ESPNet2 ASR Librispeech Conformer (100h)

ESPNet2 ASR Librispeech Conformer (100h)

Transcribes English speech to text with an ESPnet2 Conformer model trained on 100 hours of Librispeech, 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 is a demo of English speech recognition. By its name it runs an ESPnet2 Conformer model trained on 100 hours of Librispeech. The record names one model from the same author, and the repository holds an examples folder with sample audio.

Who it suits: It suits speech researchers and students who want to try a Conformer recognizer without setting up code, and developers who want a small ESPnet2 demo to read. The record gives no accuracy figures, so judge results by listening.

Industry | Who uses it | How they use it | Example
Research | Speech researchers | Try a Conformer model on English audio | Run an example clip and read the transcript
Education | Students of speech technology | See what a model trained on limited data produces | Compare the text with what you hear
Software | Python developers | Read a Gradio wrapper around an ESPnet2 model | 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
Transcribe English audio | Researchers and students | Text from the Conformer model
Try the bundled examples | Anyone | A quick sense of what the model hears
Study the wrapper | Developers | A short Gradio script around an ESPnet2 model

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 its main file gradio_asr_en_libri100.py.

pip install -U huggingface_hub
hf download jkang/espnet2_asr_librispeech_100h --repo-type space --local-dir espnet2_asr_librispeech_100h
cd espnet2_asr_librispeech_100h
pip install gradio
pip install -r requirements.txt
python gradio_asr_en_libri100.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.

python gradio_asr_en_libri100.py

Example: You open the Space and run it on one of the example audio clips. The app returns the English text the model hears in the clip. The record does not describe the controls, so check the page.

For newcomers and experts: Speech recognition means a program listens and writes down what it hears. Start by opening the Space and running one of the example clips.

If you know the field, the model is a Conformer trained on a 100 hour slice of Librispeech, which is a modest slice of that corpus. The record gives no error rates, so measure on your own audio.

You choose an example or give audio
The Gradio page sends it to the script
The ESPnet2 Conformer model decodes it
The page shows the English text

Good to know:

• License: MIT, as listed on the Space
• Cost to run: none named
• Data it reaches: the audio you give it, the model it names and the example clips
• Not for: non-English speech, since the model is named for English

On Hugging Face, 13 November 2026

• Likes: 0
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/jkang/espnet2_asr_librispeech_100h
• Files: https://huggingface.co/spaces/jkang/espnet2_asr_librispeech_100h/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.