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| import gradio as gr | |
| from huggingface_hub import HfApi, hf_hub_download | |
| from huggingface_hub.repocard import metadata_load | |
| import requests | |
| import re | |
| import pandas as pd | |
| from huggingface_hub import ModelCard | |
| import os | |
| def pass_emoji(passed): | |
| if passed is True: | |
| passed = "✅" | |
| else: | |
| passed = "❌" | |
| return passed | |
| api = HfApi() | |
| USERNAMES_DATASET_ID = "huggingface-course/audio-course-u7-hands-on" | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| def get_user_models(hf_username, task): | |
| """ | |
| List the user's models for a given task | |
| :param hf_username: User HF username | |
| """ | |
| models = api.list_models(author=hf_username, filter=[task]) | |
| user_model_ids = [x.modelId for x in models] | |
| match task: | |
| case "audio-classification": | |
| dataset = 'marsyas/gtzan' | |
| case "automatic-speech-recognition": | |
| dataset = 'PolyAI/minds14' | |
| case "text-to-speech": | |
| dataset = "" | |
| case _: | |
| print("Unsupported task") | |
| dataset_specific_models = [] | |
| if dataset == "": | |
| return user_model_ids | |
| else: | |
| for model in user_model_ids: | |
| meta = get_metadata(model) | |
| if meta is None: | |
| continue | |
| try: | |
| if meta["datasets"] == [dataset]: | |
| dataset_specific_models.append(model) | |
| except: | |
| continue | |
| return dataset_specific_models | |
| def calculate_best_result(user_models, task): | |
| """ | |
| Calculate the best results of a unit for a given task | |
| :param user_model_ids: models of a user | |
| """ | |
| best_model = "" | |
| if task == "audio-classification": | |
| best_result = -100 | |
| larger_is_better = True | |
| elif task == "automatic-speech-recognition": | |
| best_result = 100 | |
| larger_is_better = False | |
| for model in user_models: | |
| meta = get_metadata(model) | |
| if meta is None: | |
| continue | |
| metric = parse_metrics(model, task) | |
| if larger_is_better: | |
| if metric > best_result: | |
| best_result = metric | |
| best_model = meta['model-index'][0]["name"] | |
| else: | |
| if metric < best_result: | |
| best_result = metric | |
| best_model = meta['model-index'][0]["name"] | |
| return best_result, best_model | |
| def get_metadata(model_id): | |
| """ | |
| Get model metadata (contains evaluation data) | |
| :param 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 extract_metric(model_card_content, task): | |
| """ | |
| Extract the metric value from the models' model card | |
| :param model_card_content: model card content | |
| """ | |
| accuracy_pattern = r"Accuracy: (\d+\.\d+)" | |
| wer_pattern = r"Wer: (\d+\.\d+)" | |
| if task == "audio-classification": | |
| pattern = accuracy_pattern | |
| elif task == "automatic-speech-recognition": | |
| pattern = wer_pattern | |
| match = re.search(pattern, model_card_content) | |
| if match: | |
| metric = match.group(1) | |
| return float(metric) | |
| else: | |
| return None | |
| def parse_metrics(model, task): | |
| """ | |
| Get model card and parse it | |
| :param model_id: model id | |
| """ | |
| card = ModelCard.load(model) | |
| return extract_metric(card.content, task) | |
| def certification(hf_username): | |
| results_certification = [ | |
| { | |
| "unit": "Unit 4: Audio Classification", | |
| "task": "audio-classification", | |
| "baseline_metric": 0.87, | |
| "best_result": 0, | |
| "best_model_id": "", | |
| "passed_": False | |
| }, | |
| { | |
| "unit": "Unit 5: Automatic Speech Recognition", | |
| "task": "automatic-speech-recognition", | |
| "baseline_metric": 0.37, | |
| "best_result": 0, | |
| "best_model_id": "", | |
| "passed_": False | |
| }, | |
| { | |
| "unit": "Unit 6: Text-to-Speech", | |
