| 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) | |
| # | |