Basic speech-to-text interface.

app.py

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+import streamlit as st
+from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
+import torch
+import torchaudio
+import torchaudio.functional as F
+
+st.set_page_config(
+    page_title='Swedish Speech-to-Text',
+    page_icon='🎙️'
+)
+
+
+# Import model and processor
+model_name = 'viktor-enzell/wav2vec2-large-voxrex-swedish-4gram'
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device)
+processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
+
+
+def run_inference(file):
+    waveform, sample_rate = torchaudio.load(file)
+
+    if sample_rate == 16_000:
+        waveform = waveform[0]
+    else:
+        waveform = F.resample(waveform, sample_rate, 16_000)[0]
+
+    inputs = processor(
+        waveform,
+        sampling_rate=16_000,
+        return_tensors='pt',
+        padding=True
+    ).to(device)
+
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    return processor.batch_decode(logits.cpu().numpy()).text[0].lower()
+
+
+uploaded_file = st.file_uploader('Choose a file', type=['.wav'])
+if uploaded_file is not None:
+    transcript = run_inference(uploaded_file)
+
+    st.write(transcript)

requirements.txt

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+torch==1.10.1
+torchaudio==0.10.1