FunASR check in.

Author: odincodeshen

content/learning-paths/servers-and-cloud-computing/funASR/1_asr.md

@@ -10,9 +10,9 @@ layout: learningpathall
 
 Automatic Speech Recognition [ASR](https://en.wikipedia.org/wiki/Speech_recognition), also known as speech-to-text, is a rapidly evolving field that empowers computers to understand and transcribe human speech.
 
-This technology has become an integral part of our daily lives, powering a wide range of applications and services.   
+This technology has become deeply integrated into our daily lives, powering a wide range of applications and services we often take for granted, many of which are optimized for and run on Arm CPU architecture.
 
-At its core, ASR involves converting spoken audio into written text. This seemingly simple task is actually quite complex, requiring sophisticated algorithms and models to accurately interpret the nuances of human speech, including variations in pronunciation, accents, and background noise.   
+At its core, ASR involves converting spoken audio into written text. While seemingly simple, this process is quite complex, requiring sophisticated algorithms and models to accurately interpret the nuances of human speech, including variations in pronunciation, accents, and background noise.
 
 ### Key Applications of ASR
 
@@ -47,8 +47,8 @@ ASR is now used in a myriad of applications across various domains:
 
 While the potential applications of ASR are vast and inspiring, it's important to acknowledge the inherent challenges in developing and deploying accurate and reliable ASR systems. These challenges stem from the complexities of human speech, environmental factors, and the intricacies of language itself. These challenges are particularly pronounced for Chinese ASR, which needs to address unique linguistic characteristics such as:
 
-* **Tonal Variations:**
-    Mandarin Chinese is a tonal language, where the meaning of a syllable changes depending on its tone. Accurately recognizing these tonal variations is crucial for understanding spoken Chinese.
+* **Complexities of Chinese Language:** 
+    Mandarin Chinese involves tonal variations where the meaning of a syllable changes depending on its tone, and punctuation is crucial to convey meaning and avoid ambiguity. Accurately recognizing these nuances is essential for understanding spoken Chinese.
 
 * **Noise Robustness:** 
     ASR systems need to be able to filter out background noise to accurately transcribe speech. This is particularly challenging in noisy environments like crowded streets or busy offices.
@@ -59,4 +59,4 @@ While the potential applications of ASR are vast and inspiring, it's important t
 * **Homophones:**
     Chinese has a high prevalence of homophones, words that sound alike but have different meanings. Disambiguating these homophones requires understanding the context and semantics of the spoken words.
 
-Fortunately, advancements in AI and open-source technologies are paving the way for innovative solutions to address these challenges. In the following sections, we'll explore one such solution that leverages the power of ModelScope and Arm CPUs to enable efficient and accurate Chinese ASR.
+In the following sections, we'll explore one such solution that leverages the power of ModelScope and Arm CPUs to enable efficient and accurate Chinese ASR.

content/learning-paths/servers-and-cloud-computing/funASR/2_modelscope.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Before you begin
 
-To follow the instructions for this Learning Path, you will need an Arm server running Ubuntu 22.04 LTS or later version with at least 16 cores, 16GB of RAM, and 50GB of disk storage.
+To follow the instructions for this Learning Path, you will need an Arm server running Ubuntu 22.04 LTS or later version with at least 8 cores, 16GB of RAM, and 30GB of disk storage.
 
 ## Introduce ModelScope
 [ModelScope](https://github.com/modelscope/modelscope/) is an open-source platform that makes it easy to use AI models in your applications. 
@@ -27,11 +27,9 @@ Key benefits of ModelScope include:
 
 
 ## Arm CPU Acceleration
-ModelScope fully support Pytorch 1.8+ and other machine learing framework which can be efficiently deployed on Arm Neoverse CPUs, taking advantage of Arm's performance and power-efficiency characteristics. 
+ModelScope fully supports Pytorch 1.8+ and other machine learning frameworks, which can be efficiently deployed on Arm Neoverse CPUs, taking advantage of Arm's performance and power-efficiency characteristics.
 
-Arm provides optimized software and tools, such as the Kleidi, to accelerate AI inference on Arm-based platforms. 
-
-This makes Arm Neoverse CPUs an ideal choice for running ModelScope models in edge devices and other resource-constrained environments.
+Arm provides optimized software and tools, such as Kleidi, to accelerate AI inference on Arm-based platforms. This makes Arm Neoverse CPUs an ideal choice for running ModelScope models in edge devices and other resource-constrained environments.
 
 You can learn more about [Faster PyTorch Inference using Kleidi on Arm Neoverse](https://community.arm.com/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/faster-pytorch-inference-kleidi-arm-neoverse) from Arm community website.
 
@@ -58,20 +56,23 @@ pip3 install numpy packaging addict datasets simplejson sortedcontainers transfo
 
 ```
 {{% notice Note %}}
-This learning path will use execute model in Arm Neoverse, so we only need install PyTorch CPU package.
+This learning path will execute models on Arm Neoverse, so we only need to install the PyTorch CPU package.
 {{% /notice %}}
 
 ## Create a sample example
 
 After completing the installation, we will use an example related to Chinese semantic understanding to illustrate how to use ModelScope.
 
-There is a fundamental difference between Chinese and English writing. The relationship between Chinese characters and their meanings is somewhat analogous to the difference between words and phrases in English. Some Chinese characters, like English words, have clear meanings on their own, such as "人" (person), "山" (mountain), and "水" (water).
+There is a fundamental difference between Chinese and English writing. 
+The relationship between Chinese characters and their meanings is somewhat analogous to the difference between words and phrases in English. 
+Some Chinese characters, like English words, have clear meanings on their own, such as “人” (person), “山” (mountain), and “水” (water).
 
-However, more often, Chinese characters need to be combined with other characters to express more complete meanings, just like phrases in English. For example, "祝福" (blessing) can be broken down into "祝" (wish) and "福" (good fortune); "分享" (share) can be broken down into "分" (divide) and "享" (enjoy); "生成" (generate) is composed of "生" (produce) and "成" (become).
+However, more often, Chinese characters need to be combined with other characters to express more complete meanings, just like phrases in English. 
+For example, “祝福” (blessing) can be broken down into “祝” (wish) and “福” (good fortune); “分享” (share) can be broken down into “分” (divide) and “享” (enjoy); “生成” (generate) is composed of “生” (produce) and “成” (become).
 
 For computers to understand Chinese sentences, we need to understand the rules of Chinese characters, vocabulary, and grammar to accurately understand and express meaning.
 
-Here ia a simple example using a general-domain Chinese word segmentation [model(https://www.modelscope.cn/models/iic/nlp_structbert_word-segmentation_chinese-base)], which can break down Chinese sentences into individual words, facilitating analysis and understanding by computers.
+Here ia a simple example using a general-domain Chinese [word segmentation model](https://www.modelscope.cn/models/iic/nlp_structbert_word-segmentation_chinese-base), which can break down Chinese sentences into individual words, facilitating analysis and understanding by computers.
 
 ```python
 from modelscope.pipelines import pipeline
@@ -128,4 +129,3 @@ The segmentation model has correctly identified the following words:
 
 
 The segmentation model has successfully identified the word boundaries and separated the sentence into meaningful units, which is essential for further natural language processing tasks like machine translation or sentiment analysis.
-