cohere-transcribe-03-2026-onnx

Cohere Transcribe (03-2026) — ONNX export for Vernacula

ONNX export of CohereLabs/cohere-transcribe-03-2026, a 2B-parameter Conformer encoder + Transformer decoder ASR model covering 14 languages, packaged for use with Vernacula and ONNX Runtime.

• Conversion script: scripts/cohere_export/
• Vernacula: github.com/christopherthompson81/vernacula
• Upstream model: CohereLabs/cohere-transcribe-03-2026

Highlights

• 38× realtime on RTX 3090 (RTF 0.026) via duration-sorted batched segments and cudaMemGetInfo-driven batch sizing (B ≤ 32, 2 GB safety margin). Decoder weights read once per step, amortised across the batch.
• 48-layer positional-embedding deduplication. The encoder was patched to share PE across layers and constant folding was disabled to prevent per-layer duplication: the export size went from ~7 GB to ~120 MB without changing semantics.
• input_lengths masking on a re-exported encoder kills padding contamination. At a 2× length ratio in a batch, the original global self-attention catastrophically looped on shorter segments; the patched attention masks padded positions pre-softmax.
• Float32 encoder + float16 KV-cache hybrid. A float16 encoder hallucinated on low-energy segments; bfloat16 was blocked by the ONNX Conv opset gap. The KV cache cast to f16 halves footprint, with logits cast back to f32 at the graph boundary.
• IOBinding output ordering matters. Decoder outputs must be bound in grouped passes (all self_key, then all self_val, then all cross_key, then all cross_val) — interleaving causes MatMul dim mismatches on step 1.
• torch.export dynamo path with KV-cache split as default. Opset auto-clamped to ≥ 18 (onnxscript lacks v17 adapters for some ops). Encoder parity tightened from 5.83e-4 to 3e-6, decoder-step from 3.18e-2 to 4.8e-5 vs the legacy TorchScript path on fp32.

Contents

Export provenance

Exported via scripts/cohere_export/ in the Vernacula repo. The decoder is split into a prefill graph (decoder_init.onnx) and a per-step graph (decoder_step.onnx) so autoregressive generation reuses cached attention KV tensors instead of recomputing them on every token — typical KV-cache decoder layout for ORT.

Performance

Measured on a local RTX 3090 with Vernacula's batched ONNX pipeline:

• 600 s audio / 157 segments
• VAD: 2.0 s
• ASR: 13.5 s
• Total pipeline: 15.5 s
• RTF: 0.026 (≈ 38× realtime)

Uses batched encoder inference plus the KV-cache decoder split.

License

Apache-2.0, inherited from the upstream CohereLabs/cohere-transcribe-03-2026 model. The upstream HF repo is gated (one-time acceptance flow before download), but the license itself is permissive — commercial use is allowed with attribution.

Using these files

In Vernacula, select Cohere Transcribe as the ASR backend in Settings and the package will be downloaded and verified against manifest.json automatically. Outside Vernacula, pull with huggingface_hub and load with onnxruntime:

from huggingface_hub import snapshot_download
path = snapshot_download(repo_id="christopherthompson81/cohere-transcribe-03-2026-onnx")

The decoder split (decoder_init.onnx + decoder_step.onnx) follows the standard ORT KV-cache contract — see scripts/cohere_export/README.md for the input / output tensor names and shapes.

Limitations

Numerical behavior matches the upstream CohereLabs/cohere-transcribe-03-2026 model. Language coverage (14 languages, listed above), accuracy, and known failure modes inherit from the upstream model card.

Citation

@misc{julian_mack_2026,
    author       = { Julian Mack and Ekagra Ranjan and Walter Beller-Morales and Bharat Venkitesh and Pierre Richemond },
    title        = { cohere-transcribe-03-2026 (Revision d96e814) },
    year         = 2026,
    url          = { https://huggingface.co/CohereLabs/cohere-transcribe-03-2026 },
    doi          = { 10.57967/hf/8653 },
    publisher    = { Hugging Face }
}

Acknowledgments

• Original model: Cohere Labs
• ONNX repackaging: Chris Thompson for Vernacula

Issues with the ONNX export specifically: open an issue on the Vernacula repo. Issues with the underlying model: contact [email protected] or open an issue on the upstream model card.

See also

• Vernacula on GitHub — the speech pipeline app this package is built for
• Conversion script (scripts/cohere_export/) — the export pipeline that produced these files
• CohereLabs/cohere-transcribe-03-2026 — upstream model card
• Cohere Transcribe demo Space — official upstream demo
• Other Vernacula model packages