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| import pandas as pd | |
| import streamlit as st | |
| from huggingface_hub import HfApi | |
| from utils import ascending_metrics, metric_ranges, CV11_LANGUAGES, FLEURS_LANGUAGES | |
| import numpy as np | |
| from st_aggrid import AgGrid, GridOptionsBuilder, JsCode | |
| from os.path import exists | |
| import threading | |
| st.set_page_config(layout="wide") | |
| def get_model_infos(): | |
| api = HfApi() | |
| model_infos = api.list_models(filter="model-index", cardData=True) | |
| return model_infos | |
| def parse_metric_value(value): | |
| if isinstance(value, str): | |
| "".join(value.split("%")) | |
| try: | |
| value = float(value) | |
| except: # noqa: E722 | |
| value = None | |
| elif isinstance(value, list): | |
| if len(value) > 0: | |
| value = value[0] | |
| else: | |
| value = None | |
| value = round(value, 4) if isinstance(value, float) else None | |
| return value | |
| def parse_metrics_rows(meta, only_verified=False): | |
| if not isinstance(meta["model-index"], list) or len(meta["model-index"]) == 0 or "results" not in meta["model-index"][0]: | |
| return None | |
| for result in meta["model-index"][0]["results"]: | |
| if not isinstance(result, dict) or "dataset" not in result or "metrics" not in result or "type" not in result["dataset"]: | |
| continue | |
| dataset = result["dataset"]["type"] | |
| if dataset == "": | |
| continue | |
| row = {"dataset": dataset, "split": "-unspecified-", "config": "-unspecified-"} | |
| if "split" in result["dataset"]: | |
| row["split"] = result["dataset"]["split"] | |
| if "config" in result["dataset"]: | |
| row["config"] = result["dataset"]["config"] | |
| no_results = True | |
| incorrect_results = False | |
| for metric in result["metrics"]: | |
| name = metric["type"].lower().strip() | |
| if name in ("model_id", "dataset", "split", "config", "pipeline_tag", "only_verified"): | |
| # Metrics are not allowed to be named "dataset", "split", "config", "pipeline_tag" | |
| continue | |
| value = parse_metric_value(metric.get("value", None)) | |
| if value is None: | |
| continue | |
| if name in row: | |
| new_metric_better = value < row[name] if name in ascending_metrics else value > row[name] | |
| if name not in row or new_metric_better: | |
| # overwrite the metric if the new value is better. | |
| if only_verified: | |
| if "verified" in metric and metric["verified"]: | |
| no_results = False | |
| row[name] = value | |
| if name in metric_ranges: | |
| if value < metric_ranges[name][0] or value > metric_ranges[name][1]: | |
| incorrect_results = True | |
| else: | |
| no_results = False | |
| row[name] = value | |
| if name in metric_ranges: | |
| if value < metric_ranges[name][0] or value > metric_ranges[name][1]: | |
| incorrect_results = True | |
| if no_results or incorrect_results: | |
| continue | |
| yield row | |
| def get_data_wrapper(): | |
| def get_data(dataframe=None, verified_dataframe=None): | |
| data = [] | |
| verified_data = [] | |
| print("getting model infos") | |
| model_infos = get_model_infos() | |
| print("got model infos") | |
| for model_info in model_infos: | |
| meta = model_info.cardData | |
| if meta is None: | |
| continue | |
| for row in parse_metrics_rows(meta): | |
| if row is None: | |
| continue | |
| row["model_id"] = model_info.id | |
| row["pipeline_tag"] = model_info.pipeline_tag | |
| row["only_verified"] = False | |
| data.append(row) | |
| for row in parse_metrics_rows(meta, only_verified=True): | |
| if row is None: | |
| continue | |
| row["model_id"] = model_info.id | |
| row["pipeline_tag"] = model_info.pipeline_tag | |
| row["only_verified"] = True | |
| data.append(row) | |
| dataframe = pd.DataFrame.from_records(data) | |
| dataframe.to_pickle("cache.pkl") | |
| if exists("cache.pkl"): | |
| # If we have saved the results previously, call an asynchronous process | |
| # to fetch the results and update the saved file. Don't make users wait | |
| # while we fetch the new results. Instead, display the old results for | |
| # now. The new results should be loaded when this method | |
| # is called again. | |
| dataframe = pd.read_pickle("cache.pkl") | |
| t = threading.Thread(name="get_data procs", target=get_data) | |
| t.start() | |
| else: | |
| # We have to make the users wait during the first startup of this app. | |
| get_data() | |
| dataframe = pd.read_pickle("cache.pkl") | |
| return dataframe | |
| dataframe = get_data_wrapper() | |
| st.markdown("# 🤗 Whisper Event: Final Leaderboard") | |
| # query params are used to refine the browser URL as more options are selected | |
| query_params = st.experimental_get_query_params() | |
| if "first_query_params" not in st.session_state: | |
| st.session_state.first_query_params = query_params | |
| first_query_params = st.session_state.first_query_params | |
| # define the scope of the leaderboard | |
