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Update optional dependency version ranges and remove SDK dependencies

Browse Source 
Widen version ranges for stable packages (anthropic, azure, deepgram,
groq, livekit, nvidia-riva-client, fastapi, ormsgpack, opentelemetry,
faster-whisper) and add upper bounds to previously uncapped packages
(hume, pyjwt, livekit-api, camb).

Replace CartesiaHttpTTSService's internal use of the Cartesia SDK with
direct aiohttp calls, accepting an optional aiohttp_session parameter.

Replace fal-client SDK calls in FalSTTService and FalImageGenService
with direct HTTP to bypass the SDK's aggressive retry/backoff logic
that caused significant latency regressions.
This commit is contained in:
Mark Backman
2026-03-05 15:06:54 -05:00
parent 05fa727c22
commit 3f97c91983
8 changed files with 246 additions and 308 deletions
Download Patch File Download Diff File Expand all files Collapse all files

95
examples/foundational/07-interruptible-cartesia-http.py
View File

@@ -6,6 +6,7 @@
import os import os
import aiohttp
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger from loguru import logger
@@ -52,60 +53,64 @@ transport_params = {
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot") logger.info(f"Starting bot")
stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY")) async with aiohttp.ClientSession() as session:
stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
tts = CartesiaHttpTTSService( tts = CartesiaHttpTTSService(
api_key=os.getenv("CARTESIA_API_KEY"), api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
) aiohttp_session=session,
)
llm = OpenAILLMService( llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"), api_key=os.getenv("OPENAI_API_KEY"),
system_instruction="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.", system_instruction="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() context = LLMContext()
user_aggregator, assistant_aggregator = LLMContextAggregatorPair( user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context, context,
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()), user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
) )
pipeline = Pipeline( pipeline = Pipeline(
[ [
transport.input(), # Transport user input transport.input(), # Transport user input
stt, stt,
user_aggregator, # User responses user_aggregator, # User responses
llm, # LLM llm, # LLM
tts, # TTS tts, # TTS
transport.output(), # Transport bot output transport.output(), # Transport bot output
assistant_aggregator, # Assistant spoken responses assistant_aggregator, # Assistant spoken responses
] ]
) )
task = PipelineTask( task = PipelineTask(
pipeline, pipeline,
params=PipelineParams( params=PipelineParams(
enable_metrics=True, enable_metrics=True,
enable_usage_metrics=True, enable_usage_metrics=True,
), ),
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
) )
@transport.event_handler("on_client_connected") @transport.event_handler("on_client_connected")
async def on_client_connected(transport, client): async def on_client_connected(transport, client):
logger.info(f"Client connected") logger.info(f"Client connected")
# Kick off the conversation. # Kick off the conversation.
context.add_message({"role": "system", "content": "Please introduce yourself to the user."}) context.add_message(
await task.queue_frames([LLMRunFrame()]) {"role": "system", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected") @transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client): async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected") logger.info(f"Client disconnected")
await task.cancel() await task.cancel()
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
await runner.run(task) await runner.run(task)
async def bot(runner_args: RunnerArguments): async def bot(runner_args: RunnerArguments):

2
examples/foundational/07c-interruptible-deepgram-sagemaker.py
View File

@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client): async def on_client_connected(transport, client):
logger.info(f"Client connected") logger.info(f"Client connected")
# Kick off the conversation. # Kick off the conversation.
context.add_message({"role": "system", "content": "Please introduce yourself to the user."}) context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
await task.queue_frames([LLMRunFrame()]) await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected") @transport.event_handler("on_client_disconnected")

