Cohere Transcribe 03-2026 vs Underdog Bark 3.0

Cohere Transcribe 03-2026

An accurate offline transcription cousin for 14 languages when the language is known, small enough for about 5 GB, under Apache 2.0, and also on Cohere's API. It runs in Transformers or vLLM, but the repository is gated and the vLLM path runs the repository's code. Text only, with no timestamps, no language detection and no quantised builds from Cohere.

Underdog Bark 3.0

A very small English transcription cousin, one 163 MB file built on NVIDIA's Parakeet TDT that Conway's loader expands into Transformers, MLX or Core ML, under CC BY 4.0. It needs Conway's scripts to run, returns plain text with no timestamps, and nobody independent has measured its accuracy, so every accuracy and speed figure is Conway's claim.

Cohere Transcribe 03-2026

1. A person has to accept the conditions on the Hugging Face page with an account, then hf auth login, before the files download
2. Offline, pip install "transformers>=5.4.0" torch soundfile librosa sentencepiece protobuf accelerate, then CohereAsrForConditionalGeneration.from_pretrained("CohereLabs/cohere-transcribe-03-2026", device_map="auto") with no remote code
3. Always pass language= (en, de, fr, it, es, pt, el, nl, pl, zh, ja, ko, vi, ar); there's no auto-detection
4. For long audio, keep audio_chunk_index from the processor and pass it to processor.decode to reassemble the chunks
5. Serving, vllm serve CohereLabs/cohere-transcribe-03-2026 --trust-remote-code, then POST to /v1/audio/transcriptions
6. Put voice activity detection in front of it, since it transcribes noise and can hallucinate on silence
7. Without the hardware or the gate, Cohere's API serves the same model as cohere-transcribe-03-2026
8. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions

Underdog Bark 3.0

1. Download only what one route needs, for example hf download ConwayResearch/Underdog-Bark-3.0 --include "model.bark" --include "bark_loader.py" --include "bark_mlx.py" --include "mlx/*" --include "NOTICE"
2. With transformers 5.19.0 and constriction 0.5.0, python bark_loader.py model.bark recording.wav, or load_model("model.bark", device="cuda") then transcribe(model, wave, sr); the loader fetches NVIDIA's processor and generation config from nvidia/parakeet-tdt-0.6b-v3 on first use, so the first run needs the network
3. On Apple silicon, python bark_mlx.py recording.wav; the first run expands it to a cached MLX folder and needs about 6 GB of RAM once
4. Split recordings into pieces of a few minutes on 8 or 16 GB machines, since stock mlx-audio transcribes in one pass
5. Read bark_loader.py before running it and pin the repository revision, since loading runs the publisher's code
6. Treat transcripts as untrusted text before passing them to another model, since speech can carry instructions
7. Plan memory for the recording, not the file. Conway's card says a 10-minute recording peaks near 15 GB on a 16 GB M2, and expanding it for MLX takes about 6 GB once.