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Your code is a well-structured FastAPI application for transcribing audio files using NVIDIA NeMo ASR models, supporting multiple Indian languages and batch processing. It includes logging, audio preprocessing, and error handling, which are solid foundations for a production-ready service. However, there are several areas where improvements can be made to reduce latency, increase throughput (serve more users), and ensure production readiness. Below, I’ll suggest enhancements and identify missing elements for a robust production deployment. Key Improvements for Latency and Scalability
Optimize Model Inference
Mixed Precision: Enable mixed-precision inference with torch.cuda.amp.autocast (currently commented out) to reduce latency on CUDA GPUs. This uses bfloat16 or float16, cutting computation time without significant accuracy loss. python
with torch.cuda.amp.autocast(dtype=torch.bfloat16): rnnt_texts = asr_manager.model.transcribe(chunk_file_paths, batch_size=1, language_id=language_id) CUDA Graphs: If using NeMo 2.0.0+, enable CUDA Graphs for repeated inference calls to reduce kernel launch overhead. This requires a fixed input shape, so test compatibility with dynamic audio lengths. Batch Size: Dynamically adjust batch_size based on input size or GPU memory. Currently, it’s hardcoded (1 for single files, len(files) for batch). For larger batches, process in smaller chunks to avoid OOM errors: python
batch_size = min(len(chunk_file_paths), 32) # Cap at 32, adjust based on GPU memory rnnt_texts = asr_manager.model.transcribe(chunk_file_paths, batch_size=batch_size, language_id=language_id)
Model Management
Preload Models: Loading a new model for every language switch (e.g., load_model) is slow and memory-intensive. Preload all required models at startup if memory allows, or use a caching mechanism: python
class ASRModelManager: def init(self, default_language="kn"): self.models = {} self.default_language = default_language self.load_initial_model(default_language) def load_initial_model(self, language_id): model = self.load_model(language_id) self.models[language_id] = model def get_model(self, language_id): if language_id not in self.models: self.models[language_id] = self.load_model(language_id) return self.models[language_id] Then update /transcribe/ and /transcribe_batch/ to use asr_manager.get_model(language_id) instead of reloading. Model Sharing: Ensure thread-safety when sharing models across requests. FastAPI runs async, so use a lock if multiple workers access the same model: python
from threading import Lock class ASRModelManager: def init(self, default_language="kn"): self.model_locks = {lang: Lock() for lang in self.model_language.keys()} ... async def transcribe(self, paths, language_id, batch_size): with self.model_locks[language_id]: model = self.get_model(language_id) return model.transcribe(paths, batch_size=batch_size, language_id=language_id)
Audio Preprocessing
In-Memory Processing: Avoid writing to disk with temporary files (tempfile.NamedTemporaryFile) and splitting chunks to disk (split_audio). Process audio in memory to reduce I/O latency: python
def split_audio_in_memory(self, audio_segment, chunk_duration_ms=15000): duration_ms = len(audio_segment) if duration_ms <= chunk_duration_ms: return [audio_segment] chunks = [audio_segment[i:i + chunk_duration_ms] for i in range(0, duration_ms, chunk_duration_ms)] return chunks Modify /transcribe/ to: python
audio_chunks = asr_manager.split_audio_in_memory(audio) chunk_buffers = [io.BytesIO() for _ in audio_chunks] for chunk, buffer in zip(audio_chunks, chunk_buffers): chunk.export(buffer, format="wav") buffer.seek(0) rnnt_texts = asr_manager.model.transcribe(chunk_buffers, batch_size=len(chunk_buffers), language_id=language_id)
Async Preprocessing: Offload audio conversion (e.g., sample rate adjustment) to an async task or worker queue to free up the main thread.
Async and Concurrency
Worker Queue: For heavy loads, integrate a task queue (e.g., Celery with Redis) to handle transcription jobs asynchronously. This decouples preprocessing and inference from the HTTP response: python
from celery import Celery celery_app = Celery('asr', broker='redis://localhost:6379/0')
@celery_app.task def transcribe_task(file_paths, language_id): model = asr_manager.get_model(language_id) return model.transcribe(file_paths, batch_size=len(file_paths), language_id=language_id) @app.post("/transcribe_async/") async def transcribe_async(file: UploadFile = File(...), language: str = Query(...)): # Save file temporarily or process in memory task = transcribe_task.delay([tmp_file_path], asr_manager.model_language[language]) return {"task_id": task.id} Increase Workers: Run FastAPI with multiple Uvicorn workers (uvicorn --workers 4) to handle concurrent requests, leveraging multiple CPU cores.
FastAPI Performance
Response Streaming: For long transcriptions, stream results back to the client instead of waiting for full processing: python
from fastapi.responses import StreamingResponse async def stream_transcriptions(chunk_file_paths, language_id): model = asr_manager.get_model(language_id) for chunk in chunk_file_paths: text = model.transcribe([chunk], batch_size=1, language_id=language_id)[0] yield f"data: {text}\n\n" @app.post("/transcribe_stream/") async def transcribe_stream(file: UploadFile = File(...), language: str = Query(...)): audio_chunks = asr_manager.split_audio(tmp_file_path) return StreamingResponse(stream_transcriptions(audio_chunks, asr_manager.model_language[language]), media_type="text/event-stream") Rate Limiting: Add rate limiting (e.g., slowapi) to prevent overload: python
from slowapi import Limiter from slowapi.util import get_remote_address limiter = Limiter(key_func=get_remote_address) app.state.limiter = limiter @app.post("/transcribe/", response_model=TranscriptionResponse) @limiter.limit("10/minute") async def transcribe_audio(...): ...
