Update app.py

app.py

@@ -3,23 +3,22 @@ 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)
+# Load model directly from Hugging Face Hub
+model_id = "onepunchgin/ASR-Model"
+
+processor = Wav2Vec2Processor.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.from_pretrained(model_id)
 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)
+        waveform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000)(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
+    return processor.batch_decode(predicted_ids)[0]
 
 iface = gr.Interface(fn=transcribe,
                      inputs=gr.Audio(type="filepath"),