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 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](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)**  
@@ -33,7 +33,7 @@ pip install -U torch torchaudio transformers
 
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-## Quickstart — Quantize a WAV into cochlear tokens
+## Quickstart — Quantize a waveform into cochlear tokens
 
 ```python
 import torch, torchaudio
@@ -65,8 +65,8 @@ print("Token IDs shape:", token_ids.shape)
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 ## Intended uses & limitations
-- **Uses:** tokenization for speech LM training; compact storage/streaming of speech as discrete IDs in a biologically plausible manner.  
-- **Limitations:** trained only on spoken english, so might not perform as well for other languages and non-speech sounds.
+- **Uses:** tokenization for speech LM training; compact storage/streaming of speech as discrete IDs, loosely inspired by human biology.  
+- **Limitations:** trained only on spoken English, so might not perform as well for other languages and non-speech sounds.
 
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