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import gradio as gr |
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from transformers import pipeline |
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import numpy as np |
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transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") |
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def transcribe(stream, new_chunk): |
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sr, y = new_chunk |
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y = y.astype(np.float32) |
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y /= np.max(np.abs(y)) |
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if stream is not None: |
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stream = np.concatenate([stream, y]) |
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else: |
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stream = y |
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return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"] |
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demo = gr.Interface( |
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transcribe, |
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["state", gr.Audio(sources=["microphone"], streaming=True)], |
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["state", "text"], |
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live=True, |
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) |
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demo.launch() |
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import base64 |
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import pandas as pd |
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data = {'Column1': [1, 2], 'Column2': [3, 4]} |
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df = pd.DataFrame(data) |
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def to_base64_csv(df): |
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csv = df.to_csv(index=False) |
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b64 = base64.b64encode(csv.encode()).decode() |
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return f"data:text/csv;base64,{b64}" |
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def to_base64_txt(df): |
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txt = df.to_csv(index=False, sep='\t') |
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b64 = base64.b64encode(txt.encode()).decode() |
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return f"data:text/plain;base64,{b64}" |
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csv_link = to_base64_csv(df) |
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txt_link = to_base64_txt(df) |
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markdown_csv_link = f"**[📥 Download Dataset as CSV]({csv_link})**" |
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markdown_txt_link = f"**[📥 Download Dataset as TXT]({txt_link})**" |
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print(markdown_csv_link) |
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print(markdown_txt_link) |
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import gradio as gr |
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def process_live_input(input_stream): |
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processed_output = some_processing_function(input_stream) |
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return processed_output |
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iface = gr.Interface(fn=process_live_input, |
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inputs=gr.inputs.Video(source="webcam", streaming=True), |
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outputs="video") |
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iface.launch() |
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