99
examples/foundational/07w-interruptible-fal.py
View File

@@ -7,6 +7,7 @@
import os import os
import aiohttp
from dotenv import load_dotenv from dotenv import load_dotenv
from loguru import logger from loguru import logger
@@ -53,62 +54,66 @@ transport_params = {
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments): async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
logger.info(f"Starting bot") logger.info(f"Starting bot")
stt = FalSTTService( async with aiohttp.ClientSession() as session:
api_key=os.getenv("FAL_KEY"), stt = FalSTTService(
) api_key=os.getenv("FAL_KEY"),
aiohttp_session=session,
)
tts = CartesiaTTSService( tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"), api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
) )
llm = OpenAILLMService( llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"), api_key=os.getenv("OPENAI_API_KEY"),
system_instruction="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.", system_instruction="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() context = LLMContext()
user_aggregator, assistant_aggregator = LLMContextAggregatorPair( user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
context, context,
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()), user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
) )
pipeline = Pipeline( pipeline = Pipeline(
[ [
transport.input(), # Transport user input transport.input(), # Transport user input
stt, # STT stt, # STT
user_aggregator, # User responses user_aggregator, # User responses
llm, # LLM llm, # LLM
tts, # TTS tts, # TTS
transport.output(), # Transport bot output transport.output(), # Transport bot output
assistant_aggregator, # Assistant spoken responses assistant_aggregator, # Assistant spoken responses
] ]
) )
task = PipelineTask( task = PipelineTask(
pipeline, pipeline,
params=PipelineParams( params=PipelineParams(
enable_metrics=True, enable_metrics=True,
enable_usage_metrics=True, enable_usage_metrics=True,
), ),
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs, idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
) )
@transport.event_handler("on_client_connected") @transport.event_handler("on_client_connected")
async def on_client_connected(transport, client): async def on_client_connected(transport, client):
logger.info(f"Client connected") logger.info(f"Client connected")
# Kick off the conversation. # Kick off the conversation.
context.add_message({"role": "system", "content": "Please introduce yourself to the user."}) context.add_message(
await task.queue_frames([LLMRunFrame()]) {"role": "system", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected") @transport.event_handler("on_client_disconnected")
async def on_client_disconnected(transport, client): async def on_client_disconnected(transport, client):
logger.info(f"Client disconnected") logger.info(f"Client disconnected")
await task.cancel() await task.cancel()
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint) runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
await runner.run(task) await runner.run(task)
async def bot(runner_args: RunnerArguments): async def bot(runner_args: RunnerArguments):

