Update app.py

app.py

@@ -18,7 +18,8 @@ def speech_file_to_array_fn(path, max_seconds=10):
     batch = {"file": path}
     speech_array, sampling_rate = torchaudio.load(batch["file"])
     if sampling_rate != 16000:
-      transform = torchaudio.transforms.Resample(orig_freq=sampling_rate,new_freq=16000)
+      transform = torchaudio.transforms.Resample(orig_freq=sampling_rate,
+                                                 new_freq=16000)
       speech_array = transform(speech_array)
     speech_array = speech_array[0]
     if max_seconds > 0:
@@ -42,17 +43,15 @@ def inference(audio):
     with torch.no_grad():
       logits = model(input_values).logits
 
-    #pred_ids = torch.argmax(logits, dim=-1)
-    h = logits.numpy()[0,:,:]
-    v = np.pad(h, [0, 2], mode='constant')
+    output = processor.decode(logits.numpy()[0]).text
+    print(output)
 
-    output = processor.decode(v).text
-
-    return output[:-4]
+    return output
 
 inputs = gr.inputs.Audio(label="Record Audio", source="microphone", type='file')
 outputs =  gr.outputs.Textbox(label="Output Text")
 title = "Annarabic Speech Recognition System"
-description = "Gradio demo for Annarabic ASR. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below."
-examples=[['Aya.mp3'], ['Loubna.mp3']]
-gr.Interface(inference, inputs, outputs, title=title, description=description, examples=examples).launch()
+description = 'Demo for <b>Annarabic ASR</b>. To use it, simply upload your audio, or click on one of the examples to load them. Only the 10 first seconds of the audio will be transcribed and GPU runtime is not used. For more information, contact Ahmed Jaafari via email: <a href = "mailto: a.jaafari@aui.ma">a.jaafari@aui.ma</a> or phone: <a href = "tel: +212658537105">+212658537105</a>.'
+examples=[['Aya.mp3'], ['Loubna.mp3'], ['Omar.wav'], ['Yassir.wav']]
+article="* The ASR never trained on the given examples."
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()