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import gradio as gr |
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from dataset import Dataset |
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from model import Model |
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from utils import hf_login, compute_wer |
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hf_login() |
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dataset = Dataset() |
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models = Model() |
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def run_tests (dataset_choice:str, model:str): |
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MoDeL = Model() |
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MoDeL.select(model) |
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MoDeL.load() |
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DaTaSeT = Dataset(100) |
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DaTaSeT.load(dataset_choice) |
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references, predictions = MoDeL.process(DaTaSeT) |
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wer = compute_wer(references=references, predictions=predictions) |
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return wer |
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def eval(data_subset:str, model_1:str, model_2:str)->str: |
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wer_result_1 = run_tests(data_subset, model_1) |
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wer_result_2 = run_tests(data_subset, model_2) |
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return f"WER Score: {wer_result_1}", f"WER Score: {wer_result_2}" |
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def get_card(selected_model:str)->str: |
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if selected_model == "None": |
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return "" |
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elif selected_model == "Model2": |
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return "A very good model indeed" |
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elif selected_model == "Model3": |
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return "Also very good" |
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else: |
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return "Unknown Model" |
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def is_own(data_subset:str): |
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if data_subset == "own": |
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own_audio = gr.Audio(sources=['microphone'],streaming=False) |
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own_transcription = gr.TextArea(lines=2) |
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return own_audio, own_transcription |
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own_audio = None |
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own_transcription = None |
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return own_audio, own_transcription |
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with gr.Blocks() as demo: |
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gr.Markdown('# <p style="text-align: center;">VocalVenturer 💬</p>') |
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gr.Markdown("-------") |
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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""") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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pass |
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with gr.Column(scale=5): |
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data_subset = gr.Radio( |
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value="LibriSpeech Clean", |
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choices=dataset.get_options(), |
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label="Data subset / Own Sample", |
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) |
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with gr.Column(scale=1): |
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pass |
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with gr.Row(): |
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own_audio = gr.Audio(sources=['microphone'],streaming=False,visible=False) |
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own_transcription = gr.TextArea(lines=2, visible=False) |
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data_subset.change(is_own, inputs=[data_subset], outputs=[own_audio, own_transcription]) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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model_1 = gr.Dropdown( |
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choices=models.get_options(), |
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label="Select Model" |
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) |
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model_1_card = gr.Markdown("") |
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with gr.Column(scale=1): |
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model_2 = gr.Dropdown( |
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choices=models.get_options(), |
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label="Select Model" |
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) |
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model_2_card = gr.Markdown("") |
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model_1.change(get_card, inputs=model_1, outputs=model_1_card) |
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model_2.change(get_card, inputs=model_2, outputs=model_2_card) |
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eval_btn = gr.Button( |
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value="Evaluate", |
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variant="primary", |
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size="sm" |
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) |
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gr.Markdown('## <p style="text-align: center;">Results</p>') |
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gr.Markdown("-------") |
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with gr.Row(): |
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with gr.Column(scale=1): |
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results_model_1 = gr.Markdown("") |
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with gr.Column(scale=1): |
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results_model_2 = gr.Markdown("") |
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eval_btn.click(eval, [data_subset, model_1, model_2], [results_model_1, results_model_2]) |
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demo.launch(debug=True) |
