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Wav2Vec 2.0
Wav2Vec 2.0 learns speech representations from unlabeled audio, then can be fine-tuned with transcripts for automatic speech recognition.
Déglul tegtal biiIA-nettali · ~1 min
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Résumé
XLS-R is a distinct cross-lingual model family built on the wav2vec 2.0 approach; its paper reports training on nearly half a million hours in 128 languages.
Plongeur bu xóot
Wav2Vec 2.0 uses a convolutional feature encoder and a Transformer context network. During pretraining, it masks latent spans and predicts quantized targets with a contrastive objective. The authors show fine-tuning can use relatively little transcribed speech in specified LibriSpeech experiments. XLS-R is a later cross-lingual model based on wav2vec 2.0; its paper reports models trained on nearly 500,000 hours of public speech across 128 languages. These results are specific to the published models and benchmarks.
Doxal ci àdduna dëgg
Pretraining the Wav2Vec 2.0 encoder on unlabeled audio before ASR fine-tuning.
Testing the original paper’s low-resource LibriSpeech fine-tuning setup.
Using XLS-R as a cross-lingual representation model trained across 128 languages.
Comparing a model’s performance on the dataset and language used for evaluation.
Ëlëgu Wav2Vec 2.0
Evaluate recognition on target languages, domains and recording conditions. The published language count or low-label result does not guarantee performance for every language or deployment.
Teg ko ci jëf
Lu tax mu am solo
- Dugg ak yegg. Dafay gëna yombal jëfandikoo gi jaaraleko ci transkripsioŋ, nettali ak interfaasu baat.
- Njëgg ak budget. Ekipu mejaa yi mën nañu yónnee audio bu leer ci anam wu gëna gaaw te seen xaalis gëna néew.
- Gaawaay ak yaatuwaay. Sistem yiy jàkkarloo ak kiliyaan bi mën nañu def waxtaan ci anam wu gëna yaatu.
Moytul
- Jëfandikoo baat ci anam wu jaarul yoon ak niru ak nit dafay gëna yokk sudee nanguwul.
- Jaar-jaar mën na wàññeeku ci aksan yi, dialect yi wala barab yu bari xumbaay.
- Audio synthetik mën nañu ko jaawale ak wax ju dëggu sudee amul etiket bu leer.
Jéego yi ci topp
- Wutal ndigal bu leer ngir jàpp baat bi, klone ko ak jëfandikoowaat ko.
- Saytu kalite ci kàddukat yu bari ak anam yu bari ci ginaaw.
- Mandargal kañ la nit wara xoolaat wala nangu ay génne.
- Etiketu audio synthetik te nga denc dokimaa ci fimu bawoo ngir mëna lim.
Weyal di banneexu
Bulletin bu amul fayda
Jàngal xibaar AI bu bes
Ñatti jaar-jaari IA yuñ firndeel bis bu nekk ci ayu-bis bi, ñu bind leen ci Àngle bu leer. Gratis ba fàww, amul yëgle.
Benn email bis bu nekk ci ayu-bis bi. Benn klik nga dindi abonemaa bi. Dunu musa jaay wala séddoo sa adres.
Nattal sa bopp
Take the Wav2Vec 2.0 quiz
Feedback ci saasi ci bépp tontu, ak sertifikaa buñ mëna séddoo ak ID buñ mëna saytu soo jàllee ci njàng mi.
Jàppale njàngum IA te doo fay. AI Understanding mbootaay la buy def te Yàlla tax — amul yëgle, amul paywall, musul am. Defal ab sarax
Laaj yi ñuy faral di laaj
What is Wav2Vec 2.0?
Wav2Vec 2.0 learns speech representations from unlabeled audio, then can be fine-tuned with transcripts for automatic speech recognition. XLS-R is a distinct cross-lingual model family built on the wav2vec 2.0 approach; its paper reports training on nearly half a million hours in 128 languages.
Ban jafe-jafe bu mag ci xàmmee kàddu lañu defar ngir saafara Wav2Vec 2.0?
Wav2Vec 2.0 wàññi na dependence ci audio transcription bu seer ndax dafay njëkka tàggat kàddu yu bari te amul etiket.
Ban xeetu mébetu jàng la Wav2Vec 2.0 di jëfandikoo ci diiru tàggat-yaram bi?
Dafay maske span vecteur audio yu nëbbu yi ba noppi jëfandikoo perte contraste ngir tànn unité quantifié bu dëggu bi ci distracteur yi.
Ban lëf mooy njëkka defar jëmmu onde bu ñor ci Wav2Vec 2.0?
Benn pilu diisaay convolutionnel dafay kode forme onde bu ñor bi ci vecteur yu nëbbu yi laata ñuy maskeer ak Transformateur bi.
In a reported LibriSpeech experiment, what small labeled-data setup demonstrated low-label potential?
The paper reports experiments using 10 minutes of labels after 53,000 hours of unlabeled pretraining; this is a specific benchmark setup, not a universal guarantee.
Lan mooy wareefu téere kode biñ jàng ci Wav2Vec 2.0?
Téere kode bi dafay xayma limu représentation yu wéy yi ci benn ensemble bu am àpp ci ay unité yu wuute, di joxe ay target ngir objectif contrastif bi.
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