Create app.py

app.py

@@ -0,0 +1,32 @@
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import gradio as gr
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "distil-whisper/distil-small.en"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+def audio2text(audio_file):
+  output=pipe(audio_file)
+  return output['text']
+
+
+gr.Interface(fn=audio2text, inputs=[gr.Audio, label='upload your audio file', source='upload', type='filepath'], outputs=[gr.Textbox, label="transcription"]).launch()