36
pyproject.toml
View File

@@ -53,37 +53,37 @@ Changelog = "https://github.com/pipecat-ai/pipecat/blob/main/CHANGELOG.md"
[project.optional-dependencies] [project.optional-dependencies]
aic = [ "aic-sdk~=2.1.0" ] aic = [ "aic-sdk~=2.1.0" ]
anthropic = [ "anthropic~=0.49.0" ] anthropic = [ "anthropic>=0.49.0,<1" ]
assemblyai = [ "pipecat-ai[websockets-base]" ] assemblyai = [ "pipecat-ai[websockets-base]" ]
asyncai = [ "pipecat-ai[websockets-base]" ] asyncai = [ "pipecat-ai[websockets-base]" ]
aws = [ "aioboto3~=15.5.0", "pipecat-ai[websockets-base]" ] aws = [ "aioboto3>=15.5.0,<16", "pipecat-ai[websockets-base]" ]
aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.2.0; python_version>='3.12'" ] aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.4.0; python_version>='3.12'" ]
azure = [ "azure-cognitiveservices-speech~=1.47.0"] azure = [ "azure-cognitiveservices-speech>=1.47.0,<2"]
cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ] cartesia = [ "pipecat-ai[websockets-base]" ]
camb = [ "camb-sdk>=1.5.4" ] camb = [ "camb-sdk>=1.5.4,<2" ]
cerebras = [] cerebras = []
daily = [ "daily-python~=0.23.0" ] daily = [ "daily-python~=0.23.0" ]
deepgram = [ "deepgram-sdk~=6.0.1", "pipecat-ai[websockets-base]" ] deepgram = [ "deepgram-sdk>=6.0.1,<7", "pipecat-ai[websockets-base]" ]
deepseek = [] deepseek = []
elevenlabs = [ "pipecat-ai[websockets-base]" ] elevenlabs = [ "pipecat-ai[websockets-base]" ]
fal = [ "fal-client~=0.5.9" ] fal = []
fireworks = [] fireworks = []
fish = [ "ormsgpack~=1.7.0", "pipecat-ai[websockets-base]" ] fish = [ "ormsgpack>=1.7.0,<2", "pipecat-ai[websockets-base]" ]
gladia = [ "pipecat-ai[websockets-base]" ] gladia = [ "pipecat-ai[websockets-base]" ]
google = [ "google-cloud-speech>=2.33.0,<3", "google-cloud-texttospeech>=2.31.0,<3", "google-genai>=1.57.0,<2", "pipecat-ai[websockets-base]" ] google = [ "google-cloud-speech>=2.33.0,<3", "google-cloud-texttospeech>=2.31.0,<3", "google-genai>=1.57.0,<2", "pipecat-ai[websockets-base]" ]
gradium = [ "pipecat-ai[websockets-base]" ] gradium = [ "pipecat-ai[websockets-base]" ]
grok = [] grok = []
groq = [ "groq~=0.23.0" ] groq = [ "groq>=0.23.0,<2" ]
gstreamer = [ "pygobject~=3.50.0" ] gstreamer = [ "pygobject~=3.50.0" ]
heygen = [ "livekit>=1.0.13", "pipecat-ai[websockets-base]" ] heygen = [ "livekit>=1.0.13,<2", "pipecat-ai[websockets-base]" ]
hume = [ "hume>=0.11.2" ] hume = [ "hume>=0.11.2,<1" ]
inworld = [] inworld = []
koala = [ "pvkoala~=2.0.3" ] koala = [ "pvkoala~=2.0.3" ]
kokoro = [ "kokoro-onnx>=0.5.0,<1", "requests>=2.32.5,<3" ] kokoro = [ "kokoro-onnx>=0.5.0,<1", "requests>=2.32.5,<3" ]
krisp = [ "pipecat-ai-krisp~=0.4.0" ] krisp = [ "pipecat-ai-krisp~=0.4.0" ]
langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ] langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ]
lemonslice = [ "pipecat-ai[daily]" ] lemonslice = [ "pipecat-ai[daily]" ]
livekit = [ "livekit~=1.0.13", "livekit-api~=1.0.5", "tenacity>=8.2.3,<10.0.0", "pyjwt>=2.10.1" ] livekit = [ "livekit>=1.0.13,<2", "livekit-api>=1.0.5,<2", "tenacity>=8.2.3,<10.0.0", "pyjwt>=2.10.1,<3" ]
lmnt = [ "pipecat-ai[websockets-base]" ] lmnt = [ "pipecat-ai[websockets-base]" ]
local = [ "pyaudio~=0.2.14" ] local = [ "pyaudio~=0.2.14" ]
local-smart-turn = [ "coremltools>=8.0", "transformers>=4.48.0,<6", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ] local-smart-turn = [ "coremltools>=8.0", "transformers>=4.48.0,<6", "torch>=2.5.0,<3", "torchaudio>=2.5.0,<3" ]
@@ -94,7 +94,7 @@ 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,<6" ] moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0,<6" ]
neuphonic = [ "pipecat-ai[websockets-base]" ] neuphonic = [ "pipecat-ai[websockets-base]" ]
noisereduce = [ "noisereduce~=3.0.3" ] noisereduce = [ "noisereduce~=3.0.3" ]
nvidia = [ "nvidia-riva-client~=2.21.1" ] nvidia = [ "nvidia-riva-client>=2.21.1,<3" ]
openai = [ "pipecat-ai[websockets-base]" ] openai = [ "pipecat-ai[websockets-base]" ]
rnnoise = [ "pyrnnoise~=0.4.1" ] rnnoise = [ "pyrnnoise~=0.4.1" ]
openpipe = [ "openpipe>=4.50.0,<6" ] openpipe = [ "openpipe>=4.50.0,<6" ]
@@ -106,7 +106,7 @@ remote-smart-turn = []
resembleai = [ "pipecat-ai[websockets-base]" ] resembleai = [ "pipecat-ai[websockets-base]" ]
rime = [ "pipecat-ai[websockets-base]" ] rime = [ "pipecat-ai[websockets-base]" ]
riva = [ "pipecat-ai[nvidia]" ] riva = [ "pipecat-ai[nvidia]" ]
runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<0.128.0", "pipecat-ai-small-webrtc-prebuilt>=2.3.0"] runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<1", "pipecat-ai-small-webrtc-prebuilt>=2.3.0"]
sagemaker = ["aws_sdk_sagemaker_runtime_http2; python_version>='3.12'"] sagemaker = ["aws_sdk_sagemaker_runtime_http2; python_version>='3.12'"]
sambanova = [] sambanova = []
sarvam = [ "sarvamai==0.1.26a2", "pipecat-ai[websockets-base]" ] sarvam = [ "sarvamai==0.1.26a2", "pipecat-ai[websockets-base]" ]
@@ -119,12 +119,12 @@ speechmatics = [ "speechmatics-voice[smart]~=0.2.8" ]
strands = [ "strands-agents>=1.9.1,<2" ] strands = [ "strands-agents>=1.9.1,<2" ]
tavus=[] tavus=[]
together = [] together = []
tracing = [ "opentelemetry-sdk>=1.33.0", "opentelemetry-api>=1.33.0", "opentelemetry-instrumentation>=0.54b0" ] tracing = [ "opentelemetry-sdk>=1.33.0,<2", "opentelemetry-api>=1.33.0,<2", "opentelemetry-instrumentation>=0.54b0,<1" ]
ultravox = [ "pipecat-ai[websockets-base]" ] ultravox = [ "pipecat-ai[websockets-base]" ]
webrtc = [ "aiortc>=1.14.0,<2", "opencv-python>=4.11.0.86,<5" ] webrtc = [ "aiortc>=1.14.0,<2", "opencv-python>=4.11.0.86,<5" ]
websocket = [ "pipecat-ai[websockets-base]", "fastapi>=0.115.6,<0.128.0" ] websocket = [ "pipecat-ai[websockets-base]", "fastapi>=0.115.6,<1" ]
websockets-base = [ "websockets>=13.1,<16.0" ] websockets-base = [ "websockets>=13.1,<16.0" ]
whisper = [ "faster-whisper~=1.1.1" ] whisper = [ "faster-whisper~=1.2.1" ]
[dependency-groups] [dependency-groups]
dev = [ dev = [

