anaksor-ocr
Sokheng · Kit
Reads Khmer and English text from documents, receipts and photos of signs, using a detector for text lines and a recogniser for each line. Suits developers who…
Transcribes speech to text entirely in your browser using WebGPU, with two Parakeet models of different sizes. Suits people who want local speech recognition and developers studying in-browser ASR.
Parakeet Redux WebGPU is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub hf download mrfakename/parakeet-redux-webgpu --repo-type space --local-dir parakeet-redux-webgpu cd parakeet-redux-webgpu
This Space runs speech recognition inside your browser. It loads one of two Parakeet models, a smaller one with a 1.58-bit encoder and a larger one, and turns audio into text with word timestamps. Nothing needs installing, because the page does the work on your own graphics card.
It suits people who want to transcribe audio without sending it to a server, and developers who want a working example of speech recognition in a web page.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Media | Editors and journalists | Transcribe interview clips and read the word timings | Find the exact moment a quote starts |
| Education | Teachers and students | Turn a recorded lecture into text | Get notes from a short spoken lesson |
| Software | Web developers | Study how a speech model runs in a page | Read the worker and app scripts for the pipeline |
| Research | ML engineers | Compare a small and a large Parakeet model in the browser | Run the same clip through both models |
You can skip it if you need live captions in a finished product or support for a specific language not shown in its examples.
| Use case | Who | What they get back |
|---|---|---|
| Transcribe a clip | Anyone with audio | Text from the speech in the clip |
| Read word timings | Editors and developers | Timestamps for each word |
| Compare two models | ML engineers | The same audio recognized by a small and a large model |
You can use the online page with no setup, and a local copy needs only Python to download it.
pip install -U huggingface_hub hf download mrfakename/parakeet-redux-webgpu --repo-type space --local-dir parakeet-redux-webgpu cd parakeet-redux-webgpu
Open the page, choose the model you want and give it audio, then wait for the text. The first run downloads the model weights, and the browser keeps them so later runs start faster. The page also offers example clips to try first.
You pick the smaller model and load one of the example clips, such as a public-domain speech. The page returns the spoken words as text, with a timestamp on each word.
If you are new to this, open the page and try an example clip first. Seeing text appear from audio with nothing installed is the quickest way to learn what it does.
If you know the field, the page uses ONNX Runtime Web. The Conformer encoder runs on WebGPU with a WASM fallback, the transducer's prediction and joint network run on WASM, and the greedy decoding loop is plain JavaScript. Word times come from predicted token durations in 80 ms frames. Clips over 30 seconds get cut at the quietest spot in the 20 to 30 second range. Weights download with parallel range requests.
The smaller model has 149M parameters and the larger one has 627M, so the larger one needs more memory and time in the browser. Model weights stay in the browser cache after the first download.
Transcribes speech to text entirely in your browser using WebGPU, with two Parakeet models of different sizes. Suits people who want local speech recognition and developers studying in-browser ASR.
Made by mrfakename on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.
What it is: This Space runs speech recognition inside your browser. It loads one of two Parakeet models, a smaller one with a 1.58-bit encoder and a larger one, and turns audio into text with word timestamps. Nothing needs installing, because the page does the work on your own graphics card.
Who it suits: It suits people who want to transcribe audio without sending it to a server, and developers who want a working example of speech recognition in a web page.
Industry | Who uses it | How they use it | Example
Media | Editors and journalists | Transcribe interview clips and read the word timings | Find the exact moment a quote starts
Education | Teachers and students | Turn a recorded lecture into text | Get notes from a short spoken lesson
Software | Web developers | Study how a speech model runs in a page | Read the worker and app scripts for the pipeline
Research | ML engineers | Compare a small and a large Parakeet model in the browser | Run the same clip through both models
You can skip it if you need live captions in a finished product or support for a specific language not shown in its examples.
Where to use it:
Use case | Who | What they get back
Transcribe a clip | Anyone with audio | Text from the speech in the clip
Read word timings | Editors and developers | Timestamps for each word
Compare two models | ML engineers | The same audio recognized by a small and a large model
How to set it up: You can use the online page with no setup, and a local copy needs only Python to download it.
pip install -U huggingface_hub
hf download mrfakename/parakeet-redux-webgpu --repo-type space --local-dir parakeet-redux-webgpu
cd parakeet-redux-webgpu
How to use it: Open the page, choose the model you want and give it audio, then wait for the text. The first run downloads the model weights, and the browser keeps them so later runs start faster. The page also offers example clips to try first.
Example: You pick the smaller model and load one of the example clips, such as a public-domain speech. The page returns the spoken words as text, with a timestamp on each word.
For newcomers and experts: If you are new to this, open the page and try an example clip first. Seeing text appear from audio with nothing installed is the quickest way to learn what it does.
If you know the field, the page uses ONNX Runtime Web. The Conformer encoder runs on WebGPU with a WASM fallback, the transducer's prediction and joint network run on WASM, and the greedy decoding loop is plain JavaScript. Word times come from predicted token durations in 80 ms frames. Clips over 30 seconds get cut at the quietest spot in the 20 to 30 second range. Weights download with parallel range requests.
You load an audio clip
Long audio is split at a quiet point
The encoder runs on WebGPU
The decoder loop runs in JavaScript
Text and word times appear
Good to know: The smaller model has 149M parameters and the larger one has 627M, so the larger one needs more memory and time in the browser. Model weights stay in the browser cache after the first download.
Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.
README.md 4 KB
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