Create app.py

app.py

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+import gradio as gr
+import torchaudio
+import torch
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+
+# Load local model
+model_dir = "ccc_wav2vec_model"
+processor = Wav2Vec2Processor.from_pretrained(model_dir)
+model = Wav2Vec2ForCTC.from_pretrained(model_dir)
+model.eval()
+
+def transcribe(audio):
+    waveform, sr = torchaudio.load(audio)
+    if sr != 16000:
+        resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)
+        waveform = resampler(waveform)
+    inputs = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)[0]
+    return transcription
+
+iface = gr.Interface(fn=transcribe,
+                     inputs=gr.Audio(type="filepath"),
+                     outputs="text",
+                     title="Kannada Speech Recognition",
+                     description="Upload a Kannada audio file to transcribe using CCC-wav2vec model")
+
+iface.launch()