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Update app.py
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app.py
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@@ -2,6 +2,69 @@ import os
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import gradio as gr
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from pipelines.pipeline import InferencePipeline
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pipelines = {
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"VSR(mediapipe)": InferencePipeline("./configs/LRS3_V_WER19.1.ini", device="cpu", face_track=True, detector="mediapipe"),
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"ASR": InferencePipeline("./configs/LRS3_A_WER1.0.ini", device="cpu", face_track=True, detector="mediapipe"),
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@@ -22,26 +85,11 @@ def fn(pipeline_type, filename):
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print(f"transcript: {transcript}")
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return transcript
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demo = gr.Blocks()
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with demo:
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gr.HTML(
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"""
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<div style="text-align: center; max-width: 1200px; margin: 20px auto;">
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<h1 style="font-weight: 900; font-size: 3rem; margin: 0rem">
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Auto-AVSR
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</h1>
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<h3 style="font-weight: 450; font-size: 1rem; margin: 0rem">
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[<a href="https://arxiv.org/abs/2303.14307" style="color:blue;">arXiv</a>]
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[<a href="https://github.com/mpc001/auto_avsr" style="color:blue;">Code</a>]
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</h3>
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<h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem">
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🔥 <b>Notes</b>: We share this demo only for non-commercial purposes.
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</h2>
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</div>
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""")
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dropdown_list = gr.inputs.Dropdown(["ASR", "VSR(mediapipe)", "AVSR(mediapipe)"], label="model")
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@@ -51,4 +99,6 @@ with demo:
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btn.click(fn, inputs=[dropdown_list, video_file], outputs=text)
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demo.launch()
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import gradio as gr
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from pipelines.pipeline import InferencePipeline
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TITLE = """
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<div style="text-align: center; max-width: 650px; margin: 0 auto;">
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<div
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style="
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display: inline-flex;
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align-items: center;
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gap: 0.8rem;
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font-size: 1.75rem;
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"
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>
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<h1 style="font-weight: 900; margin-bottom: 7px;">
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Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels
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</h1>
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<h3 style="font-weight: 450; font-size: 1rem; margin: 0rem">
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[<a href="https://arxiv.org/abs/2303.14307" style="color:blue;">arXiv</a>]
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[<a href="https://github.com/mpc001/auto_avsr" style="color:blue;">Code</a>]
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</h3>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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Want to recognise the content from audio or visual information?<br>The Auto-AVSR is here to get you answers!
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</p>
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</div>
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"""
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ARTICLE = """
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<div style="text-align: center; max-width: 650px; margin: 0 auto;">
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<p>
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Server busy? You can also run on <a href="https://colab.research.google.com/drive/1jfb6e4xxhXHbmQf-nncdLno1u0b4j614?usp=sharing">Google Colab</a>
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</p>
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<p>
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We share this demo only for non-commercial purposes.
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</p>
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</div>
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"""
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CSS = """
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#col-container {margin-left: auto; margin-right: auto;}
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a {text-decoration-line: underline; font-weight: 600;}
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.animate-spin {
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animation: spin 1s linear infinite;
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}
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@keyframes spin {
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from { transform: rotate(0deg); }
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to { transform: rotate(360deg); }
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}
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#share-btn-container {
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display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
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}
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#share-btn {
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all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
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}
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#share-btn * {
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all: unset;
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}
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#share-btn-container div:nth-child(-n+2){
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width: auto !important;
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min-height: 0px !important;
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}
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#share-btn-container .wrap {
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display: none !important;
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}
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"""
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pipelines = {
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"VSR(mediapipe)": InferencePipeline("./configs/LRS3_V_WER19.1.ini", device="cpu", face_track=True, detector="mediapipe"),
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"ASR": InferencePipeline("./configs/LRS3_A_WER1.0.ini", device="cpu", face_track=True, detector="mediapipe"),
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print(f"transcript: {transcript}")
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return transcript
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demo = gr.Blocks(css=CSS)
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with demo:
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gr.HTML(TITLE)
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dropdown_list = gr.inputs.Dropdown(["ASR", "VSR(mediapipe)", "AVSR(mediapipe)"], label="model")
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btn.click(fn, inputs=[dropdown_list, video_file], outputs=text)
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gr.HTML(ARTICLE)
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demo.launch()
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