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import streamlit as st
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import tempfile
model_name = "oyqiz/uzbek_stt"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
st.title("Ovozni matnga o'girish")
st.write("Audio faylingizni yuklang:")
# File uploader
uploaded_file = st.file_uploader("Audio faylingizni tanlang...", type=["wav", "mp3", "ogg"])
def transcribe_audio(audio_file):
waveform, sample_rate = torchaudio.load(audio_file)
if sample_rate != 16000:
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
sample_rate = 16000
input_values = processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_values
with torch.no_grad():
input_values = input_values.squeeze(1)
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
return transcription
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_file_path = tmp_file.name
transcription = transcribe_audio(tmp_file_path)
st.write("Natija:")
st.write(transcription)
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