from cProfile import label
from distutils.command.check import check
from doctest import Example
import gradio as gr
import os
import sys
import numpy as np
import logging
import torch
from xml.sax import saxutils
#import nltk
from bark import SAMPLE_RATE, generate_audio
from bark.clonevoice import clone_voice
from bark.generation import SAMPLE_RATE, preload_models, codec_decode, generate_coarse, generate_fine, generate_text_semantic
from scipy.io.wavfile import write as write_wav
from parseinput import split_and_recombine_text, build_ssml, is_ssml, create_clips_from_ssml
from datetime import datetime
from tqdm.auto import tqdm
OUTPUTFOLDER = "Outputs"
def generate_with_settings(text_prompt, semantic_temp=0.7, semantic_top_k=50, semantic_top_p=0.95, coarse_temp=0.7, coarse_top_k=50, coarse_top_p=0.95, fine_temp=0.5, voice_name=None, use_semantic_history_prompt=True, use_coarse_history_prompt=True, use_fine_history_prompt=True, output_full=False):
# generation with more control
x_semantic = generate_text_semantic(
text_prompt,
history_prompt=voice_name if use_semantic_history_prompt else None,
temp=semantic_temp,
top_k=semantic_top_k,
top_p=semantic_top_p
)
x_coarse_gen = generate_coarse(
x_semantic,
history_prompt=voice_name if use_coarse_history_prompt else None,
temp=coarse_temp,
top_k=coarse_top_k,
top_p=coarse_top_p
)
x_fine_gen = generate_fine(
x_coarse_gen,
history_prompt=voice_name if use_fine_history_prompt else None,
temp=fine_temp
)
if output_full:
full_generation = {
'semantic_prompt': x_semantic,
'coarse_prompt': x_coarse_gen,
'fine_prompt': x_fine_gen
}
return full_generation, codec_decode(x_fine_gen)
return codec_decode(x_fine_gen)
def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, quick_generation, complete_settings, progress=gr.Progress(track_tqdm=True)):
if text == None or len(text) < 1:
raise gr.Error('No text entered!')
# Chunk the text into smaller pieces then combine the generated audio
# generation settings
if selected_speaker == 'None':
selected_speaker = None
voice_name = selected_speaker
semantic_temp = text_temp
semantic_top_k = 50
semantic_top_p = 0.95
coarse_temp = waveform_temp
coarse_top_k = 50
coarse_top_p = 0.95
fine_temp = 0.5
use_semantic_history_prompt = "Use semantic history" in complete_settings
use_coarse_history_prompt = "Use coarse history" in complete_settings
use_fine_history_prompt = "Use fine history" in complete_settings
use_last_generation_as_history = "Use last generation as history" in complete_settings
progress(0, desc="Generating")
silenceshort = np.zeros(int(0.25 * SAMPLE_RATE), dtype=np.float32) # quarter second of silence
silencelong = np.zeros(int(0.50 * SAMPLE_RATE), dtype=np.float32) # half a second of silence
all_parts = []
text = text.lstrip()
if is_ssml(text):
list_speak = create_clips_from_ssml(text)
prev_speaker = None
for i, clip in tqdm(enumerate(list_speak), total=len(list_speak)):
selected_speaker = clip[0]
# Add pause break between speakers
if i > 0 and selected_speaker != prev_speaker:
all_parts += [silencelong.copy()]
prev_speaker = selected_speaker
text = clip[1]
text = saxutils.unescape(text)
if selected_speaker == "None":
selected_speaker = None
print(f"\nGenerating Text ({i+1}/{len(list_speak)}) -> {selected_speaker}:`{text}`")
audio_array = generate_audio(text, selected_speaker, text_temp, waveform_temp)
if len(list_speak) > 1:
save_wav(audio_array, create_filename(OUTPUTFOLDER, "audioclip",".wav"))
all_parts += [audio_array]
else:
texts = split_and_recombine_text(text)
for i, text in tqdm(enumerate(texts), total=len(texts)):
print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker}:`{text}`")
if quick_generation == True:
audio_array = generate_audio(text, selected_speaker, text_temp, waveform_temp)
else:
full_generation, audio_array = generate_with_settings(
text,
semantic_temp=semantic_temp,
semantic_top_k=semantic_top_k,
semantic_top_p=semantic_top_p,
coarse_temp=coarse_temp,
coarse_top_k=coarse_top_k,
coarse_top_p=coarse_top_p,
