From ab859d65a6bded904133a163a081a651b3938d42 Mon Sep 17 00:00:00 2001 From: Sergey Mankovsky Date: Wed, 17 Sep 2025 18:52:03 +0200 Subject: [PATCH] feat: self-hosted gpu api (#636) * Self-hosted gpu api * Refactor self-hosted api * Rename model api tests * Use lifespan instead of startup event * Fix self hosted imports * Add newlines * Add response models * Move gpu dir to the root * Add project description * Refactor lifespan * Update env var names for model api tests * Preload diarizarion service * Refactor uploaded file paths --- gpu/modal_deployments/.gitignore | 33 + .../gpu => gpu}/modal_deployments/README.md | 0 .../modal_deployments/reflector_diarizer.py | 0 .../reflector_transcriber.py | 0 .../reflector_transcriber_parakeet.py | 0 .../modal_deployments/reflector_translator.py | 0 gpu/self_hosted/.env.example | 2 + gpu/self_hosted/.gitignore | 38 + gpu/self_hosted/Dockerfile | 46 + gpu/self_hosted/README.md | 73 + gpu/self_hosted/app/auth.py | 19 + gpu/self_hosted/app/config.py | 12 + gpu/self_hosted/app/factory.py | 30 + gpu/self_hosted/app/routers/diarization.py | 30 + gpu/self_hosted/app/routers/transcription.py | 109 + gpu/self_hosted/app/routers/translation.py | 28 + gpu/self_hosted/app/services/diarizer.py | 42 + gpu/self_hosted/app/services/transcriber.py | 208 ++ gpu/self_hosted/app/services/translator.py | 44 + gpu/self_hosted/app/utils.py | 107 + gpu/self_hosted/compose.yml | 10 + gpu/self_hosted/main.py | 3 + gpu/self_hosted/pyproject.toml | 19 + gpu/self_hosted/runserver.sh | 17 + gpu/self_hosted/uv.lock | 3013 +++++++++++++++++ server/docs/gpu/api-transcription.md | 2 +- server/pyproject.toml | 4 +- server/tests/test_model_api_diarization.py | 63 + ...script.py => test_model_api_transcript.py} | 28 +- server/tests/test_model_api_translation.py | 56 + 30 files changed, 4020 insertions(+), 16 deletions(-) create mode 100644 gpu/modal_deployments/.gitignore rename {server/gpu => gpu}/modal_deployments/README.md (100%) rename {server/gpu => gpu}/modal_deployments/reflector_diarizer.py (100%) rename {server/gpu => gpu}/modal_deployments/reflector_transcriber.py (100%) rename {server/gpu => gpu}/modal_deployments/reflector_transcriber_parakeet.py (100%) rename {server/gpu => gpu}/modal_deployments/reflector_translator.py (100%) create mode 100644 gpu/self_hosted/.env.example create mode 100644 gpu/self_hosted/.gitignore create mode 100644 gpu/self_hosted/Dockerfile create mode 100644 gpu/self_hosted/README.md create mode 100644 gpu/self_hosted/app/auth.py create mode 100644 gpu/self_hosted/app/config.py create mode 100644 gpu/self_hosted/app/factory.py create mode 100644 gpu/self_hosted/app/routers/diarization.py create mode 100644 gpu/self_hosted/app/routers/transcription.py create mode 100644 gpu/self_hosted/app/routers/translation.py create mode 100644 gpu/self_hosted/app/services/diarizer.py create mode 100644 gpu/self_hosted/app/services/transcriber.py create mode 100644 gpu/self_hosted/app/services/translator.py create mode 100644 gpu/self_hosted/app/utils.py create mode 100644 gpu/self_hosted/compose.yml create mode 100644 gpu/self_hosted/main.py create mode 100644 gpu/self_hosted/pyproject.toml create mode 100644 gpu/self_hosted/runserver.sh create mode 100644 gpu/self_hosted/uv.lock create mode 100644 server/tests/test_model_api_diarization.py rename server/tests/{test_gpu_modal_transcript.py => test_model_api_transcript.py} (94%) create mode 100644 server/tests/test_model_api_translation.py diff --git a/gpu/modal_deployments/.gitignore b/gpu/modal_deployments/.gitignore new file mode 100644 index 00000000..734bd3b2 --- /dev/null +++ b/gpu/modal_deployments/.gitignore @@ -0,0 +1,33 @@ +# OS / Editor +.DS_Store +.vscode/ +.idea/ + +# Python +__pycache__/ +*.py[cod] +*$py.class + +# Logs +*.log + +# Env and secrets +.env +.env.* +*.env +*.secret + +# Build / dist +build/ +dist/ +.eggs/ +*.egg-info/ + +# Coverage / test +.pytest_cache/ +.coverage* +htmlcov/ + +# Modal local state (if any) +modal_mounts/ +.modal_cache/ diff --git a/server/gpu/modal_deployments/README.md b/gpu/modal_deployments/README.md similarity index 100% rename from server/gpu/modal_deployments/README.md rename to gpu/modal_deployments/README.md diff --git a/server/gpu/modal_deployments/reflector_diarizer.py b/gpu/modal_deployments/reflector_diarizer.py similarity index 100% rename from server/gpu/modal_deployments/reflector_diarizer.py rename to gpu/modal_deployments/reflector_diarizer.py diff --git a/server/gpu/modal_deployments/reflector_transcriber.py b/gpu/modal_deployments/reflector_transcriber.py similarity index 100% rename from server/gpu/modal_deployments/reflector_transcriber.py rename to gpu/modal_deployments/reflector_transcriber.py diff --git a/server/gpu/modal_deployments/reflector_transcriber_parakeet.py b/gpu/modal_deployments/reflector_transcriber_parakeet.py