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OpenAI compatible transcription api

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This commit is contained in:
Sergey Mankovsky
2025-01-17 01:02:40 +01:00
parent b9b9292821
commit 99ff06ff17
4 changed files with 209 additions and 134 deletions
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164
server/gpu/modal_deployments/reflector_transcriber.py
View File

@@ -1,89 +1,64 @@
"""
Reflector GPU backend - transcriber
===================================
"""
import os
import tempfile
import threading
from modal import Image, Secret, App, asgi_app, method, enter
import modal
from pydantic import BaseModel
# Whisper
WHISPER_MODEL: str = "large-v2"
WHISPER_COMPUTE_TYPE: str = "float16"
WHISPER_NUM_WORKERS: int = 1
MODELS_DIR = "/models"
MODEL_NAME = "large-v2"
MODEL_COMPUTE_TYPE: str = "float16"
MODEL_NUM_WORKERS: int = 1
MINUTES = 60 # seconds
volume = modal.Volume.from_name("models", create_if_missing=True)
app = modal.App("reflector-transcriber")
WHISPER_MODEL_DIR = "/root/transcription_models"
def download_model():
from faster_whisper import download_model
app = App(name="reflector-transcriber")
volume.reload()
download_model(MODEL_NAME, cache_dir=MODELS_DIR)
volume.commit()
def download_whisper():
from faster_whisper.utils import download_model
print("Downloading Whisper model")
download_model(WHISPER_MODEL, cache_dir=WHISPER_MODEL_DIR)
print("Whisper model downloaded")
def migrate_cache_llm():
"""
XXX The cache for model files in Transformers v4.22.0 has been updated.
Migrating your old cache. This is a one-time only operation. You can
interrupt this and resume the migration later on by calling
`transformers.utils.move_cache()`.
"""
from transformers.utils.hub import move_cache
print("Moving LLM cache")
move_cache(cache_dir=WHISPER_MODEL_DIR, new_cache_dir=WHISPER_MODEL_DIR)
print("LLM cache moved")
transcriber_image = (
Image.debian_slim(python_version="3.10.8")
.apt_install("git")
.apt_install("wget")
.apt_install("libsndfile-dev")
image = (
modal.Image.debian_slim(python_version="3.12")
.pip_install(
"faster-whisper",
"requests",
"torch",
"transformers==4.34.0",
"sentencepiece",
"protobuf",
"huggingface_hub==0.16.4",
"gitpython",
"torchaudio",
"fairseq2",
"pyyaml",
"hf-transfer~=0.1"
"huggingface_hub==0.27.1",
"hf-transfer==0.1.9",
"torch==2.5.1",
"faster-whisper==1.1.1",
)
.run_function(download_whisper)
.run_function(migrate_cache_llm)
.env(
{
"HF_HUB_ENABLE_HF_TRANSFER": "1",
"LD_LIBRARY_PATH": (
"/usr/local/lib/python3.10/site-packages/nvidia/cudnn/lib/:"
"/opt/conda/lib/python3.10/site-packages/nvidia/cublas/lib/"
)
"/usr/local/lib/python3.12/site-packages/nvidia/cudnn/lib/:"
"/opt/conda/lib/python3.12/site-packages/nvidia/cublas/lib/"
),
}
)
.run_function(download_model, volumes={MODELS_DIR: volume})
)
@app.cls(
gpu="A10G",
timeout=60 * 5,
container_idle_timeout=60 * 5,
timeout=5 * MINUTES,
container_idle_timeout=5 * MINUTES,
allow_concurrent_inputs=6,
image=transcriber_image,
image=image,
volumes={MODELS_DIR: volume},
)
class Transcriber:
@enter()
@modal.enter()
def enter(self):
import faster_whisper
import torch
@@ -92,21 +67,20 @@ class Transcriber:
self.use_gpu = torch.cuda.is_available()
self.device = "cuda" if self.use_gpu else "cpu"