Production Readiness: Missing Elements
Scalability
Load Balancing: Deploy behind a load balancer (e.g., NGINX, HAProxy) to distribute requests across multiple instances. Containerization: Use Docker for consistent deployment: dockerfile
FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04 RUN apt-get update && apt-get install -y python3-pip ffmpeg RUN pip3 install torch nemo_toolkit[asr] fastapi uvicorn pydub COPY . /app WORKDIR /app CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]
Build and run: bash
docker build -t asr-api . docker run --gpus all -p 8000:8000 asr-api
Horizontal Scaling: Use Kubernetes or Docker Swarm to scale instances based on demand.
Monitoring and Logging
Metrics: Add Prometheus metrics (e.g., prometheus-fastapi-instrumentator) to track latency, request rate, and errors: python
from prometheus_fastapi_instrumentator import Instrumentator Instrumentator().instrument(app).expose(app)
Distributed Logging: Send logs to a centralized system (e.g., ELK Stack, Loki) instead of local files for better analysis.
Security
Authentication: Add API key or JWT authentication (e.g., fastapi-users) to restrict access. Input Validation: Validate audio file size and duration to prevent abuse: python
MAX_FILE_SIZE = 50 * 1024 * 1024 # 50MB if len(file_content) > MAX_FILE_SIZE: raise HTTPException(status_code=400, detail="File too large") HTTPS: Configure SSL/TLS with NGINX or a cloud provider.
Error Handling and Resilience
Retry Logic: Add retries for transient failures (e.g., model inference errors) using tenacity: python
from tenacity import retry, stop_after_attempt, wait_fixed @retry(stop=stop_after_attempt(3), wait=wait_fixed(2)) def transcribe_with_retry(model, paths, batch_size, language_id): return model.transcribe(paths, batch_size=batch_size, language_id=language_id) Graceful Degradation: If a model fails to load, fall back to a default (e.g., Kannada).
Configuration
Environment Variables: Use python-dotenv or pydantic-settings for configurable settings (e.g., port, host, chunk duration): python
from pydantic_settings import BaseSettings class Settings(BaseSettings): host: str = "127.0.0.1" port: int = 8000 chunk_duration_ms: int = 15000 settings = Settings() uvicorn.run(app, host=settings.host, port=settings.port)
Final Optimized Code Snippet Here’s an example incorporating some key improvements: python
import torch import nemo.collections.asr as nemo_asr from fastapi import FastAPI, File, UploadFile, HTTPException, Query from fastapi.responses import JSONResponse from pydub import AudioSegment import io import logging from threading import Lock
app = FastAPI() logging.basicConfig(level=logging.INFO)
class ASRModelManager: def init(self, default_language="kn"): self.default_language = default_language self.model_language = {...} # Same as original self.config_models = {...} # Same as original self.models = {} self.model_locks = {lang: Lock() for lang in self.model_language.keys()} self.load_initial_model(default_language)
def load_model(self, language_id):
model = nemo_asr.models.ASRModel.from_pretrained(self.config_models[language_id])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
return model.to(device).eval()
def load_initial_model(self, language_id):
self.models[language_id] = self.load_model(language_id)
def get_model(self, language_id):
if language_id not in self.models:
with self.model_locks[language_id]:
if language_id not in self.models: # Double-check locking
self.models[language_id] = self.load_model(language_id)
return self.models[language_id]
def split_audio_in_memory(self, audio_segment, chunk_duration_ms=15000):
duration_ms = len(audio_segment)
if duration_ms <= chunk_duration_ms:
return [audio_segment]
return [audio_segment[i:i + chunk_duration_ms] for i in range(0, duration_ms, chunk_duration_ms)]
asr_manager = ASRModelManager()
@app.post("/transcribe/") async def transcribe_audio(file: UploadFile = File(...), language: str = Query(...)): file_content = await file.read() audio = AudioSegment.from_file(io.BytesIO(file_content), format=file.filename.split(".")[-1].lower()) if audio.frame_rate != 16000: audio = audio.set_frame_rate(16000).set_channels(1)
audio_chunks = asr_manager.split_audio_in_memory(audio)
chunk_buffers = [io.BytesIO() for _ in audio_chunks]
for chunk, buffer in zip(audio_chunks, chunk_buffers):
chunk.export(buffer, format="wav")
buffer.seek(0)
language_id = asr_manager.model_language.get(language, asr_manager.default_language)
model = asr_manager.get_model(language_id)
model.cur_decoder = "rnnt"
with torch.cuda.amp.autocast(dtype=torch.bfloat16):
rnnt_texts = model.transcribe(chunk_buffers, batch_size=min(len(chunk_buffers), 32), language_id=language_id)
text = " ".join(rnnt_texts)
return JSONResponse(content={"text": text})
if name == "main": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000, workers=4)
Summary
Latency: Mixed precision, in-memory processing, and dynamic batching reduce inference time.
Scalability: Preloaded models, async workers, and Triton (as an alternative) handle more users.
Production: Add monitoring, security, and containerization for reliability.
For maximum performance, consider switching to NVIDIA Triton Inference Server (as suggested previously) instead of FastAPI if inference throughput is the top priority. Let me know if you’d like a deeper dive into any specific improvement!