import numpy as np import gradio as gr from bark import SAMPLE_RATE, generate_audio, preload_models from bark.generation import SUPPORTED_LANGS 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: audio_arr = generate_audio(text, history_prompt=history_prompt)#, text_temp=temp_semantic, waveform_temp=temp_waveform) audio_arr = (audio_arr * 32767).astype(np.int16) return (SAMPLE_RATE, audio_arr) iface = gr.Interface( fn=gen_tts, inputs=[ gr.Textbox(label="Input Text", lines=2, value=default_text), gr.Dropdown(AVAILABLE_PROMPTS, value="Speaker 1 (en)", label="Acoustic Prompt"), # gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label="Temp 1", info="Gen. temperature of semantic tokens. (lower is more conservative, higher is more diverse)"), # gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label="Temp 2", info="Gen. temperature of waveform tokens. (lower is more conservative, higher is more diverse)"), ], outputs=[ gr.Audio(label="Generated Audio", type="numpy"), ], title=title, description=description, article=article, examples=examples, cache_examples=False, ) iface.launch(enable_queue=True)