| "task": "text-to-speech", | |
| "baseline_metric": 0, | |
| "best_result": 0, | |
| "best_model_id": "", | |
| "passed_": False | |
| }, | |
| { | |
| "unit": "Unit 7: Audio applications", | |
| "task": "demo", | |
| "baseline_metric": 0, | |
| "best_result": 0, | |
| "best_model_id": "", | |
| "passed_": False | |
| }, | |
| ] | |
| for unit in results_certification: | |
| unit["passed"] = pass_emoji(unit["passed_"]) | |
| match unit["task"]: | |
| case "audio-classification": | |
| try: | |
| user_ac_models = get_user_models(hf_username, task = "audio-classification") | |
| best_result, best_model_id = calculate_best_result(user_ac_models, task = "audio-classification") | |
| unit["best_result"] = best_result | |
| unit["best_model_id"] = best_model_id | |
| if unit["best_result"] >= unit["baseline_metric"]: | |
| unit["passed_"] = True | |
| unit["passed"] = pass_emoji(unit["passed_"]) | |
| except: print("Either no relevant models found, or no metrics in the model card for audio classificaiton") | |
| case "automatic-speech-recognition": | |
| try: | |
| user_asr_models = get_user_models(hf_username, task = "automatic-speech-recognition") | |
| best_result, best_model_id = calculate_best_result(user_asr_models, task = "automatic-speech-recognition") | |
| unit["best_result"] = best_result | |
| unit["best_model_id"] = best_model_id | |
| if unit["best_result"] <= unit["baseline_metric"]: | |
| unit["passed_"] = True | |
| unit["passed"] = pass_emoji(unit["passed_"]) | |
| except: print("Either no relevant models found, or no metrics in the model card for automatic speech recognition") | |
| case "text-to-speech": | |
| try: | |
| user_tts_models = get_user_models(hf_username, task = "text-to-speech") | |
| if user_tts_models: | |
| unit["best_result"] = 0 | |
| unit["best_model_id"] = user_tts_models[0] | |
| unit["passed_"] = True | |
| unit["passed"] = pass_emoji(unit["passed_"]) | |
| except: print("Either no relevant models found, or no metrics in the model card for automatic speech recognition") | |
| case "demo": | |
| u7_usernames = hf_hub_download(USERNAMES_DATASET_ID, repo_type = "dataset", filename="usernames.csv", token=HF_TOKEN) | |
| u7_users = pd.read_csv(u7_usernames) | |
| if hf_username in u7_users['username']: | |
| unit["best_result"] = 0 | |
| unit["best_model_id"] = "Demo check passed, no model id" | |
| unit["passed_"] = True | |
| unit["passed"] = pass_emoji(unit["passed_"]) | |
| case _: | |
| print("Unknown task") | |
| print(results_certification) | |
| df = pd.DataFrame(results_certification) | |
| df = df[['passed', 'unit', 'task', 'baseline_metric', 'best_result', 'best_model_id']] | |
| return df | |
| with gr.Blocks() as demo: | |
| gr.Markdown(f""" | |
| # 🏆 Check your progress in the Audio Course 🏆 | |
| - To get a certificate of completion, you must **pass 3 out of 4 assignments before July 31st 2023**. | |
| - To get an honors certificate, you must **pass 4 out of 4 assignments before July 31st 2023**. | |
| For the assignments where you have to train a model, your model's metric should be equal to or better than the baseline metric. | |
| For the Unit 7 assignment, first, check your demo with the [Unit 7 assessment space](https://huggingface.co/spaces/huggingface-course/audio-course-u7-assessment) | |
| Make sure that you have uploaded your model(s) to Hub, and that your Unit 7 demo is public. | |
| To check your progress, type your Hugging Face Username here (in my case MariaK) | |
| """) | |
| hf_username = gr.Textbox(placeholder="MariaK", label="Your Hugging Face Username") | |
| check_progress_button = gr.Button(value="Check my progress") | |
| output = gr.components.Dataframe(value=certification(hf_username)) | |
| check_progress_button.click(fn=certification, inputs=hf_username, outputs=output) | |
| demo.launch() |