Update README.md

README.md

@@ -16,7 +16,7 @@ pretty_name: WavCoch (8192-code speech tokenizer)
 
 # WavCochV8192 — 8,192-code speech tokenizer (cochlear tokens)
 
-**WavCochV8192** is a biologically-inspired, learned **audio quantizer** that maps a raw waveform to **discrete "cochlear tokens".** It is used as the tokenizer for the AuriStream autoregressive speech/language model (e.g., [TuKoResearch/AuriStream1B_librilight_ckpt500k](https://huggingface.co/TuKoResearch/AuriStream1B_librilight_ckpt500k)). The model is trained on LibriSpeech960 and encodes audio into a time–frequency representation ([Cochleagram](https://github.com/jenellefeather/chcochleagram)) and reads out **8,192-way discrete codes** through a low-bit latent bottleneck (LFQ). These tokens can be fed to a transformer LM for **representation learning** and **next-token prediction** (speech continuation).
+**WavCochV8192** is a biologically-inspired, learned **audio quantizer** that maps a raw waveform to **discrete "cochlear tokens".** It is used as the tokenizer for the AuriStream autoregressive speech/language model (e.g., [TuKoResearch/AuriStream1B_librilight_ckpt500k](https://huggingface.co/TuKoResearch/AuriStream1B_librilight_ckpt500k)). The model is trained on LibriSpeech960 and encodes audio into a time–frequency representation ([Cochleagram; Feather et al., 2023 Nat Neuro](https://github.com/jenellefeather/chcochleagram)) and reads out **8,192-way discrete codes** through a low-bit latent bottleneck (LFQ). These tokens can be fed to a transformer LM for **representation learning** and **next-token prediction** (speech continuation).
 
 > **API at a glance**
 > - **Input:** mono waveform at 16 kHz (pytorch tensor float32), shape **(B, 1, T)**