app.py

import gradio as gr
from processing import run
import json
from huggingface_hub import login
import os




# LOG INTO HUGGING FACE
hf_token = os.getenv("HF_Token")
login(hf_token)

# def hf_login():
#     hf_token = os.getenv("HF_Token")
#     if hf_token is None:
#         with open("credentials.json", "r") as f:
#             hf_token = json.load(f)["token"]
#     login(token=hf_token, add_to_git_credential=True)

# hf_login()





# GENERAL OPTIONS FOR MODELS AND DATASETS
MODEL_OPTIONS = ["openai/whisper-tiny.en", "facebook/s2t-medium-librispeech-asr", "facebook/wav2vec2-base-960h","openai/whisper-large-v2"]
DATASET_OPTIONS = ["Common Voice", "Librispeech ASR clean", "Librispeech ASR other", "OWN Recoding/Sample"]

# HELPER FUNCTIONS
def get_card(selected_model:str)->str:

    with open("cards.txt", "r") as f:
        cards = f.read()
    
    cards = cards.split("@@")
    for card in cards:
        if "ID: "+selected_model in card:
            return card

    return "Unknown Model"

def is_own(selected_option):
    if selected_option == "OWN Recoding/Sample":
        return gr.update(visible=True), gr.update(visible=True)
    else:
        return gr.update(visible=False), gr.update(visible=False)

def make_visible():
    return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)





# THE ACTUAL APP
with gr.Blocks() as demo:


    gr.Markdown('# <p style="text-align: center;">ASR Model Comparison 💬</p>')
    gr.Markdown("""

""")


    gr.Markdown("""### Welcome to ASR Model Comparison Hub! 🎉

Hey there, and welcome to an app designed just for developers like you, who are passionate about pushing the boundaries of Automatic Speech Recognition (ASR) technology!

Here, you can easily compare different ASR models by selecting a dataset and choosing two models from the dropdown to see how they stack up against each other. If you're feeling creative, go ahead and select 'OWN' as your dataset option to upload your own audio file or record something new right in the app. Don’t forget to provide a transcription, and the app will handle the rest!

ASR Model Comparison Hub uses the Word Error Rate (WER) ⬇️ (the lower the better) metric to give you a clear picture of each model's performance. And hey, don't miss out on checking the **Amazing Leaderboard** where you can see how a wide range of models have been evaluated—[Check it out here](https://huggingface.co/spaces/hf-audio/open_asr_leaderboard).

Happy experimenting and comparing! 🚀""")

    

    with gr.Row():
        with gr.Column(scale=1):
            pass
        with gr.Column(scale=5):
            data_subset = gr.Radio(
                value="Common Voice",
                choices=DATASET_OPTIONS,
                label="Data subset / Own Sample",
            )
            own_audio = gr.Audio(sources=['microphone'], visible=False)
            own_transcription = gr.TextArea(lines=2, visible=False)
            data_subset.change(is_own, inputs=[data_subset], outputs=[own_audio, own_transcription])
        with gr.Column(scale=1):
            pass


    with gr.Row():

        with gr.Column(scale=1):
            model_1 = gr.Dropdown(
                choices=MODEL_OPTIONS,
                label="Select Model"
            )
            model_1_card = gr.Markdown("")

        with gr.Column(scale=1):
            model_2 = gr.Dropdown(
                choices=MODEL_OPTIONS,
                label="Select Model"
            )
            model_2_card = gr.Markdown("")


        model_1.change(get_card, inputs=model_1, outputs=model_1_card)
        model_2.change(get_card, inputs=model_2, outputs=model_2_card)


    eval_btn = gr.Button(
        value="Evaluate",
        variant="primary",
        size="sm")
    
    results_title = gr.Markdown('## <p style="text-align: center;">Results</p>', visible=False)
    results_md = gr.Markdown("")
    results_plot = gr.Plot(show_label=False, visible=False)
    results_df = gr.DataFrame(
        visible=False,
        row_count=(5, "dynamic"),  # Allow dynamic rows
        interactive=False,  # Allow users to interact with the DataFrame
        wrap=True,  # Ensure text wraps to multiple lines
    )
    eval_btn.click(make_visible, outputs=[results_plot, results_df, results_title])
    eval_btn.click(run, [data_subset, model_1, model_2, own_audio, own_transcription], [results_md, results_plot, results_df], show_progress=False)

demo.launch(debug=True)