from transformers import pipeline import gradio as gr from pytube import YouTube pipe = pipeline(model="howlbz/whisper-small-hi") # 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) #