Upload app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+import time
+
+p = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish")
+pc = pipeline("automatic-speech-recognition",model="softcatala/wav2vec2-large-xlsr-catala")
+pe = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-english")
+
+
+
+
+def transcribe(language,audio, state=""):#language="Spanish",
+    time.sleep(2)
+    if language=="Spanish":
+        state=""
+        text = p(audio)["text"]
+    if language=="Catalan":
+        state=""
+        text = pc(audio)["text"]
+    if language=="English":
+        state=""
+        text = pe(audio)["text"]
+    state += text + " "
+    #text2="Esto es loq ue te he entendido"
+    return state, state
+
+demo=gr.Interface(
+    fn=transcribe, 
+    
+    title="TEDCAS Offline Speech recognition",
+    description="1)Select language 2)Click on 'record from microphone' and talk 3)Click on 'stop recording' 4)Click on submit 5)Before starting again, click on 'clear'",
+
+    inputs=[
+        gr.Dropdown(["Spanish","Catalan","English"]),
+        #gr.Audio(source="microphone", type="filepath", streaming=True), 
+        gr.inputs.Audio(source="microphone", type="filepath"), 
+        "state"#,"language"
+    ],
+    outputs=[
+        "textbox",
+        "state"
+    ],
+    #live=True).launch()
+)
+demo.launch()
+#gr.Interface(
+#    fn=transcribe, 
+#    inputs=[
+#        gr.inputs.Audio(source="microphone", type="filepath"), 
+#        "state"
+#    ],
+#    outputs=[
+#        "textbox",
+#        "state"
+#    ],
+#    live=True).launch()