similarity index 100% rename from server/gpu/modal_deployments/reflector_transcriber_parakeet.py rename to gpu/modal_deployments/reflector_transcriber_parakeet.py diff --git a/server/gpu/modal_deployments/reflector_translator.py b/gpu/modal_deployments/reflector_translator.py similarity index 100% rename from server/gpu/modal_deployments/reflector_translator.py rename to gpu/modal_deployments/reflector_translator.py diff --git a/gpu/self_hosted/.env.example b/gpu/self_hosted/.env.example new file mode 100644 index 00000000..a55584ba --- /dev/null +++ b/gpu/self_hosted/.env.example @@ -0,0 +1,2 @@ +REFLECTOR_GPU_APIKEY= +HF_TOKEN= diff --git a/gpu/self_hosted/.gitignore b/gpu/self_hosted/.gitignore new file mode 100644 index 00000000..2773c2e2 --- /dev/null +++ b/gpu/self_hosted/.gitignore @@ -0,0 +1,38 @@ +cache/ + +# OS / Editor +.DS_Store +.vscode/ +.idea/ + +# Python +__pycache__/ +*.py[cod] +*$py.class + +# Env and secrets +.env +*.env +*.secret +HF_TOKEN +REFLECTOR_GPU_APIKEY + +# Virtual env / uv +.venv/ +venv/ +ENV/ +uv/ + +# Build / dist +build/ +dist/ +.eggs/ +*.egg-info/ + +# Coverage / test +.pytest_cache/ +.coverage* +htmlcov/ + +# Logs +*.log diff --git a/gpu/self_hosted/Dockerfile b/gpu/self_hosted/Dockerfile new file mode 100644 index 00000000..4865fcc0 --- /dev/null +++ b/gpu/self_hosted/Dockerfile @@ -0,0 +1,46 @@ +FROM python:3.12-slim + +ENV PYTHONUNBUFFERED=1 \ + UV_LINK_MODE=copy \ + UV_NO_CACHE=1 + +WORKDIR /tmp +RUN apt-get update \ + && apt-get install -y \ + ffmpeg \ + curl \ + ca-certificates \ + gnupg \ + wget \ + && apt-get clean +# Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12 +ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb +RUN dpkg -i /cuda-keyring.deb \ + && rm /cuda-keyring.deb \ + && apt-get update \ + && apt-get install -y --no-install-recommends \ + cuda-cudart-12-6 \ + libcublas-12-6 \ + libcudnn9-cuda-12 \ + libcudnn9-dev-cuda-12 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* +ADD https://astral.sh/uv/install.sh /uv-installer.sh +RUN sh /uv-installer.sh && rm /uv-installer.sh +ENV PATH="/root/.local/bin/:$PATH" +ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH" + +RUN mkdir -p /app +WORKDIR /app +COPY pyproject.toml uv.lock /app/ + + +COPY ./app /app/app +COPY ./main.py /app/ +COPY ./runserver.sh /app/ + +EXPOSE 8000 + +CMD ["sh", "/app/runserver.sh"] + + diff --git a/gpu/self_hosted/README.md b/gpu/self_hosted/README.md new file mode 100644 index 00000000..0180a8ae --- /dev/null +++ b/gpu/self_hosted/README.md @@ -0,0 +1,73 @@ +# Self-hosted Model API + +Run transcription, translation, and diarization services compatible with Reflector's GPU Model API. Works on CPU or GPU. + +Environment variables + +- REFLECTOR_GPU_APIKEY: Optional Bearer token. If unset, auth is disabled. +- HF_TOKEN: Optional. Required for diarization to download pyannote pipelines + +Requirements + +- FFmpeg must be installed and on PATH (used for URL-based and segmented transcription) +- Python 3.12+ +- NVIDIA GPU optional. If available, it will be used automatically + +Local run +Set env vars in self_hosted/.env file +uv sync + +uv run uvicorn main:app --host 0.0.0.0 --port 8000 + +Authentication + +- If REFLECTOR_GPU_APIKEY is set, include header: Authorization: Bearer + +Endpoints + +- POST /v1/audio/transcriptions + + - multipart/form-data + - fields: file (single file) OR files[] (multiple files), language, batch (true/false) + - response: single { text, words, filename } or { results: [ ... ] } + +- POST /v1/audio/transcriptions-from-url + + - application/json + - body: { audio_file_url, language, timestamp_offset } + - response: { text, words } + +- POST /translate + + - text: query parameter + - body (application/json): { source_language, target_language } + - response: { text: { : original, : translated } } + +- POST /diarize + - query parameters: audio_file_url, timestamp (optional) + - requires HF_TOKEN to be set (for pyannote) + - response: { diarization: [ { start, end, speaker } ] } + +OpenAPI docs + +- Visit /docs when the server is running + +Docker + +- Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above + +Conformance tests + +# From this directory + +TRANSCRIPT_URL=http://localhost:8000 \ +TRANSCRIPT_API_KEY=dev-key \ +uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_transcript.py + +TRANSLATION_URL=http://localhost:8000 \ +TRANSLATION_API_KEY=dev-key \ +uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_translation.py + +DIARIZATION_URL=http://localhost:8000 \ +DIARIZATION_API_KEY=dev-key \ +uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_diarization.py diff --git a/gpu/self_hosted/app/auth.py b/gpu/self_hosted/app/auth.py new file mode 100644 index 00000000..9c74e90c --- /dev/null +++ b/gpu/self_hosted/app/auth.py @@ -0,0 +1,19 @@ +import os + +from fastapi import