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from transformers import pipeline
import gradio as gr
from pytube import YouTube
pipe = pipeline(model="kk90ujhun/whisper-small-zh") # change to "your-username/the-name-you-picked"
def transcribe(audio,url):
if url:
youtubeObject = YouTube(url).streams.first().download()
audio = youtubeObject
text = pipe(audio)["text"]
return text
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.Audio(source="microphone", type="filepath"),
gr.inputs.Textbox(label="give me an url",default ="https://www.youtube.com/watch?v=YzGsIavAo_E")
],
outputs="text",
title="Whisper Small Chinese",
description="Realtime demo for chinese speech recognition using a fine-tuned Whisper small model.",
)
iface.launch()
# import gradio as gr
# import numpy as np
# from PIL import Image
# import requests
#
# import hopsworks
# import joblib
#
# project = hopsworks.login()
# fs = project.get_feature_store()
#
# #HwJaWmtvaCzFra3g.89QYueFGuScRnJkiepzG2tiWtKSrqNHCCJrnVie9fwhIMeJxRUpAGAT7mF36MDMv
# mr = project.get_model_registry()
# model = mr.get_model("iris_modal", version=1)
# model_dir = model.download()
# model = joblib.load(model_dir + "/iris_model.pkl")
#
#
# def iris(sepal_length, sepal_width, petal_length, petal_width):
# input_list = []
# input_list.append(sepal_length)
# input_list.append(sepal_width)
# input_list.append(petal_length)
# input_list.append(petal_width)
# # 'res' is a list of predictions returned as the label.
# res = model.predict(np.asarray(input_list).reshape(1, -1))
# # We add '[0]' to the result of the transformed 'res', because 'res' is a list, and we only want
# # the first element.
# flower_url = "https://raw.githubusercontent.com/featurestoreorg/serverless-ml-course/main/src/01-module/assets/" + res[0] + ".png"
# img = Image.open(requests.get(flower_url, stream=True).raw)
# return img
#
# demo = gr.Interface(
# fn=iris,
# title="Iris Flower Predictive Analytics",
# description="Experiment with sepal/petal lengths/widths to predict which flower it is.",
# allow_flagging="never",
# inputs=[
# gr.inputs.Number(default=1.0, label="sepal length (cm)"),
# gr.inputs.Number(default=1.0, label="sepal width (cm)"),
# gr.inputs.Number(default=1.0, label="petal length (cm)"),
# gr.inputs.Number(default=1.0, label="petal width (cm)"),
# ],
# outputs=gr.Image(type="pil"))
#
# demo.launch(share = True)
#
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