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launch-ui.py
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launch-ui.py
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# coding: utf-8
import argparse
import logging
import os
import pathlib
import time
import tempfile
import platform
import webbrowser
import sys
print(f"default encoding is {sys.getdefaultencoding()},file system encoding is {sys.getfilesystemencoding()}")
print(f"You are using Python version {platform.python_version()}")
if(sys.version_info[0]<3 or sys.version_info[1]<7):
print("The Python version is too low and may cause problems")
if platform.system().lower() == 'windows':
temp = pathlib.PosixPath
pathlib.PosixPath = pathlib.WindowsPath
else:
temp = pathlib.WindowsPath
pathlib.WindowsPath = pathlib.PosixPath
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
import langid
langid.set_languages(['en', 'zh', 'ja'])
import nltk
nltk.data.path = nltk.data.path + [os.path.join(os.getcwd(), "nltk_data")]
import torch
import torchaudio
import random
import numpy as np
from data.tokenizer import (
AudioTokenizer,
tokenize_audio,
)
from data.collation import get_text_token_collater
from models.vallex import VALLE
from utils.g2p import PhonemeBpeTokenizer
from descriptions import *
from macros import *
from examples import *
import gradio as gr
import whisper
from vocos import Vocos
import multiprocessing
thread_count = multiprocessing.cpu_count()
print("Use",thread_count,"cpu cores for computing")
torch.set_num_threads(thread_count)
torch.set_num_interop_threads(thread_count)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._set_graph_executor_optimize(False)
text_tokenizer = PhonemeBpeTokenizer(tokenizer_path="./utils/g2p/bpe_69.json")
text_collater = get_text_token_collater()
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
if torch.backends.mps.is_available():
device = torch.device("mps")
# VALL-E-X model
if not os.path.exists("./checkpoints/"): os.mkdir("./checkpoints/")
if not os.path.exists(os.path.join("./checkpoints/", "vallex-checkpoint.pt")):
import wget
try:
logging.info("Downloading model from https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt ...")
# download from https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt to ./checkpoints/vallex-checkpoint.pt
wget.download("https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt",
out="./checkpoints/vallex-checkpoint.pt", bar=wget.bar_adaptive)
except Exception as e:
logging.info(e)
raise Exception(
"\n Model weights download failed, please go to 'https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt'"
"\n manually download model weights and put it to {} .".format(os.getcwd() + "\checkpoints"))
model = VALLE(
N_DIM,
NUM_HEAD,
NUM_LAYERS,
norm_first=True,
add_prenet=False,
prefix_mode=PREFIX_MODE,
share_embedding=True,
nar_scale_factor=1.0,
prepend_bos=True,
num_quantizers=NUM_QUANTIZERS,
)
checkpoint = torch.load("./checkpoints/vallex-checkpoint.pt", map_location='cpu')
missing_keys, unexpected_keys = model.load_state_dict(
checkpoint["model"], strict=True
)
assert not missing_keys
model.eval()
# Encodec model
audio_tokenizer = AudioTokenizer(device)
# Vocos decoder
vocos = Vocos.from_pretrained('charactr/vocos-encodec-24khz').to(device)
# ASR
if not os.path.exists("./whisper/"): os.mkdir("./whisper/")
try:
whisper_model = whisper.load_model("medium",download_root=os.path.join(os.getcwd(), "whisper")).cpu()
except Exception as e:
logging.info(e)
raise Exception(
"\n Whisper download failed or damaged, please go to "
"'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt'"
"\n manually download model and put it to {} .".format(os.getcwd() + "\whisper"))
# Voice Presets
preset_list = os.walk("./presets/").__next__()[2]
preset_list = [preset[:-4] for preset in preset_list if preset.endswith(".npz")]
def clear_prompts():
try:
path = tempfile.gettempdir()
for eachfile in os.listdir(path):
filename = os.path.join(path, eachfile)
if os.path.isfile(filename) and filename.endswith(".npz"):
lastmodifytime = os.stat(filename).st_mtime
endfiletime = time.time() - 60
if endfiletime > lastmodifytime:
os.remove(filename)
except:
return
def transcribe_one(model, audio_path):
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio_path)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
lang = max(probs, key=probs.get)
# decode the audio
options = whisper.DecodingOptions(temperature=1.0, best_of=5, fp16=False if device == torch.device("cpu") else True, sample_len=150)
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)
text_pr = result.text
if text_pr.strip(" ")[-1] not in "?!.,。，？！。、":
text_pr += "."
