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'{model_name}' 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 < 1.0: # 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_row(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: return row return None @st.cache(ttl=600) 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 row = parse_metrics_row(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)