Update README.md

Several solutions for lightweight TTS have shown promising results. Still, they either rely on a hand-crafted design that reaches non-optimum size or use a neural architecture search but often suffer training costs. We present Nix-TTS, a lightweight TTS achieved via knowledge distillation to a high-quality yet large-sized, non-autoregressive, and end-to-end (vocoder-free) TTS teacher model. Specifically, we offer module-wise distillation, enabling flexible and independent distillation to the encoder and decoder module. The resulting Nix-TTS inherited the advantageous properties of being non-autoregressive and end-to-end from the teacher, yet significantly smaller in size, with only 5.23M parameters or up to 89.34% reduction of the teacher model; it also achieves over 3.04x and 8.36x inference speedup on Intel-i7 CPU and Raspberry Pi 3B respectively and still retains a fair voice naturalness and intelligibility compared to the teacher model. We provide pretrained models and audio samples of Nix-TTS.

README.md

@@ -1,3 +1,17 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- Congliu/Chinese-DeepSeek-R1-Distill-data-110k
+language:
+- en
+metrics:
+- brier_score
+base_model:
+- deepseek-ai/DeepSeek-R1
+new_version: Qwen/QwQ-32B
+pipeline_tag: text-generation
+library_name: allennlp
+tags:
+- finance
+- legal
+---