31
src/pipecat/services/cartesia/tts.py
View File

@@ -13,6 +13,7 @@ from dataclasses import dataclass, field
from enum import Enum from enum import Enum
from typing import Any, AsyncGenerator, List, Literal, Mapping, Optional from typing import Any, AsyncGenerator, List, Literal, Mapping, Optional
import aiohttp
from loguru import logger from loguru import logger
from pydantic import BaseModel, Field from pydantic import BaseModel, Field
@@ -33,13 +34,8 @@ from pipecat.utils.text.base_text_aggregator import BaseTextAggregator
from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator
from pipecat.utils.tracing.service_decorators import traced_tts from pipecat.utils.tracing.service_decorators import traced_tts
# Suppress regex warnings from pydub (used by cartesia)
warnings.filterwarnings("ignore", message="invalid escape sequence", category=SyntaxWarning)
# See .env.example for Cartesia configuration needed # See .env.example for Cartesia configuration needed
try: try:
from cartesia import AsyncCartesia
from websockets.asyncio.client import connect as websocket_connect from websockets.asyncio.client import connect as websocket_connect
from websockets.protocol import State from websockets.protocol import State
except ModuleNotFoundError as e: except ModuleNotFoundError as e:
@@ -721,6 +717,7 @@ class CartesiaHttpTTSService(TTSService):
model: str = "sonic-3", model: str = "sonic-3",
base_url: str = "https://api.cartesia.ai", base_url: str = "https://api.cartesia.ai",
cartesia_version: str = "2024-11-13", cartesia_version: str = "2024-11-13",
aiohttp_session: Optional[aiohttp.ClientSession] = None,
sample_rate: Optional[int] = None, sample_rate: Optional[int] = None,
encoding: str = "pcm_s16le", encoding: str = "pcm_s16le",
container: str = "raw", container: str = "raw",
@@ -735,6 +732,8 @@ class CartesiaHttpTTSService(TTSService):
model: TTS model to use (e.g., "sonic-3"). model: TTS model to use (e.g., "sonic-3").
base_url: Base URL for Cartesia HTTP API. base_url: Base URL for Cartesia HTTP API.
cartesia_version: API version string for Cartesia service. cartesia_version: API version string for Cartesia service.
aiohttp_session: Optional aiohttp ClientSession for HTTP requests.
If not provided, a session will be created and managed internally.
sample_rate: Audio sample rate. If None, uses default. sample_rate: Audio sample rate. If None, uses default.
encoding: Audio encoding format. encoding: Audio encoding format.
container: Audio container format. container: Audio container format.
@@ -766,10 +765,8 @@ class CartesiaHttpTTSService(TTSService):
self._base_url = base_url self._base_url = base_url
self._cartesia_version = cartesia_version self._cartesia_version = cartesia_version
self._client = AsyncCartesia( self._session: aiohttp.ClientSession | None = aiohttp_session
api_key=api_key, self._owns_session = aiohttp_session is None
base_url=base_url,
)
def can_generate_metrics(self) -> bool: def can_generate_metrics(self) -> bool:
"""Check if this service can generate processing metrics. """Check if this service can generate processing metrics.
@@ -798,6 +795,14 @@ class CartesiaHttpTTSService(TTSService):
""" """
await super().start(frame) await super().start(frame)
self._settings.output_sample_rate = self.sample_rate self._settings.output_sample_rate = self.sample_rate
if self._owns_session:
self._session = aiohttp.ClientSession()
async def _close_session(self):
"""Close the HTTP session if we own it."""
if self._owns_session and self._session:
await self._session.close()
self._session = None
async def stop(self, frame: EndFrame): async def stop(self, frame: EndFrame):
"""Stop the Cartesia HTTP TTS service. """Stop the Cartesia HTTP TTS service.
@@ -806,7 +811,7 @@ class CartesiaHttpTTSService(TTSService):
frame: The end frame. frame: The end frame.
""" """
await super().stop(frame) await super().stop(frame)
await self._client.close() await self._close_session()
async def cancel(self, frame: CancelFrame): async def cancel(self, frame: CancelFrame):
"""Cancel the Cartesia HTTP TTS service. """Cancel the Cartesia HTTP TTS service.
@@ -815,7 +820,7 @@ class CartesiaHttpTTSService(TTSService):
frame: The cancel frame. frame: The cancel frame.
""" """
await super().cancel(frame) await super().cancel(frame)
await self._client.close() await self._close_session()
@traced_tts @traced_tts
async def run_tts(self, text: str, context_id: str) -> AsyncGenerator[Frame, None]: async def run_tts(self, text: str, context_id: str) -> AsyncGenerator[Frame, None]:
@@ -874,8 +879,6 @@ class CartesiaHttpTTSService(TTSService):
yield TTSStartedFrame(context_id=context_id) yield TTSStartedFrame(context_id=context_id)
session = await self._client._get_session()
headers = { headers = {
"Cartesia-Version": self._cartesia_version, "Cartesia-Version": self._cartesia_version,
"X-API-Key": self._api_key, "X-API-Key": self._api_key,
@@ -884,7 +887,7 @@ class CartesiaHttpTTSService(TTSService):
url = f"{self._base_url}/tts/bytes" url = f"{self._base_url}/tts/bytes"
async with session.post(url, json=payload, headers=headers) as response: async with self._session.post(url, json=payload, headers=headers) as response:
if response.status != 200: if response.status != 200:
error_text = await response.text() error_text = await response.text()
yield ErrorFrame(error=f"Cartesia API error: {error_text}") yield ErrorFrame(error=f"Cartesia API error: {error_text}")

