granite-speech-5.0-turboctc-nc

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What granite-speech-5.0-turboctc-nc is

Granite Speech 5.0 470M TurboCTC NC Disclaimer: This model is intended for research and non-commercial use-only. See Granite Speech 5.0 Turbo CTC if interested in commercial use. Model Summary: Granite Speech 5.0 TurboCTC Non-commercial is a compact 470 million parameter English ASR model with very high inference speed that is released for research and noncommercial purposes only. We invite users to refer to granite-speech-5.0-470m-turboctc for other use cases. The model consists of a conformer acoustic encoder with block self-attention, self-conditioning and temporal downsampling with an output layer corresponding to 16,384 BPE units. It was trained on approximately 75,000 hours of English audio from public corpora using Connectionist Temporal Classification (CTC) and inference is done non-autoregressively with greedy decoding. Evaluations: We evaluated granite-speech-5.0-470m-turboctc-nc on standard short-form English ASR benchmarks from the Open ASR leaderboard: Performance on the Open ASR leaderboard (official results as of August 25, 2026, public test sets only, RTFx measured on 1 H200): Performance on noisy and reverberant speech from the FFASR leaderboard (official results as of August 25, 2026, RTFx measured on 1 L4 GPU) Release Date: August 25, 2026 License: CC-BY-NC-SA-4.0 Supported Languages: English Intended Use: Use of this model is governed by the terms of the CC-BY-NC-SA-4.0 license. Usage: Granite Speech 5.0 TurboCTC NC is supported natively in transformers=5.16.0: Model Architecture: The architecture of granite-speech-5.0-470m-turboctc-nc consists of 16 conformer blocks trained with Connectionist Temporal Classification (CTC) with a 16,384 BPE classification head (see configuration below). We perform temporal subsampling by a factor of 8 to reduce frame rates from 100Hz to 12.5Hz: first by stacking and skipping consecutive logmel+delta frames (2x) followed by strided convolutions and pooled residuals in the first two conformer blocks (4x) as shown in the figure below. In addition, the encoder uses block-attention with blocks of 128 frames and self-conditioned CTC from the middle layer. Training Data: Our training data is entirely comprised of pu

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys granite-speech-5-0-turboctc-nc for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (granite-speech-5-0-turboctc-nc below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="granite-speech-5-0-turboctc-nc" -F file=@audio.mp3

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