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| import gradio as gr | |
| import pandas as pd | |
| import json | |
| from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS, LEADERBOARD_CSS | |
| from init import is_model_on_hub, upload_file, load_all_info_from_dataset_hub | |
| from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message | |
| from datetime import datetime, timezone | |
| LAST_UPDATED = "Nov 22th 2024" | |
| column_names = { | |
| "MODEL": "Model", | |
| "Avg. WER": "Average WER ⬇️", | |
| "RTFx": "RTFx ⬆️️", | |
| "AMI WER": "AMI", | |
| "Earnings22 WER": "Earnings22", | |
| "Gigaspeech WER": "Gigaspeech", | |
| "LS Clean WER": "LS Clean", | |
| "LS Other WER": "LS Other", | |
| "SPGISpeech WER": "SPGISpeech", | |
| "Tedlium WER": "Tedlium", | |
| "Voxpopuli WER": "Voxpopuli", | |
| } | |
| whisper_column_names = { | |
| "MODEL": "Model", | |
| "Avg. WER": "Average WER ⬇️", | |
| "RTFx": "RTFx ⬆️️", | |
| "Backend": "Backend", | |
| "Hardware": "Device", | |
| "AMI WER": "AMI", | |
| "Earnings22 WER": "Earnings22", | |
| "Gigaspeech WER": "Gigaspeech", | |
| "LS Clean WER": "LS Clean", | |
| "LS Other WER": "LS Other", | |
| "SPGISpeech WER": "SPGISpeech", | |
| "Tedlium WER": "Tedlium", | |
| "Voxpopuli WER": "Voxpopuli", | |
| } | |
| eval_queue_repo, requested_models, csv_results, whisper_eval_queue_repo, whisper_csv_results = load_all_info_from_dataset_hub() | |
| if not csv_results.exists(): | |
| raise Exception(f"CSV file {csv_results} does not exist locally") | |
| if not whisper_csv_results.exists(): | |
| raise Exception(f"CSV file {whisper_csv_results} does not exist locally") | |
| # Get csv with data and parse columns | |
| original_df = pd.read_csv(csv_results) | |
| whisper_df = pd.read_csv(whisper_csv_results) | |
| # Formats the columns | |
| def formatter(x): | |
| if type(x) is str: | |
| x = x | |
| else: | |
| x = round(x, 2) | |
| return x | |
| for col in original_df.columns: | |
| if col == "model": | |
| original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x))) | |
| else: | |
| original_df[col] = original_df[col].apply(formatter) # For numerical values | |
| whisper_df[col] = whisper_df[col].apply(formatter) # For numerical values | |
| original_df.rename(columns=column_names, inplace=True) | |
| original_df.sort_values(by='Average WER ⬇️', inplace=True) | |
| whisper_df.rename(columns=whisper_column_names, inplace=True) | |
| whisper_df.sort_values(by='Average WER ⬇️', inplace=True) | |
| COLS = [c.name for c in fields(AutoEvalColumn)] | |
| TYPES = [c.type for c in fields(AutoEvalColumn)] | |
| def request_model(model_text, chbcoco2017): | |
| # Determine the selected checkboxes | |
| dataset_selection = [] | |
| if chbcoco2017: | |
| dataset_selection.append("ESB Datasets tests only") | |
| if len(dataset_selection) == 0: | |
| return styled_error("You need to select at least one dataset") | |
| base_model_on_hub, error_msg = is_model_on_hub(model_text) | |
| if not base_model_on_hub: | |
| return styled_error(f"Base model '{model_text}' {error_msg}") | |
| # Construct the output dictionary | |
| current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") | |
| required_datasets = ', '.join(dataset_selection) | |
| eval_entry = { | |
| "date": current_time, | |
| "model": model_text, | |
| "datasets_selected": required_datasets | |
| } | |
| # Prepare file path | |
| DIR_OUTPUT_REQUESTS.mkdir(parents=True, exist_ok=True) | |
| fn_datasets = '@ '.join(dataset_selection) | |
| filename = model_text.replace("/","@") + "@@" + fn_datasets | |
| if filename in requested_models: | |
| return styled_error(f"A request for this model '{model_text}' and dataset(s) was already made.") | |
| try: | |
| filename_ext = filename + ".txt" | |
| out_filepath = DIR_OUTPUT_REQUESTS / filename_ext | |
| # Write the results to a text file | |
| with open(out_filepath, "w") as f: | |
