app.py

import gradio as gr
from transformers import pipeline,WhisperProcessor, WhisperForConditionalGeneration
import torch
import librosa
import datasets
from transformers.pipelines.pt_utils import KeyDataset
from tqdm.auto import tqdm

transcriber = pipeline(model="openai/whisper-large-v2",device_map="auto")
# checkpoint = "/innev/open-ai/huggingface/openai/whisper-base"
image_to_text_model = pipeline("image-classification")
text_to_audio_model = pipeline("text-to-speech")
pipe_audio = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
dataset = datasets.load_dataset("superb", name="asr", split="test")

for out in tqdm(pipe(KeyDataset(dataset, "file"))):
    print(out)
    # {"text": "NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD NIGHT HUSBAND"}
    # {"text": ....}
    # ....
def image_to_text(input_image):
    # Convertir la imagen a texto
    text_output = image_to_text_model(input_image)[0]['label']
    print(text_output)
    #texts = transcriber(text_output)
    return text_output

#with gr.Blocks() as demo:
#    gr.Markdown("Start typing below and then click **Run** to see the output.")
#    with gr.Row():
#        inp = gr.Image()
#        out = gr.Textbox(placeholder=image_to_text(inp))
#    gr.Interface(fn=image_to_text, inputs=inp, outputs=out,interpretation="default")

#demo.launch()