app.py

import os
import sys

os.system("git clone https://github.com/C0untFloyd/bark-gui.git")
sys.path.append("./bark-gui/")

from cProfile import label
from distutils.command.check import check
from doctest import Example
import dataclasses
import gradio as gr
import numpy as np
import logging
import torch
import pytorch_seed
import time

import torchaudio
from speechbrain.pretrained import SpectralMaskEnhancement

enhance_model = SpectralMaskEnhancement.from_hparams(
    source="speechbrain/metricgan-plus-voicebank",
    savedir="pretrained_models/metricgan-plus-voicebank",
    run_opts={"device":"cuda"},
)

from xml.sax import saxutils
from bark.api import generate_with_settings
from bark.api import save_as_prompt
from settings import Settings
#import nltk

from bark import SAMPLE_RATE
from bark.clonevoice import clone_voice
from bark.generation import SAMPLE_RATE, preload_models, _load_history_prompt, codec_decode
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
from id3tagging import add_id3_tag

import shutil

import string
import argparse
import json

import gc, copy
from datetime import datetime
from huggingface_hub import hf_hub_download
from pynvml import *
nvmlInit()
gpu_h = nvmlDeviceGetHandleByIndex(0)
ctx_limit = 1536
title = "RWKV-4-Raven-7B-v12-Eng98%-Other2%-20230521-ctx8192"

os.environ["RWKV_JIT_ON"] = '1'
os.environ["RWKV_CUDA_ON"] = '1' # if '1' then use CUDA kernel for seq mode (much faster)

from rwkv.model import RWKV
model_path1 = hf_hub_download(repo_id="BlinkDL/rwkv-4-raven", filename=f"{title}.pth")
model1 = RWKV(model=model_path1, strategy='cuda fp16i8 *8 -> cuda fp16')
from rwkv.utils import PIPELINE, PIPELINE_ARGS
pipeline = PIPELINE(model1, "20B_tokenizer.json")

def generate_prompt(instruction, input=None):
    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    if input:
        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Input:
{input}
# Response:
"""
    else:
        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
# Instruction:
{instruction}
# Response:
"""

def evaluate(
    instruction,
    input=None,
    token_count=200,
    temperature=1.0,
    top_p=0.7,
    presencePenalty = 0.1,
    countPenalty = 0.1,
):
    args = PIPELINE_ARGS(temperature = max(0.2, float(temperature)), top_p = float(top_p),
                     alpha_frequency = countPenalty,
                     alpha_presence = presencePenalty,
                     token_ban = [], # ban the generation of some tokens
                     token_stop = [0]) # stop generation whenever you see any token here

    instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
    input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
    ctx = generate_prompt(instruction, input)
    
    all_tokens = []
    out_last = 0
    out_str = ''
    occurrence = {}
    state = None
    for i in range(int(token_count)):
        out, state = model1.forward(pipeline.encode(ctx)[-ctx_limit:] if i == 0 else [token], state)
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        if token in args.token_stop:
            break
        all_tokens += [token]
        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1
        
        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            out_str += tmp
            yield out_str.strip()
            out_last = i + 1

    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  
    del out
    del state
    gc.collect()
    torch.cuda.empty_cache()
    yield out_str.strip()

examples = [
    ["Tell me about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Write a python function to mine 1 BTC, with details and comments.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Write a song about ravens.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Explain the following metaphor: Life is like cats.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["Write a story using the following information", "A man named Alex chops a tree down", 300, 1.2, 0.5, 0.4, 0.4],
    ["Generate a list of adjectives that describe a person as brave.", "", 300, 1.2, 0.5, 0.4, 0.4],
    ["You have $100, and your goal is to turn that into as much money as possible with AI and Machine Learning. Please respond with detailed plan.", "", 300, 1.2, 0.5, 0.4, 0.4],
]

##########################################################################

chat_intro = '''The following is a coherent verbose detailed conversation between <|user|> and an AI girl named <|bot|>.
<|user|>: Hi <|bot|>, Would you like to chat with me for a while?
<|bot|>: Hi <|user|>. Sure. What would you like to talk about? I'm listening.
'''

def user(message, chatbot):
    chatbot = chatbot or []
    # print(f"User: {message}")
    return "", chatbot + [[message, None]]

def alternative(chatbot, history):
    if not chatbot or not history:
        return chatbot, history
    
    chatbot[-1][1] = None
    history[0] = copy.deepcopy(history[1])

