docs/optimisations_grok.md

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
1. 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)

2. 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)

3. 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.

4. 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.

5. 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
1. 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.

2. 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.

3. 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.

4. 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).

5. 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!