250-Hour Saudi Arabic Recordings

@ramantoor5

Hello, I will begin by organizing a team of native speakers fluent in Najdi, Khaleeji, and Hijazi dialects to record 250 hours of clear, natural, and studio-quality Arabic narration. The recordings will be delivered as clean 48 kHz / 16-bit WAV files with consistent volume and no background noise, following your requirements for simple, conversational narration. To ensure a balanced dataset, we will aim for roughly equal coverage of each dialect. I propose starting with a small initial milestone where we provide a five-minute sample from each dialect for your review before moving on to full production. This will allow us to ensure that the audio chain and accents are spot-on before completing the full 250 hours. Best regards,

@mossaalshareef

السلام عليكم ورحمة الله وبركاته I am an Arab who lived in Saudi Arabia, so I speak Saudi Arabic fluently and can single-handedly record the full 250 hours across the Najdi, Khaleeji, and Hijazi dialects without needing to rely on a team. I use a professional studio microphone to ensure clean 48 kHz / 16-bit WAV files with zero background noise, and my extensive background in editing means I can easily manage the precise file trimming and gentle normalization you require. I will also keep the tracking spreadsheet perfectly organized as I deliver the raw and final segments. I am ready to record and send over the initial 5-minute samples for your review right now so we can get started.

@harshita768

As an experienced audio engineer and native speaker of Arabic (including the Najdi, Khaleeji, and Hijazi dialects), I am the perfect fit for your project. I understand the importance of clear, high-quality audio recordings with balanced coverage among the dialects. With a strong technical background in delivering clean, noise-free 48kHZ/16-bit WAV files, I can ensure professional-grade recordings that meet your specifications to assist in training your Generative AI model. Having worked on numerous projects requiring precise sound engineering, I guarantee consistent volume levels across all files through gentle normalization. Moreover, I not only possess the skills to record alone but can also coordinate a small team for efficient work delivery. My proficiency as a voice talent and fluency in these specific dialects will enable me to provide natural and conversational narrations needed for your corpus. Evidenced by my high rating for on-time delivery and budget management, I consistently achieve my clients' goals without compromising quality. I’ll approach this project the same way and provide milestone-based deliverables according to your specifications. Ultimately, my aim is to ensure that each file, from the raw takes to the final trimmed version, exceeds your expectations in terms of clarity and content per the provided one-line descriptions.

@munnamhm14

Send me 1 minute of your track — I’ll mix it FREE so you can hear the quality. Hi, I’m Munna Mhm — Professional Audio Mixing Engineer & Music Producer with 13+ years of experience. I deliver clean, powerful, radio-ready mixes across Hip-Hop, Pop, EDM, Rock, R&B, cinematic music, podcasts, and voiceovers. Using industry-standard EQ, compression, saturation, stereo imaging, reverb, delay & more — I turn your track into a polished, industry-level sound.

@SkillCrafts

I’ve successfully delivered high-fidelity speech datasets for regional Arabic dialects, and I understand the specific phonetic nuances required to make a Saudi Generative AI model sound truly authentic. For a 250-hour corpus, the priority isn't just raw volume; it’s capturing the distinct prosody, glottal stops, and lexical variety of Najdi and Hejazi regions to ensure the model generalizes effectively across the Kingdom's linguistic landscape. My experience managing large-scale data collection pipelines for TTS and ASR ensures that every hour of your requirement meets the rigorous standards needed for high-performance machine learning. To execute this, I will implement a multi-stage validation pipeline using professional-grade recording environments and high-quality condensers sampling at 48kHz for maximum spectral clarity. I’ll curate a diverse roster of native Saudi narrators, ensuring a balanced gender distribution while monitoring for acoustic consistency and precise silence-to-speech ratios via automated LUFS normalization. Each segment will undergo Signal-to-Noise Ratio (SNR) checks followed by manual linguistic auditing to prevent dialect drift or phonetic inaccuracies. The final dataset will be delivered with structured metadata in JSON format, mapping each file to its transcript for seamless integration into your training architecture. Are you looking for a specific distribution across the major Saudi provinces, or should the corpus lean toward a more standardized Modern Standard Arabic? Additionally, do you have specific preferences for the narration content—such as conversational or literary—to better align with the model's intended application? I’m available to discuss how we can scale this collection efficiently while maintaining the highest linguistic integrity. Let’s chat to align on your technical specs or jump on a brief call to finalize the roadmap.

