fix

README.md

@@ -1,13 +1,13 @@
 ---
-title: SpeechLeaderboard
-emoji: 💩
-colorFrom: gray
-colorTo: gray
+title: Speech Recognition Leaderboard
+emoji: 📈
+colorFrom: red
+colorTo: yellow
 sdk: streamlit
-sdk_version: 1.2.0
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
 ---
 
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

app.py

@@ -0,0 +1,132 @@
+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 < 1.0:
+        # 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)

requirements.txt

@@ -0,0 +1 @@
+pandas