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app.py

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+import gradio as gr
+from transformers import pipeline
+
+
+
+#def predict(image):
+#  predictions = pipeline(image)
+ #  return {p["label"]: p["score"] for p in predictions}
+
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+ from datasets import load_dataset
+ import torch
+ 
+ def predict(speech):
+ # load model and tokenizer
+   processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+   model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+ 
+   #pipeline = pipeline(task="speech-classification", model="facebook/wav2vec2-base-960h")
+     
+ # load dummy dataset and read soundfiles
+   ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+ 
+ # tokenize
+   input_values = processor(ds[0]["audio"]["array"], return_tensors="pt", padding="longest").input_values  # Batch size 1
+ 
+ # retrieve logits
+   logits = model(input_values).logits
+ 
+ # take argmax and decode
+   predicted_ids = torch.argmax(logits, dim=-1)
+   transcription = processor.batch_decode(predicted_ids)
+   return transcription
+   
+demo = gr.Interface(fn=speech, inputs="text", outputs="text")
+
+demo.launch()
+
+
+#gr.Interface(
+#    predict,
+#    inputs=gr.inputs.speech(label="Upload", type="filepath"),
+#    outputs=gr.outputs.Label(num_top_classes=2),
+#    title="Audio",
+#).launch()