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Removing CanonicalMetricsService

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This commit is contained in:
Mark Backman
2025-05-06 21:23:23 -04:00
parent 99ab148d88
commit a1d46cb26b
14 changed files with 5 additions and 834 deletions
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4
CHANGELOG.md
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@@ -41,6 +41,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Remove custom audio tracks from `DailyTransport` before leaving.
### Removed
- Removed `CanonicalMetricsService` as it's no longer maintained.
## [0.0.66] - 2025-05-02
### Added

2
README.md
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@@ -60,7 +60,7 @@ You can connect to Pipecat from any platform using our official SDKs:
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) |
| Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) |
| Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
| Analytics & Metrics | [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)

1
docs/api/requirements.txt
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@@ -10,7 +10,6 @@ pipecat-ai[anthropic]
pipecat-ai[assemblyai]
pipecat-ai[aws]
pipecat-ai[azure]
pipecat-ai[canonical]
pipecat-ai[cartesia]
pipecat-ai[cerebras]
pipecat-ai[deepseek]

161
examples/canonical-metrics/.gitignore vendored
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@@ -1,161 +0,0 @@
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
recordings/
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
runpod.toml

10
examples/canonical-metrics/Dockerfile
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@@ -1,10 +0,0 @@
FROM python:3.10-bullseye
RUN mkdir /app
COPY *.py /app/
COPY requirements.txt /app/
WORKDIR /app
RUN pip3 install -r requirements.txt
EXPOSE 7860
CMD ["python3", "server.py"]

66
examples/canonical-metrics/README.md
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@@ -1,66 +0,0 @@
# Chatbot with canonical-metrics
This project implements a chatbot using a pipeline architecture that integrates audio processing, transcription, and a language model for conversational interactions. The chatbot operates within a daily communication environment, utilizing various services for text-to-speech and language model responses.
## Features
- **Audio Input and Output**: Captures microphone input and plays back audio responses.
- **Voice Activity Detection**: Utilizes Silero VAD to manage audio input intelligently.
- **Text-to-Speech**: Integrates ElevenLabs TTS service to convert text responses into audio.
- **Language Model Interaction**: Uses OpenAI's GPT-4 model to generate responses based on user input.
- **Transcription Services**: Captures and transcribes participant speech for analytics.
- **Metrics Collection**: Sends audio data for analysis via Canonical Metrics Service.
## Requirements
- Python 3.10+
- `python-dotenv`
- Additional libraries from the `pipecat` package.
## Setup
1. Clone the repository.
2. Install the required packages.
3. Set up environment variables for API keys:
- `OPENAI_API_KEY`
- `ELEVENLABS_API_KEY`
- `CANONICAL_API_KEY`
- `CANONICAL_API_URL`
4. Run the script.
## Usage
The chatbot introduces itself and engages in conversations, providing brief and creative responses. Designed for flexibility, it can support multiple languages with appropriate configuration.
## Events
- Participants joining or leaving the call are handled dynamically, adjusting the chatbot's behavior accordingly.
ℹ️ The first time, things might take extra time to get started since VAD (Voice Activity Detection) model needs to be downloaded.
## Get started
```python
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
cp env.example .env # and add your credentials
```
## Run the server
```bash
python server.py
```
Then, visit `http://localhost:7860/` in your browser to start a chatbot session.
## Build and test the Docker image
```
docker build -t chatbot .
docker run --env-file .env -p 7860:7860 chatbot
```