    return chatbot, history

def chat(
        prompt,
        user,
        bot,
        chatbot,
        history,
        temperature=1.0,
        top_p=0.8,
        presence_penalty=0.1,
        count_penalty=0.1,
):
    args = PIPELINE_ARGS(temperature=max(0.2, float(temperature)), top_p=float(top_p),
                         alpha_frequency=float(count_penalty),
                         alpha_presence=float(presence_penalty),
                         token_ban=[],  # ban the generation of some tokens
                         token_stop=[])  # stop generation whenever you see any token here
    
    if not chatbot:
        return chatbot, history

    message = chatbot[-1][0]
    message = message.strip().replace('\r\n','\n').replace('\n\n','\n')
    ctx = f"{user}: {message}\n\n{bot}:"

    if not history:
        prompt = prompt.replace("<|user|>", user.strip())
        prompt = prompt.replace("<|bot|>", bot.strip())
        prompt = prompt.strip()
        prompt = f"\n{prompt}\n\n"

        out, state = model1.forward(pipeline.encode(prompt), None)
        history = [state, None, []]  # [state, state_pre, tokens]
        # print("History reloaded.")

    [state, _, all_tokens] = history
    state_pre_0 = copy.deepcopy(state)

    out, state = model1.forward(pipeline.encode(ctx)[-ctx_limit:], state)
    state_pre_1 = copy.deepcopy(state)    # For recovery

    # print("Bot:", end='')

    begin = len(all_tokens)
    out_last = begin
    out_str: str = ''
    occurrence = {}
    for i in range(300):
        if i <= 0:
            nl_bias = -float('inf')
        elif i <= 30:
            nl_bias = (i - 30) * 0.1
        elif i <= 130:
            nl_bias = 0
        else:
            nl_bias = (i - 130) * 0.25
        out[187] += nl_bias
        for n in occurrence:
            out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)

        token = pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p)
        next_tokens = [token]
        if token == 0:
            next_tokens = pipeline.encode('\n\n')
        all_tokens += next_tokens

        if token not in occurrence:
            occurrence[token] = 1
        else:
            occurrence[token] += 1

        out, state = model1.forward(next_tokens, state)

        tmp = pipeline.decode(all_tokens[out_last:])
        if '\ufffd' not in tmp:
            # print(tmp, end='', flush=True)
            out_last = begin + i + 1
            out_str += tmp

            chatbot[-1][1] = out_str.strip()
            history = [state, all_tokens]
            yield chatbot, history

        out_str = pipeline.decode(all_tokens[begin:])
        out_str = out_str.replace("\r\n", '\n').replace('\\n', '\n')

        if '\n\n' in out_str:
            break

        # State recovery
        if f'{user}:' in out_str or f'{bot}:' in out_str:
            idx_user = out_str.find(f'{user}:')
            idx_user = len(out_str) if idx_user == -1 else idx_user
            idx_bot = out_str.find(f'{bot}:')
            idx_bot = len(out_str) if idx_bot == -1 else idx_bot
            idx = min(idx_user, idx_bot)

            if idx < len(out_str):
                out_str = f" {out_str[:idx].strip()}\n\n"
                tokens = pipeline.encode(out_str)

                all_tokens = all_tokens[:begin] + tokens
                out, state = model1.forward(tokens, state_pre_1)
                break

    gpu_info = nvmlDeviceGetMemoryInfo(gpu_h)
    print(f'vram {gpu_info.total} used {gpu_info.used} free {gpu_info.free}')  

    gc.collect()
    torch.cuda.empty_cache()

    chatbot[-1][1] = out_str.strip()
    history = [state, state_pre_0, all_tokens]
    yield chatbot, history

from TTS.tts.utils.synthesis import synthesis
from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
try:
  from TTS.utils.audio import AudioProcessor
except:
  from TTS.utils.audio import AudioProcessor


from TTS.tts.models import setup_model
from TTS.config import load_config
from TTS.tts.models.vits import *

from TTS.tts.utils.speakers import SpeakerManager
from pydub import AudioSegment

# from google.colab import files
import librosa

from scipy.io.wavfile import write, read

import subprocess


OUTPUTFOLDER = "Outputs"

def speechbrain(aud):
  # Load and add fake batch dimension
  noisy = enhance_model.load_audio(
      aud
  ).unsqueeze(0)
  enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))
  torchaudio.save('enhanced.wav', enhanced.cpu(), 16000)
  return 'enhanced.wav'

def generate_text_to_speech(text, selected_speaker, text_temp, waveform_temp, eos_prob, quick_generation, complete_settings, seed, batchcount, progress=gr.Progress(track_tqdm=True)):
    # Chunk the text into smaller pieces then combine the generated audio

    # generation settings
    if selected_speaker == 'None':
        selected_speaker = None

    voice_name = selected_speaker

    if text == None or len(text) < 1:
       if selected_speaker == None:
            raise gr.Error('No text entered!')

