import numpy as np import gradio as gr from bark_infinity import SAMPLE_RATE, generate_audio, preload_models from bark_infinity.generation import SUPPORTED_LANGS from share_btn import community_icon_html, loading_icon_html, share_js DEBUG_MODE = False if not DEBUG_MODE: _ = preload_models() AVAILABLE_PROMPTS = ["Unconditional", "Announcer"] PROMPT_LOOKUP = {} for _, lang in SUPPORTED_LANGS: for n in range(10): label = f"Speaker {n} ({lang})" AVAILABLE_PROMPTS.append(label) PROMPT_LOOKUP[label] = f"{lang}_speaker_{n}" PROMPT_LOOKUP["Unconditional"] = None PROMPT_LOOKUP["Announcer"] = "announcer" default_text = "Hello, my name is Suno. And, uh — and I like pizza. [laughs]\nBut I also have other interests such as playing tic tac toe." title = "# 🐶 Bark" description = """
Bark is a universal text-to-audio model created by [Suno](www.suno.ai), with code publicly available [here](https://github.com/suno-ai/bark). \ Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. \ This demo should be used for research purposes only. Commercial use is strictly prohibited. \ The model output is not censored and the authors do not endorse the opinions in the generated content. \ Use at your own risk. """ article = """ ## 🌎 Foreign Language Bark supports various languages out-of-the-box and automatically determines language from input text. \ When prompted with code-switched text, Bark will even attempt to employ the native accent for the respective languages in the same voice. Try the prompt: ``` Buenos días Miguel. Tu colega piensa que tu alemán es extremadamente malo. But I suppose your english isn't terrible. ``` ## 🤭 Non-Speech Sounds Below is a list of some known non-speech sounds, but we are finding more every day. \ Please let us know if you find patterns that work particularly well on Discord! * [laughter] * [laughs] * [sighs] * [music] * [gasps] * [clears throat] * — or ... for hesitations * ♪ for song lyrics * capitalization for emphasis of a word * MAN/WOMAN: for bias towards speaker Try the prompt: ``` " [clears throat] Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as... ♪ singing ♪." ``` ## 🎶 Music Bark can generate all types of audio, and, in principle, doesn't see a difference between speech and music. \ Sometimes Bark chooses to generate text as music, but you can help it out by adding music notes around your lyrics. Try the prompt: ``` ♪ In the jungle, the mighty jungle, the lion barks tonight ♪ ``` ## 🧬 Voice Cloning Bark has the capability to fully clone voices - including tone, pitch, emotion and prosody. \ The model also attempts to preserve music, ambient noise, etc. from input audio. \ However, to mitigate misuse of this technology, we limit the audio history prompts to a limited set of Suno-provided, fully synthetic options to choose from. ## 👥 Speaker Prompts You can provide certain speaker prompts such as NARRATOR, MAN, WOMAN, etc. \ Please note that these are not always respected, especially if a conflicting audio history prompt is given. Try the prompt: ``` WOMAN: I would like an oatmilk latte please. MAN: Wow, that's expensive! ``` ## Details Bark model by [Suno](https://suno.ai/), including official [code](https://github.com/suno-ai/bark) and model weights. \ Gradio demo supported by 🤗 Hugging Face. Bark is licensed under a non-commercial license: CC-BY 4.0 NC, see details on [GitHub](https://github.com/suno-ai/bark). """ examples = [ ["Please surprise me and speak in whatever voice you enjoy. Vielen Dank und Gesundheit!", "Unconditional"], # , 0.7, 0.7], ["Hello, my name is Suno. And, uh — and I like pizza. [laughs] But I also have other interests such as playing tic tac toe.", "Speaker 1 (en)"], # , 0.7, 0.7], ["Buenos días Miguel. Tu colega piensa que tu alemán es extremadamente malo. But I suppose your english isn't terrible.", "Speaker 0 (es)"], # , 0.7, 0.7], ] def gen_tts(text, history_prompt): # , temp_semantic, temp_waveform): history_prompt = PROMPT_LOOKUP[history_prompt] if DEBUG_MODE: audio_arr = np.zeros(SAMPLE_RATE) else: # , text_temp=temp_semantic, waveform_temp=temp_waveform) audio_arr = generate_audio(text, history_prompt=history_prompt) audio_arr = (audio_arr * 32767).astype(np.int16) return (SAMPLE_RATE, audio_arr) css = """ #share-btn-container { display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; margin-top: 10px; margin-left: auto; flex: unset !important; } #share-btn { all: initial; color: #ffffff; font-weight: 600; cursor: pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important; right:0; } #share-btn * { all: unset !important; } #share-btn-container div:nth-child(-n+2){ width: auto !important; min-height: 0px !important; } #share-btn-container .wrap { display: none !important; } """ with gr.Blocks(css=css) as block: gr.Markdown(title) gr.Markdown(description) with gr.Row(): with gr.Column(): input_text = gr.Textbox( label="Input Text", lines=2, value=default_text, elem_id="input_text") options = gr.Dropdown( AVAILABLE_PROMPTS, value="Speaker 1 (en)", label="Acoustic Prompt", elem_id="speaker_option") run_button = gr.Button(text="Generate Audio", type="button") with gr.Column(): audio_out = gr.Audio(label="Generated Audio", type="numpy", elem_id="audio_out") with gr.Row(visible=False) as share_row: with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html) loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button( "Share to community", elem_id="share-btn") share_button.click(None, [], [], _js=share_js) inputs = [input_text, options] outputs = [audio_out] gr.Examples(examples=examples, fn=gen_tts, inputs=inputs, outputs=outputs, cache_examples=True) gr.Markdown(article) run_button.click(fn=lambda: gr.update(visible=False), inputs=None, outputs=share_row, queue=False).then( fn=gen_tts, inputs=inputs, outputs=outputs, queue=True).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=share_row, queue=False) block.queue() block.launch()