--- title: "Phonon-2.5 English" description: "Phonon-2.5 English downloads, benchmarks and runtimes. A 158 MB open speech recognition model for English at 4.82 % average word error on the seven Open ASR Leaderboard sets, for Mac, Linux, Windows and NVIDIA GPUs." canonical: "https://www.fermionresearch.com/models/phonon-2-5-english/" source: "Fermion Research" --- # Phonon-2.5 English Our most accurate English speech recognition model, in a 158 MB download. 4.82 % average word error on the seven Open ASR Leaderboard sets, better than Phonon-2 on all seven. ## Overview Phonon-2.5 English is an open speech recognition model for English in a 158 MB download. Across the Open ASR Leaderboard’s seven English sets it averages 4.82 % word error, against 4.94 % for Underdog Bark 3.0, the other low-bit model of this size, and 5.21 % for Phonon-2. It is better than Phonon-2 on all seven sets, and it is our most accurate English model. Some workloads want English text and nothing else: dictation, captions, compliance and search pipelines built around English. Phonon-2.5 English is for those. The same file runs on Apple silicon and on Linux (x86-64 and Arm) and Windows CPUs, with no GPU needed, on NVIDIA GPUs and in the CPU and CUDA Docker images. It writes punctuated, capitalized text with a start and end time for every word, and the weights are released under CC-BY-4.0. For 25 languages, [Phonon-2.5](https://www.fermionresearch.com/models/phonon-2-5/) is the same size. ## Models Phonon-2.5 English is a single file. Phonon-2.5, the 25-language model, and Phonon-2 remain available beside it. [The Phonon-2.5 spec](https://www.fermionresearch.com/models/phonon-2-5/)[The Phonon-2 spec](https://www.fermionresearch.com/models/phonon-2/) ## Evaluation Phonon-2.5 English averages 4.82 % word error on the seven public test sets of the Open ASR Leaderboard, scored with the board’s own code on the full test sets, with Earnings-22 as the leaderboard’s chunked set. Table 1 sets it beside Phonon-2.5, Phonon-2, Underdog Bark 3.0, Parakeet Redux and the 2,508 MB Parakeet TDT 0.6B v3 the Phonon-2 family derives from. | Model | Download | LS clean | LS other | AMI | Earnings-22 | GigaSpeech | SPGISpeech | VoxPopuli | Average | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Phonon-2.5 English | 158 MB | 1.64 | 3.56 | 8.76 | 6.22 | 7.99 | 3.31 | 2.28 | 4.82 | | Phonon-2.525 languages | 156 MB | 1.60 | 3.41 | 9.12 | 6.72 | 8.04 | 3.31 | 3.23 | 5.06 | | Phonon-2 | 164 MB | 1.72 | 3.92 | 9.37 | 6.96 | 8.35 | 3.70 | 2.46 | 5.21 | | Underdog Bark 3.0 | 163 MB | 1.89 | 4.08 | 7.82 | 7.05 | 7.95 | 3.72 | 2.05 | 4.94 | | Parakeet TDT 0.6B v3† | 2,508 MB | 1.52 | 3.13 | 9.42 | 5.85 | 7.99 | 3.63 | 3.19 | 4.96 | | Parakeet Redux | 178 MB | 1.94 | 4.35 | 9.16 | 7.90 | 8.62 | 4.01 | 3.87 | 5.69 | - Phonon-2.5 English158 MB LS clean1.64 LS other3.56 AMI8.76 Earnings-226.22 GigaSpeech7.99 SPGISpeech3.31 VoxPopuli2.28 Average4.82 - Phonon-2.525 languages156 MB LS clean1.60 LS other3.41 AMI9.12 Earnings-226.72 GigaSpeech8.04 SPGISpeech3.31 VoxPopuli3.23 Average5.06 - Phonon-2164 MB LS clean1.72 LS other3.92 AMI9.37 Earnings-226.96 GigaSpeech8.35 SPGISpeech3.70 VoxPopuli2.46 Average5.21 - Underdog Bark 3.0163 MB LS clean1.89 LS other4.08 AMI7.82 Earnings-227.05 GigaSpeech7.95 SPGISpeech3.72 VoxPopuli2.05 Average4.94 - Parakeet TDT 0.6B v3†2,508 MB LS clean1.52 LS other3.13 AMI9.42 Earnings-225.85 GigaSpeech7.99 SPGISpeech3.63 