Depends, HTTPException, status +from fastapi.security import OAuth2PasswordBearer + +oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") + + +def apikey_auth(apikey: str = Depends(oauth2_scheme)): + required_key = os.environ.get("REFLECTOR_GPU_APIKEY") + if not required_key: + return + if apikey == required_key: + return + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid API key", + headers={"WWW-Authenticate": "Bearer"}, + ) diff --git a/gpu/self_hosted/app/config.py b/gpu/self_hosted/app/config.py new file mode 100644 index 00000000..5c466f00 --- /dev/null +++ b/gpu/self_hosted/app/config.py @@ -0,0 +1,12 @@ +from pathlib import Path + +SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"] +SAMPLE_RATE = 16000 +VAD_CONFIG = { + "batch_max_duration": 30.0, + "silence_padding": 0.5, + "window_size": 512, +} + +# App-level paths +UPLOADS_PATH = Path("/tmp/whisper-uploads") diff --git a/gpu/self_hosted/app/factory.py b/gpu/self_hosted/app/factory.py new file mode 100644 index 00000000..72dadcd7 --- /dev/null +++ b/gpu/self_hosted/app/factory.py @@ -0,0 +1,30 @@ +from contextlib import asynccontextmanager + +from fastapi import FastAPI + +from .routers.diarization import router as diarization_router +from .routers.transcription import router as transcription_router +from .routers.translation import router as translation_router +from .services.transcriber import WhisperService +from .services.diarizer import PyannoteDiarizationService +from .utils import ensure_dirs + + +@asynccontextmanager +async def lifespan(app: FastAPI): + ensure_dirs() + whisper_service = WhisperService() + whisper_service.load() + app.state.whisper = whisper_service + diarization_service = PyannoteDiarizationService() + diarization_service.load() + app.state.diarizer = diarization_service + yield + + +def create_app() -> FastAPI: + app = FastAPI(lifespan=lifespan) + app.include_router(transcription_router) + app.include_router(translation_router) + app.include_router(diarization_router) + return app diff --git a/gpu/self_hosted/app/routers/diarization.py b/gpu/self_hosted/app/routers/diarization.py new file mode 100644 index 00000000..113a8957 --- /dev/null +++ b/gpu/self_hosted/app/routers/diarization.py @@ -0,0 +1,30 @@ +from typing import List + +from fastapi import APIRouter, Depends, Request +from pydantic import BaseModel + +from ..auth import apikey_auth +from ..services.diarizer import PyannoteDiarizationService +from ..utils import download_audio_file + +router = APIRouter(tags=["diarization"]) + + +class DiarizationSegment(BaseModel): + start: float + end: float + speaker: int + + +class DiarizationResponse(BaseModel): + diarization: List[DiarizationSegment] + + +@router.post( + "/diarize", dependencies=[Depends(apikey_auth)], response_model=DiarizationResponse +) +def diarize(request: Request, audio_file_url: str, timestamp: float = 0.0): + with download_audio_file(audio_file_url) as (file_path, _ext): + file_path = str(file_path) + diarizer: PyannoteDiarizationService = request.app.state.diarizer + return diarizer.diarize_file(file_path, timestamp=timestamp) diff --git a/gpu/self_hosted/app/routers/transcription.py b/gpu/self_hosted/app/routers/transcription.py new file mode 100644 index 00000000..04f1f7f7 --- /dev/null +++ b/gpu/self_hosted/app/routers/transcription.py @@ -0,0 +1,109 @@ +import uuid +from typing import Optional, Union + +from fastapi import APIRouter, Body, Depends, Form, HTTPException, Request, UploadFile +from pydantic import BaseModel +from pathlib import Path +from ..auth import apikey_auth +from ..config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH +from ..services.transcriber import MODEL_NAME +from ..utils import cleanup_uploaded_files, download_audio_file + +router = APIRouter(prefix="/v1/audio", tags=["transcription"]) + + +class WordTiming(BaseModel): + word: str + start: float + end: float + + +class TranscriptResult(BaseModel): + text: str + words: list[WordTiming] + filename: Optional[str] = None + + +class TranscriptBatchResponse(BaseModel): + results: list[TranscriptResult] + + +@router.post( + "/transcriptions", + dependencies=[Depends(apikey_auth)], + response_model=Union[TranscriptResult, TranscriptBatchResponse], +) +def transcribe( + request: Request, + file: UploadFile = None, + files: list[UploadFile] | None = None, + model: str = Form(MODEL_NAME), + language: str = Form("en"), + batch: bool = Form(False), +): + service = request.app.state.whisper + if not file and not files: + raise HTTPException( + status_code=400, detail="Either 'file' or 'files' parameter is required" + ) + if batch and not files: + raise HTTPException( + status_code=400, detail="Batch transcription requires 'files'" + ) + + upload_files = [file] if file else files + + uploaded_paths: list[Path] = [] + with cleanup_uploaded_files(uploaded_paths): + for upload_file in upload_files: + audio_suffix = upload_file.filename.split(".")