       # Extract audio data from speaker if no text and speaker selected
       voicedata = _load_history_prompt(voice_name)
       audio_arr = codec_decode(voicedata["fine_prompt"])
       result = create_filename(OUTPUTFOLDER, "None", "extract",".wav")
       save_wav(audio_arr, result)
       return result

    if batchcount < 1:
        batchcount = 1


    silenceshort = np.zeros(int((float(settings.silence_sentence) / 1000.0) * SAMPLE_RATE), dtype=np.int16)  # quarter second of silence
    silencelong = np.zeros(int((float(settings.silence_speakers) / 1000.0) * SAMPLE_RATE), dtype=np.float32)  # half a second of silence
    use_last_generation_as_history = "Use last generation as history" in complete_settings
    save_last_generation = "Save generation as Voice" in complete_settings
    for l in range(batchcount):
        currentseed = seed
        if seed != None and seed > 2**32 - 1:
            logger.warning(f"Seed {seed} > 2**32 - 1 (max), setting to random")
            currentseed = None
        if currentseed == None or currentseed <= 0:
            currentseed = np.random.default_rng().integers(1, 2**32 - 1)
        assert(0 < currentseed and currentseed < 2**32)

        progress(0, desc="Generating")

        full_generation = None

        all_parts = []
        complete_text = ""
        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} (Seed {currentseed}):`{text}`")
                complete_text += text
                with pytorch_seed.SavedRNG(currentseed):
                    audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
                    currentseed = torch.random.initial_seed()
                if len(list_speak) > 1:
                    filename = create_filename(OUTPUTFOLDER, currentseed, "audioclip",".wav")
                    save_wav(audio_array, filename)
                    add_id3_tag(filename, text, selected_speaker, currentseed)

                all_parts += [audio_array]
        else:
            texts = split_and_recombine_text(text, settings.input_text_desired_length, settings.input_text_max_length)
            for i, text in tqdm(enumerate(texts), total=len(texts)):
                print(f"\nGenerating Text ({i+1}/{len(texts)}) -> {selected_speaker} (Seed {currentseed}):`{text}`")
                complete_text += text
                if quick_generation == True:
                    with pytorch_seed.SavedRNG(currentseed):
                        audio_array = generate_with_settings(text_prompt=text, voice_name=selected_speaker, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)
                        currentseed = torch.random.initial_seed()
                else:
                    full_output = use_last_generation_as_history or save_last_generation
                    if full_output:
                        full_generation, audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob, output_full=True)
                    else:
                        audio_array = generate_with_settings(text_prompt=text, voice_name=voice_name, semantic_temp=text_temp, coarse_temp=waveform_temp, eos_p=eos_prob)

                # 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:
                    filename = create_filename(OUTPUTFOLDER, currentseed, "audioclip",".wav")
                    save_wav(audio_array, filename)
                    add_id3_tag(filename, text, selected_speaker, currentseed)

                if quick_generation == False and (save_last_generation == True or use_last_generation_as_history == True):
                    # save to npz
                    voice_name = create_filename(OUTPUTFOLDER, seed, "audioclip", ".npz")
                    save_as_prompt(voice_name, full_generation)
                    if use_last_generation_as_history:
                        selected_speaker = voice_name

                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, currentseed, "final",".wav")
        save_wav(np.concatenate(all_parts), result)
        # write id3 tag with text truncated to 60 chars, as a precaution...
        add_id3_tag(result, complete_text, selected_speaker, currentseed)

    return result

def create_filename(path, seed, 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)

    time_str = now.strftime("%H-%M-%S")
    file_name = f"{name}_{time_str}_s{seed}{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)


def apply_settings(themes, input_server_name, input_server_port, input_server_public, input_desired_len, input_max_len, input_silence_break, input_silence_speaker):
    settings.selected_theme = themes
    settings.server_name = input_server_name
    settings.server_port = input_server_port
    settings.server_share = input_server_public
    settings.input_text_desired_length = input_desired_len
    settings.input_text_max_length = input_max_len
    settings.silence_sentence = input_silence_break
    settings.silence_speaker = input_silence_speaker
    settings.save()

def restart():
    global restart_server
    restart_server = True


def create_version_html():
    python_version = ".".join([str(x) for x in sys.version_info[0:3]])
    versions_html = f"""
python: <span title="{sys.version}">{python_version}</span>
 • 
torch: {getattr(torch, '__long_version__',torch.__version__)}
 • 
gradio: {gr.__version__}
"""
    return versions_html

