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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 = "πŸ€— Audio Transformers Course: Unit 7 Assessment"
DESCRIPTION = """
Check that you have successfully completed the hands-on exercise for Unit 7 of the πŸ€— 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! βœ…
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()