self.model = faster_whisper.WhisperModel(
WHISPER_MODEL,
MODEL_NAME,
device=self.device,
compute_type=WHISPER_COMPUTE_TYPE,
num_workers=WHISPER_NUM_WORKERS,
download_root=WHISPER_MODEL_DIR,
local_files_only=True
compute_type=MODEL_COMPUTE_TYPE,
num_workers=MODEL_NUM_WORKERS,
download_root=MODELS_DIR,
local_files_only=True,
)
@method()
@modal.method()
def transcribe_segment(
self,
audio_data: str,
audio_suffix: str,
source_language: str,
timestamp: float = 0
language: str,
):
with tempfile.NamedTemporaryFile("wb+", suffix=f".{audio_suffix}") as fp:
fp.write(audio_data)
@@ -114,40 +88,23 @@ class Transcriber:
with self.lock:
segments, _ = self.model.transcribe(
fp.name,
language=source_language,
language=language,
beam_size=5,
word_timestamps=True,
vad_filter=True,
vad_parameters={"min_silence_duration_ms": 500},
)
multilingual_transcript = {}
transcript_source_lang = ""
text = ""
words = []
if segments:
segments = list(segments)
for segment in segments:
transcript_source_lang += segment.text
for word in segment.words:
words.append(
{
"text": word.word,
"start": round(timestamp + word.start, 3),
"end": round(timestamp + word.end, 3),
}
text += segment.text
words.extend(
{"word": word.word, "start": word.start, "end": word.end}
for word in segment.words
)
multilingual_transcript[source_language] = transcript_source_lang
return {
"text": multilingual_transcript,
"words": words
}
# -------------------------------------------------------------------
# Web API
# -------------------------------------------------------------------
return {"text": text, "words": words}
@app.function(
@@ -155,21 +112,23 @@ class Transcriber:
timeout=60,
allow_concurrent_inputs=40,
secrets=[
Secret.from_name("reflector-gpu"),
modal.Secret.from_name("reflector-gpu"),
],
volumes={MODELS_DIR: volume},
)
@asgi_app()
@modal.asgi_app()
def web():
from fastapi import Body, Depends, FastAPI, HTTPException, UploadFile, status
from fastapi.security import OAuth2PasswordBearer
from typing_extensions import Annotated
transcriberstub = Transcriber()
transcriber = Transcriber()
app = FastAPI()
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
supported_audio_file_types = ["wav", "mp3", "ogg", "flac"]
supported_file_types = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
def apikey_auth(apikey: str = Depends(oauth2_scheme)):
if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
@@ -182,21 +141,20 @@ def web():
class TranscriptResponse(BaseModel):
result: dict
@app.post("/transcribe", dependencies=[Depends(apikey_auth)])
@app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
def transcribe(
file: UploadFile,
source_language: Annotated[str, Body(...)] = "en",
timestamp: Annotated[float, Body()] = 0.0
model: str = "whisper-1",
language: Annotated[str, Body(...)] = "en",
) -> TranscriptResponse:
audio_data = file.file.read()
audio_suffix = file.filename.split(".")[-1]
assert audio_suffix in supported_audio_file_types
assert audio_suffix in supported_file_types
func = transcriberstub.transcribe_segment.spawn(
func = transcriber.transcribe_segment.spawn(
audio_data=audio_data,
audio_suffix=audio_suffix,
source_language=source_language,
timestamp=timestamp
language=language,
)
result = func.get()
return result

