AI Dataset2026
Around 80K short audio clips paired with transcripts in JSON, organized for easy loading with the Hugging Face datasets ecosystem—designed for short-form speech tasks (ASR, TTS, fine-tuning) and quick prototyping with common Python data tools.
AI Dataset2026
End-to-end evaluation framework for conversational voice agents that runs bot-to-bot audio simulations and scores agents on task accuracy (EVA-A) and interaction experience (EVA-X). Includes per-scenario backend state, accent/noise perturbations, and 213 scenarios across airline, healthcare HR, and enterprise IT domains.
AI Model2026
Adds discrete audio tokens and an audio encoder to a 30B MoE text LLM so a single model can perform ASR, speech translation, TTS, text-to-audio and speech-to-speech while preserving text reasoning and long-context capabilities; supports thinking/instruct modes and up to 1M-token context.
AI Audio2026
Transcribes Arabic speech to text using a CohereLabs-trained ASR model compatible with the Hugging Face Transformers pipeline. Provides safetensors weights, endpoint compatibility and a DOI-tagged release; suitable for Arabic transcription workflows but may require adaptation for diverse dialects or noisy audio.
AI Dataset2026
Provides over 1.1M hours of high-bandwidth, multichannel multilingual speech with segment- and word-level timestamps, English translations, and per-file metadata for ASR, TTS and audio-representation research. Preserves original 48kHz multichannel OPUS audio and is released under CC BY 3.0.
AI Audio2026
Multilingual, real-time ASR for edge CPUs that uses heterogeneous quantization to reduce model size (4.62→1.58 GB) and lower inference latency. Trades some accuracy for 1.6–2.3× faster inference vs. Whisper.cpp and real-time capability on a few CPU threads, making it suitable for memory- and compute-constrained on-device transcription.
AI Dataset2026
Benchmark for joint speaker diarization and speaker-attributed ASR across all 22 scheduled Indian languages, providing ~108 hours of human-corrected, time-aligned, speaker-attributed transcripts. Includes near-field, far-field and in-the-wild recordings with code-mixing and speaker overlap.
AI Audio2026
An end-to-end 11B full-duplex speech model for real-time conversational AI that jointly performs streaming speech understanding and generation, enabling ~450 ms turn-taking, barge‑in and live tool calling in a single unified architecture; research use only.
AI Dataset2026
100-hour, single-narrator Egyptian Arabic speech corpus with 15,653 aligned clips at 24 kHz for TTS and ASR fine-tuning; studio-consistent audio, machine-generated undiacritized transcripts, CC BY-NC 4.0 (research/non-commercial use).
AI Model2026
Converts raw ASR transcripts into clean written text: adds punctuation and capitalization, expands spoken numbers/dates/times/currencies/emails, removes fillers and resolves self-corrections. Fine-tuned from Qwen3-0.6B (≈0.6B params), 94.8% token accuracy on a 7,519-case English test set; designed for CPU/edge deployment and deterministic post-processing.
AI Model2026
Open-weights LLM fine-tuned for phone-based voice agents that prioritizes low latency and reliable tool/function calling. Based on NVIDIA Nemotron 3 Nano (30B total, 3.5B active), supports very long contexts (262,144 tokens) and recommends temperature=0 with thinking disabled for deployment.