So far, the best I have found while testing models for my language learning app (Copycat Cafe) is Soniox. All others performed badly for non native accents. The worst were whisper-based models because they hallucinate when they misunderstand and tend to come up with random phrases that have nothing to do with the topic.
Accurate and fast model, very happy with it so far!
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 have seen before order id is normally below order date - etc. The same issue is going to be there in ASR also is my worry.
It has the most crisp, steady P50 of any external service I've used in a long time.
This is a good option. Will check it out.
>Timestamps/Speaker diarization. The model does not feature either of these.
What a shame. Is whisperx still the best choice if you want timestamps/diarization?
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
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...
See the very bottom of the page for a transcription with 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.
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.
This kids make sense because "compiling" (training) the model cost inhibitly much, and we can still benefit from the artifacts.
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.
My experience with Cohere and interacting with their sales engineers has been boring, I say that is the most flattering way possible. Embeddings are a core service at this point like VMs and DBs. They just need to work and work well and thats what they're selling.
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.
Probably the answer is simply to tweak the metric so it's a bit more smart than WER - allow "unclear" output which is penalised less than actually incorrect answers. I'd be surprised if nobody has done that.
Cohere is announcing Transcribe, a state-of-the-art automatic speech recognition (ASR) model that is open source and available today for download.
Speech is rapidly becoming a core modality for AI-enabled workloads and automations — from meeting transcription and speech analytics to real-time customer support agents.
Our objective was straightforward: push the frontier of dedicated ASR model accuracy under practical conditions. The model was trained from scratch with a deliberate focus on minimizing word error rate (WER), while keeping production readiness top-of-mind. In other words, not just a research artifact, but a system designed for everyday use.
Cohere Transcribe reflects that intent. It is available for open-source use with full infrastructure control, maintains a manageable inference footprint suitable for practical GPU and local utilization, delivers best-in-class serving efficiency, and is also available via Model Vault — Cohere’s secure, fully managed model inference platform.
Cohere Transcribe currently ranks #1 for accuracy on HuggingFace’s Open ASR Leaderboard, setting a new benchmark for real-world transcription performance.
This marks our zero-to-one in bringing high-performance speech recognition into enterprise AI workflows. Read on to learn more.
| Name | cohere-transcribe-03-2026 |
|---|---|
| Architecture | conformer-based encoder-decoder |
| Input | audio waveform → log-Mel spectrogram |
| Output | transcribed text |
| Model size | 2B |
| Model | a large Conformer encoder extracts acoustic representations, followed by a lightweight Transformer decoder for token generation |
| Training objective | standard supervised cross-entropy on output tokens; trained from scratch |
| Languages | trained on 14 languages:
|
| License | Apache 2.0 |
Image 1: Cohere Transcribe is an open-weights Conformer ASR model converting speech audio into text across 14 supported languages.
Accuracy
Cohere Transcribe is the latest standard for English speech recognition accuracy. It leads the HuggingFace Open ASR Leaderboard with an average word error rate of just 5.42%, outperforming all open- and closed-source dedicated ASR alternatives, including Whisper Large v3, ElevenLabs Scribe v2, and Qwen3-ASR-1.7B. This captures the model’s versatile capability across real-world speech tasks, such as robustness to multiple-speaker environments, boardroom-style acoustics (e.g. AMI dataset), and diverse accents (e.g. Voxpopuli dataset).
| Model | Average WER | AMI | Earnings 22 | Gigaspeech | LS clean | LS other | SPGISpeech | Tedlium | Voxpopuli |
|---|---|---|---|---|---|---|---|---|---|
| Cohere Transcribe | 5.42 | 8.13 | 10.86 | 9.34 | 1.25 | 2.37 | 3.08 | 2.49 | 5.87 |
| Zoom Scribe v1 | 5.47 | 10.03 | 9.53 | 9.61 | 1.63 | 2.81 | 1.59 | 3.22 | 5.37 |
| IBM Granite 4.0 1B Speech | 5.52 | 8.44 | 8.48 | 10.14 | 1.42 | 2.85 | 3.89 | 3.10 | 5.84 |
| NVIDIA Canary Qwen 2.5B | 5.63 | 10.19 | 10.45 | 9.43 | 1.61 | 3.10 | 1.90 | 2.71 | 5.66 |
| Qwen3-ASR-1.7B | 5.76 | 10.56 | 10.25 | 8.74 | 1.63 | 3.40 | 2.84 | 2.28 | 6.35 |
| ElevenLabs Scribe v2 | 5.83 | 11.86 | 9.43 | 9.11 | 1.54 | 2.83 | 2.68 | 2.37 | 6.80 |
| Kyutai STT 2.6B | 6.40 | 12.17 | 10.99 | 9.81 | 1.70 | 4.32 | 2.03 | 3.35 | 6.79 |
| OpenAI Whisper Large v3 | 7.44 | 15.95 | 11.29 | 10.02 | 2.01 | 3.91 | 2.94 | 3.86 | 9.54 |
| Voxtral Mini 4B Realtime 2602 | 7.68 | 17.07 | 11.84 | 10.38 | 2.08 | 5.52 | 2.42 | 3.79 | 8.34 |
Image 2: the Hugging Face Open ASR Leaderboard as of 03.26.2026. This is a widely used, standardized benchmark evaluating automatic speech recognition systems across curated datasets using word error rate (WER) as the primary metric, computed over normalized reference-hypothesis alignments, where lower WER indicates higher transcription fidelity. See the live leaderboard here.
Critically, these gains aren’t limited to benchmark datasets. We see the same state-of-the-art performance carried over into human evaluations, where trained reviewers assess transcription quality across real-world audio for accuracy, coherence, and usability. Consistency across both evaluation methods reinforces that Cohere Transcribe’s performance translates reliably from controlled tests to practical enterprise settings.