[-1].lower() + if audio_suffix not in SUPPORTED_FILE_EXTENSIONS: + raise HTTPException( + status_code=400, + detail=( + f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}" + ), + ) + unique_filename = f"{uuid.uuid4()}.{audio_suffix}" + file_path = UPLOADS_PATH / unique_filename + with open(file_path, "wb") as f: + content = upload_file.file.read() + f.write(content) + uploaded_paths.append(file_path) + + if batch and len(upload_files) > 1: + results = [] + for path in uploaded_paths: + result = service.transcribe_file(str(path), language=language) + result["filename"] = path.name + results.append(result) + return {"results": results} + + results = [] + for path in uploaded_paths: + result = service.transcribe_file(str(path), language=language) + result["filename"] = path.name + results.append(result) + + return {"results": results} if len(results) > 1 else results[0] + + +@router.post( + "/transcriptions-from-url", + dependencies=[Depends(apikey_auth)], + response_model=TranscriptResult, +) +def transcribe_from_url( + request: Request, + audio_file_url: str = Body(..., description="URL of the audio file to transcribe"), + model: str = Body(MODEL_NAME), + language: str = Body("en"), + timestamp_offset: float = Body(0.0), +): + service = request.app.state.whisper + with download_audio_file(audio_file_url) as (file_path, _ext): + file_path = str(file_path) + result = service.transcribe_vad_url_segment( + file_path=file_path, timestamp_offset=timestamp_offset, language=language + ) + return result diff --git a/gpu/self_hosted/app/routers/translation.py b/gpu/self_hosted/app/routers/translation.py new file mode 100644 index 00000000..d2025416 --- /dev/null +++ b/gpu/self_hosted/app/routers/translation.py @@ -0,0 +1,28 @@ +from typing import Dict + +from fastapi import APIRouter, Body, Depends +from pydantic import BaseModel + +from ..auth import apikey_auth +from ..services.translator import TextTranslatorService + +router = APIRouter(tags=["translation"]) + +translator = TextTranslatorService() + + +class TranslationResponse(BaseModel): + text: Dict[str, str] + + +@router.post( + "/translate", + dependencies=[Depends(apikey_auth)], + response_model=TranslationResponse, +) +def translate( + text: str, + source_language: str = Body("en"), + target_language: str = Body("fr"), +): + return translator.translate(text, source_language, target_language) diff --git a/gpu/self_hosted/app/services/diarizer.py b/gpu/self_hosted/app/services/diarizer.py new file mode 100644 index 00000000..2935ffc5 --- /dev/null +++ b/gpu/self_hosted/app/services/diarizer.py @@ -0,0 +1,42 @@ +import os +import threading + +import torch +import torchaudio +from pyannote.audio import Pipeline + + +class PyannoteDiarizationService: + def __init__(self): + self._pipeline = None + self._device = "cpu" + self._lock = threading.Lock() + + def load(self): + self._device = "cuda" if torch.cuda.is_available() else "cpu" + self._pipeline = Pipeline.from_pretrained( + "pyannote/speaker-diarization-3.1", + use_auth_token=os.environ.get("HF_TOKEN"), + ) + self._pipeline.to(torch.device(self._device)) + + def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict: + if self._pipeline is None: + self.load() + waveform, sample_rate = torchaudio.load(file_path) + with self._lock: + diarization = self._pipeline( + {"waveform": waveform, "sample_rate": sample_rate} + ) + words = [] + for diarization_segment, _, speaker in diarization.itertracks(yield_label=True): + words.append( + { + "start": round(timestamp + diarization_segment.start, 3), + "end": round(timestamp + diarization_segment.end, 3), + "speaker": int(speaker[-2:]) + if speaker and speaker[-2:].isdigit() + else 0, + } + ) + return {"diarization": words} diff --git a/gpu/self_hosted/app/services/transcriber.py b/gpu/self_hosted/app/services/transcriber.py new file mode 100644 index 00000000..26a313cc --- /dev/null +++ b/gpu/self_hosted/app/services/transcriber.py @@ -0,0 +1,208 @@ +import os +import shutil +import subprocess +import threading +from typing import Generator + +import faster_whisper +import librosa +import numpy as np +import torch +from fastapi import HTTPException +from silero_vad import VADIterator, load_silero_vad + +from ..config import SAMPLE_RATE, VAD_CONFIG + +# Whisper configuration (service-local defaults) +MODEL_NAME = "large-v2" +# None delegates compute type to runtime: float16 on CUDA, int8 on CPU +MODEL_COMPUTE_TYPE = None +MODEL_NUM_WORKERS = 1 +CACHE_PATH = os.path.join(os.path.expanduser("~"), ".cache", "reflector-whisper") +from ..utils import NoStdStreams + + +class WhisperService: + def __init__(self): + self.model = None + self.device = "cpu" + self.lock = threading.Lock() + + def load(self): + self.device = "cuda" if