "python_full_version < '3.12'", specifier = ">=0.4.1" },
{ name = "websockets", marker = "extra == 'websockets-base'", specifier = ">=13.1,<16.0" },
]
provides-extras = ["aic", "anthropic", "assemblyai", "asyncai", "aws", "aws-nova-sonic", "azure", "cartesia", "cerebras", "deepseek", "daily", "deepgram", "elevenlabs", "fal", "fireworks", "fish", "gladia", "google", "grok", "groq", "gstreamer", "heygen", "hume", "inworld", "krisp", "koala", "langchain", "livekit", "lmnt", "local", "mcp", "mem0", "mistral", "mlx-whisper", "moondream", "nim", "neuphonic", "noisereduce", "openai", "openpipe", "openrouter", "perplexity", "playht", "qwen", "rime", "riva", "runner", "sambanova", "sarvam", "sentry", "local-smart-turn", "local-smart-turn-v3", "remote-smart-turn", "silero", "simli", "soniox", "soundfile", "speechmatics", "strands", "tavus", "together", "tracing", "ultravox", "webrtc", "websocket", "websockets-base", "whisper"]
provides-extras = ["aic", "anthropic", "assemblyai", "asyncai", "aws", "aws-nova-sonic", "azure", "cartesia", "cerebras", "deepseek", "daily", "deepgram", "elevenlabs", "fal", "fireworks", "fish", "gladia", "google", "grok", "groq", "gstreamer", "heygen", "hume", "inworld", "krisp", "koala", "langchain", "livekit", "lmnt", "local", "mcp", "mem0", "mistral", "mlx-whisper", "moondream", "nim", "neuphonic", "noisereduce", "openai", "openpipe", "openrouter", "perplexity", "playht", "qwen", "rime", "riva", "runner", "sambanova", "sarvam", "sentry", "local-smart-turn", "local-smart-turn-v3", "remote-smart-turn", "sagemaker", "silero", "simli", "soniox", "soundfile", "speechmatics", "strands", "tavus", "together", "tracing", "ultravox", "webrtc", "websocket", "websockets-base", "whisper"]
[package.metadata.requires-dev]
dev = [
@@ -6522,16 +6540,16 @@ wheels = [
[[package]]
name = "smithy-aws-core"
version = "0.1.1"
version = "0.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aws-sdk-signers", marker = "python_full_version >= '3.12'" },
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
{ name = "smithy-http", marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/56/d3/f847e0fd36b95aa36ce3a4c9ce1a08e16b2aa9a56b71714045c9c924e282/smithy_aws_core-0.1.1.tar.gz", hash = "sha256:78dfd7040fc2bc72b6af293096642fc9a7bfd2db28ddbdf7c4110535eab9d662", size = 11196, upload-time = "2025-10-21T20:21:18.648Z" }
sdist = { url = "https://files.pythonhosted.org/packages/c1/c8/5970c869527972b23a1733de3993d54283c84a2340e84acdd48a11aa0ff4/smithy_aws_core-0.2.0.tar.gz", hash = "sha256:dfa1ecd311d6f0a16f48c86d793085e2a0a33a46de897d129dd1f142a43bf7f6", size = 11344, upload-time = "2025-11-21T18:33:01.928Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/d0/04/87cb06f0f6d664b5cffdef6d4042dd52c11c138436084d30ffdaa3543031/smithy_aws_core-0.1.1-py3-none-any.whl", hash = "sha256:0d1634f276c2999dc2a04fafef63b9d28309de50d939d1d49df952773a7063c4", size = 18963, upload-time = "2025-10-21T20:21:17.692Z" },
{ url = "https://files.pythonhosted.org/packages/88/25/739c0005a6cb4effbc2d37fe23590660b508fe314200f4acf94410a8f315/smithy_aws_core-0.2.0-py3-none-any.whl", hash = "sha256:d112082ef77758e1977f8694cf690ac35c76570c12a6590fccd5da085a3ce507", size = 18966, upload-time = "2025-11-21T18:33:00.812Z" },
]
[package.optional-dependencies]
@@ -6544,35 +6562,35 @@ json = [
[[package]]
name = "smithy-aws-event-stream"
version = "0.1.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/49/26/8ff24194efed60b2df18f610ea05fa2a4c6546858b80a0a51335a4943b9b/smithy_aws_event_stream-0.1.0.tar.gz", hash = "sha256:6634691a3bf5d4801a2c29f0761db2dc4771f3ae43cdee50c10d4b4bb2f86475", size = 12216, upload-time = "2025-09-29T19:37:14.659Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/90/c4/2b63d31af58fc359577e5515bf730348a235f2f2fa10e17af8640495c81c/smithy_aws_event_stream-0.1.0-py3-none-any.whl", hash = "sha256:17a7300a85cb90df4c6c23f895ca6343361fa419203c3cf80019edd7d3b5f036", size = 15581, upload-time = "2025-09-29T19:37:13.589Z" },
]
[[package]]
name = "smithy-core"
