app.py

import gradio as gr
import transformers
#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()