torch.cuda.is_available() else "cpu" + compute_type = MODEL_COMPUTE_TYPE or ( + "float16" if self.device == "cuda" else "int8" + ) + self.model = faster_whisper.WhisperModel( + MODEL_NAME, + device=self.device, + compute_type=compute_type, + num_workers=MODEL_NUM_WORKERS, + download_root=CACHE_PATH, + ) + + def pad_audio(self, audio_array, sample_rate: int = SAMPLE_RATE): + audio_duration = len(audio_array) / sample_rate + if audio_duration < VAD_CONFIG["silence_padding"]: + silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"]) + silence = np.zeros(silence_samples, dtype=np.float32) + return np.concatenate([audio_array, silence]) + return audio_array + + def enforce_word_timing_constraints(self, words: list[dict]) -> list[dict]: + if len(words) <= 1: + return words + enforced: list[dict] = [] + for i, word in enumerate(words): + current = dict(word) + if i < len(words) - 1: + next_start = words[i + 1]["start"] + if current["end"] > next_start: + current["end"] = next_start + enforced.append(current) + return enforced + + def transcribe_file(self, file_path: str, language: str = "en") -> dict: + input_for_model: str | "object" = file_path + try: + audio_array, _sample_rate = librosa.load( + file_path, sr=SAMPLE_RATE, mono=True + ) + if len(audio_array) / float(SAMPLE_RATE) < VAD_CONFIG["silence_padding"]: + input_for_model = self.pad_audio(audio_array, SAMPLE_RATE) + except Exception: + pass + + with self.lock: + with NoStdStreams(): + segments, _ = self.model.transcribe( + input_for_model, + language=language, + beam_size=5, + word_timestamps=True, + vad_filter=True, + vad_parameters={"min_silence_duration_ms": 500}, + ) + + segments = list(segments) + text = "".join(segment.text for segment in segments).strip() + words = [ + { + "word": word.word, + "start": round(float(word.start), 2), + "end": round(float(word.end), 2), + } + for segment in segments + for word in segment.words + ] + words = self.enforce_word_timing_constraints(words) + return {"text": text, "words": words} + + def transcribe_vad_url_segment( + self, file_path: str, timestamp_offset: float = 0.0, language: str = "en" + ) -> dict: + def load_audio_via_ffmpeg(input_path: str, sample_rate: int) -> np.ndarray: + ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg" + cmd = [ + ffmpeg_bin, + "-nostdin", + "-threads", + "1", + "-i", + input_path, + "-f", + "f32le", + "-acodec", + "pcm_f32le", + "-ac", + "1", + "-ar", + str(sample_rate), + "pipe:1", + ] + try: + proc = subprocess.run( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True + ) + except Exception as e: + raise HTTPException(status_code=400, detail=f"ffmpeg failed: {e}") + audio = np.frombuffer(proc.stdout, dtype=np.float32) + return audio + + def vad_segments( + audio_array, + sample_rate: int = SAMPLE_RATE, + window_size: int = VAD_CONFIG["window_size"], + ) -> Generator[tuple[float, float], None, None]: + vad_model = load_silero_vad(onnx=False) + iterator = VADIterator(vad_model, sampling_rate=sample_rate) + start = None + for i in range(0, len(audio_array), window_size): + chunk = audio_array[i : i + window_size] + if len(chunk) < window_size: + chunk = np.pad( + chunk, (0, window_size - len(chunk)), mode="constant" + ) + speech = iterator(chunk) + if not speech: + continue + if "start" in speech: + start = speech["start"] + continue + if "end" in speech and start is not None: + end = speech["end"] + yield (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE)) + start = None + iterator.reset_states() + + audio_array = load_audio_via_ffmpeg(file_path, SAMPLE_RATE) + + merged_batches: list[tuple[float, float]] = [] + batch_start = None + batch_end = None + max_duration = VAD_CONFIG["batch_max_duration"] + for seg_start, seg_end in vad_segments(audio_array): + if batch_start is None: + batch_start, batch_end = seg_start, seg_end + continue + if seg_end - batch_start <= max_duration: + batch_end = seg_end + else: + merged_batches.append((batch_start, batch_end)) + batch_start, batch_end = seg_start, seg_end + if batch_start is not None and batch_end is not None: + merged_batches.append((batch_start, batch_end)) + + all_text = [] + all_words = [] + for start_time, end_time in merged_batches: + s_idx = int(start_time * SAMPLE_RATE) + e_idx = int(end_time * SAMPLE_RATE) + segment = audio_array[s_idx:e_idx] + segment = self.pad_audio(segment, SAMPLE_RATE) + with self.lock: + segments, _ = self.model.transcribe( + segment, + language=language, + beam_size=5, + word_timestamps=True, + vad_filter=True, + vad_parameters={"min_silence_duration_ms": 500}, + ) + segments = list(segments) + text = "".join(seg.text for seg in segments).strip() + words = [ + { + "word": w.word, + "start": round(float(w.start) + start_time + timestamp_offset, 2), + "end": round(float(w.end) + start_time + timestamp_offset, 2), + } + for seg in segments + for