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Merge branch 'main' of github.com:Monadical-SAS/reflector into sara/recorder-memory

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Sara
2023-10-13 19:10:55 +02:00
parent fb7a7602f3 628c69f81c
commit 21473b584f
17 changed files with 333 additions and 291 deletions
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1
server/env.example
View File

@@ -48,6 +48,7 @@
## Using serverless modal.com (require reflector-gpu-modal deployed)
#TRANSCRIPT_BACKEND=modal
#TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
#TRANSLATE_URL=https://xxxxx--reflector-translator-web.modal.run
#TRANSCRIPT_MODAL_API_KEY=xxxxx
## Using serverless banana.dev (require reflector-gpu-banana deployed)

138
server/gpu/modal/reflector_transcriber.py
View File

@@ -14,34 +14,12 @@ WHISPER_MODEL: str = "large-v2"
WHISPER_COMPUTE_TYPE: str = "float16"
WHISPER_NUM_WORKERS: int = 1
# Seamless M4T
SEAMLESSM4T_MODEL_SIZE: str = "medium"
SEAMLESSM4T_MODEL_CARD_NAME: str = f"seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}"
SEAMLESSM4T_VOCODER_CARD_NAME: str = "vocoder_36langs"
HF_SEAMLESS_M4TEPO: str = f"facebook/seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}"
HF_SEAMLESS_M4T_VOCODEREPO: str = "facebook/seamless-m4t-vocoder"
SEAMLESS_GITEPO: str = "https://github.com/facebookresearch/seamless_communication.git"
SEAMLESS_MODEL_DIR: str = "m4t"
WHISPER_MODEL_DIR = "/root/transcription_models"
stub = Stub(name="reflector-transcriber")
def install_seamless_communication():
import os
import subprocess
initial_dir = os.getcwd()
subprocess.run(["ssh-keyscan", "-t", "rsa", "github.com", ">>", "~/.ssh/known_hosts"])
subprocess.run(["rm", "-rf", "seamless_communication"])
subprocess.run(["git", "clone", SEAMLESS_GITEPO, "." + "/seamless_communication"])
os.chdir("seamless_communication")
subprocess.run(["pip", "install", "-e", "."])
os.chdir(initial_dir)
def download_whisper():
from faster_whisper.utils import download_model
@@ -50,18 +28,6 @@ def download_whisper():
print("Whisper model downloaded")
def download_seamlessm4t_model():
from huggingface_hub import snapshot_download
print("Downloading Transcriber model & tokenizer")
snapshot_download(HF_SEAMLESS_M4TEPO, cache_dir=SEAMLESS_MODEL_DIR)
print("Transcriber model & tokenizer downloaded")
print("Downloading vocoder weights")
snapshot_download(HF_SEAMLESS_M4T_VOCODEREPO, cache_dir=SEAMLESS_MODEL_DIR)
print("Vocoder weights downloaded")
def migrate_cache_llm():
"""
XXX The cache for model files in Transformers v4.22.0 has been updated.
@@ -76,52 +42,6 @@ def migrate_cache_llm():
print("LLM cache moved")
def configure_seamless_m4t():
import os
import yaml
ASSETS_DIR: str = "./seamless_communication/src/seamless_communication/assets/cards"
with open(f'{ASSETS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml', 'r') as file:
model_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f'{ASSETS_DIR}/vocoder_36langs.yaml', 'r') as file:
vocoder_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f'{ASSETS_DIR}/unity_nllb-100.yaml', 'r') as file:
unity_100_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f'{ASSETS_DIR}/unity_nllb-200.yaml', 'r') as file:
unity_200_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
model_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}/snapshots"
available_model_versions = os.listdir(model_dir)
latest_model_version = sorted(available_model_versions)[-1]
model_name = f"multitask_unity_{SEAMLESSM4T_MODEL_SIZE}.pt"
model_path = os.path.join(os.getcwd(), model_dir, latest_model_version, model_name)
