Create app.py

app.py

@@ -0,0 +1,21 @@
+import soundfile as sf
+import gradio as gr
+import torch
+from transformers import Speech2Text2Processor, SpeechEncoderDecoder
+model = SpeechEncoderDecoder.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
+processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
+def map_to_array(file):
+   speech, _ = sf.read(file)
+   return speech
+ 
+def inference(audio):
+  inputs = processor(map_to_array(audio.name), sampling_rate=16_000, return_tensors="pt")
+  generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
+  transcription = processor.batch_decode(generated_ids)
+  return transcription[0]
+inputs = gr.inputs.Audio(label="Input Audio", type="file")
+outputs =  gr.outputs.Textbox(label="Output Text")
+title = "Robust wav2vec 2.0"
+description = "Gradio demo for Robust wav2vec 2.0. To use it, simply upload your audio, or click one of the examples to load them. Read more at the links below. Currently supports .wav and .flac files"
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2104.01027' target='_blank'>Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training</a> | <a href='https://github.com/pytorch/fairseq' target='_blank'>Github Repo</a></p>"
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article).launch()