from Service Now AI: Apriel Voice 27B Preview An open model for speech, reasoning, and tool use from ServiceNow Apriel Voice 27B Preview understands spoken requests, reasons over conversational context, and generates text responses and tool calls through a shared Gemma 4 backbone. It adds a speech interface while retaining text and image input capabilities. Pair it with an external speech synthesizer to build interactive voice assistants, customer support workflows, and spoken tool use. Try Apriel Live · Read the blog · Technical report Model capabilities Capability What it supports Speech understanding Transcription, translation, and spoken question answering. Reasoning and tool use Following instructions, handling multi-step requests, and generating structured tool calls. Multimodal context Audio, text, images, and tool results in a shared conversation. Voice applications Text responses paired with external speech synthesis and application-managed turn-taking. The model supports transcription, speech translation, spoken instruction following, reasoning, and function calling. The application executes tools and returns their results to the conversation. Real-time voice interaction is supported by the application and serving stack; the checkpoint itself generates text and tool calls rather than native full-duplex speech. A Whisper large-v3-turbo speech encoder connects to the Gemma 4 26B-A4B backbone through a learned projector. Training first aligns this speech interface, then adapts it with the backbone using speech tasks and text replay. Cross-entropy and teacher-distillation supervision vary by data source; the technical report describes the full recipe. Long audio The processor automatically splits longer 16 kHz mono recordings into 30-second chunks. In testing, we observed good performance on continuous recordings up to around 30 minutes. Longer recordings are supported, but performance can decline as duration grows; thinking mode is recommended for longer audio. Audio chunks and the rest of the conversation share the total context budget. Practical input length also depends on GPU memory and the encoder cache. The serving examples below include settings for 30-minute audio, with optional settings for one-hour input. English results Apriel Voice achieves 51.27% EVA-A, 74.13% EVA-X, and 65.51% τ-Voice task success, with 1.91 seconds mean τ-Voice response latency. On LibriSpeech, word error rate is 2.23% on test-clean and 5.15% on test-other; lower WER is better. System Type Source EVA-A ↑ EVA-X ↑ τ-Voice pass@1 ↑ Latency (s) ↓ GPT Realtime 2.1 S2S Proprietary 59.03 61.6 69.82 1.55 Gemini-3.8 Live Extended Thinking S2S Proprietary 54.82 73.5 84.19 1.50 Flux + Gemma 4-26B 3-stage Mixed 36.07 29.22 65.92 2.92 Parakeet + Gemma 4-26B 3-stage Open source 39.97 3.93 67.78 3.01 UltraVox v0.6 (Qwen) 2-stage Open source 8.94 1.91 26.80 3.95 Gemma 4-12B-IT 2-stage Open source 13.25 5.97 24.20 3.61 Audex-30B-A3B 2-stage Open source 5.66 8.2 17.15 2.83 Apriel Voice 27B Preview 2-stage Open source 51.27 74.13 65.51 1.91 EVA-A measures service accuracy and EVA-X measures conversational experience. EVA-A/X and τ-Voice pass@1 use a 0–100 scale; higher is better. Headline EVA and τ-Voice results use equal-domain means, rather than weighting domains by their number of cases. Apriel Voice uses thinking mode for these evaluations. S2S denotes native speech-to-speech systems, which use their own synthesis. Three-stage systems use ASR + LLM + TTS; two-stage systems connect a speech interface to a reasoning backbone and use external synthesis. All evaluated two- and three-stage systems use Cartesia Sonic 3.5. Latency describes the complete evaluated voice system, including response generation and synthesis, and depends on deployment settings. The radar's audio reasoning axis is the mean of eight benchmarks: BFCL, Big Bench Audio, IFEval, MT-Bench, Spoken SQuAD, Alpaca Audio, OpenHermes, and GSM8K (85.84 for Apriel Voice). Its responsiveness axis transforms latency into a higher-is-better score; it is not a measured accuracy. For latency t in seconds, the score is 100 × clip((4.0 − t) / (4.0 − 0.25), 0, 1). Text reasoning and tool use These results use text input in thinking mode for both Gemma 4 26B-A4B and Apriel Voice. Scores use a 0–100 scale; higher is better. The τ² result is the mean across three domains with GPT-5.2 as the user simulator. Benchmark Gemma 4 26B-A4B Apriel Voice MMLU-Pro 85.27 84.42 GPQA Diamond 77.80 80.40 AIME 2024 92.00 92.00 AIME 2025 81.67 81.67 MATH-500 92.76 92.88 AMC 2023 100.00 100.00 τ² (GPT-5.2 user simulator) 68.93 70.95 English speech recognition Word error rate (WER, %) on selected English benchmarks; lower is better. Benchmark Apriel Voice WER ↓ LibriSpeech test-clean 2.23 LibriSpeech test-other 5.15 GigaSpeech 10.20 μ-Bench (en-US) 10.76 FLEURS (en-US) 3.55 Earnings21 11.25 Speech reasoning and instruction following These results use audio input in thinking mode. Scores use a 0–100 scale; higher is better. The mean gives equal weight to each of the eight benchmarks. Benchmark Apriel Voice ↑ BFCL 81.13 Big Bench Audio 98.90 IFEval 86.47 MT-Bench 86.19 Spoken SQuAD 82.24 Alpaca Audio 76.8 OpenHermes 81.8 GSM8K 93.18 See the technical report for domain breakdowns, evaluation protocols, and text and image evaluations.   submitted by   /u/jacek2023 [link]   [comments]