129
server/poetry.lock generated
View File

@@ -1068,6 +1068,17 @@ wrapt = ">=1.10,<2"
[package.extras]
dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
[[package]]
name = "distro"
version = "1.9.0"
description = "Distro - an OS platform information API"
optional = false
python-versions = ">=3.6"
files = [
{file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
{file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
]
[[package]]
name = "dnspython"
version = "2.4.2"
@@ -1696,6 +1707,91 @@ files = [
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
]
[[package]]
name = "jiter"
version = "0.8.2"
description = "Fast iterable JSON parser."
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
name = "jiwer"
version = "3.0.3"
@@ -2267,6 +2363,31 @@ packaging = "*"
protobuf = "*"
sympy = "*"
[[package]]
name = "openai"
version = "1.59.7"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.8"
files = [
{file = "openai-1.59.7-py3-none-any.whl", hash = "sha256:cfa806556226fa96df7380ab2e29814181d56fea44738c2b0e581b462c268692"},
{file = "openai-1.59.7.tar.gz", hash = "sha256:043603def78c00befb857df9f0a16ee76a3af5984ba40cb7ee5e2f40db4646bf"},
]
[package.dependencies]
anyio = ">=3.5.0,<5"
distro = ">=1.7.0,<2"
httpx = ">=0.23.0,<1"
jiter = ">=0.4.0,<1"
pydantic = ">=1.9.0,<3"
sniffio = "*"
tqdm = ">4"
typing-extensions = ">=4.11,<5"
[package.extras]
datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
realtime = ["websockets (>=13,<15)"]
[[package]]
name = "packaging"
version = "23.2"
@@ -4041,13 +4162,13 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
[[package]]
name = "typing-extensions"
version = "4.8.0"
version = "4.12.2"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
{file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
{file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
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{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
]
[[package]]
@@ -4483,4 +4604,4 @@ multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
content-hash = "3ba5402ab9fbec271f255c345c89c67385c722735b1d79a959e887c7c34a4047"
content-hash = "6f5213b520e038f396de4a6b6e742a54eec1e6e9ea67cb1fa36e134c8a946cbe"

1
server/pyproject.toml
View File

@@ -39,6 +39,7 @@ faster-whisper = "^0.10.0"
transformers = "^4.36.2"
black = "24.1.1"
jsonschema = "^4.23.0"
openai = "^1.59.7"
[tool.poetry.group.dev.dependencies]

45
server/reflector/processors/audio_transcript_modal.py
View File

@@ -12,51 +12,46 @@ API will be a POST request to TRANSCRIPT_URL:
"""
import httpx
from openai import AsyncOpenAI
from reflector.processors.audio_transcript import AudioTranscriptProcessor
from reflector.processors.audio_transcript_auto import AudioTranscriptAutoProcessor
from reflector.processors.types import AudioFile, Transcript, Word
from reflector.settings import settings
from reflector.utils.retry import retry
class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
def __init__(self, modal_api_key: str):
super().__init__()
self.transcript_url = settings.TRANSCRIPT_URL + "/transcribe"
self.transcript_url = settings.TRANSCRIPT_URL + "/v1"
self.timeout = settings.TRANSCRIPT_TIMEOUT
self.api_key = settings.TRANSCRIPT_MODAL_API_KEY
self.headers = {"Authorization": f"Bearer {modal_api_key}"}
async def _transcript(self, data: AudioFile):
async with httpx.AsyncClient() as client:
self.logger.debug(f"Try to transcribe audio {data.name}")
files = {
"file": (data.name, data.fd),
}
source_language = self.get_pref("audio:source_language", "en")
json_payload = {"source_language": source_language}
response = await retry(client.post)(
self.transcript_url,
files=files,
async with AsyncOpenAI(
base_url=self.transcript_url,
api_key=self.api_key,
timeout=self.timeout,
headers=self.headers,
params=json_payload,
follow_redirects=True,
)
) as client:
self.logger.debug(f"Try to transcribe audio {data.name}")
self.logger.debug(
f"Transcript response: {response.status_code} {response.content}"
audio_file = open(data.path, "rb")
transcription = await client.audio.transcriptions.create(
file=audio_file,
model="whisper-1",
response_format="verbose_json",
language=self.get_pref("audio:source_language", "en"),
timestamp_granularities=["word"],
)
response.raise_for_status()
result = response.json()
self.logger.debug(f"Transcription: {transcription}")
transcript = Transcript(
words=[
Word(
text=word["text"],
start=word["start"],
end=word["end"],
text=word.word,
start=word.start,
end=word.end,
)
for word in result["words"]
for word in transcription.words
],
)
transcript.add_offset(data.timestamp)