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 = True
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,
)
with gr.Group(elem_id="share-btn-container", visible=False):
share_button = gr.Button("Share to community", elem_id="share-btn")
iface.launch(enable_queue=True)