    

logger = logging.getLogger(__name__)
APPTITLE = "Bark UI Enhanced v0.4.8"


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()

settings = Settings('config.yaml')

# Collect all existing speakers/voices in dir
speakers_list = []

for root, dirs, files in os.walk("./bark/assets/prompts"):
	for file in files:
		if(file.endswith(".npz")):
			pathpart = root.replace("./bark/assets/prompts", "")
			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, 'None')

available_themes = ["Default", "gradio/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "gstaff/xkcd", "freddyaboulton/dracula_revamped", "ysharma/steampunk"]

seed = -1
server_name = settings.server_name
if len(server_name) < 1:
    server_name = None
server_port = settings.server_port
if server_port <= 0:
    server_port = None
global run_server
global restart_server

run_server = True





'''
from google.colab import drive
drive.mount('/content/drive')
src_path = os.path.join(os.path.join(os.path.join(os.path.join(os.getcwd(), 'drive'), 'MyDrive'), 'Colab Notebooks'), 'best_model_latest.pth.tar')
dst_path = os.path.join(os.getcwd(), 'best_model.pth.tar')
shutil.copy(src_path, dst_path)
'''

TTS_PATH = "TTS/"

# add libraries into environment
sys.path.append(TTS_PATH) # set this if TTS is not installed globally

# Paths definition

OUT_PATH = 'out/'

# create output path
os.makedirs(OUT_PATH, exist_ok=True)

# model vars 
MODEL_PATH = 'best_model.pth.tar'
CONFIG_PATH = 'config.json'
TTS_LANGUAGES = "language_ids.json"
TTS_SPEAKERS = "speakers.json"
USE_CUDA = torch.cuda.is_available()

# load the config
C = load_config(CONFIG_PATH)

# load the audio processor
ap = AudioProcessor(**C.audio)

speaker_embedding = None

C.model_args['d_vector_file'] = TTS_SPEAKERS
C.model_args['use_speaker_encoder_as_loss'] = False

model = setup_model(C)
model.language_manager.set_language_ids_from_file(TTS_LANGUAGES)
# print(model.language_manager.num_languages, model.embedded_language_dim)
# print(model.emb_l)
cp = torch.load(MODEL_PATH, map_location=torch.device('cpu'))
# remove speaker encoder
model_weights = cp['model'].copy()
for key in list(model_weights.keys()):
  if "speaker_encoder" in key:
    del model_weights[key]

model.load_state_dict(model_weights)

model.eval()

if USE_CUDA:
    model = model.cuda()

# synthesize voice
use_griffin_lim = False

# Paths definition

CONFIG_SE_PATH = "config_se.json"
CHECKPOINT_SE_PATH = "SE_checkpoint.pth.tar"

# Load the Speaker encoder

SE_speaker_manager = SpeakerManager(encoder_model_path=CHECKPOINT_SE_PATH, encoder_config_path=CONFIG_SE_PATH, use_cuda=USE_CUDA)

# Define helper function

def compute_spec(ref_file):
  y, sr = librosa.load(ref_file, sr=ap.sample_rate)
  spec = ap.spectrogram(y)
  spec = torch.FloatTensor(spec).unsqueeze(0)
  return spec


def voice_conversion(ta, ra, da):

  target_audio = 'target.wav'
  reference_audio = 'reference.wav'
  driving_audio = 'driving.wav'

  write(target_audio, ta[0], ta[1])
  write(reference_audio, ra[0], ra[1])
  write(driving_audio, da[0], da[1])          

  # !ffmpeg-normalize $target_audio -nt rms -t=-27 -o $target_audio -ar 16000 -f
  # !ffmpeg-normalize $reference_audio -nt rms -t=-27 -o $reference_audio -ar 16000 -f
  # !ffmpeg-normalize $driving_audio -nt rms -t=-27 -o $driving_audio -ar 16000 -f

  files = [target_audio, reference_audio, driving_audio]

  for file in files:
      subprocess.run(["ffmpeg-normalize", file, "-nt", "rms", "-t=-27", "-o", file, "-ar", "16000", "-f"])