VoxPopuli3.19 Average4.96 - Parakeet Redux178 MB LS clean1.94 LS other4.35 AMI9.16 Earnings-227.90 GigaSpeech8.62 SPGISpeech4.01 VoxPopuli3.87 Average5.69 Table 1Word error rate, %, lower is better; the best value in each column is underlined. † The Open ASR Leaderboard’s published row; the other rows were scored with its code on the full test sets, with Earnings-22 as the leaderboard’s chunked set. The Phonon-2 row is the one published with its release. ## Speed Phonon-2.5 English has the architecture, the file format and the engines of Phonon-2, so it runs at about the same speed as Phonon-2, and it will get a lot faster in the coming weeks. For reference, these are Phonon-2’s published figures, from its release post: 174 times realtime on a base M5 MacBook Air, which is an hour of audio in about 20 seconds; 143 times realtime on eight AMD Zen 5 cores; and 6,680 times realtime batched on an H100. ## Run it Python 3.10 or newer. The fermion command line installs from PyPI; name the model as `phonon-2.5-en`. ```text $ pip install fermion-research $ fermion transcribe phonon-2.5-en meeting.wav $ fermion transcribe phonon-2.5-en meeting.wav --json $ fermion serve phonon-2.5-en --port 8010 ``` It transcribes files, adds a start and end time for every word under --json, and serves an OpenAI-compatible /v1/audio/transcriptions endpoint. ```text $ pip install "fermion-research[mlx]" ``` On Apple silicon it runs the model on the GPU through MLX. ```text $ pip install --no-deps torch --index-url https://download.pytorch.org/whl/cpu $ pip install fermion-research ``` On Linux (x86-64 and Arm) and Windows the same package runs the CPU engine; on Linux, install torch from its CPU wheel index first to skip the GPU build. [Weights on Hugging Face](https://huggingface.co/FermionResearch/Phonon-2.5-English)[Documentation](https://www.fermionresearch.com/docs/speech/)[fermion-research on PyPI](https://pypi.org/project/fermion-research/)[Engines on GitHub](https://github.com/fermionresearch/phonon) ## Availability ### License The weights are released under the Creative Commons Attribution 4.0 licence, which Phonon-2.5 English inherits from NVIDIA’s Parakeet TDT 0.6B v3; the repository’s NOTICE carries the attribution and lists the changes. The licence permits commercial use, modification and redistribution with attribution. The command line is released under Apache 2.0. ### Citation ```text @misc{fermionresearch2026phonon25english, title = {Phonon-2.5 English}, author = {{Fermion Research}}, year = {2026}, url = {https://fermionresearch.com/models/phonon-2-5-english/} } ``` ## Related research ![A field of warm mineral light gathering into a single bright band](https://www.fermionresearch.com/images/launch/phonon-hero.jpg) ResearchOctober 22, 2026 ### Introducing Phonon-2.5 Open speech recognition in 25 languages from a 156 MB download, and a new English model of the same size. Both run on a laptop CPU at the speed of Phonon-2. ![A field of warm mineral light gathering into a single bright band](https://www.fermionresearch.com/images/launch/phonon-hero.jpg) ResearchSeptember 29, 2026 ### Introducing Phonon-2 The most accurate open speech recognition model under 900 MB, in a 164 MB download that turns an hour of audio into text in about 20 seconds on a MacBook Air. Source: [https://www.fermionresearch.com/models/phonon-2-5-english/](https://www.fermionresearch.com/models/phonon-2-5-english/)