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added scores
Browse files- .gitignore +1 -0
- app.py +82 -0
- css_html_js.py +111 -0
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__pycache__
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app.py
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import gradio as gr
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import pandas as pd
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from css_html_js import custom_css
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TITLE = """<h1 align="center" id="space-title">π²πΎ Malaysian Speech-to-Text Leaderboard</h1>"""
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INTRODUCTION_TEXT = """
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π The π²πΎ Malaysian Speech-to-Text Leaderboard aims to track, rank and evaluate Malaysian Speech-to-Text models. All notebooks at https://github.com/mesolitica/malaysian-stt-benchmarks
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## Dataset
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π We evaluate models based on 2 datasets,
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1. Fleurs MY-MS test set, Malay language, https://huggingface.co/datasets/malaysia-ai/fleurs-my-ms
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2. IMDA TTS first 700 audio files, English language but with Manglish slang, https://huggingface.co/datasets/mesolitica/IMDA-TTS
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During test we,
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1. Lowercase.
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2. Remove punctuations.
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"""
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open_source = [
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{
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'model': 'openai/whisper-large-v3',
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'Fleurs MY-MS CER': 0.027414635425413655,
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'Fleurs MY-MS WER': 0.0912705436045907,
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'IMDA TTS CER': 0.016648493852990828,
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'IMDA TTS WER': 0.0386282289139432,
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},
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{
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'model': 'openai/whisper-medium',
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'Fleurs MY-MS CER': 0.045260198639505075,
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'Fleurs MY-MS WER': 0.14913723876746685,
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'IMDA TTS CER': 0.02065587879424904,
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'IMDA TTS WER': 0.047277690563404855,
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},
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{
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'model': 'openai/whisper-small',
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'Fleurs MY-MS CER': 0.07028889922090295,
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'Fleurs MY-MS WER': 0.2327510905228186,
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'IMDA TTS CER': 0.024812471688517194,
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'IMDA TTS WER': 0.058901277294134434,
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},
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{
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'model': 'openai/whisper-base',
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'Fleurs MY-MS CER': 0.24820848114299138,
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'Fleurs MY-MS WER': 0.5164123884823085,
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'IMDA TTS CER': 0.03914533450681607,
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'IMDA TTS WER': 0.08951682444539587,
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},
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{
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'model': 'mesolitica/malaysian-whisper-medium',
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},
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{
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'model': 'mesolitica/malaysian-whisper-small',
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'Fleurs MY-MS CER': 0.03596621199151582,
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'Fleurs MY-MS WER': 0.12024457480764372,
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'IMDA TTS CER': 0.024228721439634855,
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'IMDA TTS WER': 0.05546294182008469,
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},
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{
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'model': 'mesolitica/malaysian-whisper-base',
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'Fleurs MY-MS CER': 0.07478803508650385,
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'Fleurs MY-MS WER': 0.21823941044294087,
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'IMDA TTS CER': 0.03982418421412676,
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'IMDA TTS WER': 0.08917690642690643,
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},
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{
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'model': 'mesolitica/malaysian-whisper-tiny',
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},
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]
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data = pd.DataFrame(open_source)
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demo = gr.Blocks(css=custom_css)
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with demo:
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gr.HTML(TITLE)
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gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
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gr.DataFrame(data)
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demo.launch()
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css_html_js.py
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custom_css = """
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.markdown-text {
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font-size: 16px !important;
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}
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#models-to-add-text {
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font-size: 18px !important;
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}
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#citation-button span {
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font-size: 16px !important;
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}
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#citation-button textarea {
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font-size: 16px !important;
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}
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#citation-button > label > button {
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margin: 6px;
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transform: scale(1.3);
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}
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#leaderboard-table {
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margin-top: 15px
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}
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#leaderboard-table-lite {
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margin-top: 15px
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}
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#search-bar-table-box > div:first-child {
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background: none;
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border: none;
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}
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#search-bar {
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padding: 0px;
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}
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/* Hides the final AutoEvalColumn */
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#llm-benchmark-tab-table table td:last-child,
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#llm-benchmark-tab-table table th:last-child {
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display: none;
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}
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/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
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table td:first-child,
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table th:first-child {
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max-width: 400px;
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overflow: auto;
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white-space: nowrap;
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}
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.tab-buttons button {
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font-size: 20px;
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}
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#scale-logo {
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border-style: none !important;
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box-shadow: none;
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display: block;
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margin-left: auto;
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margin-right: auto;
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max-width: 600px;
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}
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#scale-logo .download {
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display: none;
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}
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#filter_type{
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border: 0;
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padding-left: 0;
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padding-top: 0;
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}
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#filter_type label {
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display: flex;
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}
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#filter_type label > span{
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margin-top: var(--spacing-lg);
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margin-right: 0.5em;
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}
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#filter_type label > .wrap{
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width: 103px;
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}
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#filter_type label > .wrap .wrap-inner{
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padding: 2px;
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}
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#filter_type label > .wrap .wrap-inner input{
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width: 1px
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}
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#filter-columns-type{
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border:0;
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padding:0.5;
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}
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#filter-columns-size{
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border:0;
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padding:0.5;
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}
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#box-filter > .form{
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border: 0
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}
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"""
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get_window_url_params = """
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function(url_params) {
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const params = new URLSearchParams(window.location.search);
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url_params = Object.fromEntries(params);
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return url_params;
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}
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"""
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