  # ta_ = read(target_audio)

  target_emb = SE_speaker_manager.compute_d_vector_from_clip([target_audio])
  target_emb = torch.FloatTensor(target_emb).unsqueeze(0)

  driving_emb = SE_speaker_manager.compute_d_vector_from_clip([reference_audio])
  driving_emb = torch.FloatTensor(driving_emb).unsqueeze(0)

  # Convert the voice

  driving_spec = compute_spec(driving_audio)
  y_lengths = torch.tensor([driving_spec.size(-1)])
  if USE_CUDA:
      ref_wav_voc, _, _ = model.voice_conversion(driving_spec.cuda(), y_lengths.cuda(), driving_emb.cuda(), target_emb.cuda())
      ref_wav_voc = ref_wav_voc.squeeze().cpu().detach().numpy()
  else:
      ref_wav_voc, _, _ = model.voice_conversion(driving_spec, y_lengths, driving_emb, target_emb)
      ref_wav_voc = ref_wav_voc.squeeze().detach().numpy()

  # print("Reference Audio after decoder:")
  # IPython.display.display(Audio(ref_wav_voc, rate=ap.sample_rate))

  return (ap.sample_rate, ref_wav_voc)


while run_server:
    print(f'Launching {APPTITLE} Server')

    # Create Gradio Blocks

    with gr.Blocks(title=f"{APPTITLE}", mode=f"{APPTITLE}", theme=settings.selected_theme) as barkgui:
        gr.Markdown("# <center>🐶🥳🎶 - Bark拟声，开启声音真实复刻的新纪元！</center>")
        gr.Markdown("### <center>🦄 - [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</center>")
        gr.Markdown(
                f""" 
                ### <center>🤗 - Powered by [Bark Enhanced](https://github.com/C0untFloyd/bark-gui). Thanks to C0untFloyd.</center>
                ### <center>1. 您可以复制该程序并用GPU运行: <a href="https://huggingface.co/spaces/{os.getenv('SPACE_ID')}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></center>
                ### <center>2. 更多精彩应用，尽在[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>
            """
        )
        with gr.Tab("Instruct mode"):
            gr.Markdown(f"Raven is [RWKV 7B](https://github.com/BlinkDL/ChatRWKV) 100% RNN [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) finetuned to follow instructions. *** Please try examples first (bottom of page) *** (edit them to use your question). Demo limited to ctxlen {ctx_limit}. Finetuned on alpaca, gpt4all, codealpaca and more. For best results, *** keep you prompt short and clear ***. <b>UPDATE: now with Chat (see above, as a tab) ==> turn off as of now due to VRAM leak caused by buggy code.</b>.")
            with gr.Row():
                with gr.Column():
                    instruction = gr.Textbox(lines=2, label="Instruction", value="Tell me about ravens.")
                    input = gr.Textbox(lines=2, label="Input", placeholder="none")
                    token_count = gr.Slider(10, 300, label="Max Tokens", step=10, value=300)
                    temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=1.2)
                    top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.5)
                    presence_penalty = gr.Slider(0.0, 1.0, label="Presence Penalty", step=0.1, value=0.4)
                    count_penalty = gr.Slider(0.0, 1.0, label="Count Penalty", step=0.1, value=0.4)
                with gr.Column():
                    with gr.Row():
                        submit = gr.Button("Submit", variant="primary")
                        clear = gr.Button("Clear", variant="secondary")
                    output = gr.Textbox(label="Output", lines=5)
            data = gr.Dataset(components=[instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], samples=examples, label="Example Instructions", headers=["Instruction", "Input", "Max Tokens", "Temperature", "Top P", "Presence Penalty", "Count Penalty"])
            submit.click(evaluate, [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty], [output])
            clear.click(lambda: None, [], [output])
            data.click(lambda x: x, [data], [instruction, input, token_count, temperature, top_p, presence_penalty, count_penalty])
    
        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", lines=4, placeholder=placeholder)
                with gr.Column():
                    convert_to_ssml_button = gr.Button("Convert Input Text to SSML")
                    seedcomponent = gr.Number(label="Seed (default -1 = Random)", precision=0, value=-1)
                    batchcount = gr.Number(label="Batch count", precision=0, value=1)

            with gr.Row():
                with gr.Column():
                    gr.Markdown("查看Bark官方[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)
                    settings_checkboxes = ["Use last generation as history", "Save generation as Voice"]
                    complete_settings = gr.CheckboxGroup(choices=settings_checkboxes, value=settings_checkboxes, label="Detailed Generation Settings", type="value", interactive=True, visible=False)
                with gr.Column():
                    eos_prob = gr.Slider(0.0, 0.5, value=0.05, label="End of sentence probability")

