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import gradio as gr
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import pandas as pd
from sklearn.model_selection import train_test_split
processor = Wav2Vec2Processor.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
model = Wav2Vec2ForCTC.from_pretrained("gagan3012/wav2vec2-xlsr-nepali")
from torchaudio.transforms import Resample
import numpy as np
def transcribe_audio(audio_file):
input_arr, sampling_rate =torchaudio.load(audio_file)
resampler = Resample(orig_freq=sampling_rate, new_freq=16000)
input_arr = resampler(input_arr).squeeze().numpy()
sampling_rate = 16000
inputs = processor(input_arr, sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_words= processor.batch_decode(predicted_ids)
return predicted_words[0]
audio_input = gr.inputs.Audio(source="upload", type="filepath")
iface = gr.Interface(fn=transcribe_audio, inputs=audio_input,
outputs=["textbox"], title="Speech To Text",
description="Upload an audio file and hit the 'Submit'\
button")
iface.launch(inline=False)
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