Cohere Transcribe 03-2026 vs Ming-3.0-audio-flash

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.

Ming-3.0-audio-flash

Ming-3.0-audio-flash is a cousin for Chinese and English transcription, questions about long audio and spoken replies in built-in or cloned voices, under MIT. It needs about 260 GB on a multi-GPU server, with inclusionAI's own runtime branches or Transformers remote code. The main risks are running repository code to load it and no published safety results.

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

Ming-3.0-audio-flash

1. Start it with inclusionAI's recipe, hf download inclusionAI/Ming-3.0-audio-flash --local-dir inclusionAI/Ming-3.0-audio-flash && export MODEL_PATH="$PWD/inclusionAI/Ming-3.0-audio-flash" && pip install -r requirements-vllm-omni.txt && MING_OUTPUT_MODE=asr ./scripts/serve_vllm_omni.sh "$MODEL_PATH", run from a clone of github.com/inclusionAI/Ming-3.0-audio-flash
2. The 128K profile uses tensor parallelism over eight 80 GB H800 GPUs, the SGLang profile four and SGLang-Omni's cookbook two. It doesn't fit the 107.4 GB reference machine
3. Use MING_OUTPUT_MODE=asr with the validated prompt and greedy decoding for transcripts, and split the language label from the text at the tab
4. Use MING_OUTPUT_MODE=text for audio questions and the Talker profile with modalities=["text", "audio"] for spoken replies
5. Send audio as a base64 data URL in an audio_url item for vLLM-Omni, or as input_audio for SGLang
6. Pick a voice with audio.voice and an id, or clone one with ref_audio and its exact prompt_text, never both
7. Turn thinking on per request with chat_template_kwargs {"enable_thinking": true}, it's off by default and the reasoning comes back in think tags
8. Tool calls follow the template's <tool_call> with <arg_key> and <arg_value> pairs, and no parser is named, so parse them yourself
9. Review the repository's Python and the talker/ code, .pyc files included, before loading with trust_remote_code