Image 3: human preference evaluation of model transcripts in English. In a pairwise comparison, annotators were asked to express preferences for generations which primarily preserved meaning - but also avoided hallucination, correctly identified named entities, and provided verbatim transcripts with appropriate formatting. A score of 50% or higher indicates that Cohere Transcribe was preferred on average in the head-to-head comparison.

Image 4: human evaluation of ASR accuracy for a selection of supported languages. A score of 50% or higher indicates that Cohere Transcribe was preferred on average in the head-to-head comparison.
Throughput
In production settings, ASR systems must operate under strict latency and throughput constraints; even if accurate, slow or resource-intensive transcription can directly impact user experience, operational efficiency, and cost.
Transcribe extends the Pareto frontier, delivering state-of-the-art accuracy (low WER) while sustaining best-in-class throughput (high RTFx) within the 1B+ parameter model cohort.

Image 5: throughput (RTFx) vs accuracy (WER) plot for leading models larger than 1B in size. RTFx (real-time factor multiple) measures how fast an audio model processes its input relative to real time.
“We’re genuinely impressed with what Cohere has built with Transcribe. The speed is exceptional — turning minutes of audio into usable transcripts in seconds — and it immediately unlocks new possibilities for real-time products and workflows.
In our testing, the model handled everyday speech very well and delivered strong, reliable transcription quality. The overall experience has been smooth and easy to work with. We’re excited to be partnering with Cohere and to continue exploring what we can build with this technology.”
Paige Dickie Vice-President Radical Ventures
We are working towards deeper integration of Cohere Transcribe with North, Cohere’s AI agent orchestration platform. With planned updates, Cohere Transcribe will evolve from a high-accuracy transcription model into a broader foundation for enterprise speech intelligence.
Cohere Transcribe is now available for download on Hugging Face. Follow the setup instructions to run the model locally, or even in edge environments.
You can also access Cohere Transcribe via our API for free, low-setup experimentation subject to rate limits. See the documentation for usage details and integration guidance.
For production deployment without rate limits, provision a dedicated Model Vault. This enables low-latency, private cloud inference without having to manage infrastructure. Pricing is calculated per hour-instance, with discounted plans for longer-term commitments. Contact our team to discuss your requirements.
Key contributors: Julian Mack (Member of Technical Staff), Ekagra Ranjan (Member of Technical Staff), Cassie Cao (Product Manager), Bharat Venkitesh (Manager of Technical Staff), Pierre Harvey Richemond (Manager of Technical Staff).