w in seg.words + ] + if text: + all_text.append(text) + all_words.extend(words) + + all_words = self.enforce_word_timing_constraints(all_words) + return {"text": " ".join(all_text), "words": all_words} diff --git a/gpu/self_hosted/app/services/translator.py b/gpu/self_hosted/app/services/translator.py new file mode 100644 index 00000000..bda7373f --- /dev/null +++ b/gpu/self_hosted/app/services/translator.py @@ -0,0 +1,44 @@ +import threading + +from transformers import MarianMTModel, MarianTokenizer, pipeline + + +class TextTranslatorService: + """Simple text-to-text translator using HuggingFace MarianMT models. + + This mirrors the modal translator API shape but uses text translation only. + """ + + def __init__(self): + self._pipeline = None + self._lock = threading.Lock() + + def load(self, source_language: str = "en", target_language: str = "fr"): + # Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr + model_name = self._resolve_model_name(source_language, target_language) + tokenizer = MarianTokenizer.from_pretrained(model_name) + model = MarianMTModel.from_pretrained(model_name) + self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer) + + def _resolve_model_name(self, src: str, tgt: str) -> str: + # Minimal mapping; extend as needed + pair = (src.lower(), tgt.lower()) + mapping = { + ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr", + ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en", + ("en", "es"): "Helsinki-NLP/opus-mt-en-es", + ("es", "en"): "Helsinki-NLP/opus-mt-es-en", + ("en", "de"): "Helsinki-NLP/opus-mt-en-de", + ("de", "en"): "Helsinki-NLP/opus-mt-de-en", + } + return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr") + + def translate(self, text: str, source_language: str, target_language: str) -> dict: + if self._pipeline is None: + self.load(source_language, target_language) + with self._lock: + results = self._pipeline( + text, src_lang=source_language, tgt_lang=target_language + ) + translated = results[0]["translation_text"] if results else "" + return {"text": {source_language: text, target_language: translated}} diff --git a/gpu/self_hosted/app/utils.py b/gpu/self_hosted/app/utils.py new file mode 100644 index 00000000..679804cb --- /dev/null +++ b/gpu/self_hosted/app/utils.py @@ -0,0 +1,107 @@ +import logging +import os +import sys +import uuid +from contextlib import contextmanager +from typing import Mapping +from urllib.parse import urlparse +from pathlib import Path + +import requests +from fastapi import HTTPException + +from .config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH + +logger = logging.getLogger(__name__) + + +class NoStdStreams: + def __init__(self): + self.devnull = open(os.devnull, "w") + + def __enter__(self): + self._stdout, self._stderr = sys.stdout, sys.stderr + self._stdout.flush() + self._stderr.flush() + sys.stdout, sys.stderr = self.devnull, self.devnull + + def __exit__(self, exc_type, exc_value, traceback): + sys.stdout, sys.stderr = self._stdout, self._stderr + self.devnull.close() + + +def ensure_dirs(): + UPLOADS_PATH.mkdir(parents=True, exist_ok=True) + + +def detect_audio_format(url: str, headers: Mapping[str, str]) -> str: + url_path = urlparse(url).path + for ext in SUPPORTED_FILE_EXTENSIONS: + if url_path.lower().endswith(f".{ext}"): + return ext + + content_type = headers.get("content-type", "").lower() + if "audio/mpeg" in content_type or "audio/mp3" in content_type: + return "mp3" + if "audio/wav" in content_type: + return "wav" + if "audio/mp4" in content_type: + return "mp4" + + raise HTTPException( + status_code=400, + detail=( + f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}" + ), + ) + + +def download_audio_to_uploads(audio_file_url: str) -> tuple[Path, str]: + response = requests.head(audio_file_url, allow_redirects=True) + if response.status_code == 404: + raise HTTPException(status_code=404, detail="Audio file not found") + + response = requests.get(audio_file_url, allow_redirects=True) + response.raise_for_status() + + audio_suffix = detect_audio_format(audio_file_url, response.headers) + unique_filename = f"{uuid.uuid4()}.