Create app.py

app.py

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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 = "🤗 Audio Transformers Course: Unit 7 Assessment"
+DESCRIPTION = """
+Check that you have successfully completed the hands-on exercise for Unit 7 of the 🤗 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! ✅
+
+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()