Upload app.py

app.py

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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)
+#