app.py

import gradio as gr
import tensorflow as tf
from transformers import Wav2Vec2Processor, TFWav2Vec2Model
import librosa

# Load the model and processor
processor = Wav2Vec2Processor.from_pretrained("openai/whisper-tiny")
model = TFWav2Vec2Model.from_pretrained("kobrasoft/kobraspeech-rnn-cs")

def transcribe(audio):
    # Load audio
    audio, rate = librosa.load(audio, sr=16000)
    
    # Process audio
    inputs = processor(audio, sampling_rate=rate, return_tensors="tf", padding="longest")
    logits = model(inputs.input_values).logits
    
    # Decode the logits
    predicted_ids = tf.argmax(logits, axis=-1)
    transcription = processor.batch_decode(predicted_ids)[0]
    return transcription

# Create Gradio interface
iface = gr.Interface(
    fn=transcribe,
    inputs=gr.inputs.Audio(source="microphone", type="filepath"),
    outputs="text",
    title="ASR Model Demo",
    description="Upload an audio file or record your voice to get the transcription."
)

if __name__ == "__main__":
    iface.launch()