fine_temp=fine_temp,
voice_name=voice_name,
use_semantic_history_prompt=use_semantic_history_prompt,
use_coarse_history_prompt=use_coarse_history_prompt,
use_fine_history_prompt=use_fine_history_prompt,
output_full=True,
)
# Noticed this in the HF Demo - convert to 16bit int -32767/32767 - most used audio format
# audio_array = (audio_array * 32767).astype(np.int16)
if len(texts) > 1:
save_wav(audio_array, create_filename(OUTPUTFOLDER, "audioclip",".wav"))
if quick_generation == False & use_last_generation_as_history:
# save to npz
voice_name = create_filename(OUTPUTFOLDER, "audioclip", "")
save_voice(voice_name,
full_generation['semantic_prompt'],
full_generation['coarse_prompt'],
full_generation['fine_prompt'])
# loading voice from custom folder needs to have extension
voice_name = voice_name + ".npz"
all_parts += [audio_array]
# Add short pause between sentences
if text[-1] in "!?.\n" and i > 1:
all_parts += [silenceshort.copy()]
# save & play audio
result = create_filename(OUTPUTFOLDER, "final",".wav")
save_wav(np.concatenate(all_parts), result)
return result
def create_filename(path, name, extension):
now = datetime.now()
date_str = now.strftime("%m-%d-%Y")
outputs_folder = os.path.join(os.getcwd(), path)
if not os.path.exists(outputs_folder):
os.makedirs(outputs_folder)
sub_folder = os.path.join(outputs_folder, date_str)
if not os.path.exists(sub_folder):
os.makedirs(sub_folder)
now = datetime.now()
time_str = now.strftime("%H-%M-%S")
file_name = f"{name}_{time_str}{extension}"
return os.path.join(sub_folder, file_name)
def save_wav(audio_array, filename):
write_wav(filename, SAMPLE_RATE, audio_array)
def save_voice(filename, semantic_prompt, coarse_prompt, fine_prompt):
np.savez_compressed(
filename,
semantic_prompt=semantic_prompt,
coarse_prompt=coarse_prompt,
fine_prompt=fine_prompt
)
def on_quick_gen_changed(checkbox):
if checkbox == False:
return gr.CheckboxGroup.update(visible=True)
return gr.CheckboxGroup.update(visible=False)
def delete_output_files(checkbox_state):
if checkbox_state:
outputs_folder = os.path.join(os.getcwd(), OUTPUTFOLDER)
if os.path.exists(outputs_folder):
purgedir(outputs_folder)
return False
# https://stackoverflow.com/a/54494779
def purgedir(parent):
for root, dirs, files in os.walk(parent):
for item in files:
# Delete subordinate files
filespec = os.path.join(root, item)
os.unlink(filespec)
for item in dirs:
# Recursively perform this operation for subordinate directories
purgedir(os.path.join(root, item))
def convert_text_to_ssml(text, selected_speaker):
return build_ssml(text, selected_speaker)
logger = logging.getLogger(__name__)
autolaunch = False
if len(sys.argv) > 1:
autolaunch = "-autolaunch" in sys.argv
if torch.cuda.is_available() == False:
os.environ['BARK_FORCE_CPU'] = 'True'
logger.warning("No CUDA detected, fallback to CPU!")
print(f'smallmodels={os.environ.get("SUNO_USE_SMALL_MODELS", False)}')
print(f'enablemps={os.environ.get("SUNO_ENABLE_MPS", False)}')
print(f'offloadcpu={os.environ.get("SUNO_OFFLOAD_CPU", False)}')
print(f'forcecpu={os.environ.get("BARK_FORCE_CPU", False)}')
print(f'autolaunch={autolaunch}\n\n')
#print("Updating nltk\n")
#nltk.download('punkt')
print("Preloading Models\n")
preload_models()
# Collect all existing speakers/voices in dir
speakers_list = []
for root, dirs, files in os.walk("./bark/assets/prompts/v2"):
for file in files:
if(file.endswith(".npz")):
pathpart = root.replace("./bark/assets/prompts/v2", "")
name = os.path.join(pathpart, file[:-4])
if name.startswith("/") or name.startswith("\\"):
name = name[1:]
speakers_list.append(name)
speakers_list = sorted(speakers_list, key=lambda x: x.lower())
speakers_list.insert(0, "nana.npz")
#speakers_list.insert(0, 'None')
# Create Gradio Blocks
with gr.Blocks(title="🐶🥳🎶 - Bark声音合成，开启声音真实复刻的新纪元！", mode="Bark Enhanced") as barkgui:
gr.Markdown("# 🐶🥳🎶 - Bark声音合成，开启声音真实复刻的新纪元！")
gr.Markdown("### 🦄 - [Bark](https://github.com/suno-ai/bark)拟声，能够实现语音、语调及说话情感的真实复刻")
gr.Markdown(
f"""