vocoder_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-vocoder/snapshots"
available_vocoder_versions = os.listdir(vocoder_dir)
latest_vocoder_version = sorted(available_vocoder_versions)[-1]
vocoder_name = "vocoder_36langs.pt"
vocoder_path = os.path.join(os.getcwd(), vocoder_dir, latest_vocoder_version, vocoder_name)
tokenizer_name = "tokenizer.model"
tokenizer_path = os.path.join(os.getcwd(), model_dir, latest_model_version, tokenizer_name)
model_yaml_data['checkpoint'] = f"file:/{model_path}"
vocoder_yaml_data['checkpoint'] = f"file:/{vocoder_path}"
unity_100_yaml_data['tokenizer'] = f"file:/{tokenizer_path}"
unity_200_yaml_data['tokenizer'] = f"file:/{tokenizer_path}"
with open(f'{ASSETS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml', 'w') as file:
yaml.dump(model_yaml_data, file)
with open(f'{ASSETS_DIR}/vocoder_36langs.yaml', 'w') as file:
yaml.dump(vocoder_yaml_data, file)
with open(f'{ASSETS_DIR}/unity_nllb-100.yaml', 'w') as file:
yaml.dump(unity_100_yaml_data, file)
with open(f'{ASSETS_DIR}/unity_nllb-200.yaml', 'w') as file:
yaml.dump(unity_200_yaml_data, file)
transcriber_image = (
Image.debian_slim(python_version="3.10.8")
.apt_install("git")
@@ -131,7 +51,7 @@ transcriber_image = (
"faster-whisper",
"requests",
"torch",
"transformers",
"transformers==4.34.0",
"sentencepiece",
"protobuf",
"huggingface_hub==0.16.4",
@@ -141,9 +61,6 @@ transcriber_image = (
"pyyaml",
"hf-transfer~=0.1"
)
.run_function(install_seamless_communication)
.run_function(download_seamlessm4t_model)
.run_function(configure_seamless_m4t)
.run_function(download_whisper)
.run_function(migrate_cache_llm)
.env(
@@ -167,7 +84,6 @@ class Transcriber:
def __enter__(self):
import faster_whisper
import torch
from seamless_communication.models.inference.translator import Translator
self.use_gpu = torch.cuda.is_available()
self.device = "cuda" if self.use_gpu else "cpu"
@@ -178,12 +94,6 @@ class Transcriber:
num_workers=WHISPER_NUM_WORKERS,
download_root=WHISPER_MODEL_DIR
)
self.translator = Translator(
SEAMLESSM4T_MODEL_CARD_NAME,
SEAMLESSM4T_VOCODER_CARD_NAME,
torch.device(self.device),
dtype=torch.float32
)
@method()
def transcribe_segment(
@@ -229,38 +139,6 @@ class Transcriber:
"words": words
}
def get_seamless_lang_code(self, lang_code: str):
"""
The codes for SeamlessM4T is different from regular standards.
For ex, French is "fra" and not "fr".
"""
# TODO: Enhance with complete list of lang codes
seamless_lang_code = {
"en": "eng",
"fr": "fra"
}
return seamless_lang_code.get(lang_code, "eng")
@method()
def translate_text(
self,
text: str,
source_language: str,
target_language: str
):
translated_text, _, _ = self.translator.predict(
text,
"t2tt",
src_lang=self.get_seamless_lang_code(source_language),
tgt_lang=self.get_seamless_lang_code(target_language),
ngram_filtering=True
)
return {
"text": {
source_language: text,
target_language: str(translated_text)
}
}
# -------------------------------------------------------------------
# Web API
# -------------------------------------------------------------------
@@ -316,18 +194,4 @@ def web():
result = func.get()
return result
@app.post("/translate", dependencies=[Depends(apikey_auth)])
async def translate(
text: str,
source_language: Annotated[str, Body(...)] = "en",
target_language: Annotated[str, Body(...)] = "fr",
) -> TranscriptResponse:
func = transcriberstub.translate_text.spawn(
text=text,
source_language=source_language,
target_language=target_language,
)
result = func.get()
return result
return app