version = "0.1.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/b9/8d/16028d03456071d21de7591f1e1e6a1cc81b2389e53ef8663dbf59caf9cd/smithy_core-0.1.0.tar.gz", hash = "sha256:b159b8905264e1e4c613eab9f74cec0b2f5b8119c42fbadddb4da0a8ed8050e9", size = 48415, upload-time = "2025-09-29T19:37:16.873Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/ca/5b/563cb2beadcfa40597b0c3ff3f2d42e21f065b14782c4ba9cb41a44b745f/smithy_core-0.1.0-py3-none-any.whl", hash = "sha256:cb44e9355fb89e89f2c6ba6a1d59c5db4f2f7282c72d31d9307b6202d66cd0fa", size = 62895, upload-time = "2025-09-29T19:37:15.917Z" },
]
[[package]]
name = "smithy-http"
version = "0.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/3c/1c/44e99a7dfb8c39bf0c3d998accdf4573a7a3488863b90f21af260cec2d45/smithy_http-0.2.0.tar.gz", hash = "sha256:2382562fa9af326be455f14b18615f16ffe9db756e51b2a4ca0d23e1b881cff8", size = 26729, upload-time = "2025-10-21T20:21:06.146Z" }
sdist = { url = "https://files.pythonhosted.org/packages/31/90/78283c21484f8cf9862982e53bc2769b784910735fb5fb2400a17bfb5fdd/smithy_aws_event_stream-0.2.0.tar.gz", hash = "sha256:99700a11346e7ab1435ff2e53e6f6d60a1e857f2b2ee1941d40b54270adf3323", size = 12278, upload-time = "2025-11-21T18:33:03.79Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/d4/e2/d475fad81ac74ec0e145cb6d72afe5ecde4e2358bd632c2fd5d3f4bc87dc/smithy_http-0.2.0-py3-none-any.whl", hash = "sha256:49ee2402d7737798d70f99f491fbfb2a5767283ae562e21b6f86e3fd14f3e3e0", size = 37328, upload-time = "2025-10-21T20:21:05.362Z" },
{ url = "https://files.pythonhosted.org/packages/ca/f5/08b997eee81b55150496ce565f0e03c72d0c80e5b218170bdeae7c46a5a4/smithy_aws_event_stream-0.2.0-py3-none-any.whl", hash = "sha256:679a0c7d944e67d3a55d287541b3ca1e61f9d6a62e13401367dcc034e75aa55d", size = 15567, upload-time = "2025-11-21T18:33:02.711Z" },
]
[[package]]
name = "smithy-core"
version = "0.2.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/c7/f6/140f0be9331dd7cd8fa012b3ca4735df39a1a81d03eea89728f997249116/smithy_core-0.2.0.tar.gz", hash = "sha256:05c3e3309df5dcb9cf53e241bd57a96510e4575186443ea157db9dbb59b6c85e", size = 50334, upload-time = "2025-11-21T18:33:05.697Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/16/e3/d0defa2acf50b91625fe15e3ddb0c8e41ff64363a1f4cd9b8f19ae2ec0c6/smithy_core-0.2.0-py3-none-any.whl", hash = "sha256:db4620da3497abb60f79ac1d8a738d3eac46d7e820bfb50c777c36e932915239", size = 64777, upload-time = "2025-11-21T18:33:04.591Z" },
]
[[package]]
name = "smithy-http"
version = "0.3.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/1c/c7/4d8be56e897f99f3b6ffcdf52ba00a468febc939fca85b90f1c122450830/smithy_http-0.3.0.tar.gz", hash = "sha256:55dcc3af315eee6863d2f3f58ada1d9cb4bcc3a57faac10a1b21d4a93722f520", size = 28674, upload-time = "2025-11-21T18:33:07.387Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/2d/e5/59ae79ecdc9a935ad10512c581b3054ebb1afd90498ecc8afaf141dbc22b/smithy_http-0.3.0-py3-none-any.whl", hash = "sha256:972924304febd77c7134a7cffab83ce3b48423ff966dcc1f257e2c0d58fa9b18", size = 40520, upload-time = "2025-11-21T18:33:06.312Z" },
]
[package.optional-dependencies]
@@ -6582,15 +6600,15 @@ awscrt = [
[[package]]
name = "smithy-json"
version = "0.1.0"
version = "0.2.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "ijson", marker = "python_full_version >= '3.12'" },
{ name = "smithy-core", marker = "python_full_version >= '3.12'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/e2/5b/0ecb10007475e1b8faca3bbff1be2fc6edb3ea12ffc5e939e2249be95325/smithy_json-0.1.0.tar.gz", hash = "sha256:84fb48e445b87d850c240d837702c16b259ea53bad76b655ac1bbd8094d48912", size = 7086, upload-time = "2025-09-29T19:37:20.432Z" }
sdist = { url = "https://files.pythonhosted.org/packages/89/cf/e319a2a299b27bc0addf46ee3d4b9c25ec0817e3a0507b2b7a33eddc19f1/smithy_json-0.2.0.tar.gz", hash = "sha256:0946066fdda15d6a579dfdd4b61a547ab915eb057bd176fc2bc17d01dc789499", size = 7157, upload-time = "2025-11-21T18:33:08.968Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/62/95/e11c04e56aae12b62e38c49000004a1dc598a64dc207018c08448efde322/smithy_json-0.1.0-py3-none-any.whl", hash = "sha256:80ff64734dccdabf1ba6a2908555b97e60f62c07c3a27df48e421ee058413cb9", size = 9914, upload-time = "2025-09-29T19:37:19.459Z" },
{ url = "https://files.pythonhosted.org/packages/2e/b1/33012ac5b2e5940a00b6e1ccc313330e6f8692152a151f72a398cd6be0e0/smithy_json-0.2.0-py3-none-any.whl", hash = "sha256:5018a4e61731afa3094a02d737d4f956dbf270c271410c089045a17d86fc3b3b", size = 9911, upload-time = "2025-11-21T18:33:08.267Z" },
]
[[package]]