Update app.py

app.py

@@ -36,15 +36,15 @@ feat_ext3 = WhisperFeatureExtractor.from_pretrained(M3, use_auth_token=auth_toke
 
 
 # make quantized model
-quantized_model1 = torch.quantization.quantize_dynamic(
-    model1, {torch.nn.Linear}, dtype=torch.qint8
-)
+# quantized_model1 = torch.quantization.quantize_dynamic(
+#     model1, {torch.nn.Linear}, dtype=torch.qint8
+# )
 
 
 p1 = pipeline('automatic-speech-recognition', model=model1, tokenizer=tokenizer1, feature_extractor=feat_ext1)
 p2 = pipeline('automatic-speech-recognition', model=model2, tokenizer=tokenizer2, feature_extractor=feat_ext2)
 p3 = pipeline('automatic-speech-recognition', model=model3, tokenizer=tokenizer3, feature_extractor=feat_ext3)
-p1_quant = pipeline('automatic-speech-recognition', model=quantized_model1, tokenizer=tokenizer1, feature_extractor=feat_ext1)
+# p1_quant = pipeline('automatic-speech-recognition', model=quantized_model1, tokenizer=tokenizer1, feature_extractor=feat_ext1)
 
 def transcribe(mic_input, upl_input, model_type):
     if mic_input:
@@ -57,8 +57,8 @@ def transcribe(mic_input, upl_input, model_type):
         text = p2(audio)["text"]
     elif model_type == 'CleanFinetuned':
         text = p3(audio)["text"]
-    elif model_type == 'DistilledAndQuantised':
-        text = p1_quant(audio)['text']
+    # elif model_type == 'DistilledAndQuantised':
+    #     text = p1_quant(audio)['text']
     else:
         text = p1(audio)["text"]
     end_time = time.time()
@@ -115,7 +115,7 @@ if __name__ == "__main__":
                 )
 
                 with gr.Row():
-                    model_type = gr.inputs.Dropdown(["RobustDistillation", "NoisyFinetuned", "CleanFinetuned", "DistilledAndQuantised"], label='Model Type')
+                    model_type = gr.inputs.Dropdown(["RobustDistillation", "NoisyFinetuned", "CleanFinetuned"], label='Model Type')
 
                 with gr.Row():
                     clr_btn = gr.Button(value="Clear", variant="secondary")