Cohere Transcribe 03-2026
June 28, 2026 · View on GitHub
Cohere's CohereLabs/cohere-transcribe-03-2026
ported to transcribe.cpp. A Conformer encoder with a Transformer encoder-decoder
head (cross-attention, tied token embedding).
What it's for
Offline multilingual speech-to-text covering 14 languages: English, French, German, Spanish, Italian, Portuguese, Dutch, Polish, Greek, Arabic, Japanese, Chinese, Vietnamese, Korean. The model takes a 16 kHz mono WAV and produces a transcript. Cohere uses an encoder-decoder with cross-attention, decoding is autoregressive.
See Cohere's model card for training data, intended use, and upstream evaluation methodology.
Licensed Apache-2.0. Ported from upstream commit
76b8b23,
pinned 2026-04-16.
Input limits
Accepts up to about 6.7 minutes (400 s) of 16 kHz mono audio per call — the
encoder's positional table is the binding limit. Longer audio is rejected up
front with TRANSCRIBE_ERR_INPUT_TOO_LONG rather than silently truncated; split
it into shorter segments. See the input-length contract.
Download
| Quantization | Download | Size | WER (LibriSpeech test-clean) |
|---|---|---|---|
| BF16 | cohere-transcribe-03-2026-BF16.gguf | 4.10 GB | 1.26% |
| F16 | cohere-transcribe-03-2026-F16.gguf | 4.11 GB | 1.26% |
| Q8_0 | cohere-transcribe-03-2026-Q8_0.gguf | 2.41 GB | 1.27% |
| Q6_K | cohere-transcribe-03-2026-Q6_K.gguf | 1.97 GB | 1.27% |
| Q5_K_M | cohere-transcribe-03-2026-Q5_K_M.gguf | 1.76 GB | 1.25% |
| Q4_K_M | cohere-transcribe-03-2026-Q4_K_M.gguf | 1.55 GB | 1.25% |
WER is measured on the full LibriSpeech test-clean split (2620 utterances)
with greedy decoding and no external LM. BF16 reference baseline: 1.26%.
Cohere's self-reported number on the same split is 1.25% (Open ASR Leaderboard,
as of 2026-03-26). Text normalizer: Whisper EnglishTextNormalizer — the same
normalizer the Open ASR Leaderboard uses, so the comparison is apples-to-apples.
Quick Start
cmake -B build
cmake --build build
build/bin/transcribe-cli \
-m models/cohere-transcribe-03-2026/cohere-transcribe-03-2026-F16.gguf \
samples/jfk.wav
If your audio is not already 16 kHz mono WAV, convert it first:
ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav
Performance
Cells are wall-clock latency (mean over 3 iterations after 1 warmup),
with speedup over realtime in parentheses. Units: ms below 1 s, s
above (2 decimal places).
Apple M4 Max
| Backend | Sample | Q8_0 | Q4_K_M |
|---|---|---|---|
| Metal | jfk (11.0s) | 150 ms (74×) | 154 ms (71×) |
| Metal | dots (35.3s) | 491 ms (72×) | 465 ms (76×) |
| CPU | jfk (11.0s) | 1.21 s (9×) | 1.05 s (11×) |
| CPU | dots (35.3s) | 4.13 s (9×) | 3.49 s (10×) |
macOS 26.4.1, transcribe.cpp e0fa0f6.
AMD Ryzen 7 4750U Pro
| Backend | Sample | Q8_0 | Q4_K_M |
|---|---|---|---|
| Vulkan | jfk (11.0s) | 1.43 s (8×) | 1.33 s (8×) |
| Vulkan | dots (35.3s) | 4.25 s (8×) | 4.25 s (8×) |
| CPU | jfk (11.0s) | 3.57 s (3×) | 2.90 s (4×) |
| CPU | dots (35.3s) | 12.40 s (3×) | 10.08 s (4×) |
Fedora 43, transcribe.cpp 2ab01b8. Vulkan device: AMD Radeon Graphics (RADV RENOIR).