### 🤗 - Powered by [Bark Enhanced v0.4.0](https://github.com/C0untFloyd/bark-gui). Thanks to C0untFloyd.
### 1. 您可以复制该程序并用GPU运行: 
### 2. 更多精彩应用，敬请关注[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕
### 3. [Bark+声音克隆极速版](https://huggingface.co/spaces/kevinwang676/Bark-with-Voice-Cloning)也已上线！🤩
"""
)
with gr.Tab("🐶 - Bark拟声"):
with gr.Row():
with gr.Column():
placeholder = "想让Bark说些什么呢？"
input_text = gr.Textbox(label="用作声音合成的文本(长文本效果更好哦)", lines=4, placeholder=placeholder)
with gr.Column():
convert_to_ssml_button = gr.Button("Convert Text to SSML", visible=False)
with gr.Row():
with gr.Column():
examples = [
"Special meanings: [laughter] [laughs] [sighs] [music] [gasps] [clears throat] MAN: WOMAN:",
"♪ Never gonna make you cry, never gonna say goodbye, never gonna tell a lie and hurt you ♪",
"And now — a picture of a larch [laughter]",
"""
WOMAN: I would like an oatmilk latte please.
MAN: Wow, that's expensive!
""",
"""
Look at that drunk guy!
Who is he?
WOMAN: [clears throat] 10 years ago, he proposed me and I rejected him.
Oh my God [laughs] he is still celebrating
"""
]
examples = gr.Examples(examples=examples, inputs=input_text)
with gr.Row():
with gr.Column():
gr.Markdown("查看Bark官方的[语言库](https://suno-ai.notion.site/8b8e8749ed514b0cbf3f699013548683?v=bc67cff786b04b50b3ceb756fd05f68c)")
speaker = gr.Dropdown(speakers_list, value=speakers_list[0], label="中英双语的不同声音供您选择")
with gr.Column():
text_temp = gr.Slider(0.1, 1.0, value=0.7, label="Generation Temperature", info="1.0 more diverse, 0.1 more conservative")
waveform_temp = gr.Slider(0.1, 1.0, value=0.7, label="Waveform temperature", info="1.0 more diverse, 0.1 more conservative")
with gr.Row():
with gr.Column():
quick_gen_checkbox = gr.Checkbox(label="是否要快速合成语音", value=True)
with gr.Column():
settings_checkboxes = ["Use semantic history", "Use coarse history", "Use fine history", "Use last generation as history"]
complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
with gr.Row():
with gr.Column():
tts_create_button = gr.Button("开始生成语音吧")
with gr.Column():
hidden_checkbox = gr.Checkbox(visible=False)
button_delete_files = gr.Button("Clear output folder", visible=False)
with gr.Row():
output_audio = gr.Audio(label="听听Bark为您复刻的语音吧", type="filepath")
with gr.Tab("🎶 - 声音克隆"):
input_audio_filename = gr.Audio(label="请上传一段5秒左右的声音", source="upload", type="filepath")
transcription_text = gr.Textbox(label="请填写该声音文件对应的文本", lines=1, placeholder="您上传的声音都说了些什么呢？")
initialname = "Custom_voice"
#inputAudioFilename = gr.Textbox(label="Filename of Input Audio", lines=1, placeholder="audio.wav")
output_voice = gr.Textbox(label="为您的专属声音起个名字吧", lines=1, placeholder=initialname, value=initialname)
clone_voice_button = gr.Button("生成.npz文件")
dummy = gr.Text(label="进度")
npz_file_1 = gr.File(label="请下载您的专属.npz文件")
convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, quick_gen_checkbox, complete_settings],outputs=output_audio)
# Javascript hack to display modal confirmation dialog
js = "(x) => confirm('Are you sure? This will remove all files from output folder')"
button_delete_files.click(None, None, hidden_checkbox, _js=js)
hidden_checkbox.change(delete_output_files, [hidden_checkbox], [hidden_checkbox])
clone_voice_button.click(clone_voice, inputs=[input_audio_filename, transcription_text, output_voice], outputs=[dummy,npz_file_1])
gr.HTML('''
''')
barkgui.queue().launch(show_error=True)