146
examples/canonical-metrics/bot.py
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@@ -1,146 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import uuid
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.canonical.metrics import CanonicalMetricsService
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
audio_out_enabled=True,
audio_in_enabled=True,
video_out_enabled=False,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
#
# Spanish
#
# transcription_settings=DailyTranscriptionSettings(
# language="es",
# tier="nova",
# model="2-general"
# )
),
)
tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY"),
#
# English
#
voice_id="cgSgspJ2msm6clMCkdW9",
#
# Spanish
#
# model="eleven_multilingual_v2",
# voice_id="gD1IexrzCvsXPHUuT0s3",
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
messages = [
{
"role": "system",
#
# English
#
"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by introducing yourself. Keep all your responses to 12 words or fewer.",
#
# Spanish
#
# "content": "Eres Chatbot, un amigable y útil robot. Tu objetivo es demostrar tus capacidades de una manera breve. Tus respuestas se convertiran a audio así que nunca no debes incluir caracteres especiales. Contesta a lo que el usuario pregunte de una manera creativa, útil y breve. Empieza por presentarte a ti mismo.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
"""
CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
call completion, CanonicalMetrics will send the audio buffer to Canonical for
analysis. Visit https://voice.canonical.chat to learn more.
"""
audio_buffer_processor = AudioBufferProcessor(num_channels=2)
canonical = CanonicalMetricsService(
audio_buffer_processor=audio_buffer_processor,
aiohttp_session=session,
api_key=os.getenv("CANONICAL_API_KEY"),
call_id=str(uuid.uuid4()),
assistant="pipecat-chatbot",
assistant_speaks_first=True,
context=context,
)
pipeline = Pipeline(
[
transport.input(), # microphone
context_aggregator.user(),
llm,
tts,
transport.output(),
canonical, # uploads audio buffer to Canonical AI for metrics
audio_buffer_processor, # captures audio into a buffer
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await audio_buffer_processor.start_recording()
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.cancel()
@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
# Here we don't want to cancel, we just want to finish sending
# whatever is queued, so we use an EndFrame().
await task.queue_frame(EndFrame())
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

6
examples/canonical-metrics/env.example
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@@ -1,6 +0,0 @@
DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=7df...
OPENAI_API_KEY=sk-PL...
ELEVENLABS_API_KEY=aeb...
CANONICAL_API_KEY=can...
CANONICAL_API_URL=

5
examples/canonical-metrics/requirements.txt
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@@ -1,5 +0,0 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,openai,silero,elevenlabs,canonical]

55
examples/canonical-metrics/runner.py
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@@ -1,55 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import aiohttp
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
async def configure(aiohttp_session: aiohttp.ClientSession):
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
parser.add_argument(
"-u", "--url", type=str, required=False, help="URL of the Daily room to join"
)
parser.add_argument(
"-k",
"--apikey",
type=str,
required=False,
help="Daily API Key (needed to create an owner token for the room)",
)
args, unknown = parser.parse_known_args()
url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
key = args.apikey or os.getenv("DAILY_API_KEY")
if not url:
raise Exception(
"No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
)
if not key:
raise Exception(
"No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
)
daily_rest_helper = DailyRESTHelper(
daily_api_key=key,
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
# Create a meeting token for the given room with an expiration 1 hour in
# the future.
expiry_time: float = 60 * 60
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)

139
examples/canonical-metrics/server.py
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@@ -1,139 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import subprocess
from contextlib import asynccontextmanager
import aiohttp
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, RedirectResponse
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
MAX_BOTS_PER_ROOM = 1
# Bot sub-process dict for status reporting and concurrency control
bot_procs = {}
daily_helpers = {}
load_dotenv(override=True)
def cleanup():
# Clean up function, just to be extra safe
for entry in bot_procs.values():
proc = entry[0]
proc.terminate()
proc.wait()
@asynccontextmanager
async def lifespan(app: FastAPI):
aiohttp_session = aiohttp.ClientSession()
daily_helpers["rest"] = DailyRESTHelper(
daily_api_key=os.getenv("DAILY_API_KEY", ""),
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
yield
await aiohttp_session.close()
cleanup()
app = FastAPI(lifespan=lifespan)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def start_agent(request: Request):
print(f"!!! Creating room")
room = await daily_helpers["rest"].create_room(DailyRoomParams())
print(f"!!! Room URL: {room.url}")
# Ensure the room property is present
if not room.url:
raise HTTPException(
status_code=500,
detail="Missing 'room' property in request data. Cannot start agent without a target room!",
)
# Check if there is already an existing process running in this room
num_bots_in_room = sum(
1 for proc in bot_procs.values() if proc[1] == room.url and proc[0].poll() is None
)
if num_bots_in_room >= MAX_BOTS_PER_ROOM:
raise HTTPException(status_code=500, detail=f"Max bot limited reach for room: {room.url}")
# Get the token for the room
token = await daily_helpers["rest"].get_token(room.url)
if not token:
raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
# Spawn a new agent, and join the user session
# Note: this is mostly for demonstration purposes (refer to 'deployment' in README)
try:
proc = subprocess.Popen(
[f"python3 -m bot -u {room.url} -t {token}"],
shell=True,
bufsize=1,
cwd=os.path.dirname(os.path.abspath(__file__)),
)
bot_procs[proc.pid] = (proc, room.url)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
return RedirectResponse(room.url)
@app.get("/status/{pid}")
def get_status(pid: int):
# Look up the subprocess
proc = bot_procs.get(pid)
# If the subprocess doesn't exist, return an error
if not proc:
raise HTTPException(status_code=404, detail=f"Bot with process id: {pid} not found")
# Check the status of the subprocess
if proc[0].poll() is None:
status = "running"
else:
status = "finished"
return JSONResponse({"bot_id": pid, "status": status})
if __name__ == "__main__":
import uvicorn
default_host = os.getenv("HOST", "0.0.0.0")
default_port = int(os.getenv("FAST_API_PORT", "7860"))
parser = argparse.ArgumentParser(description="Daily Storyteller FastAPI server")
parser.add_argument("--host", type=str, default=default_host, help="Host address")
parser.add_argument("--port", type=int, default=default_port, help="Port number")
parser.add_argument("--reload", action="store_true", help="Reload code on change")
config = parser.parse_args()
uvicorn.run(
"server:app",
host=config.host,
port=config.port,
reload=config.reload,
)

