# Alexander-H-Liu/End-to-end-ASR-Pytorch

This is an open source project (formerly named Listen, Attend and Spell - PyTorch Implementation) for end-to-end ASR implemented with Pytorch, the well known deep learning toolkit.

Repository: https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch
Canonical: https://ross.abutalabs.com/products/end-to-end-asr-pytorch
Language: Python
License: MIT
License Family: permissive
Last push: 2020-12-19T07:53:25+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3190, "days_push": 2083, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1208, forks 314 (observed 2026-08-28T04:03:59.674762+00:00)

## What it is
A PyTorch implementation of end-to-end automatic speech recognition (ASR), formerly known as Listen, Attend and Spell. It supports seq2seq and CTC-based models with techniques like subword encoding, RNN language models, and joint CTC-attention decoding.

## Use cases
- train an end-to-end speech recognition model in pytorch
- implement listen attend and spell ASR
- build a CTC-based speech recognizer
- run beam search decoding for ASR
- train an RNN language model for joint ASR decoding
- experiment with subword tokenization for speech recognition

## When to choose
- you want a research-oriented PyTorch codebase for end-to-end ASR
- you need seq2seq, CTC, or hybrid CTC-attention speech recognition models
- you want on-the-fly feature extraction with torchaudio and TensorBoard visualization

## When to avoid
- you need a production-ready, actively maintained ASR system
- you want pretrained models or a simple inference API without training
- you lack a high-end GPU, which is required for training

## Facets
- artifact type: library
- maturity: maintenance
- function: speech-recognition, machine-learning, deep-learning
- domain: speech-processing, machine-learning, deep-learning
- platform: python
- tags: asr, pytorch, listen-attend-spell, ctc, seq2seq, beam-search, gpu

## Member repositories
- Alexander-H-Liu/End-to-end-ASR-Pytorch (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:59.674762+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:18:38.009556+00:00, confidence not recorded.
  - readme: https://github.com/Alexander-H-Liu/End-to-end-ASR-Pytorch (fetched 2026-08-28T04:03:59.674762+00:00, sha 591efb09c9e2)
- Data as of 2026-08-30T08:39:29.467469+00:00.