app.py

from transformers import pipeline
import gradio as gr
import librosa
import numpy as np

from transformers import WhisperProcessor, WhisperForConditionalGeneration, pipeline

# Load model and tokenizer
processor = WhisperProcessor.from_pretrained("kadriu/whisper-turbo-sq")
model = WhisperForConditionalGeneration.from_pretrained("kadriu/whisper-turbo-sq")
#tokenizer = processor.tokenizer

# Create pipeline
# pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer, feature_extractor=processor)

def transcribe(audio):
    audio_input, _ = librosa.load(audio, sr=16000)
    input_features = processor(audio_input, sampling_rate=16000, return_tensors="pt").input_features 
    # generate token ids
    predicted_ids = model.generate(input_features)
    # decode token ids to text
    #transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
    text = transcription
    #text = pipe(audio)["text"]
    return text

iface = gr.Interface(
    fn=transcribe, 
    inputs=gr.Audio(source="microphone", type="filepath"), 
    outputs="text",
    title="ASR Albanian",
    description="Realtime demo for Sq speech recognition",
)

iface.launch(share=True)