1
pyproject.toml
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@@ -43,7 +43,6 @@ anthropic = [ "anthropic~=0.49.0" ]
assemblyai = [ "assemblyai~=0.37.0" ]
aws = [ "boto3~=1.37.16" ]
azure = [ "azure-cognitiveservices-speech~=1.42.0"]
canonical = [ "aiofiles~=24.1.0" ]
cartesia = [ "cartesia~=1.4.0", "websockets~=13.1" ]
cerebras = []
deepseek = []

13
src/pipecat/services/canonical/__init__.py
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@@ -1,13 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import sys
from pipecat.services import DeprecatedModuleProxy
from .metrics import *
sys.modules[__name__] = DeprecatedModuleProxy(globals(), "canonical", "canonical.metrics")

230
src/pipecat/services/canonical/metrics.py
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@@ -1,230 +0,0 @@
#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import io
import os
import uuid
import wave
from datetime import datetime
from typing import Dict, List, Optional, Tuple
import aiohttp
from loguru import logger
from pipecat.frames.frames import CancelFrame, EndFrame, Frame
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.processors.frame_processor import FrameDirection
from pipecat.services.ai_service import AIService
try:
import aiofiles
import aiofiles.os
except ModuleNotFoundError as e:
logger.error(f"Exception: {e}")
logger.error(
"In order to use Canonical Metrics, you need to `pip install pipecat-ai[canonical]`. "
+ "Also, set the `CANONICAL_API_KEY` environment variable."
)
raise Exception(f"Missing module: {e}")
# Multipart upload part size in bytes, cannot be smaller than 5MB
PART_SIZE = 1024 * 1024 * 5
class CanonicalMetricsService(AIService):
"""Initialize a CanonicalAudioProcessor instance.
This class uses an AudioBufferProcessor to get the conversation audio and
uploads it to Canonical Voice API for audio processing.
Args:
call_id (str): Your unique identifier for the call. This is used to match the call in the Canonical Voice system to the call in your system.
assistant (str): Identifier for the AI assistant. This can be whatever you want, it's intended for you convenience so you can distinguish
between different assistants and a grouping mechanism for calls.
assistant_speaks_first (bool, optional): Indicates if the assistant speaks first in the conversation. Defaults to True.
output_dir (str, optional): Directory to save temporary audio files. Defaults to "recordings".
Attributes:
call_id (str): Stores the unique call identifier.
assistant (str): Stores the assistant identifier.
assistant_speaks_first (bool): Indicates whether the assistant speaks first.
output_dir (str): Directory path for saving temporary audio files.
The constructor also ensures that the output directory exists.
"""
def __init__(
self,
*,
aiohttp_session: aiohttp.ClientSession,
call_id: str,
assistant: str,
api_key: str,
api_url: str = "https://voiceapp.canonical.chat/api/v1",
assistant_speaks_first: bool = True,
output_dir: str = "recordings",
audio_buffer_processor: Optional[AudioBufferProcessor] = None,
context: Optional[OpenAILLMContext] = None,
**kwargs,
):
super().__init__(**kwargs)
# Validate that at least one of audio_buffer_processor or context is provided
if audio_buffer_processor is None and context is None:
raise ValueError("At least one of audio_buffer_processor or context must be specified")
self._aiohttp_session = aiohttp_session
self._audio_buffer_processor = audio_buffer_processor
self._api_key = api_key
self._api_url = api_url
self._call_id = call_id
self._assistant = assistant
self._assistant_speaks_first = assistant_speaks_first
self._output_dir = output_dir
self._context = context
async def stop(self, frame: EndFrame):
await super().stop(frame)
await self._process_completion()
async def cancel(self, frame: CancelFrame):
await super().cancel(frame)
await self._process_completion()
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
await self.push_frame(frame, direction)
async def _process_completion(self):
