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| import requests | |
| import pandas as pd | |
| from tqdm.auto import tqdm | |
| import streamlit as st | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| cer_langs = ["ja", "zh-CN", "zh-HK", "zh-TW"] | |
| def make_clickable(model_name): | |
| link = "https://huggingface.co/" + model_name | |
| return f'<a target="_blank" href="{link}">{model_name}</a>' | |
| def get_model_ids(): | |
| api = HfApi() | |
| models = api.list_models(filter="robust-speech-event") | |
| model_ids = [x.modelId for x in models] | |
| return model_ids | |
| def get_metadata(model_id): | |
| try: | |
| readme_path = hf_hub_download(model_id, filename="README.md") | |
| return metadata_load(readme_path) | |
| except requests.exceptions.HTTPError: | |
| # 404 README.md not found | |
| return None | |
| def parse_metric_value(value): | |
| if isinstance(value, str): | |
| "".join(value.split("%")) | |
| try: | |
| value = float(value) | |
| except: # noqa: E722 | |
| value = None | |
| elif isinstance(value, float) and value < 1.1: | |
| # assuming that WER is given in 0.xx format | |
| value = 100 * value | |
| elif isinstance(value, list): | |
| if len(value) > 0: | |
| value = value[0] | |
| else: | |
| value = None | |
| value = round(value, 2) if value is not None else None | |
| return value | |
| def parse_metrics_rows(meta): | |
| if "model-index" not in meta or "language" not in meta: | |
| return None | |
| lang = meta["language"] | |
| lang = lang[0] if isinstance(lang, list) else lang | |
| for result in meta["model-index"][0]["results"]: | |
| if "dataset" not in result or "metrics" not in result: | |
| continue | |
| dataset = result["dataset"]["type"] | |
| if "args" not in result["dataset"]: | |
| continue | |
| dataset_config = result["dataset"]["args"] | |
| row = {"dataset": dataset, "lang": lang} | |
| for metric in result["metrics"]: | |
| type = metric["type"].lower().strip() | |
| if type not in ["wer", "cer"]: | |
| continue | |
| value = parse_metric_value(metric["value"]) | |
| if value is None: | |
| continue | |
| if type not in row or value < row[type]: | |
| # overwrite the metric if the new value is lower (e.g. with LM) | |
| row[type] = value | |
| if "wer" in row or "cer" in row: | |
| yield row | |
| def get_data(): | |
| data = [] | |
| model_ids = get_model_ids() | |
| for model_id in tqdm(model_ids): | |
| meta = get_metadata(model_id) | |
| if meta is None: | |
| continue | |
| for row in parse_metrics_rows(meta): | |
| if row is None: | |
| continue | |
| row["model_id"] = model_id | |
| data.append(row) | |
| return pd.DataFrame.from_records(data) | |
| dataframe = get_data() | |
| dataframe = dataframe.fillna("") | |
| dataframe["model_id"] = dataframe["model_id"].apply(make_clickable) | |
| _, col_center = st.columns([3, 6]) | |
| with col_center: | |
| st.image("logo.png", width=200) | |
| st.markdown("# Speech Models Leaderboard") | |
| lang = st.selectbox( | |
| "Language", | |
| sorted(dataframe["lang"].unique()), | |
| index=0, | |
| ) | |
| lang_df = dataframe[dataframe.lang == lang] | |
| dataset = st.selectbox( | |
| "Dataset", | |
| sorted(lang_df["dataset"].unique()), | |
| index=0, | |
| ) | |
| dataset_df = lang_df[lang_df.dataset == dataset] | |
| if lang in cer_langs: | |
| dataset_df = dataset_df[["model_id", "cer"]] | |
| dataset_df.sort_values("cer", inplace=True) | |
| else: | |
| dataset_df = dataset_df[["model_id", "wer"]] | |
| dataset_df.sort_values("wer", inplace=True) | |
| dataset_df.rename( | |
| columns={ | |
| "model_id": "Model", | |
| "wer": "WER (lower is better)", | |
| "cer": "CER (lower is better)", | |
| }, | |
| inplace=True, | |
| ) | |
| st.write(dataset_df.to_html(escape=False, index=None), unsafe_allow_html=True) | |