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app.py

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+
+import gradio as gr
+from transformers import pipeline
+from pathlib import Path
+
+examples = list(Path().rglob('*mp3'))
+
+model_id = 'SamuelM0422/whisper-small-pt'
+pipe = pipeline('automatic-speech-recognition', model=model_id)
+
+def transcribe_speech(filepath):
+  output = pipe(
+      filepath,
+      max_new_tokens=256,
+      generate_kwargs={
+          'task': 'transcribe',
+          'language': 'portuguese'
+      },
+      chunck_length_s=30,
+      batch_size=8
+  )
+  return output['text']
+
+demo = gr.Blocks()
+
+title='Audio Transcriber (PT) 🎙️'
+description='A fine-tuned Whisper model for the Portuguese language.'
+
+mic_transcribe = gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(sources='microphone', type='filepath'),
+    outputs=gr.components.Textbox(),
+    flagging_mode='never',
+    examples=examples,
+    description=description
+)
+
+file_transcribe=gr.Interface(
+    fn=transcribe_speech,
+    inputs=gr.Audio(sources='upload', type='filepath'),
+    outputs=gr.components.Textbox(),
+    flagging_mode='never',
+    examples=examples,
+    description=description
+)
+
+with demo:
+  gr.TabbedInterface([mic_transcribe, file_transcribe], ['Transcribe Microphone', 'Transcribe Audio File'], 
+                     title=title)
+
+
+demo.launch()