Update app.py

app.py

@@ -1,27 +1,42 @@
 
+# import gradio as gr
+# from googletrans import Translator
+# import torch
+# # Initialize Translator
+# from transformers import pipeline
+# translator = Translator()
+# MODEL_NAME = "openai/whisper-base"
+# device = 0 if torch.cuda.is_available() else "cpu"
+# pipe = pipeline(
+#     task="automatic-speech-recognition",
+#     model=MODEL_NAME,
+#     chunk_length_s=30,
+#     device=device,
+# )
+
+
+# def transcribe_audio(audio):
+#    text = pipe(audio)["text"]
+#    return text
+#     # return translated_text
+
+# audio_record = gr.inputs.Audio(source='microphone', label='Record Audio')
+# output_text = gr.outputs.Textbox(label='Transcription')
+
+# interface = gr.Interface(fn=transcribe_audio, inputs=audio_record, outputs=output_text)
+# interface.launch()
+
 import gradio as gr
-from googletrans import Translator
-import torch
-# Initialize Translator
 from transformers import pipeline
-translator = Translator()
-MODEL_NAME = "openai/whisper-base"
-device = 0 if torch.cuda.is_available() else "cpu"
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-
-def transcribe_audio(audio):
-   text = pipe(audio)["text"]
-   return text
-    # return translated_text
-
-audio_record = gr.inputs.Audio(source='microphone', label='Record Audio')
-output_text = gr.outputs.Textbox(label='Transcription')
-
-interface = gr.Interface(fn=transcribe_audio, inputs=audio_record, outputs=output_text)
-interface.launch()
+
+modelo = pipeline("automatic-speech-recognition", model="openai/whisper-base")
+
+def transcribe(audio):
+  text = modelo(audio)["text"]
+  return text
+
+gr.Interface(
+    fn=transcribe,
+    inputs=[gr.Audio(source="microphone", type="filepath")],
+    outputs=["textbox"]
+).launch()