28
src/pipecat/services/fal/image.py
View File

@@ -25,13 +25,6 @@ from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame
from pipecat.services.image_service import ImageGenService from pipecat.services.image_service import ImageGenService
from pipecat.services.settings import ImageGenSettings from pipecat.services.settings import ImageGenSettings
try:
import fal_client
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error("In order to use Fal, you need to `pip install pipecat-ai[fal]`.")
raise Exception(f"Missing module: {e}")
@dataclass @dataclass
class FalImageGenSettings(ImageGenSettings): class FalImageGenSettings(ImageGenSettings):
@@ -91,6 +84,7 @@ class FalImageGenService(ImageGenService):
super().__init__(settings=FalImageGenSettings(model=model), **kwargs) super().__init__(settings=FalImageGenSettings(model=model), **kwargs)
self._params = params self._params = params
self._aiohttp_session = aiohttp_session self._aiohttp_session = aiohttp_session
self._api_key = key or os.getenv("FAL_KEY", "")
if key: if key:
os.environ["FAL_KEY"] = key os.environ["FAL_KEY"] = key
@@ -112,10 +106,22 @@ class FalImageGenService(ImageGenService):
logger.debug(f"Generating image from prompt: {prompt}") logger.debug(f"Generating image from prompt: {prompt}")
response = await fal_client.run_async( headers = {
self._settings.model, "Authorization": f"Key {self._api_key}",
arguments={"prompt": prompt, **self._params.model_dump(exclude_none=True)}, "Content-Type": "application/json",
) }
payload = {"prompt": prompt, **self._params.model_dump(exclude_none=True)}
async with self._aiohttp_session.post(
f"https://fal.run/{self._settings.model}",
json=payload,
headers=headers,
) as resp:
if resp.status != 200:
error_text = await resp.text()
yield ErrorFrame(error=f"Fal API error ({resp.status}): {error_text}")
return
response = await resp.json()
image_url = response["images"][0]["url"] if response else None image_url = response["images"][0]["url"] if response else None

