Provides a comprehensive benchmark for instruction-based audio editing across seven audio modalities and eight operation types, with 2,000 high-fidelity samples and a rubric that decomposes tasks into 17,741 verifiable criteria for multi-dimensional evaluation.
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 Dataset2026
Provides kanji-level evaluation data for Japanese TTS: disambiguated sentence contexts targeting 4,378 kanji-reading pairs (2,136 Jōyō kanji) with 13,095 native-speaker–verified sentences and katakana-marked ground-truth readings for kanji-level error metrics.
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 Audio2026
Generates streaming, low‑latency neural speech for real‑time dialogue by autoregressively producing audio frames as text arrives; joint text–speech training preserves natural prosody. Optimized for vLLM streaming (~50 ms first chunk), supports short‑clip voice cloning and four languages.
AI Audio2026
Local English text-to-waveform TTS producing a single fixed synthetic voice in a deployable package below 10M parameters. Offers deterministic seeds, punctuation-aware long-text chunking, CPU/CUDA and ONNX runtime options, measured evaluations and a compact FP32 footprint; English-only, one voice.
AI Audio2026
Generates English speech locally from text into 24 kHz waveforms with a fixed synthetic male voice. Complete text-to-waveform TTS under ~4M parameters (≈16 MB FP32), supports CPU/CUDA inference, deterministic seeds, long-text chunking and an ONNX export path under Apache-2.0 license.
AI Dataset2026
Provides ~1,467 single-speaker Sanskrit chant audio clips (≈5.3 hours) with aligned transcripts and prosodic metadata for meter-aware TTS training. Two recording/config styles (style_a/style_b), 24 kHz mono WAVs, metadata includes Devanagari, SLP1, Kannada text, meter, duration, session/take. CC-BY-4.0.
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
Multilingual neural text-to-speech model (0.6B params) with zero-shot voice cloning and a bundled 44.1 kHz codec. Preview release targets 11 recommended languages and aims to deliver near-SOTA quality in a compact checkpoint suited for voice cloning and multilingual TTS prototypes.
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.