{audio_suffix}" + file_path: Path = UPLOADS_PATH / unique_filename + + with open(file_path, "wb") as f: + f.write(response.content) + + return file_path, audio_suffix + + +@contextmanager +def download_audio_file(audio_file_url: str): + """Download an audio file to UPLOADS_PATH and remove it after use. + + Yields (file_path: Path, audio_suffix: str). + """ + file_path, audio_suffix = download_audio_to_uploads(audio_file_url) + try: + yield file_path, audio_suffix + finally: + try: + file_path.unlink(missing_ok=True) + except Exception as e: + logger.error("Error deleting temporary file %s: %s", file_path, e) + + +@contextmanager +def cleanup_uploaded_files(file_paths: list[Path]): + """Ensure provided file paths are removed after use. + + The provided list can be populated inside the context; all present entries + at exit will be deleted. + """ + try: + yield file_paths + finally: + for path in list(file_paths): + try: + path.unlink(missing_ok=True) + except Exception as e: + logger.error("Error deleting temporary file %s: %s", path, e) diff --git a/gpu/self_hosted/compose.yml b/gpu/self_hosted/compose.yml new file mode 100644 index 00000000..4f04935a --- /dev/null +++ b/gpu/self_hosted/compose.yml @@ -0,0 +1,10 @@ +services: + reflector_gpu: + build: + context: . + ports: + - "8000:8000" + env_file: + - .env + volumes: + - ./cache:/root/.cache diff --git a/gpu/self_hosted/main.py b/gpu/self_hosted/main.py new file mode 100644 index 00000000..52617d24 --- /dev/null +++ b/gpu/self_hosted/main.py @@ -0,0 +1,3 @@ +from app.factory import create_app + +app = create_app() diff --git a/gpu/self_hosted/pyproject.toml b/gpu/self_hosted/pyproject.toml new file mode 100644 index 00000000..7cd3007d --- /dev/null +++ b/gpu/self_hosted/pyproject.toml @@ -0,0 +1,19 @@ +[project] +name = "reflector-gpu" +version = "0.1.0" +description = "Self-hosted GPU service for speech transcription, diarization, and translation via FastAPI." +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "fastapi[standard]>=0.116.1", + "uvicorn[standard]>=0.30.0", + "torch>=2.3.0", + "faster-whisper>=1.1.0", + "librosa==0.10.1", + "numpy<2", + "silero-vad==5.1.0", + "transformers>=4.35.0", + "sentencepiece", + "pyannote.audio==3.1.0", + "torchaudio>=2.3.0", +] diff --git a/gpu/self_hosted/runserver.sh b/gpu/self_hosted/runserver.sh new file mode 100644 index 00000000..851dd535 --- /dev/null +++ b/gpu/self_hosted/runserver.sh @@ -0,0 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a/server/docs/gpu/api-transcription.md +++ b/server/docs/gpu/api-transcription.md @@ -190,5 +190,5 @@ Use the pytest-based conformance tests to validate any new implementation (inclu ``` TRANSCRIPT_URL=https:// \ TRANSCRIPT_MODAL_API_KEY=your-api-key \ -uv run -m pytest -m gpu_modal --no-cov server/tests/test_gpu_modal_transcript.py +uv run -m pytest -m model_api --no-cov server/tests/test_model_api_transcript.py ``` diff --git a/server/pyproject.toml b/server/pyproject.toml index d055f461..1609afe0 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -118,7 +118,7 @@ addopts = "-ra -q --disable-pytest-warnings --cov --cov-report html -v" testpaths = ["tests"] asyncio_mode = "auto" markers = [ - "gpu_modal: mark test to run only with GPU Modal endpoints (deselect with '-m \"not gpu_modal\"')", + "model_api: tests for the unified model-serving HTTP API (backend- and hardware-agnostic)", ] [tool.ruff.lint] @@ -130,7 +130,7 @@ select = [ [tool.ruff.lint.per-file-ignores] "reflector/processors/summary/summary_builder.py" = ["E501"] -"gpu/**.py" = ["PLC0415"] +"gpu/modal_deployments/**.py" = ["PLC0415"] "reflector/tools/**.py" = ["PLC0415"] "migrations/versions/**.py" = ["PLC0415"] "tests/**.py" = ["PLC0415"] diff --git a/server/tests/test_model_api_diarization.py b/server/tests/test_model_api_diarization.py new file mode 100644 index 00000000..8ae719ff --- /dev/null +++ b/server/tests/test_model_api_diarization.py @@ -0,0 +1,63 @@ +""" +Tests for diarization Model API endpoint (self-hosted service compatible shape). + +Marked with the "model_api" marker and skipped unless DIARIZATION_URL is provided. + +Run with for local self-hosted server: + DIARIZATION_API_KEY=dev-key \ + DIARIZATION_URL=http://localhost:8000 \ + uv run -m pytest -m model_api --no-cov tests/test_model_api_diarization.py +""" + +import os + +import httpx +import pytest + +# Public test audio file hosted on S3 specifically for reflector pytests +TEST_AUDIO_URL = ( + "https://reflector-github-pytest.s3.us-east-1.amazonaws.com/test_mathieu_hello.mp3" +) + + +def get_modal_diarization_url(): + url = os.environ.get("DIARIZATION_URL") + if not url: + pytest.skip( + "DIARIZATION_URL environment variable is required for Model API tests" + ) + return url + + +def get_auth_headers(): + api_key = os.environ.get("DIARIZATION_API_KEY") or os.environ.get( + "REFLECTOR_GPU_APIKEY" + ) + return {"Authorization": f"Bearer {api_key}"} if api_key else {} + + +@pytest.mark.model_api +class TestModelAPIDiarization: + def test_diarize_from_url(self): + url = get_modal_diarization_url() + headers = get_auth_headers() + + with httpx.Client(timeout=60.0) as client: + response = client.post( + f"{url}/diarize", + params={"audio_file_url": TEST_AUDIO_URL, "timestamp": 0.0}, + headers=headers, + ) + + assert response.status_code == 200, f"Request failed: {response.text}" + result = response.json() + + assert "diarization" in result + assert isinstance(result["diarization"], list) + assert len(result["diarization"]) > 0 + + for seg in result["diarization"]: + assert "start" in seg and "end" in