from transformers import pipeline
from transformers import WhisperForConditionalGeneration, WhisperProcessor
from transformers import WhisperTokenizer
from transformers import WhisperFeatureExtractor
import gradio as gr

tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Spanish", task="transcribe")
model = WhisperForConditionalGeneration.from_pretrained("mirari/whisper-small-es")
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")

pipe = pipeline(task="automatic-speech-recognition",model=model, tokenizer=tokenizer,feature_extractor=feature_extractor)  



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 Hindi",
    description="Realtime demo for Spanish speech recognition using a fine-tuned Whisper small model.",
)

iface.launch()