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Cover image for title: Building a Free Arabic Speech-to-Text Engine using Hugging Face & Next.js
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admin@adawati.app

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title: Building a Free Arabic Speech-to-Text Engine using Hugging Face & Next.js

#ai #nextjs #nlp #webdev

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Hello fellow developers! 👋

Handling audio processing in web applications is always tricky, but when you add Arabic dialects and academic terminology (Arabizi) to the mix, it becomes a real engineering challenge.

Recently, while building Adawati.app (an all-in-one digital workspace for Arab students), I needed to implement a reliable Speech-to-Text (STT) feature for university lectures. Paid APIs like Google Cloud or AWS were either too expensive for a free tool or struggled heavily with local Arabic dialects.

Here is how I engineered a custom, free solution using Hugging Face open-source models.

🛑 The Technical Bottlenecks

  1. Large File Uploads & Timeouts: University lectures are often 1-2 hours long. Sending a 100MB audio file to a server in one go usually results in a 504 Gateway Timeout.
  2. Background Noise: Lecture halls are noisy. Passing raw audio to an AI model drastically reduces transcription accuracy.
  3. Dialect Nuances: Standard Arabic models fail when professors mix English technical terms with local Arabic dialects.

⚙️ The Architecture & Solution

To bypass these issues, I built a pipeline that processes the audio efficiently:

1. Audio Chunking (The Game Changer)
Instead of sending the whole file, I used the Web Audio API on the client-side to split the audio into smaller 30-second chunks before sending them to the backend. This prevents timeouts and allows parallel processing.

2. Pre-processing & Noise Reduction
Before hitting the AI model, the chunks go through a basic noise-reduction filter using FFmpeg to isolate human voice frequencies.

3. Hugging Face Inference
I connected the backend to a fine-tuned Whisper model hosted on Hugging Face, specifically trained on Arabic datasets.

Here is a conceptual snippet of how the chunking logic looks in the backend (Python/FastAPI wrapper):

`python
from pydub import AudioSegment
import requests

def process_large_audio(file_path):
audio = AudioSegment.from_file(file_path)
chunk_length_ms = 30000 # 30 seconds
chunks = [audio[i:i+chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]

full_transcript = ""

for idx, chunk in enumerate(chunks):
    chunk.export(f"temp_chunk_{idx}.wav", format="wav")
    # Send to Hugging Face API
    transcript = query_huggingface(f"temp_chunk_{idx}.wav")
    full_transcript += transcript + " "

return full_transcript`
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🚀 The Live Result
By combining this chunking architecture with Hugging Face models, I managed to create a fast, accurate, and completely free lecture transcription tool without relying on expensive enterprise APIs.

You can test the live implementation and its accuracy with Arabic audio here:
👉 Arabic Audio-to-Text Converter - Adawati
https://adawati.app/audio-to-text/

💬 Let's Discuss!
I'm curious to know from the backend engineers here:
How do you handle massive file uploads in your Next.js/Node.js applications? Do you prefer client-side chunking or streaming directly to a cloud bucket (like AWS S3) before processing?

Let me know in the comments!

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