---
language:
- ky
license: apache-2.0
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")  

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()
```