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import os | |
import gradio as gr | |
import soundfile as sf | |
import torch | |
from gradio_client import Client | |
from huggingface_hub import Repository | |
from pandas import read_csv | |
from transformers import pipeline | |
# load the results file from the private repo | |
USERNAMES_DATASET_ID = "huggingface-course/audio-course-u7-hands-on" | |
HF_TOKEN = os.environ.get("HF_TOKEN") | |
usernames_url = os.path.join("https://huggingface.co/datasets", USERNAMES_DATASET_ID) | |
usernames_repo = Repository(local_dir="usernames", clone_from=usernames_url, use_auth_token=HF_TOKEN) | |
usernames_repo.git_pull() | |
CSV_RESULTS_FILE = os.path.join("usernames", "usernames.csv") | |
all_results = read_csv(CSV_RESULTS_FILE) | |
# load the LID checkpoint | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
pipe = pipeline("audio-classification", model="facebook/mms-lid-126", device=device) | |
# define some constants | |
TITLE = "π€ Audio Transformers Course: Unit 7 Assessment" | |
DESCRIPTION = """ | |
Check that you have successfully completed the hands-on exercise for Unit 7 of the π€ Audio Transformers Course by submitting your demo to this Space. | |
As a reminder, you should start with the template Space provided at [`course-demos/speech-to-speech-translation`](https://huggingface.co/spaces/course-demos/speech-to-speech-translation), | |
and update the Space to translate from any language X to a **non-English** language Y. | |
Your demo should take as input an audio file, and return as output another audio file, matching the signature of the | |
[`speech_to_speech_translation`](https://huggingface.co/spaces/course-demos/speech-to-speech-translation/blob/3946ba6705a6632a63de8672ac52a482ab74b3fc/app.py#L35) | |
function in the template demo. | |
To submit your demo for assessment, give the repo id or URL to your demo. For the template demo, this would be `course-demos/speech-to-speech-translation`. | |
This Space will submit a test file to your demo, and check that the output is non-English audio. If your demo successfully | |
returns an audio file, and this audio file is classified as being non-English, you will pass the demo and get a green | |
tick next to your name! β | |
If you experience any issues with using this checker, [open an issue](https://huggingface.co/spaces/huggingface-course/audio-course-u7-assessment/discussions/new) | |
on this Space and tag [`@sanchit-gandhi`](https://huggingface.co/sanchit-gandhi). | |
""" | |
THRESHOLD = 0.5 | |
PASS_MESSAGE = "Congratulations! Your demo passed the assessment!" | |
def verify_demo(repo_id): | |
if "/" not in repo_id: | |
raise gr.Error(f"Ensure you pass a valid repo id to the assessor, got `{repo_id}`") | |
split_repo_id = repo_id.split("/") | |
user_name = split_repo_id[-2] | |
if len(split_repo_id) > 2: | |
repo_id = "/".join(split_repo_id[-2:]) | |
if user_name in all_results["username"]: | |
raise gr.Error(f"Username {user_name} has already passed the assessment!") | |
try: | |
client = Client(repo_id, hf_token=HF_TOKEN) | |
except Exception as e: | |
raise gr.Error(f"Error with loading Space: {e}") | |
try: | |
audio_file = client.predict("test.wav", api_name="/predict") | |
except Exception as e: | |
raise gr.Error( | |
f"Error with querying Space, ensure your Space takes an audio file as input and returns an audio as output: {e}" | |
) | |
audio, sampling_rate = sf.read(audio_file) | |
language_prediction = pipe({"array": audio, "sampling_rate": sampling_rate}) | |
label_outputs = {} | |
for pred in language_prediction: | |
label_outputs[pred["label"]] = pred["score"] | |
top_prediction = language_prediction[0] | |
if top_prediction["score"] < THRESHOLD: | |
raise gr.Error( | |
f"Model made random predictions - predicted {top_prediction['label']} with probability {top_prediction['score']}" | |
) | |
elif top_prediction["label"] == "eng": | |
raise gr.Error( | |
"Model generated an English audio - ensure the model is set to generate audio in a non-English langauge, e.g. Dutch" | |
) | |
# save and upload new evaluated usernames | |
all_results.loc[len(all_results)] = {"username": user_name} | |
all_results.to_csv(CSV_RESULTS_FILE, index=False) | |
usernames_repo.push_to_hub() | |
return PASS_MESSAGE, (sampling_rate, audio), label_outputs | |
demo = gr.Interface( | |
fn=verify_demo, | |
inputs=gr.Textbox(placeholder="course-demos/speech-to-speech-translation", label="Repo id or URL of your demo"), | |
outputs=[ | |
gr.Textbox(label="Status"), | |
gr.Audio(label="Generated Speech", type="numpy"), | |
gr.Label(label="Language prediction"), | |
], | |
title=TITLE, | |
description=DESCRIPTION, | |
) | |
demo.launch() | |