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Cohere Transcribe: Speech Recognition
41–50 of 77 posts
Re: Cohere Transcribe: Speech Recognition
#42Awesome. Going to see if I can port https://scrivvy.ai to this. based in Canada
Re: Cohere Transcribe: Speech Recognition
#43My worry is that ASR will end up like OCR. If the multi modal large AI system is good enough (latency wise), the advantage of domain understanding eats the other technlogies alive. In OCR, even when the characters are poorly scanned, the deep domain understanding these large multi modal AIs have allows it to understand what the document actually meant - this is going to be order id because in the million invoices I h…
Why are you 'worried' about it? Shouldn't we strive for better technology even if it means some will 'lose'?
Ideally, you'd be able to specify exactly what you want - do you want to write-out filled pauses ("aaah", "umm")? Do you want to get a transcription of the the disfluencies - re-starts, etc. or just get out a cleaned up version?
Re: Cohere Transcribe: Speech Recognition
#44notable omission of deepgram models in comparisons?
Re: Cohere Transcribe: Speech Recognition
#45notable omission of deepgram models in comparisons?
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Re: Cohere Transcribe: Speech Recognition
#46Ran it over our internal dataset of ~250 recordings of people saying british postcodes (all kinds of accents, etc) - it's competitive for sure!
Soniox (stt-async-v4): 176/248 (71.0%) ElevenLabs (scribe_v2): 170/248 (68.5%) AssemblyAI (universal-3-pro): 166/248 (66.9%) Deepgram (nova-3): 158/248 (63.7%) AssemblyAI (universal-2): 148/248 (59.7%) Cohere (transcribe-03-2026): 148/248 (59.7%) Speechmatics (enhanced): 134/248 (54.0%)
P.s. how do I get this to render correctly on here?
Re: Cohere Transcribe: Speech Recognition
#47Earlier quoted context omitted.
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 b…
Isn't Elevenlabs the best in this?
I've not tested their speech-to-text yet, but based on the docs it looks promising. Thanks for the suggestion!
Re: Cohere Transcribe: Speech Recognition
#48Re: Cohere Transcribe: Speech Recognition
#49My worry is that ASR will end up like OCR. If the multi modal large AI system is good enough (latency wise), the advantage of domain understanding eats the other technlogies alive. In OCR, even when the characters are poorly scanned, the deep domain understanding these large multi modal AIs have allows it to understand what the document actually meant - this is going to be order id because in the million invoices I h…
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'…
Many ASR models already support prompts/adding your own terminology. This one doesn't, but full LLMs especially such expensive ones aren't needed for that.
Re: Cohere Transcribe: Speech Recognition
#50Ran it over our internal dataset of ~250 recordings of people saying british postcodes (all kinds of accents, etc) - it's competitive for sure! Soniox (stt-async-v4): 176/248 (71.0%) ElevenLabs (scribe_v2): 170/248 (68.5%) AssemblyAI (universal-3-pro): 166/248 (66.9%) Deepgram (nova-3): 158/248 (63.7%) AssemblyAI (universal-2): 148/248 (59.7%) Cohere (transcribe-03-2026): 148/248 (59.7%) Speechmatics (enhanced): 134/…
Try two newlines between each one