Create app.py

app.py

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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)