seg and "speaker" in seg + assert isinstance(seg["start"], (int, float)) + assert isinstance(seg["end"], (int, float)) + assert seg["start"] <= seg["end"] diff --git a/server/tests/test_gpu_modal_transcript.py b/server/tests/test_model_api_transcript.py similarity index 94% rename from server/tests/test_gpu_modal_transcript.py rename to server/tests/test_model_api_transcript.py index 9a152185..f4a21283 100644 --- a/server/tests/test_gpu_modal_transcript.py +++ b/server/tests/test_model_api_transcript.py @@ -1,21 +1,21 @@ """ -Tests for GPU Modal transcription endpoints. +Tests for transcription Model API endpoints. -These tests are marked with the "gpu-modal" group and will not run by default. -Run them with: pytest -m gpu-modal tests/test_gpu_modal_transcript_parakeet.py +These tests are marked with the "model_api" group and will not run by default. +Run them with: pytest -m model_api tests/test_model_api_transcript.py Required environment variables: -- TRANSCRIPT_URL: URL to the Modal.com endpoint (required) -- TRANSCRIPT_MODAL_API_KEY: API key for authentication (optional) +- TRANSCRIPT_URL: URL to the Model API endpoint (required) +- TRANSCRIPT_API_KEY: API key for authentication (optional) - TRANSCRIPT_MODEL: Model name to use (optional, defaults to nvidia/parakeet-tdt-0.6b-v2) -Example with pytest (override default addopts to run ONLY gpu_modal tests): +Example with pytest (override default addopts to run ONLY model_api tests): TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web-dev.modal.run \ - TRANSCRIPT_MODAL_API_KEY=your-api-key \ - uv run -m pytest -m gpu_modal --no-cov tests/test_gpu_modal_transcript.py + TRANSCRIPT_API_KEY=your-api-key \ + uv run -m pytest -m model_api --no-cov tests/test_model_api_transcript.py # Or with completely clean options: - uv run -m pytest -m gpu_modal -o addopts="" tests/ + uv run -m pytest -m model_api -o addopts="" tests/ Running Modal locally for testing: modal serve gpu/modal_deployments/reflector_transcriber_parakeet.py @@ -40,14 +40,16 @@ def get_modal_transcript_url(): url = os.environ.get("TRANSCRIPT_URL") if not url: pytest.skip( - "TRANSCRIPT_URL environment variable is required for GPU Modal tests" + "TRANSCRIPT_URL environment variable is required for Model API tests" ) return url def get_auth_headers(): """Get authentication headers if API key is available.""" - api_key = os.environ.get("TRANSCRIPT_MODAL_API_KEY") + api_key = os.environ.get("TRANSCRIPT_API_KEY") or os.environ.get( + "REFLECTOR_GPU_APIKEY" + ) if api_key: return {"Authorization": f"Bearer {api_key}"} return {} @@ -58,8 +60,8 @@ def get_model_name(): return os.environ.get("TRANSCRIPT_MODEL", "nvidia/parakeet-tdt-0.6b-v2") -@pytest.mark.gpu_modal -class TestGPUModalTranscript: +@pytest.mark.model_api +class TestModelAPITranscript: """Test suite for GPU Modal transcription endpoints.""" def test_transcriptions_from_url(self): diff --git a/server/tests/test_model_api_translation.py b/server/tests/test_model_api_translation.py new file mode 100644 index 00000000..47e3141f --- /dev/null +++ b/server/tests/test_model_api_translation.py @@ -0,0 +1,56 @@ +""" +Tests for translation Model API endpoint (self-hosted service compatible shape). + +Marked with the "model_api" marker and skipped unless TRANSLATION_URL is provided +or we fallback to TRANSCRIPT_URL base (same host for self-hosted). + +Run locally against self-hosted server: + TRANSLATION_API_KEY=dev-key \ + TRANSLATION_URL=http://localhost:8000 \ + uv run -m pytest -m model_api --no-cov tests/test_model_api_translation.py +""" + +import os + +import httpx +import pytest + + +def get_translation_url(): + url = os.environ.get("TRANSLATION_URL") or os.environ.get("TRANSCRIPT_URL") + if not url: + pytest.skip( + "TRANSLATION_URL or TRANSCRIPT_URL environment variable is required for Model API tests" + ) + return url + + +def get_auth_headers(): + api_key = os.environ.get("TRANSLATION_API_KEY") or os.environ.get( + "REFLECTOR_GPU_APIKEY" + ) + return {"Authorization": f"Bearer {api_key}"} if api_key else {} + + +@pytest.mark.model_api +class TestModelAPITranslation: + def test_translate_text(self): + url = get_translation_url() + headers = get_auth_headers() + + with httpx.Client(timeout=60.0) as client: + response = client.post( + f"{url}/translate", + params={"text": "The meeting will start in five minutes."}, + json={"source_language": "en", "target_language": "fr"}, + headers=headers, + ) + + assert response.status_code == 200, f"Request failed: {response.text}" + data = response.json() + + assert "text" in data and isinstance(data["text"], dict) + assert data["text"].get("en") == "The meeting will start in five minutes." + assert isinstance(data["text"].get("fr", ""), str) + assert len(data["text"]["fr"]) > 0 + assert data["text"]["fr"] == "La réunion commencera dans cinq minutes."