datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
multilingual-tts-benchmark
Multilingual Speech Benchmark for Zero-Shot TTS
A voice-cloning and intelligibility benchmark for 8 language subsets,
with 10,100 examples selected from Common Voice 17.0. Each example supplies
a speaker reference and an independently selected target text, with human
recordings as WER/CER and speaker-similarity anchors when the corresponding
audio is available. Corpus WER and the WavLM-FT evaluation follow
seed-tts-eval.
Version 3.1. Adds ru, kk through the same S1–S4 selection… See the full description on the dataset page: https://huggingface.co/datasets/nineninesix/multilingual-tts-benchmark.clipquill-asr-benchmark
Measuring whisper-tiny vs whisper-base in a browser tab
Word error rate, wall-clock timing, transfer size and peak memory for two
quantised Whisper tiers running entirely client-side in a real Chrome window,
with the scripts that produced every number.
If you are building an in-browser transcription page, the two results worth
knowing before you pick a model tier:
On clean synthetic audio the two tiers tie. If that is all you test, you
will conclude the tier does not matter… See the full description on the dataset page: https://huggingface.co/datasets/sophia8888/clipquill-asr-benchmark.asr-benchmark-outputs
SaarAI ASR Benchmark Outputs
Raw per-utterance model outputs (transcription manifests) produced by the gsma-asr-bench runners on SaarAI/asr-leaderboard-datasets.
files: 570
utterances: 4615160
languages: 7
models: 50
Layout
data/<language_name>/<split>__<dataset_config>__<model_slug>.jsonl
index.jsonl # one record per file (language, split, model, rows, sha256, ...)
index.csv
Directories categorise by language name; the file name begins with the split name… See the full description on the dataset page: https://huggingface.co/datasets/SaarAI/asr-benchmark-outputs.script-fidelity-benchmark
Script fidelity benchmark
Anonymous supplement for the paper "Script collapse in multilingual ASR:
A reference-free metric and 100-pair benchmark."
Script Fidelity Rate (SFR) measures the fraction of ASR hypothesis characters
that belong to the expected target script. WER measures word edits, while SFR
checks whether the output is written in the target orthography.
Related resources:
PyPI package: https://pypi.org/project/script-fidelity/
Hugging Face Evaluate metric:… See the full description on the dataset page: https://huggingface.co/datasets/themechanism/script-fidelity-benchmark.nepali-asr-benchmark
Nepali ASR Benchmark
Per-utterance reference, hypothesis, WER, and CER for the six released Nepali ASR
checkpoints evaluated on three independent test sets. Released alongside the paper
Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech
Recognition.
Contents
Field
Type
Description
utterance_id
string
stable identifier {test_set}-{index}
reference
string
NFC-normalised gold transcription (Devanagari)
hypothesis
string… See the full description on the dataset page: https://huggingface.co/datasets/sumanpaudel1997/nepali-asr-benchmark.