---
language:
  - ar
  - en
tags:
  - speech-recognition
  - automatic-speech-recognition
  - onnx
  - sherpa-onnx
  - cohere-asr
  - arabic
base_model: CohereLabs/cohere-transcribe-arabic-07-2026
library_name: transformers
pipeline_tag: automatic-speech-recognition
---

# cohere-transcribe-arabic-07-2026 (ONNX, sherpa-onnx format)

ONNX export of [`CohereLabs/cohere-transcribe-arabic-07-2026`](https://huggingface.co/CohereLabs/cohere-transcribe-arabic-07-2026)
for offline inference with **sherpa-onnx** (`OfflineCohereTranscribeModelConfig`).

## Files

The `onnx/` folder contains the exported graphs. Each variant has an encoder +
decoder + shared `tokens.txt`:

| Variant | Encoder | Decoder | Notes |
|---|---|---|---|
| **q4f16** | `encoder.q4f16.onnx` + `.onnx_data` | `decoder.q4f16.onnx` + `.onnx_data` | 4-bit + fp16 (~1.5 GB) |
| **int8** | `encoder.int8.onnx` + `.onnx_data` | `decoder.int8.onnx` + `.onnx_data` | dynamic int8 (~3 GB) |

Each `*.onnx` is the graph; the `*.onnx_data` sidecar holds the weights (external
data format, since a 2B-param graph exceeds ONNX's 2 GB protobuf limit).

## sherpa-onnx I/O contract

- **Encoder**: 1 input (mel features) -> 2 outputs (`cross_k`, `cross_v`),
  each `[8, batch, 8, seq, 128]`. Runs once per utterance.
- **Decoder**: 6 inputs (`tokens, self_k, self_v, cross_k, cross_v, offset`)
  -> 3 outputs (`logits, self_k_out, self_v_out`).
- The encoder precomputes the cross-attention K/V; the decoder reuses them
  statically and only maintains its own self-attention cache.
- `model_type` metadata = `cohere-transcribe`.

## Usage (sherpa-onnx, Python)

```python
import sherpa_onnx
recognizer = sherpa_onnx.OfflineRecognizer.from_cohere_transcribe(
    encoder="./onnx/encoder.q4f16.onnx",
    decoder="./onnx/decoder.q4f16.onnx",
    tokens="./onnx/tokens.txt",
    debug=False,
)
stream = recognizer.create_stream()
stream.accept_waveform(16000, audio_samples)
stream.set_option("language", "ar")
recognizer.decode_stream(stream)
print(stream.result.text)
```

## How this was produced

Exported with `tools/cohere-conversion/convert_direct.py` (direct
`torch.onnx.export`, no optimum) from transformers 5.14.1 + torch 2.10.0.
Run `convert_direct.py --variant q4f16` (or `int8`) then `upload_to_hub.py`.

## License

Derived from the gated `CohereLabs/cohere-transcribe-arabic-07-2026`. See that
model's license terms.