Benchmark reproduction:
uv run scripts/bench/run.py \
--models cohere-transcribe-03-2026 \
--quants q8_0,q4_k_m \
--samples jfk,dots \
--backends metal,cpu,vulkan \
--iters 3 --warmup 1 \
--name cohere-transcribe-03-2026-publication
Numerical Validation
transcribe.cpp is validated tensor-by-tensor against the Transformers
reference implementation on samples/jfk.wav. All 22 checkpointed tensors
fall within family tolerance, and the final transcript matches the reference
verbatim. Last validated at commit
bf0d0b7.
| Field | Value |
|---|---|
| Reference | Transformers, CohereLabs/cohere-transcribe-03-2026 |
| Dump script | scripts/dump_reference_cohere_transformers.py |
| Manifest | tests/golden/cohere/cohere-transcribe-03-2026.manifest.json |
| Command | uv run scripts/validate.py compare --family cohere |
Selected tensors:
| Tensor | Max abs diff | Mean abs diff | Notes |
|---|---|---|---|
enc.mel.in | 2.678e-01 | 4.238e-03 | fp64 vs fp32 STFT precision gap |
enc.pre_encode.out | 8.794e+00 | 3.049e-02 | Mel gap propagated through pre-encoder |
enc.block.0.out | 4.542e+00 | 1.939e-02 | Early encoder |
enc.block.23.out | 3.173e+00 | 3.118e-02 | Mid-encoder |
enc.block.47.out | 2.039e-01 | 4.615e-03 | Final encoder block |
enc.final | 2.039e-01 | 4.615e-03 | Encoder output |
enc_dec_proj.out | 3.693e-01 | 1.074e-02 | Encoder→decoder projection |
dec.token_emb | 2.980e-08 | 2.910e-12 | Exact within fp32 round-off |
dec.pos_emb | 0.000e+00 | 0.000e+00 | Exact |
dec.embed_norm | 1.241e-01 | 1.327e-03 | LayerNorm output |
dec.block.0.out | 3.409e+00 | 2.491e-02 | Early decoder |
dec.block.7.out | 3.177e+01 | 1.416e-01 | Final decoder block (accumulated) |
dec.out_before_head | 2.465e-01 | 1.339e-02 | Pre-head projection |
dec.logits_raw | 6.675e-01 | 3.328e-02 | Raw logits |
dec.logits | nan | nan | Softmax: -inf entries produce nan; first diff index 114692 is masked |
The expected divergence is in the frontend: C++ runs the STFT in fp64 where the reference runs fp32. The gap enters at the mel spectrogram, propagates through the encoder, and attenuates to a few tenths by the final encoder block. Decoder numerics track the reference within fp32 round-off on the first autoregressive step.
Reproduction
Convert
Downloads the upstream HF repo via huggingface-cli (or an existing local
clone) and converts with the family-specific script. Output path is derived
from the repo id.
uv run --project scripts/envs/cohere \
scripts/convert-cohere.py CohereLabs/cohere-transcribe-03-2026
Quantize
Run transcribe-quantize once per target quant. Example for F16; repeat with
Q8_0, Q6_K, Q5_K_M, Q4_K_M:
build/bin/transcribe-quantize \
models/cohere-transcribe-03-2026/cohere-transcribe-03-2026-BF16.gguf \
models/cohere-transcribe-03-2026/cohere-transcribe-03-2026-F16.gguf \
--quant F16
Validate
uv run scripts/validate.py all --family cohere
Run real-model tests
cmake -B build -DTRANSCRIBE_BUILD_REAL_MODEL_TESTS=ON
cmake --build build
TRANSCRIBE_COHERE_GGUF=models/cohere-transcribe-03-2026/cohere-transcribe-03-2026-BF16.gguf \
ctest --test-dir build --output-on-failure -R 'cohere'