Update app.py

app.py

@@ -6,36 +6,29 @@ from pyctcdecode import build_ctcdecoder
 
 # Define ASR MODEL
 class Speech2Text:
-    def __init__(self):
-        self.vocab = list(processor.tokenizer.get_vocab().keys())
+    def __init__(self, model_name='masoudmzb/wav2vec2-xlsr-multilingual-53-fa'):
+        self.model = Wav2Vec2ForCTC.from_pretrained(model_name).eval()
+        self.processor = Wav2Vec2Processor.from_pretrained(model_name)
+        self.vocab = list(self.processor.tokenizer.get_vocab().keys())
         self.decoder = build_ctcdecoder(self.vocab, kenlm_model_path='kenlm.scorer')
 
     def wav2feature(self, path):
         speech_array, sampling_rate = torchaudio.load(path)
-        speech_array = librosa.resample(speech_array.squeeze().numpy(),
-                                        sampling_rate, processor.feature_extractor.sampling_rate)
-        return processor(speech_array, return_tensors="pt",
-                         sampling_rate=processor.feature_extractor.sampling_rate)
+        speech_array = librosa.resample(speech_array.squeeze().numpy(), sampling_rate, processor.feature_extractor.sampling_rate)
+        return processor(speech_array, return_tensors="pt", sampling_rate=processor.feature_extractor.sampling_rate)
 
     def feature2logits(self, features):
         with torch.no_grad():
-            return wav2vec_model(features.input_values[0].to(device)).logits.numpy()[0]
+            return self.model(features.input_values[0]).logits.numpy()[0]
 
     def __call__(self, path):
         logits = self.feature2logits(self.wav2feature(path))
         return self.decoder.decode(logits)
         
-#Loading the model and the tokenizer
-model_name = 'masoudmzb/wav2vec2-xlsr-multilingual-53-fa'
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 
-wav2vec_model = Wav2Vec2ForCTC.from_pretrained(model_name).to(device).eval()
-processor = Wav2Vec2Processor.from_pretrained(model_name)
+# Create an instance
 s2t = Speech2Text()
-
-def asr(path):
-    return s2t(path)
     
-gr.Interface(asr,
+gr.Interface(lambda path: s2t(path),
              inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Record Your Beautiful Persian Voice"),
              outputs = gr.outputs.Textbox(label="Output Text"),
              title="Persian ASR using Wav2Vec 2.0 & N-gram LM",