app.py

import streamlit as st
from pandas import read_csv
import os
import jiwer
from huggingface_hub import Repository
import zipfile

REFERENCE_NAME = "references"
SUBMISSION_NAME = "submissions"

REFERENCE_URL = os.path.join(
    "https://huggingface.co/datasets/esc-bench", REFERENCE_NAME
)
SUBMISSION_URL = os.path.join(
    "https://huggingface.co/datasets/esc-bench", SUBMISSION_NAME
)

TEST_SETS = [
    "librispeech-clean",
    "librispeech-other",
    "common-voice-9",
    "vox-populi",
    "ted-lium",
    "giga-speech",
    "spgi-speech",
    "earnings-22",
    "ami",
]
EXPECTED_TEST_FILES = [f + ".txt" for f in TEST_SETS]
OPTIONAL_TEST_SETS = ["switch-board", "call-home", "chime-4"]

CSV_RESULTS_FILE = os.path.join(SUBMISSION_NAME, "results.csv")


HF_TOKEN = os.environ.get("HF_TOKEN")


def compute_wer(pred_file, ref_file):
    with open(pred_file, "r", encoding="utf-8") as pred, open(
        ref_file, "r", encoding="utf-8"
    ) as ref:
        pred_lines = [line.strip() for line in pred.readlines()]
        ref_lines = [line.strip() for line in ref.readlines()]

    wer = jiwer.wer(ref_lines, pred_lines)
    return wer


reference_repo = Repository(
    local_dir="references", clone_from=REFERENCE_URL, use_auth_token=HF_TOKEN
)
submission_repo = Repository(
    local_dir="submissions", clone_from=SUBMISSION_URL, use_auth_token=HF_TOKEN
)
submission_repo.git_pull()

all_submissions = [
    folder
    for folder in os.listdir(SUBMISSION_NAME)
    if os.path.isdir(os.path.join(SUBMISSION_NAME, folder)) and folder != ".git"
]


COLUMN_NAMES = {
    "librispeech-clean": "ls-clean",
    "librispeech-other": "ls-other",
    "common-voice-9": "cv9",
    "vox-populi": "vox",
    "ted-lium": "ted",
    "giga-speech": "giga",
    "spgi-speech": "spgi",
    "earnings-22": "e22",
    "ami": "ami",
    "chime-4": "chime",
    "switch-board": "swbd",
}

all_results = read_csv(CSV_RESULTS_FILE)


# Write table form CSV
table = all_results.copy()

esc_column = table.pop("esc-score")
name_column = table.pop("name")
table.insert(0, "esc-score", esc_column)
# TODO: revert to scaling raw WER by 100 to retrieve % point values
table = table.select_dtypes(exclude=['object', 'string'])  # * 100
table.insert(0, "name", name_column)
table = table.round(2)
table = table.rename(columns=COLUMN_NAMES)
# start indexing from 1
table.index = table.index + 1

# Streamlit
st.markdown("# ESC: A Benchmark For Multi-Domain End-to-End Speech Recognition")

st.markdown(
    f"""
    This is the leaderboard of the End-to end Speech Challenge (ESC).
    Submitted systems are ranked by the **ESC Score** which is the average of
    all non-optional datasets: {', '.join(COLUMN_NAMES.values())}."""
)

# st.table(table)
table

st.markdown(
    """
    ESC was proposed in *ESC: A Benchmark For Multi-Domain End-to-End Speech Recognition* by ...
    \n
    The abstract of the paper is as follows:
    \n
    *Speech recognition applications cover a range of different audio and text distributions, with different speaking styles, background noise, transcription punctuation and character casing. However, many speech recognition systems require dataset-specific tuning (audio filtering, punctuation removal and normalisation of casing), therefore assuming a-priori knowledge of both the audio and text distributions. This tuning requirement can lead to systems failing to generalise to other datasets and domains. To promote the development of multi-domain speech systems, we introduce the End-to end Speech Challenge (ESC) for evaluating the performance of a single automatic speech recognition (ASR) system across a broad set of speech datasets. Benchmarked systems must use the same data pre- and post-processing algorithm across datasets - assuming the audio and text data distributions are a-priori unknown. We compare a series of state-of-the-art (SoTA) end-to-end (E2E) systems on this benchmark, demonstrating how a single speechsystem can be applied and evaluated on a wide range of data distributions. We find E2E systems to be effective across datasets: in a fair comparison, E2E systems achieve within 2.6% of SoTA systems tuned to a specific dataset. Our analysis reveals that transcription artefacts, such as punctuation and casing, pose difficulties for ASR systems and should be included in evaluation. We believe E2E benchmarking over a range of datasets promotes the research of multi-domain speech recognition systems.*
    \n
    For more information, please see the official submission on [OpenReview.net](https://openreview.net/forum?id=9OL2fIfDLK).
    """
)

st.markdown("To submit to ESC, please click on the instructions below ↓")

st.markdown("TODO: Add instructions ...")

# Using the "with" syntax
with st.form(key="my_form"):
    uploaded_file = st.file_uploader("Choose a zip file")
    submit_button = st.form_submit_button(label="Submit")

if submit_button:
    if uploaded_file is None:
        raise ValueError("Please make sure to have uploaded a zip file.")

    submission = uploaded_file.name.split(".zip")[0]
    with st.spinner(f"Uploading {submission}..."):
        with zipfile.ZipFile(uploaded_file, 'r') as zip_ref:
            zip_ref.extractall(submission_repo.local_dir)
            submission_repo.push_to_hub()

    with st.spinner(f"Computing ESC Score for {submission}..."):
        results = {"name": submission}
        submitted_files = os.listdir(os.path.join(SUBMISSION_NAME, submission))

        submitted_files = [f for f in submitted_files if f in EXPECTED_TEST_FILES]

        if sorted(EXPECTED_TEST_FILES) != sorted(submitted_files):
            raise ValueError(
                f"{', '.join(submitted_files)} were submitted, but expected {', '.join(EXPECTED_TEST_FILES)}"
            )

        for file in submitted_files:
            ref_file = os.path.join(REFERENCE_NAME, file)
            pred_file = os.path.join(SUBMISSION_NAME, submission, file)

            wer = compute_wer(pred_file, ref_file)
            results[file.split(".")[0]] = str(wer)

        wer_values = [float(results[t]) for t in TEST_SETS]
        all_wer = sum(wer_values) / len(wer_values)

        results["esc-score"] = all_wer
        all_results = all_results.append(results, ignore_index=True)

        # save and upload new evaluated results
        all_results.to_csv(CSV_RESULTS_FILE)
        commit_url = submission_repo.push_to_hub()

    st.success('Please refresh this space (CTRL+R) to see your result')