237
server/gpu/modal/reflector_translator.py Normal file
View File

@@ -0,0 +1,237 @@
"""
Reflector GPU backend - transcriber
===================================
"""
import os
import tempfile
from modal import Image, Secret, Stub, asgi_app, method
from pydantic import BaseModel
# Seamless M4T
SEAMLESSM4T_MODEL_SIZE: str = "medium"
SEAMLESSM4T_MODEL_CARD_NAME: str = f"seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}"
SEAMLESSM4T_VOCODER_CARD_NAME: str = "vocoder_36langs"
HF_SEAMLESS_M4TEPO: str = f"facebook/seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}"
HF_SEAMLESS_M4T_VOCODEREPO: str = "facebook/seamless-m4t-vocoder"
SEAMLESS_GITEPO: str = "https://github.com/facebookresearch/seamless_communication.git"
SEAMLESS_MODEL_DIR: str = "m4t"
stub = Stub(name="reflector-translator")
def install_seamless_communication():
import os
import subprocess
initial_dir = os.getcwd()
subprocess.run(["ssh-keyscan", "-t", "rsa", "github.com", ">>", "~/.ssh/known_hosts"])
subprocess.run(["rm", "-rf", "seamless_communication"])
subprocess.run(["git", "clone", SEAMLESS_GITEPO, "." + "/seamless_communication"])
os.chdir("seamless_communication")
subprocess.run(["pip", "install", "-e", "."])
os.chdir(initial_dir)
def download_seamlessm4t_model():
from huggingface_hub import snapshot_download
print("Downloading Transcriber model & tokenizer")
snapshot_download(HF_SEAMLESS_M4TEPO, cache_dir=SEAMLESS_MODEL_DIR)
print("Transcriber model & tokenizer downloaded")
print("Downloading vocoder weights")
snapshot_download(HF_SEAMLESS_M4T_VOCODEREPO, cache_dir=SEAMLESS_MODEL_DIR)
print("Vocoder weights downloaded")
def configure_seamless_m4t():
import os
import yaml
ASSETS_DIR: str = "./seamless_communication/src/seamless_communication/assets/cards"
with open(f'{ASSETS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml', 'r') as file:
model_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f'{ASSETS_DIR}/vocoder_36langs.yaml', 'r') as file:
vocoder_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f'{ASSETS_DIR}/unity_nllb-100.yaml', 'r') as file:
unity_100_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
with open(f'{ASSETS_DIR}/unity_nllb-200.yaml', 'r') as file:
unity_200_yaml_data = yaml.load(file, Loader=yaml.FullLoader)
model_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-{SEAMLESSM4T_MODEL_SIZE}/snapshots"
available_model_versions = os.listdir(model_dir)
latest_model_version = sorted(available_model_versions)[-1]
model_name = f"multitask_unity_{SEAMLESSM4T_MODEL_SIZE}.pt"
model_path = os.path.join(os.getcwd(), model_dir, latest_model_version, model_name)
vocoder_dir = f"{SEAMLESS_MODEL_DIR}/models--facebook--seamless-m4t-vocoder/snapshots"
available_vocoder_versions = os.listdir(vocoder_dir)
latest_vocoder_version = sorted(available_vocoder_versions)[-1]
vocoder_name = "vocoder_36langs.pt"
vocoder_path = os.path.join(os.getcwd(), vocoder_dir, latest_vocoder_version, vocoder_name)
tokenizer_name = "tokenizer.model"
tokenizer_path = os.path.join(os.getcwd(), model_dir, latest_model_version, tokenizer_name)
model_yaml_data['checkpoint'] = f"file:/{model_path}"
vocoder_yaml_data['checkpoint'] = f"file:/{vocoder_path}"
unity_100_yaml_data['tokenizer'] = f"file:/{tokenizer_path}"
unity_200_yaml_data['tokenizer'] = f"file:/{tokenizer_path}"
with open(f'{ASSETS_DIR}/seamlessM4T_{SEAMLESSM4T_MODEL_SIZE}.yaml', 'w') as file:
yaml.dump(model_yaml_data, file)
with open(f'{ASSETS_DIR}/vocoder_36langs.yaml', 'w') as file:
yaml.dump(vocoder_yaml_data, file)
with open(f'{ASSETS_DIR}/unity_nllb-100.yaml', 'w') as file:
yaml.dump(unity_100_yaml_data, file)
with open(f'{ASSETS_DIR}/unity_nllb-200.yaml', 'w') as file:
yaml.dump(unity_200_yaml_data, file)
transcriber_image = (
Image.debian_slim(python_version="3.10.8")
.apt_install("git")
.apt_install("wget")
.apt_install("libsndfile-dev")
.pip_install(
"requests",
"torch",
"transformers==4.34.0",
"sentencepiece",
"protobuf",
"huggingface_hub==0.16.4",
"gitpython",
"torchaudio",
"fairseq2",
"pyyaml",
"hf-transfer~=0.1"
)
.run_function(install_seamless_communication)
.run_function(download_seamlessm4t_model)
.run_function(configure_seamless_m4t)
.env(
{
"LD_LIBRARY_PATH": (
"/usr/local/lib/python3.10/site-packages/nvidia/cudnn/lib/:"
"/opt/conda/lib/python3.10/site-packages/nvidia/cublas/lib/"
)
}
)
)
@stub.cls(
gpu="A10G",
timeout=60 * 5,
container_idle_timeout=60 * 5,
image=transcriber_image,
)
class Translator:
def __enter__(self):
import torch
from seamless_communication.models.inference.translator import Translator
self.use_gpu = torch.cuda.is_available()
self.device = "cuda" if self.use_gpu else "cpu"
self.translator = Translator(
SEAMLESSM4T_MODEL_CARD_NAME,
SEAMLESSM4T_VOCODER_CARD_NAME,
torch.device(self.device),
dtype=torch.float32
)
@method()
def warmup(self):
return {"status": "ok"}
def get_seamless_lang_code(self, lang_code: str):
"""
The codes for SeamlessM4T is different from regular standards.
For ex, French is "fra" and not "fr".
"""
# TODO: Enhance with complete list of lang codes
seamless_lang_code = {
"en": "eng",
"fr": "fra"
}
return seamless_lang_code.get(lang_code, "eng")
@method()
def translate_text(
self,
text: str,
source_language: str,
target_language: str
):
translated_text, _, _ = self.translator.predict(
text,
"t2tt",
src_lang=self.get_seamless_lang_code(source_language),
tgt_lang=self.get_seamless_lang_code(target_language),
ngram_filtering=True
)
return {
"text": {
source_language: text,
target_language: str(translated_text)
}
}
# -------------------------------------------------------------------
# Web API
# -------------------------------------------------------------------
@stub.function(
container_idle_timeout=60,
timeout=60,
secrets=[
Secret.from_name("reflector-gpu"),
],
)
@asgi_app()
def web():
from fastapi import Body, Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer
from typing_extensions import Annotated
translatorstub = Translator()
app = FastAPI()
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
def apikey_auth(apikey: str = Depends(oauth2_scheme)):
if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
raise HTTPException(
status_code=status.HTTP_401_UNAUTHORIZED,
detail="Invalid API key",
headers={"WWW-Authenticate": "Bearer"},
)
class TranslateResponse(BaseModel):
result: dict
@app.post("/translate", dependencies=[Depends(apikey_auth)])
async def translate(
text: str,
source_language: Annotated[str, Body(...)] = "en",
target_language: Annotated[str, Body(...)] = "fr",
) -> TranslateResponse:
func = translatorstub.translate_text.spawn(
text=text,
source_language=source_language,
target_language=target_language,
)
result = func.get()
return result
@app.post("/warmup", dependencies=[Depends(apikey_auth)])
async def warmup():
return translatorstub.warmup.spawn().get()
return app

