Update app.py

app.py

@@ -1,18 +1,26 @@
+import os
+os.system('pip freeze')
+os.system('pip install transformers --upgrade')
+os.system('pip freeze')
 import soundfile as sf
 import gradio as gr
 import torch
-from transformers import pipeline
-
-asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-de", feature_extractor="facebook/s2t-wav2vec2-large-en-de")
-
+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):
-  translation_de = asr(audio.name)
-  return translation_de[0]
-  
+  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()
+examples=[['poem.wav']]
+gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()