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from typing import Callable |
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import gradio as gr |
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from fish_speech.i18n import i18n |
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from fish_speech.inference_engine.utils import normalize_text |
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from tools.webui.variables import HEADER_MD, TEXTBOX_PLACEHOLDER |
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def build_app(inference_fct: Callable, theme: str = "light") -> gr.Blocks: |
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with gr.Blocks(theme=gr.themes.Base()) as app: |
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gr.Markdown(HEADER_MD) |
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app.load( |
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None, |
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None, |
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js="() => {const params = new URLSearchParams(window.location.search);if (!params.has('__theme')) {params.set('__theme', '%s');window.location.search = params.toString();}}" |
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% theme, |
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) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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text = gr.Textbox( |
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label=i18n("Input Text"), placeholder=TEXTBOX_PLACEHOLDER, lines=10 |
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) |
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refined_text = gr.Textbox( |
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label=i18n("Realtime Transform Text"), |
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placeholder=i18n( |
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"Normalization Result Preview (Currently Only Chinese)" |
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), |
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lines=5, |
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interactive=False, |
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) |
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with gr.Row(): |
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normalize = gr.Checkbox( |
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label=i18n("Text Normalization"), |
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value=False, |
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) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Tab(label=i18n("Advanced Config")): |
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with gr.Row(): |
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chunk_length = gr.Slider( |
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label=i18n("Iterative Prompt Length, 0 means off"), |
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minimum=0, |
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maximum=300, |
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value=200, |
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step=8, |
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) |
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max_new_tokens = gr.Slider( |
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label=i18n( |
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"Maximum tokens per batch, 0 means no limit" |
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), |
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minimum=0, |
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maximum=2048, |
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value=0, |
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step=8, |
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) |
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with gr.Row(): |
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top_p = gr.Slider( |
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label="Top-P", |
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minimum=0.6, |
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maximum=0.9, |
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value=0.7, |
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step=0.01, |
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) |
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repetition_penalty = gr.Slider( |
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label=i18n("Repetition Penalty"), |
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minimum=1, |
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maximum=1.5, |
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value=1.2, |
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step=0.01, |
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) |
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with gr.Row(): |
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temperature = gr.Slider( |
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label="Temperature", |
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minimum=0.6, |
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maximum=0.9, |
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value=0.7, |
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step=0.01, |
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) |
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seed = gr.Number( |
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label="Seed", |
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info="0 means randomized inference, otherwise deterministic", |
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value=0, |
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) |
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with gr.Tab(label=i18n("Reference Audio")): |
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with gr.Row(): |
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gr.Markdown( |
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i18n( |
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"5 to 10 seconds of reference audio, useful for specifying speaker." |
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) |
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) |
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with gr.Row(): |
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reference_id = gr.Textbox( |
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label=i18n("Reference ID"), |
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placeholder="Leave empty to use uploaded references", |
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) |
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with gr.Row(): |
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use_memory_cache = gr.Radio( |
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label=i18n("Use Memory Cache"), |
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choices=["on", "off"], |
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value="on", |
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) |
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with gr.Row(): |
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reference_audio = gr.Audio( |
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label=i18n("Reference Audio"), |
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type="filepath", |
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) |
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with gr.Row(): |
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reference_text = gr.Textbox( |
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label=i18n("Reference Text"), |
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lines=1, |
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placeholder="在一无所知中，梦里的一天结束了，一个新的「轮回」便会开始。", |
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value="", |
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) |
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with gr.Column(scale=3): |
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with gr.Row(): |
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error = gr.HTML( |
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label=i18n("Error Message"), |
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visible=True, |
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) |
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with gr.Row(): |
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audio = gr.Audio( |
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label=i18n("Generated Audio"), |
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type="numpy", |
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interactive=False, |
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visible=True, |
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) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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generate = gr.Button( |
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value="\U0001F3A7 " + i18n("Generate"), |
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variant="primary", |
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) |
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text.input(fn=normalize_text, inputs=[text, normalize], outputs=[refined_text]) |
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generate.click( |
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inference_fct, |
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[ |
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refined_text, |
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normalize, |
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reference_id, |
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reference_audio, |
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reference_text, |
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max_new_tokens, |
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chunk_length, |
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top_p, |
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repetition_penalty, |
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temperature, |
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seed, |
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use_memory_cache, |
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], |
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[audio, error], |
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concurrency_limit=1, |
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) |
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return app |
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