Update README.md

README.md

@@ -6,43 +6,13 @@ pipeline_tag: automatic-speech-recognition
 ---
 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: 
-```bash
-!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. 
-```python
-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:
-```python
-from transformers import pipeline
-import gradio as gr
-
-pipe = pipeline(model="UlutSoftLLC/whisper-small-kyrgyz")  
+pipe = pipeline(model="the-cramer-project/AkylAI-STT-small")  
 
 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()
 ```