|
import gradio as gr |
|
|
|
from dataset import Dataset |
|
|
|
|
|
dataset = Dataset() |
|
|
|
|
|
def eval(data_subset:str, model_1:str, model_2:str, model_3:str)->str: |
|
|
|
result = f"""# HELLO THERE |
|
Data Subset: {data_subset} |
|
Model 1: {model_1} |
|
Model 2: {model_2} |
|
Model_3: {model_3} |
|
""" |
|
return result |
|
|
|
def get_card(selected_model:str)->str: |
|
|
|
if selected_model == "None": |
|
return "" |
|
elif selected_model == "Model2": |
|
return "A very good model indeed" |
|
elif selected_model == "Model3": |
|
return "Also very good" |
|
else: |
|
return "Unknown Model" |
|
|
|
def is_own(data_subset:str): |
|
if data_subset == "own": |
|
own_audio = gr.Audio(sources=['microphone'],streaming=False) |
|
own_transcription = gr.TextArea(lines=2) |
|
return own_audio, own_transcription |
|
own_audio = None |
|
own_transcription = None |
|
return own_audio, own_transcription |
|
|
|
with gr.Blocks() as demo: |
|
|
|
gr.Markdown("# VocalVenturer 💬") |
|
gr.Markdown("-------") |
|
gr.Markdown(""" |
|
Hello there, this is the VocalVenturer, this app is aimed at helping you making more informed model choices for ASR. |
|
Please choose a Data Subset to evalutate the Models on. You also have the opportunity to record and transcibe an own sample. |
|
The Models will be evaluated using the *WER* metric -> here is an amazing Leaderboard for it LINK""") |
|
|
|
with gr.Row(): |
|
with gr.Column(scale=1): |
|
pass |
|
with gr.Column(scale=3): |
|
data_subset = gr.Radio( |
|
value="Libris", |
|
choices=["Libris","Common","own"], |
|
label="Data subset / Own Sample", |
|
) |
|
with gr.Column(scale=1): |
|
pass |
|
|
|
with gr.Row(): |
|
own_audio = gr.Audio(sources=['microphone'],streaming=False,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.Row(): |
|
|
|
with gr.Column(scale=1): |
|
model_1 = gr.Dropdown( |
|
choices=["None","Model2","Model3"], |
|
label="Select Model 1" |
|
) |
|
model_1_card = gr.Markdown("") |
|
|
|
with gr.Column(scale=1): |
|
model_2 = gr.Dropdown( |
|
choices=["None","Model2","Model3"], |
|
label="Select Model 2" |
|
) |
|
model_2_card = gr.Markdown("") |
|
|
|
with gr.Column(scale=1): |
|
model_3 = gr.Dropdown( |
|
choices=["None","Model2","Model3"], |
|
label="Select Model 3" |
|
) |
|
model_3_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) |
|
model_3.change(get_card, inputs=model_3, outputs=model_3_card) |
|
|
|
eval_btn = gr.Button( |
|
value="Evaluate", |
|
variant="primary" |
|
) |
|
|
|
|
|
|
|
gr.Markdown("-------") |
|
gr.Markdown("### Results") |
|
|
|
results = gr.Markdown("") |
|
|
|
eval_btn.click(eval, [data_subset, model_1, model_2, model_3], results) |
|
|
|
demo.launch(debug=True) |