            with gr.Row():
                with gr.Column():
                    tts_create_button = gr.Button("开始声音真实复刻吧")
                with gr.Column():
                    hidden_checkbox = gr.Checkbox(visible=False)
                    button_stop_generation = gr.Button("停止生成")
            with gr.Row():
                output_audio = gr.Audio(label="真实复刻的声音")

            with gr.Row():
                inp1 = gr.Audio(label="请上传您喜欢的声音")
                inp2 = output_audio
                inp3 = output_audio
                btn = gr.Button("开始生成专属声音吧")
                out1 = gr.Audio(label="为您生成的专属声音", type="filepath")
            btn.click(voice_conversion, [inp1, inp2, inp3], [out1])

            with gr.Row():
                inp4 = out1
                btn2 = gr.Button("对专属声音降噪吧")
                out2 = gr.Audio(label="降噪后的专属声音", type="filepath")
            btn2.click(speechbrain, [inp4], [out2])
            
            

            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!
                       """,
                       """<?xml version="1.0"?>
    <speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis"
             xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
             xsi:schemaLocation="http://www.w3.org/2001/10/synthesis
                       http://www.w3.org/TR/speech-synthesis/synthesis.xsd"
             xml:lang="en-US">
    <voice name="en_speaker_9">Look at that drunk guy!</voice>
    <voice name="en_speaker_3">Who is he?</voice>
    <voice name="en_speaker_9">WOMAN: [clears throat] 10 years ago, he proposed me and I rejected him.</voice>
    <voice name="en_speaker_3">Oh my God [laughs] he is still celebrating</voice>
    </speak>"""
                       ]
                    examples = gr.Examples(examples=examples, inputs=input_text)

        with gr.Tab("🤖 - 设置"):
            with gr.Row():
                themes = gr.Dropdown(available_themes, label="Theme", info="Change needs complete restart", value=settings.selected_theme)
            with gr.Row():
                input_server_name = gr.Textbox(label="Server Name", lines=1, info="Leave blank to run locally", value=settings.server_name)
                input_server_port = gr.Number(label="Server Port", precision=0, info="Leave at 0 to use default", value=settings.server_port)
                share_checkbox = gr.Checkbox(label="Public Server", value=settings.server_share)
            with gr.Row():
                input_desired_len = gr.Slider(100, 150, value=settings.input_text_desired_length, label="Desired Input Text Length", info="Ideal length to split input sentences")
                input_max_len = gr.Slider(150, 256, value=settings.input_text_max_length, label="Max Input Text Length", info="Maximum Input Text Length")
            with gr.Row():
                input_silence_break = gr.Slider(1, 1000, value=settings.silence_sentence, label="Sentence Pause Time (ms)", info="Silence between sentences in milliseconds")
                input_silence_speakers = gr.Slider(1, 5000, value=settings.silence_speakers, label="Speaker Pause Time (ms)", info="Silence between different speakers in milliseconds")

            with gr.Row():
                button_apply_settings = gr.Button("Apply Settings")
                button_apply_restart = gr.Button("Restart Server")
                button_delete_files = gr.Button("Clear output folder")

        gr.HTML('''
            <div class="footer">
                        <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
                        </p>
            </div>
        ''')  

        quick_gen_checkbox.change(fn=on_quick_gen_changed, inputs=quick_gen_checkbox, outputs=complete_settings)
        convert_to_ssml_button.click(convert_text_to_ssml, inputs=[input_text, speaker],outputs=input_text)
        gen_click = tts_create_button.click(generate_text_to_speech, inputs=[input_text, speaker, text_temp, waveform_temp, eos_prob, quick_gen_checkbox, complete_settings, seedcomponent, batchcount],outputs=output_audio)
        button_stop_generation.click(fn=None, inputs=None, outputs=None, cancels=[gen_click])
        # 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])
        button_apply_settings.click(apply_settings, inputs=[themes, input_server_name, input_server_port, share_checkbox, input_desired_len, input_max_len, input_silence_break, input_silence_speakers])
        button_apply_restart.click(restart)
        restart_server = False
        try:
            barkgui.queue().launch(show_error=True)
        except:
            restart_server = True
            run_server = False
        try:
            while restart_server == False:
                time.sleep(1.0)
        except (KeyboardInterrupt, OSError):
            print("Keyboard interruption in main thread... closing server.")
            run_server = False
        barkgui.close()