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DeepSpeech Architecture
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The Conformer is a speech-recognition encoder architecture combining self-attention for global context with convolution for local patterns in an audio sequence.
Teerera kugwaro iriAI-yakarondedzerwa · ~1 min
The Conformer paper proposes a convolution-augmented Transformer for automatic speech recognition. Its block combines multi-head self-attention, a convolution module and two half-step feed-forward modules in a Macaron-style structure. Relative positional encoding supports attention over sequence positions. The authors evaluate on LibriSpeech and report results under specific model sizes and decoding conditions.
Self-attention models global interactions across speech frames.
A convolution module captures local patterns in the encoded sequence.
Macaron-style blocks place feed-forward modules around attention and convolution sublayers.
The original paper reports LibriSpeech word error rates for models with and without an external language model.
Compare Conformer variants on the target audio conditions and compute budget. Report model size, decoding procedure and external language-model use. The original paper does not establish that Conformer is the dominant architecture across every production speech system.
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The Conformer is a speech-recognition encoder architecture combining self-attention for global context with convolution for local patterns in an audio sequence.
Iyo Conformer inosanganisa convolution (yepatani dzepanzvimbo) nekuzvitarisa wega (yepasi rose) mukati mebhuroko rega rega.
Convolutions inobudirira pamashoko emunharaunda akatsetseka achisiyanisa foni, ukuwo mamodheru ekuzvitarisisa achitsamira pakutaura kwese; Conformer inowana zvese.
Iyo Conformer inoisa iyo yekuzvitarisa-yega uye convolution modules pakati pemafid-forward modules, imwe neimwe inoshandiswa nehafu-inorema yakasara.
The Conformer yakaunzwa neGoogle vatsvakurudzi muna 2020 uye nekukasika yakave encoder yakajairwa yekutaura.
Iyo convolution module inosungirira yakananga convolution ine gated linear unit, kudzika kwakadzika convolution, batch normalization, uye Swish activation, ichipera mune imwe pointwise conv.
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InoteveraGaidhi rinotevera
DeepSpeech Architecture
AI Yenyaya