How to Split Audio and Video into Chunks for Gemma 3n

Why Chunking Matters for Gemma 3n Transcription and Translation

Gemma 3n AI models are designed to process short audio segments (chunks)—ideally 20–30 seconds at a time. This maximizes accuracy, speed, and stability while keeping memory usage reasonable. Processing full-length audio or video in one go often leads to errors, slowdowns, or dropped content.

Below, you'll find up-to-date chunking strategies and clear explanations used in the berdaflex videoscribe.

Step 1. Extract Audio from Video and Prepare for Chunking

Extract audio from video (e.g. MP4) using FFmpeg:

ffmpeg -i input_video.mp4 -vn -acodec pcm_s16le -ar 16000 -ac 1 output_audio.wav

• Converts to 16kHz mono WAV: what Gemma 3n expects.

Step 2. Load Audio and Make It Mono

import torchaudio
import numpy as np

waveform, sample_rate = torchaudio.load("output_audio.wav")
if waveform.shape[0] > 1:  # Stereo audio
    waveform = waveform.mean(dim=0, keepdim=True)  # Convert to mono
audio_data = waveform.squeeze().numpy()

Step 3. Smart Chunking Logic

Key Parameters

• max_chunk_size = 30.0 seconds (Gemma 3n’s hard limit)
• min_chunk_size = 0.3 seconds (to avoid too-tiny/noisy chunks)
• preferred_min_size = 15.0 seconds
• preferred_max_size = 30.0 seconds
• optimal_chunk_size = 22.0 seconds

Pause-Based Chunking: Natural Splits

Why do this?

Instead of blindly cutting every X seconds, we split where there are natural speech pauses (silence).

def smart_chunk_audio(audio_data, sample_rate):
    min_silence_db = -14
    min_pause_len_ms = 500
    seek_step_ms = 100
    optimal_chunk_samples = int(22 * sample_rate)
    max_chunk_samples = int(30 * sample_rate)
    min_chunk_samples = int(0.3 * sample_rate)
    overlap_samples = int(0.5 * sample_rate)  # Overlap for smoother transitions

    envelope = np.abs(audio_data)
    silence_mask = envelope = min_pause_samples:
                boundary = current_silence + duration // 2
                boundaries.append(boundary)
            current_silence = None
    
    # Chunking loop
    chunks = []
    current_start = 0
    chunk_idx = 0
    while current_start  max_chunk_samples:
            best_boundary = current_start + max_chunk_samples
        
        chunk = audio_data[current_start:best_boundary]
        chunks.append(chunk)
        current_start = best_boundary - overlap_samples  # move with overlap
        chunk_idx += 1
    return chunks

This logic closely matches the robust logic from processor.py. The real code is more thorough (handles energy, more edge-cases), but this covers the essential mechanics.

Step 4. Save Each Chunk

You can save these chunks for batch processing and easier error recovery.

import torch

def save_chunks(chunks, sample_rate):
    for idx, chunk in enumerate(chunks):
        chunk_tensor = torch.tensor(chunk).unsqueeze(0)
        torchaudio.save(f"chunk_{idx:03d}.wav", chunk_tensor, sample_rate)

Step 5. Advanced Suitability Checks

Each chunk is analyzed to ensure it's “suitable” for transcription (good energy, not mostly silence).

def check_chunk_suitability(audio_data, sample_rate):
    duration = len(audio_data) / sample_rate
    energy = np.mean(audio_data ** 2)
    max_amplitude = np.max(np.abs(audio_data))
    silence_ratio = np.sum(np.abs(audio_data)  0.00001 and max_amplitude > 0.001 and silence_ratio < 0.99 and 0.1 <= duration <= 30.0:
        return True
    return False

Step 6. Why Not Just Cut Every X Seconds?

Simple cut-every-N-seconds methods can split words, lose context, or make transcription choppy. Smart chunking (as shown) finds pauses, natural speech boundaries, and skips untranscribable noise.

Step 7. Full Workflow Overview

From processor.py, your workflow is:

1. Extract audio
2. Chunk intelligently (pause/energy based, overlap)
3. Validate chunk suitability
4. Save chunk files
5. Transcribe/translate each chunk with Gemma 3n
6. Recombine results in order

Visual Summary:

@startuml
title Gemma 3n Audio/Video Chunking & Transcription Pipeline

start
:Input Video/Audio;
:Extract Audio;
:Smart Chunking (pause/energy based, overlap);
:Save Chunks;
:Validate Suitability (energy/noise checks);
:Transcribe/Translate each chunk (Gemma 3n);
:Combine Output;
stop

@enduml

Best Practice Tips

• Use 30s maximum chunk for Gemma 3n.
• Add 0.5s overlap for completeness.
• Always convert to mono, 16kHz.
• Use pause-based chunking where possible.
• Check chunk energy: skip silent/noisy chunks.
• Automate with Python for speed and reliability.

Final Thoughts

Chunking is key for accurate, high-speed transcription and translation with Gemma 3n. The logic in the processor.py is carefully designed for real-world use: robust, efficient, and proven in production.

With these steps and sample code, anyone—regardless of background—can split audio/video into smart chunks and unlock powerful AI transcription and translation capabilities with Gemma 3n.

Published on 8/16/2025