25
server/poetry.lock generated
View File

@@ -2173,29 +2173,6 @@ files = [
{file = "protobuf-4.24.4.tar.gz", hash = "sha256:5a70731910cd9104762161719c3d883c960151eea077134458503723b60e3667"},
]
[[package]]
name = "pyaudio"
version = "0.2.13"
description = "Cross-platform audio I/O with PortAudio"
optional = false
python-versions = "*"
files = [
{file = "PyAudio-0.2.13-cp310-cp310-win32.whl", hash = "sha256:48e29537ea22ae2ae323eebe297bfb2683831cee4f20d96964e131f65ab2161d"},
{file = "PyAudio-0.2.13-cp310-cp310-win_amd64.whl", hash = "sha256:87137cfd0ef8608a2a383be3f6996f59505e322dab9d16531f14cf542fa294f1"},
{file = "PyAudio-0.2.13-cp311-cp311-win32.whl", hash = "sha256:13915faaa780e6bbbb6d745ef0e761674fd461b1b1b3f9c1f57042a534bfc0c3"},
{file = "PyAudio-0.2.13-cp311-cp311-win_amd64.whl", hash = "sha256:59cc3cc5211b729c7854e3989058a145872cc58b1a7b46c6d4d88448a343d890"},
{file = "PyAudio-0.2.13-cp37-cp37m-win32.whl", hash = "sha256:d294e3f85b2238649b1ff49ce3412459a8a312569975a89d14646536362d7576"},
{file = "PyAudio-0.2.13-cp37-cp37m-win_amd64.whl", hash = "sha256:ff7f5e44ef51fe61da1e09c6f632f0b5808198edd61b363855cc7dd03bf4a8ac"},
{file = "PyAudio-0.2.13-cp38-cp38-win32.whl", hash = "sha256:c6b302b048c054b7463936d8ba884b73877dc47012f3c94665dba92dd658ae04"},
{file = "PyAudio-0.2.13-cp38-cp38-win_amd64.whl", hash = "sha256:1505d766ee718df6f5a18b73ac42307ba1cb4d2c0397873159254a34f67515d6"},
{file = "PyAudio-0.2.13-cp39-cp39-win32.whl", hash = "sha256:eb128e4a6ea9b98d9a31f33c44978885af27dbe8ae53d665f8790cbfe045517e"},
{file = "PyAudio-0.2.13-cp39-cp39-win_amd64.whl", hash = "sha256:910ef09225cce227adbba92622d4a3e3c8375117f7dd64039f287d9ffc0e02a1"},
{file = "PyAudio-0.2.13.tar.gz", hash = "sha256:26bccc81e4243d1c0ff5487e6b481de6329fcd65c79365c267cef38f363a2b56"},
]
[package.extras]
test = ["numpy"]
[[package]]
name = "pycparser"
version = "2.21"
@@ -3861,4 +3838,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "a85cb09a0e4b68b29c4272d550e618d2e24ace5f16b707f29e8ac4ce915c1fae"
content-hash = "61578467a70980ff9c2dc0cd787b6410b91d7c5fd2bb4c46b6951ec82690ef67"

4
server/pyproject.toml
View File

@@ -40,10 +40,6 @@ black = "^23.7.0"
stamina = "^23.1.0"
[tool.poetry.group.client.dependencies]
pyaudio = "^0.2.13"
[tool.poetry.group.tests.dependencies]
pytest-cov = "^4.1.0"
pytest-aiohttp = "^1.0.4"

6
server/reflector/processors/transcript_translator.py
View File

@@ -16,8 +16,8 @@ class TranscriptTranslatorProcessor(Processor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.transcript_url = settings.TRANSCRIPT_URL
self.timeout = settings.TRANSCRIPT_TIMEOUT
self.translate_url = settings.TRANSLATE_URL
self.timeout = settings.TRANSLATE_TIMEOUT
self.headers = {"Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}"}
async def _push(self, data: Transcript):
@@ -46,7 +46,7 @@ class TranscriptTranslatorProcessor(Processor):
async with httpx.AsyncClient() as client:
response = await retry(client.post)(
settings.TRANSCRIPT_URL + "/translate",
self.translate_url + "/translate",
headers=self.headers,
params=json_payload,
timeout=self.timeout,

4
server/reflector/settings.py
View File

@@ -38,6 +38,10 @@ class Settings(BaseSettings):
TRANSCRIPT_URL: str | None = None
TRANSCRIPT_TIMEOUT: int = 90
# Translate into the target language
TRANSLATE_URL: str | None = None
TRANSLATE_TIMEOUT: int = 90
# Audio transcription banana.dev configuration
TRANSCRIPT_BANANA_API_KEY: str | None = None
TRANSCRIPT_BANANA_MODEL_KEY: str | None = None

8
server/reflector/views/_range_requests_response.py
View File

@@ -38,7 +38,9 @@ def _get_range_header(range_header: str, file_size: int) -> tuple[int, int]:
return start, end
def range_requests_response(request: Request, file_path: str, content_type: str):
def range_requests_response(
request: Request, file_path: str, content_type: str, content_disposition: str
):
"""Returns StreamingResponse using Range Requests of a given file"""
file_size = os.stat(file_path).st_size
@@ -54,6 +56,10 @@ def range_requests_response(request: Request, file_path: str, content_type: str)
"content-range, content-encoding"
),
}
if content_disposition:
headers["Content-Disposition"] = content_disposition
start = 0
end = file_size - 1
status_code = status.HTTP_200_OK

6
server/reflector/views/transcripts.py
View File

@@ -356,10 +356,14 @@ async def transcript_get_audio_mp3(
if not transcript.audio_mp3_filename.exists():
raise HTTPException(status_code=404, detail="Audio not found")
truncated_id = str(transcript.id).split("-")[0]
filename = f"recording_{truncated_id}.mp3"
return range_requests_response(
request,
transcript.audio_mp3_filename,
content_type="audio/mp3",
content_type="audio/mpeg",
content_disposition=f"attachment; filename={filename}",
)

