Improve model card: Update license, add HF paper link, project links, and correct language in description

README.md

@@ -1,14 +1,16 @@
 ---
-license: other 
 language:
-- vi 
+- vi
 library_name: transformers
+license: apache-2.0
 pipeline_tag: automatic-speech-recognition
-arxiv: https://arxiv.org/abs/2509.02523 
+arxiv: https://arxiv.org/abs/2509.02523
+paper: https://huggingface.co/papers/2509.02523
 ---
-# Moonshine
 
-[[Paper]](https://arxiv.org/abs/2509.02523) [[Installation]](https://github.com/usefulsensors/moonshine/blob/main/README.md)
+# Moonshine: Tiny Specialized ASR Model for Vietnamese
+
+[[Hugging Face Paper]](https://huggingface.co/papers/2509.02523) [[arXiv Paper]](https://arxiv.org/abs/2509.02523) [[Code]](https://github.com/moonshine-ai/moonshine) [[Blog]](https://petewarden.com/2024/10/21/introducing-moonshine-the-new-state-of-the-art-for-speech-to-text/) [[Installation]](https://github.com/moonshine-ai/moonshine#installation)
 
 This is the model card for running the automatic speech recognition (ASR) models (Moonshine models) trained and released by Moonshine AI (f.k.a Useful Sensors.)
 
@@ -78,7 +80,7 @@ Sequence-to-sequence ASR (automatic speech recognition) and speech translation m
 
 The primary intended users of these models are AI developers that want to deploy Vietnamese speech recognition systems in platforms that are severely constrained in memory capacity and computational resources. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not safe use.
 
-The models are primarily trained and evaluated on Arabic ASR task. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.
+The models are primarily trained and evaluated on Vietnamese ASR task. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.
 
 In particular, we caution against using Moonshine models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe Vietnamese speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.
 
@@ -92,7 +94,7 @@ Our evaluations show that, the models exhibit greater accuracy on standard datas
 
 However, like any machine learning model, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.
 
-In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. It is likely that this behavior and hallucinations may be worse for short audio segments, or segments where parts of words are cut off at the beginning or the end of the segment.
+In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. It is likely that this behavior and hallucinations may be worse for short audio segments, or segments where parts of words are cut off at the beginning or at the end of the segment.
 
 ## Broader Implications
 
@@ -113,4 +115,4 @@ If you benefit from our work, please cite us:
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2509.02523}, 
 }
-```
+```