🎙️ SedaNevis_v1.0 (صدانویس)

Robust Multi-Domain Persian ASR Model with FastConformer-RNNT

SedaNevis_v1.0 is an enterprise-grade, state-of-the-art Automatic Speech Recognition (ASR) model specifically tailored for the Persian (Farsi) language. Built on top of NVIDIA's robust FastConformer-RNNT architecture, it is fine-tuned across diverse and challenging acoustic conditions, including elderly speech, conversational podcasts, studio reading, and emotional speech.

توضیح فارسی (Overview):
مدل SedaNevis_v1.0 (صدانویس) یک مدل پیشرفته و بهینه‌سازی‌شده برای بازشناسی خودکار گفتار زبان فارسی (ASR) است. این مدل با هدف عملکرد پایدار در شرایط چالش‌برانگیز آکوستیک (لهجه‌ها، گفتار سالمندان، مکالمات محاوره‌ای پادکست‌ها، و صدای همراه با احساسات) بر پایه معماری FastConformer-RNNT توسعه یافته است.

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🏆 Benchmark & Latency Comparison

Evaluation Setup: Evaluated on 1,000 diverse Persian audio files under identical hardware conditions (NVIDIA GPU).

⚡ Operational Advantage:
While achieving an unprecedented low error rate (9.78% WER), SedaNevis operates 5× to 38× faster than typical Whisper Large models, making it optimal for both real-time streaming pipelines and large-scale offline batch processing.

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⚡ Unmatched Speed & Real-World Latency Comparison

The latency figures highlight a transformative efficiency gain:

• 1 Minute vs. 39 Minutes: Transcribing the entire 1,000 benchmark clips takes only ~1 minute with SedaNevis_v1.0, whereas Whisper-Large-Persian-v4 requires nearly 39 minutes on the exact same workload!
• 37.5× Higher Throughput: Delivering real-time streaming capability without the severe computational overhead of autoregressive decoder architectures.

توضیح فارسی (تفاوت شگفت‌انگیز در زمان و سرعت پردازش):
برای درک سرعت خارق‌العاده مدل به این مقایسه توجه کنید:
پیاده‌سازی ۱,۰۰۰ فایل صوتی تست، با مدل SedaNevis_v1.0 تنها حدود ۱ دقیقه طول کشید؛ در حالی که همین تعداد فایل با مدل Whisper-Large-Persian-v4 نزدیک به ۳۹ دقیقه زمان برد!
یعنی صدانویس بیش از ۳۷ برابر سریع‌تر از ویسپر لارج عمل می‌کند و پردازش هم‌زمان صدها مکالمه را به‌صورت بلادرنگ (Real-Time) ممکن می‌سازد.

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💡 Exceptional Parameter Efficiency & Ultra-Low Footprint (~500 MB)

A standout characteristic of SedaNevis_v1.0 is its remarkable architectural efficiency:

• Massive Size Reduction: Weighing in at only ~500 MB, SedaNevis achieves sub-10% WER, comfortably outperforming large-scale models (such as Whisper-Large variants that require 1.5GB – 3.1GB of storage and massive compute).
• Edge & Production Ready: The compact footprint enables deployment in resource-constrained production settings, on-premise edge servers, CPU-only environments, and client-side applications with minimal VRAM utilization.
• Cost-Efficient Serving: Delivers enterprise-grade transcription accuracy at a fraction of the cloud hosting and hardware inference costs compared to multi-gigabyte models.

توضیح فارسی (بهره‌وری فوق‌العاده و حجم کم):
یکی از مهم‌ترین نقاط قوت مدل صدانویس، حجم کم (حدود ۵۰۰ مگابایت) در کنار دقت بسیار بالا است. در حالی که مدل‌های بزرگ و سنگین چندگیگابایتی (مانند Whisper Large) به سخت‌افزارهای گران‌قیمت با VRAM بالا نیاز دارند، صدانویس با حجمی ۶ برابر کوچک‌تر خطای کمتری ثبت کرده و به‌سادگی بر روی سرورهای اقتصادی، سیستم‌های محلی (Edge) و حتی پردازنده‌های معمولی (CPU) قابل اجراست.

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📊 Evaluation & Training Data Sources

The model's training and evaluation benchmarks leverage standard, peer-reviewed collections:

1. Elderly Speech: AliAvd/persian-elderly-asr – Focuses on disfluent pronunciations, vocal cord fatigue, and slower speaking rates.
2. Conversational Podcasts: farsi-asr/farsi-asr-dataset – Captures real-world colloquial Farsi, overlapping speech, and modern vernacular.
3. Studio Read Speech: Thomcles/Persian-Farsi-Speech – Clean acoustic baseline with clear enunciation.
4. Emotional Speech: ShEMO Database – Authentic recordings across anger, happiness, sadness, and surprise.
5. Curated Broadcast & Web Corpora: Filtered speech segments (0.4s to 30.0s) ensuring linguistic variety.

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🔬 Training Methodology

To achieve strong generalization while eliminating catastrophic forgetting, the training pipeline employed several advanced techniques:

1. Multi-Stage Adaptation & Terminal Layer Stabilization

• Progressive Fine-Tuning Pipeline: The model was trained through a multi-stage adaptation pipeline. Following comprehensive end-to-end parameter training across diverse Persian corpora, late-stage targeted freezing was utilized to stabilize foundational representations.
• Controlled Gradient Flow: In the terminal convergence phase, gradient updates were predominantly concentrated on high-level linguistic blocks and the RNN-T prediction network. This stabilized low-level acoustic representations while refining domain-specific speech nuances, dialects, and conversational patterns.

2. Speech Text Normalization & Persian ITN

• Spoken-Form Expansion: Cardinal, ordinal, and fractional digits were systematically expanded into verbal Persian words.
• Lexical Canonicalization: Normalized Persian solar calendar dates, clock times, metric units, and removed non-phonemic Arabic diacritics (Harakat) while preserving morphology.
• Zero-Width Non-Joiner (ZWNJ) Handling: Standardized ZWNJ and punctuation characters to align with token boundaries in the SentencePiece vocabulary.

3. Hyperparameters & Optimization

• Optimizer: AdamW with a base learning rate of $5.0 \times 10^{-6}$, combined with a Cosine Annealing learning rate schedule and warmup steps.
• Precision: Mixed Precision (FP16) for enhanced GPU throughput and reduced memory footprint.
• Gradient Accumulation: Step accumulation factor of 8 to simulate larger, stable effective batch sizes.

4. Cross-Corpus Data Harmonization & Balanced Ingestion

• Acoustic & Duration Harmonization: Raw audio from disparate sources exhibited varying metadata formats, acoustic properties, and audio length distributions. The entire corpus was filtered and constrained strictly within a valid temporal boundary ($0.4\text{s} \le t \le 30.0\text{s}$) to eliminate corrupted clips and prevent gradient anomalies.
• Stratified Domain Balancing: To prevent dominant domains (e.g., studio read speech) from overpowering underrepresented, challenging acoustic domains (e.g., elderly speech, spontaneous podcasts, or emotional expressions), a stratified sampling quota was enforced, yielding an evenly balanced acoustic representation across all training steps.

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💻 Inference Guide

Installation

pip install torch torchaudio nemo_toolkit[asr] soundfile