Update app.py

app.py

@@ -35,11 +35,22 @@ def parse_transcription(logits):
     transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
     return transcription
 
+# def parse(wav_file, applyLM):
+#     input_values = read_file_and_process(wav_file)
+#     with torch.no_grad():
+#         logits = model(**input_values).logits
+   
+#     if applyLM:
+#         # return parse_transcription_with_lm(logits)
+#         return "done"
+#     else:
+#         return parse_transcription(logits)
+
 def parse(wav_file, applyLM):
     input_values = read_file_and_process(wav_file)
     with torch.no_grad():
         logits = model(**input_values).logits
-   
+
     if applyLM:
         # return parse_transcription_with_lm(logits)
         return "done"
@@ -52,7 +63,7 @@ def parse(wav_file, applyLM):
 # model_id = "Harveenchadha/odia_large_wav2vec2"
 
 # It worked when first run but after that getting error
-model_id = "anuragshas/wav2vec2-large-xlsr-53-odia"
+# model_id = "anuragshas/wav2vec2-large-xlsr-53-odia"
 
 # model_id = "theainerd/wav2vec2-large-xlsr-53-odia"
 
@@ -62,21 +73,59 @@ model_id = "anuragshas/wav2vec2-large-xlsr-53-odia"
 
 # model_id = "theainerd/wav2vec2-large-xlsr-53-odia"
 
+# This is hindi
+model_id = "Harveenchadha/vakyansh-wav2vec2-hindi-him-4200"
+
+
+
+# processor = Wav2Vec2Processor.from_pretrained(model_id)
+# # processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
+# model = Wav2Vec2ForCTC.from_pretrained(model_id)
+
+    
+# input_ = gr.Audio(source="microphone", type="filepath") 
+# txtbox = gr.Textbox(
+#             label="Output from model will appear here:",
+#             lines=5
+#         )
+# chkbox = gr.Checkbox(label="Apply LM", value=False)
+
+
+# gr.Interface(parse, inputs = [input_, chkbox],  outputs=txtbox,
+#              streaming=True, interactive=True,
+#              analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);
+
+
+
 
 
 processor = Wav2Vec2Processor.from_pretrained(model_id)
-# processor_with_LM = Wav2Vec2ProcessorWithLM.from_pretrained(model_id)
 model = Wav2Vec2ForCTC.from_pretrained(model_id)
 
-    
-input_ = gr.Audio(source="microphone", type="filepath") 
+input_ = gr.inputs.File(source="upload", type="audio")  # Change input source to "upload" and type to "audio"
 txtbox = gr.Textbox(
-            label="Output from model will appear here:",
-            lines=5
-        )
+    label="Output from the model will appear here:",
+    lines=5
+)
 chkbox = gr.Checkbox(label="Apply LM", value=False)
 
-
-gr.Interface(parse, inputs = [input_, chkbox],  outputs=txtbox,
+gr.Interface(parse, inputs=[input_, chkbox], outputs=txtbox,
              streaming=True, interactive=True,
-             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);
+             analytics_enabled=False, show_tips=False, enable_queue=True).launch(inline=False);
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