Create app.py

app.py

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+#Importing all the necessary packages
+import torch, librosa, torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+from pyctcdecode import build_ctcdecoder
+
+# Define ASR MODEL
+class Speech2Text:
+    def __init__(self):
+        self.vocab = list(processor.tokenizer.get_vocab().keys())
+        self.decoder = build_ctcdecoder(self.vocab, kenlm_model_path=None)
+
+    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)
+
+    def feature2logits(self, features):
+        with torch.no_grad():
+            return model(features.input_values.to(device),
+                         attention_mask=features.attention_mask.to(device)).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)
+s2t = Speech2Text()
+
+  
+
+gr.Interface(s2t,
+             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",
+             description = "This application displays transcribed text for given audio input",
+             examples = [["Test_File1.wav"]], theme="grass").launch()