@besho87

Hello, I’ve worked on large-scale voice recording and dialect-specific projects before, and I can help you execute this corpus professionally and efficiently. I understand the technical and linguistic requirements clearly: • 48 kHz / 16-bit WAV format • Clean studio-quality audio • No background noise • Minimal processing (gentle normalization only) • Natural, conversational narration style • Balanced coverage across Najdi, Khaleeji, and Hijazi dialects I have experience coordinating native speakers and managing structured recording workflows to ensure consistency in tone, pacing, and audio levels across large datasets. I can either record directly (where appropriate) or coordinate a small team of verified native speakers for proper dialect authenticity. I also understand dataset discipline — organized file naming, raw + final trimmed versions, and a clean tracking spreadsheet with metadata (file name, length, dialect, description). I’m fully comfortable starting with the required 5-minute sample per dialect to confirm accent and audio chain before scaling production. I can manage this project with professionalism, clear milestones, and consistent delivery. Looking forward to collaborating. Best Regards Beishoy

@peculiarduke123

GenAI is only as intelligent as its training data. A Najdi recording with a hint of an outside accent, or a Hijazi clip with a -50dB noise floor, isn't just "bad audio"—it’s a data pollutant that compromises your entire model. I will provide a surgically clean 250-hour corpus where the linguistic boundaries between Najdi, Khaleeji, and Hijazi are strictly preserved. I understand the technical weight of 48 kHz / 16-bit WAV deliverables. My process ensures 100% dialect purity through a "Native-Only" vetting phase for every speaker. For a previous LLM project, I managed high-volume vocal datasets where we maintained a zero-rejection rate by enforcing strict acoustic controls (no processing, just -1.0dB normalization). You will receive a structured, error-free CSV mapping every file to its specific dialect and content description, ready for immediate model ingestion. I have three native speakers—one for each specific Saudi region—ready to record the 5-minute vetting samples to prove our audio chain and accent accuracy. Should I start with the Najdi or Hijazi sample first to establish the conversational baseline you need? send a message lets get started patiently waiting, peculiar

@BrainstackTech

Hello there, We bring 8 years of AI/ML data pipeline engineering and large-scale dataset curation for generative model training — building a balanced 250-hour Saudi Arabic corpus across Najdi, Khaleeji, and Hijazi is fundamentally a data engineering problem as much as an audio one. I want to be upfront: the listed budget won't cover full voice talent compensation for 250 hours of native-speaker narration. My bid covers the technical pipeline and project management layer — automated ingestion that validates incoming audio against your 48 kHz/16-bit WAV spec, checks SNR thresholds (rejecting noise above -60 dB), normalizes to consistent LUFS levels, and auto-generates your metadata spreadsheet with filename, duration, dialect tag, and content description. Zero manual bookkeeping errors. We'd build acoustic fingerprinting for duplicate detection and silence-trimming scripts to prevent dead air from inflating hour counts, plus a real-time dashboard tracking per-dialect hours so you never drift past ~83 hours each. Voice talent sourcing and compensation would need to be scoped separately — happy to discuss realistic numbers there. A parallel project we delivered processed 60,000+ multilingual field survey records with automated validation and quality scoring — same structure of multiple source variants feeding one unified clean dataset. Naveen Brainstack Technologies

@victorsitati

am a self-motivated and reliable individual seeking an opportunity to work in an online environment where I can contribute my skills and grow professionally. I have strong communication skills, good time management, and the ability to work independently without supervision. Through my academic experience in media and communication, I have developed research, writing, and digital content skills that are valuable in remote work. I am comfortable using online tools such as email, social media platforms, Google Workspace, and virtual meeting applications. I am a fast learner, adaptable to new systems, and committed to meeting deadlines with high-quality results. I value professionalism, clear communication, and teamwork, even in virtual settings. My goal is to build experience in the digital workspace, deliver consistent performance, and add value to any organization I work with while continuously improving my skills.