Update README.md

README.md

@@ -3,3 +3,45 @@ language:
 - ky
 ---
 Whisper ASR for Kyrgyz Language is an automatic speech recognition (ASR) solution customized for the Kyrgyz language. It is based on the pre-trained Whisper model and has undergone fine-tuning and adaptation to accurately transcribe Kyrgyz speech, taking into account its specific phonetic intricacies.
+
+To run the model, first install: 
+
+!pip install datasets>=2.6.1
+!pip install git+https://github.com/huggingface/transformers
+!pip install librosa
+!pip install evaluate>=0.30
+!pip install jiwer
+!pip install gradio==3.50.2
+
+
+
+Linking the notebook to the Hub is straightforward - it simply requires entering your Hub authentication token when prompted. 
+
+from huggingface_hub import notebook_login
+
+notebook_login()
+
+
+Now that we've fine-tuned our model, we can build a demo to show off its ASR capabilities! We'll use 🤗 Transformers pipeline, which will take care of the entire ASR pipeline, right from pre-processing the audio inputs to decoding the model predictions. We'll build our interactive demo with Gradio. Gradio is arguably the most straightforward way of building machine learning demos; with Gradio, we can build a demo in just a matter of minutes!
+
+Running the example below will generate a Gradio demo where we can record speech through the microphone of our computer and input it to our fine-tuned Whisper model to transcribe the corresponding text:
+
+from transformers import pipeline
+import gradio as gr
+
+pipe = pipeline(model="UlutSoftLLC/whisper-small-kyrgyz")  
+
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+
+iface = gr.Interface(
+    fn=transcribe, 
+    inputs=gr.Audio(source="microphone", type="filepath"), 
+    outputs="text",
+    title="Whisper Small Kyrgyz",
+    description="Realtime demo for Kyrgyz speech recognition using a fine-tuned Whisper small model.",
+)
+
+iface.launch()
+