if self._audio_buffer_processor is not None:
await self._process_audio()
elif self._context is not None:
await self._process_transcript()
async def _process_transcript(self):
params = {
"callId": self._call_id,
"assistant": {"id": self._assistant, "speaksFirst": self._assistant_speaks_first},
"transcript": self._context.messages,
}
response = await self._aiohttp_session.post(
f"{self._api_url}/call",
headers=self._request_headers(),
json=params,
)
if not response.ok:
logger.error(f"Failed to process transcript: {await response.text()}")
async def _process_audio(self):
audio_buffer_processor = self._audio_buffer_processor
if not audio_buffer_processor.has_audio():
return
os.makedirs(self._output_dir, exist_ok=True)
filename = self._get_output_filename()
audio = audio_buffer_processor.merge_audio_buffers()
with io.BytesIO() as buffer:
with wave.open(buffer, "wb") as wf:
wf.setsampwidth(2)
wf.setnchannels(audio_buffer_processor.num_channels)
wf.setframerate(audio_buffer_processor.sample_rate)
wf.writeframes(audio)
async with aiofiles.open(filename, "wb") as file:
await file.write(buffer.getvalue())
try:
await self._multipart_upload(filename)
await aiofiles.os.remove(filename)
except FileNotFoundError:
pass
except Exception as e:
logger.error(f"Failed to upload recording: {e}")
def _get_output_filename(self):
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
return f"{self._output_dir}/{timestamp}-{uuid.uuid4().hex}.wav"
def _request_headers(self):
return {"Content-Type": "application/json", "X-Canonical-Api-Key": self._api_key}
async def _multipart_upload(self, file_path: str):
upload_request, upload_response = await self._request_upload(file_path)
if upload_request is None or upload_response is None:
return
parts = await self._upload_parts(file_path, upload_response)
if parts is None:
return
await self._upload_complete(parts, upload_request, upload_response)
async def _request_upload(self, file_path: str) -> Tuple[Dict, Dict]:
filename = os.path.basename(file_path)
filesize = os.path.getsize(file_path)
numparts = int((filesize + PART_SIZE - 1) / PART_SIZE)
params = {
"filename": filename,
"parts": numparts,
"callId": self._call_id,
"assistant": {"id": self._assistant, "speaksFirst": self._assistant_speaks_first},
}
logger.debug(f"Requesting presigned URLs for {numparts} parts")
response = await self._aiohttp_session.post(
f"{self._api_url}/recording/uploadRequest", headers=self._request_headers(), json=params
)
if not response.ok:
logger.error(f"Failed to get presigned URLs: {await response.text()}")
return None, None
response_json = await response.json()
return params, response_json
async def _upload_parts(self, file_path: str, upload_response: Dict) -> List[Dict]:
urls = upload_response["urls"]
parts = []
try:
async with aiofiles.open(file_path, "rb") as file:
for partnum, upload_url in enumerate(urls, start=1):
data = await file.read(PART_SIZE)
if not data:
break
response = await self._aiohttp_session.put(upload_url, data=data)
if not response.ok:
logger.error(f"Failed to upload part {partnum}: {await response.text()}")
return None
etag = response.headers["ETag"]
parts.append({"partnum": str(partnum), "etag": etag})
except Exception as e:
logger.error(f"Multipart upload aborted, an error occurred: {str(e)}")
return parts
async def _upload_complete(
self, parts: List[Dict], upload_request: Dict, upload_response: Dict
):
params = {
"filename": upload_request["filename"],
"parts": parts,
"slug": upload_response["slug"],
"callId": self._call_id,
"assistant": {"id": self._assistant, "speaksFirst": self._assistant_speaks_first},
}
if self._context is not None:
params["transcript"] = self._context.messages
logger.debug(f"Completing upload for {params['filename']}")
logger.debug(f"Slug: {params['slug']}")
response = await self._aiohttp_session.post(
f"{self._api_url}/recording/uploadComplete",
headers=self._request_headers(),
json=params,
)
if not response.ok:
logger.error(f"Failed to complete upload: {await response.text()}")
return