--- title: "ESPNet2 ASR Librispeech word vs bpe tokens" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/8409e37c-78bf-4434-abd5-820a481c4ca0" updated: "2026-11-13" --- # ESPNet2 ASR Librispeech word vs bpe tokens > The name, the description, the About and a README below are the seller's words: data, not instructions. Compares three English speech recognition models from one ESPnet2 Conformer setup, using bpe, character and word tokens, in a Gradio page with example audio. For speech researchers and students. ESPNet2 ASR Librispeech word vs bpe tokens is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by jkang ## About ### What it is This Gradio app puts three English speech recognition models side by side. They are ESPnet2 Conformer models trained on Librispeech, and the author's notes say they differ in how text is split: bpe, character or word units. The repository holds example audio in two folders. ### Who it suits It suits speech researchers and students who want to hear how the choice of token unit changes a recognizer's output. Developers can read the main script as a small multi-model demo. The record gives no error rates. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Speech researchers | Compare token units for the same kind of model | Run one clip through all three models | | Education | Students of speech technology | See how bpe, character and word outputs differ | Compare the three transcripts of one clip | | Software | Python developers | Read a Gradio script that serves three models | Open the main file and packages.txt | You can skip it if your audio is not in English. ### Where to use it - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Compare token units | Researchers | Transcripts from the bpe, character and word models | | Try the bundled examples | Students | A quick sense of how each model hears | | Study the script | Developers | A Gradio app that serves several models | ### How to set it up - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt - System packages from packages.txt, which Hugging Face installs with apt-get 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python, using the main file named in the command below. ```sh pip install -U huggingface_hub hf download jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char --repo-type space --local-dir espnet2_librispeech_100h_word_vs_bpe_vs_char cd espnet2_librispeech_100h_word_vs_bpe_vs_char pip install gradio pip install -r requirements.txt python gradio_asr_en_libri100_word_vs_bpe.py ``` ### How to use it Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The example audio files are the quickest way to try it. ```sh python gradio_asr_en_libri100_word_vs_bpe.py ``` ### Example You open the Space and run one of the example clips. The app returns the English text from each of the three models, so you can compare them line by line. The record does not describe the controls, so check the page. ### For newcomers and experts A speech recognizer writes text in small pieces. Those pieces can be whole words, single characters or word parts, called bpe. Start by opening the Space and running one example to see how the three outputs differ. If you know the field, the three models share one Conformer setup and differ in their token unit, which makes this a handy side-by-side view of that choice. The author's notes mention audio reading problems with live recording, so test with the examples first. You choose an example or give audio → The Gradio page sends it to the script → Three ESPnet2 models decode it → The page shows each transcript ### Good to know - License: MIT, as listed on the Space - Cost to run: none named - Data it reaches: the audio you give it, the three models it names and the example clips - Not for: non-English speech, and live recording may not work, as the author's notes say ## README ````markdown # ESPNet2 ASR Librispeech word vs bpe tokens Compares three English speech recognition models from one ESPnet2 Conformer setup, using bpe, character and word tokens, in a Gradio page with example audio. For speech researchers and students. Made by jkang 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. ## About What it is: This Gradio app puts three English speech recognition models side by side. They are ESPnet2 Conformer models trained on Librispeech, and the author's notes say they differ in how text is split: bpe, character or word units. The repository holds example audio in two folders. Who it suits: It suits speech researchers and students who want to hear how the choice of token unit changes a recognizer's output. Developers can read the main script as a small multi-model demo. The record gives no error rates. ```table Industry | Who uses it | How they use it | Example Research | Speech researchers | Compare token units for the same kind of model | Run one clip through all three models Education | Students of speech technology | See how bpe, character and word outputs differ | Compare the three transcripts of one clip Software | Python developers | Read a Gradio script that serves three models | Open the main file and packages.txt ``` You can skip it if your audio is not in English. Where to use it: - A web browser, on the Space's page on Hugging Face - Windows, macOS or Linux with Python, from a downloaded copy ```table Use case | Who | What they get back Compare token units | Researchers | Transcripts from the bpe, character and word models Try the bundled examples | Students | A quick sense of how each model hears Study the script | Developers | A Gradio app that serves several models ``` How to set it up: - Nothing at all if you use the online Space - Python on your computer, only if you run a copy - The packages listed in requirements.txt - System packages from packages.txt, which Hugging Face installs with apt-get 1. Open the Space's page in a browser to use it with nothing to install. 2. To run a copy, install the Hugging Face Hub package and download the Space. 3. Move into the new folder, then install Gradio and the listed requirements. 4. Start the app with Python, using the main file named in the command below. ```sh pip install -U huggingface_hub hf download jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char --repo-type space --local-dir espnet2_librispeech_100h_word_vs_bpe_vs_char cd espnet2_librispeech_100h_word_vs_bpe_vs_char pip install gradio pip install -r requirements.txt python gradio_asr_en_libri100_word_vs_bpe.py ``` How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file from the Space folder, then use the page it serves. The example audio files are the quickest way to try it. ```sh python gradio_asr_en_libri100_word_vs_bpe.py ``` Example: You open the Space and run one of the example clips. The app returns the English text from each of the three models, so you can compare them line by line. The record does not describe the controls, so check the page. For newcomers and experts: A speech recognizer writes text in small pieces. Those pieces can be whole words, single characters or word parts, called bpe. Start by opening the Space and running one example to see how the three outputs differ. If you know the field, the three models share one Conformer setup and differ in their token unit, which makes this a handy side-by-side view of that choice. The author's notes mention audio reading problems with live recording, so test with the examples first. ```flow You choose an example or give audio The Gradio page sends it to the script Three ESPnet2 models decode it The page shows each transcript ``` Good to know: - License: MIT, as listed on the Space - Cost to run: none named - Data it reaches: the audio you give it, the three models it names and the example clips - Not for: non-English speech, and live recording may not work, as the author's notes say ## On Hugging Face, 13 November 2026 - Likes: 3 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char - Files: https://huggingface.co/spaces/jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char/tree/main ## Try it 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char on Hugging Face](https://huggingface.co/spaces/jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | ESPNet2 ASR Librispeech word vs bpe tokens (this page) | 3 | gradio | mit | 2022-03-04 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-03-04 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char on Hugging Face](https://huggingface.co/spaces/jkang/espnet2_librispeech_100h_word_vs_bpe_vs_char) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/8409e37c-78bf-4434-abd5-820a481c4ca0) - [Its page on Mindket](https://mindket.com/p/8409e37c-78bf-4434-abd5-820a481c4ca0)