6
server/tests/test_transcripts_audio_download.py
View File

@@ -35,7 +35,7 @@ async def fake_transcript(tmpdir):
@pytest.mark.parametrize(
"url_suffix,content_type",
[
["/mp3", "audio/mp3"],
["/mp3", "audio/mpeg"],
],
)
async def test_transcript_audio_download(fake_transcript, url_suffix, content_type):
@@ -51,7 +51,7 @@ async def test_transcript_audio_download(fake_transcript, url_suffix, content_ty
@pytest.mark.parametrize(
"url_suffix,content_type",
[
["/mp3", "audio/mp3"],
["/mp3", "audio/mpeg"],
],
)
async def test_transcript_audio_download_range(
@@ -74,7 +74,7 @@ async def test_transcript_audio_download_range(
@pytest.mark.parametrize(
"url_suffix,content_type",
[
["/mp3", "audio/mp3"],
["/mp3", "audio/mpeg"],
],
)
async def test_transcript_audio_download_range_with_seek(

63
server/tests/test_transcripts_rtc_ws.py
View File

@@ -31,28 +31,43 @@ class ThreadedUvicorn:
continue
@pytest.mark.asyncio
async def test_transcript_rtc_and_websocket(
tmpdir, dummy_llm, dummy_transcript, dummy_processors, ensure_casing
):
# goal: start the server, exchange RTC, receive websocket events
# because of that, we need to start the server in a thread
# to be able to connect with aiortc
@pytest.fixture
async def appserver(tmpdir):
from reflector.settings import settings
from reflector.app import app
DATA_DIR = settings.DATA_DIR
settings.DATA_DIR = Path(tmpdir)
# start server
host = "127.0.0.1"
port = 1255
base_url = f"http://{host}:{port}/v1"
config = Config(app=app, host=host, port=port)
server = ThreadedUvicorn(config)
await server.start()
yield (server, host, port)
server.stop()
settings.DATA_DIR = DATA_DIR
@pytest.mark.asyncio
async def test_transcript_rtc_and_websocket(
tmpdir,
dummy_llm,
dummy_transcript,
dummy_processors,
ensure_casing,
appserver,
):
# goal: start the server, exchange RTC, receive websocket events
# because of that, we need to start the server in a thread
# to be able to connect with aiortc
server, host, port = appserver
# create a transcript
base_url = f"http://{host}:{port}/v1"
ac = AsyncClient(base_url=base_url)
response = await ac.post("/transcripts", json={"name": "Test RTC"})
assert response.status_code == 200
@@ -167,35 +182,26 @@ async def test_transcript_rtc_and_websocket(
# check that audio/mp3 is available
resp = await ac.get(f"/transcripts/{tid}/audio/mp3")
assert resp.status_code == 200
assert resp.headers["Content-Type"] == "audio/mp3"
# stop server
server.stop()
assert resp.headers["Content-Type"] == "audio/mpeg"
@pytest.mark.asyncio
async def test_transcript_rtc_and_websocket_and_fr(
tmpdir, dummy_llm, dummy_transcript, dummy_processors, ensure_casing
tmpdir,
dummy_llm,
dummy_transcript,
dummy_processors,
ensure_casing,
appserver,
):
# goal: start the server, exchange RTC, receive websocket events
# because of that, we need to start the server in a thread
# to be able to connect with aiortc
# with target french language
from reflector.settings import settings
from reflector.app import app
settings.DATA_DIR = Path(tmpdir)
# start server
host = "127.0.0.1"
port = 1255
base_url = f"http://{host}:{port}/v1"
config = Config(app=app, host=host, port=port)
server = ThreadedUvicorn(config)
await server.start()
server, host, port = appserver
# create a transcript
base_url = f"http://{host}:{port}/v1"
ac = AsyncClient(base_url=base_url)
response = await ac.post(
"/transcripts", json={"name": "Test RTC", "target_language": "fr"}
@@ -303,6 +309,3 @@ async def test_transcript_rtc_and_websocket_and_fr(
# ensure the last event received is ended
assert events[-1]["event"] == "STATUS"
assert events[-1]["data"]["value"] == "ended"
# stop server
server.stop()