| f.write(json.dumps(eval_entry)) | |
| upload_file(filename, out_filepath) | |
| # Include file in the list of uploaded files | |
| requested_models.append(filename) | |
| # Remove the local file | |
| out_filepath.unlink() | |
| return styled_message("🤗 Your request has been submitted and will be evaluated soon!</p>") | |
| except Exception as e: | |
| return styled_error(f"Error submitting request!") | |
| with gr.Blocks(css=LEADERBOARD_CSS) as demo: | |
| gr.HTML(BANNER, elem_id="banner") | |
| gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") | |
| with gr.Tabs(elem_classes="tab-buttons") as tabs: | |
| with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0): | |
| leaderboard_table = gr.components.Dataframe( | |
| value=original_df, | |
| datatype=TYPES, | |
| elem_id="leaderboard-table", | |
| interactive=False, | |
| visible=True, | |
| ) | |
| with gr.TabItem("🔄 Whisper Model Leaderboard", elem_id="whisper-backends-tab", id=1): | |
| gr.Markdown("## Whisper Model Performance Across Different Backends", elem_classes="markdown-text") | |
| gr.Markdown("This table shows how different Whisper model implementations compare in terms of performance and speed.", elem_classes="markdown-text") | |
| with gr.Row(): | |
| backend_filter = gr.Dropdown( | |
| choices=["All"] + sorted(whisper_df["Backend"].unique().tolist()), | |
| value="All", | |
| label="Filter by Backend", | |
| elem_id="backend-filter", | |
| multiselect=True # Enable multiple selection | |
| ) | |
| device_choices = ["All"] + sorted(whisper_df["Device"].unique().tolist()) if "Device" in whisper_df.columns else ["All"] | |
| device_filter = gr.Dropdown( | |
| choices=device_choices, | |
| value="All", | |
| label="Filter by Device", | |
| elem_id="device-filter", | |
| multiselect=True # Enable multiple selection | |
| ) | |
| whisper_table = gr.components.Dataframe( | |
| value=whisper_df, | |
| datatype=TYPES, | |
| elem_id="whisper-table", | |
| interactive=False, | |
| visible=True, | |
| ) | |
| def filter_whisper_table(backends, devices): | |
| filtered_df = whisper_df.copy() | |
| # Handle backend filtering | |
| if backends and "All" not in backends: | |
| filtered_df = filtered_df[filtered_df["Backend"].isin(backends)] | |
| # Handle device filtering | |
| if devices and "All" not in devices and "Device" in filtered_df.columns: | |
| filtered_df = filtered_df[filtered_df["Device"].isin(devices)] | |
| return filtered_df | |
| backend_filter.change( | |
| filter_whisper_table, | |
| inputs=[backend_filter, device_filter], | |
| outputs=whisper_table | |
| ) | |
| device_filter.change( | |
| filter_whisper_table, | |
| inputs=[backend_filter, device_filter], | |
| outputs=whisper_table | |
| ) | |
| with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=2): | |
| gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text") | |
| with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=3): | |
| with gr.Column(): | |
| gr.Markdown("# ✉️✨ Request results for a new model here!", elem_classes="markdown-text") | |
| with gr.Column(): | |
| gr.Markdown("Select a dataset:", elem_classes="markdown-text") | |
| with gr.Column(): | |
| model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)") | |
| chb_coco2017 = gr.Checkbox(label="COCO validation 2017 dataset", visible=False, value=True, interactive=False) | |
| with gr.Column(): | |
| mdw_submission_result = gr.Markdown() | |
| btn_submitt = gr.Button(value="🚀 Request") | |
| btn_submitt.click(request_model, | |
| [model_name_textbox, chb_coco2017], | |
| mdw_submission_result) | |
| gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text") | |
| with gr.Row(): | |
| with gr.Accordion("📙 Citation", open=False): | |
| gr.Textbox( | |
| value=CITATION_TEXT, lines=7, | |
| label="Copy the BibTeX snippet to cite this source", | |
| elem_id="citation-button", | |
| show_copy_button=True, | |
| ) | |
| demo.launch(ssr_mode=False) | |