| only_verified_results = False | |
| task = "automatic-speech-recognition" | |
| selectable_datasets = ["mozilla-foundation/common_voice_11_0", "google/fleurs"] | |
| dataset_mapping = {"mozilla-foundation/common_voice_11_0": "Common Voice 11", "google/fleurs": "FLEURS"} # get a 'pretty' name for our datasets | |
| split = "test" | |
| selectable_metrics = ["wer", "cer"] | |
| default_metric = selectable_metrics[0] | |
| # select dataset from list provided | |
| dataset = st.sidebar.selectbox( | |
| "Dataset", | |
| selectable_datasets, | |
| help="Select a dataset to see the leaderboard!" | |
| ) | |
| dataset_name = dataset_mapping[dataset] | |
| # slice dataframe to entries of interest | |
| dataframe = dataframe[dataframe.only_verified == only_verified_results] | |
| dataset_df = dataframe[dataframe.dataset == dataset] | |
| dataset_df = dataset_df[dataset_df.split == split] # hardcoded to "test" | |
| dataset_df = dataset_df.dropna(axis="columns", how="all") | |
| # get potential dataset configs (languages) | |
| selectable_configs = list(set(dataset_df["config"])) | |
| selectable_configs.sort(key=lambda name: name.lower()) | |
| if "-unspecified-" in selectable_configs: | |
| selectable_configs.remove("-unspecified-") | |
| if dataset == "mozilla-foundation/common_voice_11_0": | |
| selectable_configs = [config for config in selectable_configs if config in CV11_LANGUAGES] | |
| visual_configs = [f"{config}: {CV11_LANGUAGES[config]}" for config in selectable_configs] | |
| elif dataset == "google/fleurs": | |
| selectable_configs = [config for config in selectable_configs if config in FLEURS_LANGUAGES] | |
| visual_configs = [f"{config}: {FLEURS_LANGUAGES[config]}" for config in selectable_configs] | |
| config = st.sidebar.selectbox( | |
| "Language", | |
| visual_configs, | |
| help="Filter the results on the current leaderboard by language." | |
| ) | |
| config, language = config.split(":") | |
| # just for show -> we've fixed the split to "test" | |
| split = st.sidebar.selectbox( | |
| "Split", | |
| [split], | |
| index=0, | |
| help="View the results for the `test` split for evaluation performance.", | |
| ) | |
| # update browser URL with selections | |
| current_query_params = {"dataset": [dataset], "config": [config], "split": split} | |
| st.experimental_set_query_params(**current_query_params) | |
| dataset_df = dataset_df[dataset_df.config == config] | |
| dataset_df = dataset_df.filter(["model_id"] + (["dataset"] if dataset == "-any-" else []) + selectable_metrics) | |
| dataset_df = dataset_df.dropna(thresh=2) # Want at least two non-na values (one for model_id and one for a metric). | |
| sorting_metric = st.sidebar.radio( | |
| "Sorting Metric", | |
| selectable_metrics, | |
| index=selectable_metrics.index(default_metric) if default_metric in selectable_metrics else 0, | |
| help="Select the metric to sort the leaderboard by. Click on the metric name in the leaderboard to reverse the sorting order." | |
| ) | |
| st.markdown( | |
| f"This is the leaderboard for {dataset_name} {language} ({config})." | |
| ) | |
| st.markdown( | |
| "Please click on the model's name to be redirected to its model card." | |
| ) | |
| st.markdown( | |
| "Want to beat the leaderboard? Don't see your model here? Ensure..." | |
| ) | |
| # Make the default metric appear right after model names and dataset names | |
| cols = dataset_df.columns.tolist() | |
| cols.remove(sorting_metric) | |
| sorting_metric_index = 1 if dataset != "-any-" else 2 | |
| cols = cols[:sorting_metric_index] + [sorting_metric] + cols[sorting_metric_index:] | |
| dataset_df = dataset_df[cols] | |
| # Sort the leaderboard, giving the sorting metric highest priority and then ordering by other metrics in the case of equal values. | |
| dataset_df = dataset_df.sort_values(by=cols[sorting_metric_index:], ascending=[metric in ascending_metrics for metric in cols[sorting_metric_index:]]) | |
| dataset_df = dataset_df.replace(np.nan, '-') | |
| # Make the leaderboard | |
| gb = GridOptionsBuilder.from_dataframe(dataset_df) | |
| gb.configure_default_column(sortable=False) | |
| gb.configure_column( | |
| "model_id", | |
| cellRenderer=JsCode('''function(params) {return '<a target="_blank" href="https://huggingface.co/'+params.value+'">'+params.value+'</a>'}'''), | |
| ) | |
| for name in selectable_metrics: | |
| gb.configure_column(name, type=["numericColumn", "numberColumnFilter", "customNumericFormat"], precision=2, aggFunc='sum') | |
| gb.configure_column( | |
| sorting_metric, | |
| sortable=True, | |
| cellStyle=JsCode('''function(params) { return {'backgroundColor': '#FFD21E'}}''') | |
| ) | |
| go = gb.build() | |
| fit_columns = len(dataset_df.columns) < 10 | |
| AgGrid(dataset_df, gridOptions=go, height=28*len(dataset_df) + (35 if fit_columns else 41), allow_unsafe_jscode=True, fit_columns_on_grid_load=fit_columns, enable_enterprise_modules=False) | |