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import gradio as gr
import pandas as pd
from css_html_js import custom_css
TITLE = """<h1 align="center" id="space-title">🇲🇾 Malaysian Speech-to-Text Leaderboard</h1>"""
INTRODUCTION_TEXT = """
📐 The 🇲🇾 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
## Dataset
📈 We evaluate models based on 2 datasets,
1. Fleurs MY-MS test set, Malay language, https://huggingface.co/datasets/malaysia-ai/fleurs-my-ms
2. IMDA TTS first 700 audio files, English language but with Manglish slang, https://huggingface.co/datasets/mesolitica/IMDA-TTS
During test we,
1. Lowercase.
2. Remove punctuations.
"""
open_source = [
{
'model': 'openai/whisper-large-v3',
'Fleurs MY-MS CER': 0.027414635425413655,
'Fleurs MY-MS WER': 0.0912705436045907,
'IMDA TTS CER': 0.016648493852990828,
'IMDA TTS WER': 0.0386282289139432,
},
{
'model': 'openai/whisper-medium',
'Fleurs MY-MS CER': 0.045260198639505075,
'Fleurs MY-MS WER': 0.14913723876746685,
'IMDA TTS CER': 0.02065587879424904,
'IMDA TTS WER': 0.047277690563404855,
},
{
'model': 'openai/whisper-small',
'Fleurs MY-MS CER': 0.07028889922090295,
'Fleurs MY-MS WER': 0.2327510905228186,
'IMDA TTS CER': 0.024812471688517194,
'IMDA TTS WER': 0.058901277294134434,
},
{
'model': 'openai/whisper-base',
'Fleurs MY-MS CER': 0.24820848114299138,
'Fleurs MY-MS WER': 0.5164123884823085,
'IMDA TTS CER': 0.03914533450681607,
'IMDA TTS WER': 0.08951682444539587,
},
{
'model': 'mesolitica/malaysian-whisper-medium',
},
{
'model': 'mesolitica/malaysian-whisper-small',
'Fleurs MY-MS CER': 0.03596621199151582,
'Fleurs MY-MS WER': 0.12024457480764372,
'IMDA TTS CER': 0.024228721439634855,
'IMDA TTS WER': 0.05546294182008469,
},
{
'model': 'mesolitica/malaysian-whisper-base',
'Fleurs MY-MS CER': 0.07478803508650385,
'Fleurs MY-MS WER': 0.21823941044294087,
'IMDA TTS CER': 0.03982418421412676,
'IMDA TTS WER': 0.08917690642690643,
},
{
'model': 'mesolitica/malaysian-whisper-tiny',
},
]
data = pd.DataFrame(open_source)
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
gr.DataFrame(data)
demo.launch() |