import gradio as gr
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torch
import phonemizer
import librosa
import base64


processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")

waveform, sample_rate = librosa.load(
    "harvard.wav", sr=16000
)  # Downsample 44.1kHz to 8kHz

input_values = processor(
    waveform, sampling_rate=sample_rate, return_tensors="pt"
).input_values

with torch.no_grad():
    logits = model(input_values).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)


def showTranscription(transcription):
    return transcription


iface = gr.Interface(fn=showTranscription, inputs="text", outputs="text")
iface.launch()