50
src/pipecat/services/fal/stt.py
View File

@@ -10,10 +10,12 @@ This module provides integration with Fal's Wizper API for speech-to-text
transcription using segmented audio processing. transcription using segmented audio processing.
""" """
import base64
import os import os
from dataclasses import dataclass, field from dataclasses import dataclass, field
from typing import Any, AsyncGenerator, Optional from typing import AsyncGenerator, Optional
import aiohttp
from loguru import logger from loguru import logger
from pydantic import BaseModel from pydantic import BaseModel
@@ -25,15 +27,6 @@ from pipecat.transcriptions.language import Language, resolve_language
from pipecat.utils.time import time_now_iso8601 from pipecat.utils.time import time_now_iso8601
from pipecat.utils.tracing.service_decorators import traced_stt from pipecat.utils.tracing.service_decorators import traced_stt
try:
import fal_client
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error(
"In order to use Fal, you need to `pip install pipecat-ai[fal]`. Also, set `FAL_KEY` environment variable."
)
raise Exception(f"Missing module: {e}")
def language_to_fal_language(language: Language) -> Optional[str]: def language_to_fal_language(language: Language) -> Optional[str]:
"""Convert a Language enum to Fal's Wizper language code. """Convert a Language enum to Fal's Wizper language code.
@@ -192,6 +185,7 @@ class FalSTTService(SegmentedSTTService):
self, self,
*, *,
api_key: Optional[str] = None, api_key: Optional[str] = None,
aiohttp_session: Optional[aiohttp.ClientSession] = None,
sample_rate: Optional[int] = None, sample_rate: Optional[int] = None,
params: Optional[InputParams] = None, params: Optional[InputParams] = None,
ttfs_p99_latency: Optional[float] = FAL_TTFS_P99, ttfs_p99_latency: Optional[float] = FAL_TTFS_P99,
@@ -201,6 +195,8 @@ class FalSTTService(SegmentedSTTService):
Args: Args:
api_key: Fal API key. If not provided, will check FAL_KEY environment variable. api_key: Fal API key. If not provided, will check FAL_KEY environment variable.
aiohttp_session: Optional aiohttp ClientSession for HTTP requests.
If not provided, a session will be created and managed internally.
sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate. sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate.
params: Configuration parameters for the Wizper API. params: Configuration parameters for the Wizper API.
ttfs_p99_latency: P99 latency from speech end to final transcript in seconds. ttfs_p99_latency: P99 latency from speech end to final transcript in seconds.
@@ -224,14 +220,14 @@ class FalSTTService(SegmentedSTTService):
**kwargs, **kwargs,
) )
if api_key: self._api_key = api_key or os.getenv("FAL_KEY", "")
os.environ["FAL_KEY"] = api_key if not self._api_key:
elif "FAL_KEY" not in os.environ:
raise ValueError( raise ValueError(
"FAL_KEY must be provided either through api_key parameter or environment variable" "FAL_KEY must be provided either through api_key parameter or environment variable"
) )
self._fal_client = fal_client.AsyncClient(key=api_key or os.getenv("FAL_KEY")) self._session: aiohttp.ClientSession | None = aiohttp_session
self._owns_session = aiohttp_session is None
def can_generate_metrics(self) -> bool: def can_generate_metrics(self) -> bool:
"""Check if the service can generate processing metrics. """Check if the service can generate processing metrics.
@@ -275,12 +271,26 @@ class FalSTTService(SegmentedSTTService):
try: try:
await self.start_processing_metrics() await self.start_processing_metrics()
# Send to Fal directly (audio is already in WAV format from base class) if not self._session:
data_uri = fal_client.encode(audio, "audio/x-wav") self._session = aiohttp.ClientSession()
response = await self._fal_client.run(
"fal-ai/wizper", data_uri = f"data:audio/x-wav;base64,{base64.b64encode(audio).decode()}"
arguments={"audio_url": data_uri, **self._settings.given_fields()}, payload = {"audio_url": data_uri, **self._settings.given_fields()}
) headers = {
"Authorization": f"Key {self._api_key}",
"Content-Type": "application/json",
}
async with self._session.post(
"https://fal.run/fal-ai/wizper",
json=payload,
headers=headers,
) as resp:
if resp.status != 200:
error_text = await resp.text()
yield ErrorFrame(error=f"Fal API error ({resp.status}): {error_text}")
return
response = await resp.json()
if response and "text" in response: if response and "text" in response:
text = response["text"].strip() text = response["text"].strip()

213
uv.lock generated
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