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PranjulGupta/
heroico-asr-conformer-es

Name: heroico-asr-conformer-es
Availability: InStock

espnet

audio

automatic-speech-recognition

apache-2.0

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HEROICO Spanish ASR — Conformer (text-disjoint split)

ESPnet2 ASR model trained on the West Point HEROICO corpus (LDC2006S37 / OpenSLR SLR39, Apache 2.0).

Language: Spanish (es)
Architecture: Conformer encoder / Transformer decoder, hybrid CTC/attention, character tokenization, no LM
Recipe: egs2/heroico/asr1 (espnet/espnet#6491), text-disjoint train/eval split
Dev: 24.4% WER / 8.1% CER (spontaneous answers, unseen speakers)
Test: 56.3% WER / 20.6% CER — the test set is dominated by out-of-domain conditions by design; per-subset:

test subset	utts	WER
answers (spontaneous, novel speakers)	711	18.9%
recordings, clean (held-out text)	129	54.3%
recordings, mislabeled band (corpus label noise)	55	98.4%
usma native (held-out text + channel)	1647	57.0%
usma non-native (held-out text + channel)	2028	71.3%

The test set looks hard because it is: 80% of it is USMA speech — a different recording site, mostly non-native speakers — reading exactly the sentences the corrected split holds out of training. See the recipe README's "Interpreting the test WER" section (espnet/espnet#6491) for the full analysis.

Note: this model replaces an earlier upload trained on a split with ~89% train/test text overlap, which scored an inflated 15.8% test WER via text memorization (see the PR discussion). The earlier zip was also missing exp/asr_stats_raw_es_char/train/feats_stats.npz (global-MVN stats), so it could not be loaded as packed; this upload includes it.

Usage

With the zip (heroico_asr_conformer_v2.zip):

python

from espnet_model_zoo.downloader import ModelDownloader
from espnet2.bin.asr_inference import Speech2Text

d = ModelDownloader()
speech2text = Speech2Text(**d.download_and_unpack(
    "https://huggingface.co/PranjulGupta/heroico-asr-conformer-es/resolve/main/heroico_asr_conformer_v2.zip"))

Or directly from the repo (unpacked layout):

python

from espnet2.bin.asr_inference import Speech2Text
speech2text = Speech2Text.from_pretrained("PranjulGupta/heroico-asr-conformer-es")

Input: 16 kHz mono float waveform.

@misc{LDC2006S37,
  Author = {Morgan, John},
  Title = {West Point Heroico Spanish Speech},
  Publisher = {Linguistic Data Consortium},
  Year = {2006}}