This is both good and bad. Good ASR can often understand low quality / garbled speech that I could not figure out, but it also "over corrects" sometimes and replaces correct but low prior words with incorrect but much more common ones.

With OCR the risk is you get another xerox[1] incident where all your data looks plausible but is incorrect. Hope you kept the originals!

(This is why for my personal doc scans, I use OCR only for full text search, but retain the original raw scans forever)

[1] https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...

This is exactly the case today. Multimodal LLMs like gpt-4o-transcribe are way better than traditional ASR, not only because of deeper understanding but because of the ability to actually prompt it with your company's specific terminology, org chart, etc.

For example, if the prompt includes that Caitlin is an accountant and Kaitlyn is an engineer, if you transcribe "Tell Kaitlyn to review my PR" it will know who you're referring to. That's something WER doesn't really capture.

BTW, I built an open-source Mac tool for using gpt-4o-transcribe with an OpenAI API key and custom prompts: https://github.com/corlinp/voibe

Why are you 'worried' about it? Shouldn't we strive for better technology even if it means some will 'lose'?

Even in the commercial space, there’s a lack of production grade ASR APIs that support diarization and word level timestamps.

My experiences with Google’s Chirp have been horrendous, with it sometimes skipping sections of speech entirely, hallucinating speech where the audio contains noise, and unreliable word level timestamps. And this all is even with using their new audio prefiltering feature.

AWS works slightly better, but also has trouble with keeping word level timestamps in sync.

Whisper is nice but hallucinates regularly.

OpenAI’s new transcription models are delivering accurate output but do not support word level timestamps…

A lot of this could be worked around by sending the resulting transcripts through a few layers of post processing, but… I just want to pay for an API that is reliable and saves me from doing all that work.

WhisperX is not a model but a software package built around Whisper and some other models, including diarization and alignment ones. Something similar will be built around the Cohere Transcribe model, maybe even just an integration to WhisperX itself.

For podcasts there is this https://news.ycombinator.com/item?id=47584376

I would try Qwen-ASR: https://qwen.ai/blog?id=qwen3asr

See the very bottom of the page for a transcription with timestamps.

There is also: https://github.com/linto-ai/whisper-timestamped

It doesn't use an extra model (so it supports every language that works with Whisper out of the box and use less memory), it works by applying Dynamic Time Warping to cross-attention weights.

can u comment on overall quality? their models tend to be a bit smaller and less performant overall.

Try two newlines between each one

Files can be downloaded here: https://huggingface.co/CohereLabs/cohere-transcribe-03-2026/...

And someone has already converted it to onnx format: https://huggingface.co/eschmidbauer/cohere-transcribe-03-202... - so it can be run on CPU instead of GPU.

Most use definition is just awailable weigths.

This kids make sense because "compiling" (training) the model cost inhibitly much, and we can still benefit from the artifacts.

I presume it means the model itself.

Looking at the ASR leaderboard (https://huggingface.co/spaces/hf-audio/open_asr_leaderboard), Parakeet (.6B) is still near the top on speed, but about 10th on WER.

Well, to clarify, it is both larger than parakeet in parameter count (parakeet is available in 0.6B and 1.1B), since it's 2B params, and also performs better than it on the benchmarks that hugging face publishes on the openASR leaderboard

There are many open source STT models that can run locally on Mac with good performance, such as whisper and Parakeet

If you have to ask you dont really need the answer.

Seems to not be to difficult in finding or creating training code. So a pretty decent amount of high quality training data should be many hours. And a few hours in high end data enter GPU compute, and many iterations to get it right.

It includes several European languages.

Can you clarify? I tested a few and they are rubbish and don't have the same features.