return lang, text_pr
def make_npz_prompt(name, uploaded_audio, recorded_audio, transcript_content):
global model, text_collater, text_tokenizer, audio_tokenizer
clear_prompts()
audio_prompt = uploaded_audio if uploaded_audio is not None else recorded_audio
sr, wav_pr = audio_prompt
if not isinstance(wav_pr, torch.FloatTensor):
wav_pr = torch.FloatTensor(wav_pr)
if wav_pr.abs().max() > 1:
wav_pr /= wav_pr.abs().max()
if wav_pr.size(-1) == 2:
wav_pr = wav_pr[:, 0]
if wav_pr.ndim == 1:
wav_pr = wav_pr.unsqueeze(0)
assert wav_pr.ndim and wav_pr.size(0) == 1
if transcript_content == "":
text_pr, lang_pr = make_prompt(name, wav_pr, sr, save=False)
else:
lang_pr = langid.classify(str(transcript_content))[0]
lang_token = lang2token[lang_pr]
text_pr = f"{lang_token}{str(transcript_content)}{lang_token}"
# tokenize audio
encoded_frames = tokenize_audio(audio_tokenizer, (wav_pr, sr))
audio_tokens = encoded_frames[0][0].transpose(2, 1).cpu().numpy()
# tokenize text
phonemes, _ = text_tokenizer.tokenize(text=f"{text_pr}".strip())
text_tokens, enroll_x_lens = text_collater(
[
phonemes
]
)
message = f"Detected language: {lang_pr}\n Detected text {text_pr}\n"
# save as npz file
np.savez(os.path.join(tempfile.gettempdir(), f"{name}.npz"),
audio_tokens=audio_tokens, text_tokens=text_tokens, lang_code=lang2code[lang_pr])
return message, os.path.join(tempfile.gettempdir(), f"{name}.npz")
def make_prompt(name, wav, sr, save=True):
global whisper_model
whisper_model.to(device)
if not isinstance(wav, torch.FloatTensor):
wav = torch.tensor(wav)
if wav.abs().max() > 1:
wav /= wav.abs().max()
if wav.size(-1) == 2:
wav = wav.mean(-1, keepdim=False)
if wav.ndim == 1:
wav = wav.unsqueeze(0)
assert wav.ndim and wav.size(0) == 1
torchaudio.save(f"./prompts/{name}.wav", wav, sr)
lang, text = transcribe_one(whisper_model, f"./prompts/{name}.wav")
lang_token = lang2token[lang]
text = lang_token + text + lang_token
with open(f"./prompts/{name}.txt", 'w', encoding='utf-8') as f:
f.write(text)
if not save:
os.remove(f"./prompts/{name}.wav")
os.remove(f"./prompts/{name}.txt")
whisper_model.cpu()
torch.cuda.empty_cache()
return text, lang
@torch.no_grad()
def infer_from_audio(text, language, accent, audio_prompt, record_audio_prompt, transcript_content):
global model, text_collater, text_tokenizer, audio_tokenizer
audio_prompt = audio_prompt if audio_prompt is not None else record_audio_prompt
sr, wav_pr = audio_prompt
if not isinstance(wav_pr, torch.FloatTensor):
wav_pr = torch.FloatTensor(wav_pr)
if wav_pr.abs().max() > 1:
wav_pr /= wav_pr.abs().max()
if wav_pr.size(-1) == 2:
wav_pr = wav_pr[:, 0]
if wav_pr.ndim == 1:
wav_pr = wav_pr.unsqueeze(0)
assert wav_pr.ndim and wav_pr.size(0) == 1
if transcript_content == "":
text_pr, lang_pr = make_prompt('dummy', wav_pr, sr, save=False)
else:
lang_pr = langid.classify(str(transcript_content))[0]
lang_token = lang2token[lang_pr]
text_pr = f"{lang_token}{str(transcript_content)}{lang_token}"
if language == 'auto-detect':
lang_token = lang2token[langid.classify(text)[0]]
else:
lang_token = langdropdown2token[language]
lang = token2lang[lang_token]
text = lang_token + text + lang_token
# onload model
model.to(device)
# tokenize audio
encoded_frames = tokenize_audio(audio_tokenizer, (wav_pr, sr))
audio_prompts = encoded_frames[0][0].transpose(2, 1).to(device)
# tokenize text
logging.info(f"synthesize text: {text}")
phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
text_tokens, text_tokens_lens = text_collater(
[
phone_tokens
]
)
enroll_x_lens = None
if text_pr:
text_prompts, _ = text_tokenizer.tokenize(text=f"{text_pr}".strip())
text_prompts, enroll_x_lens = text_collater(
[
text_prompts
]
)
text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
text_tokens_lens += enroll_x_lens
lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
encoded_frames = model.inference(
text_tokens.to(device),
text_tokens_lens.to(device),
audio_prompts,
enroll_x_lens=enroll_x_lens,
top_k=-100,
temperature=1,
prompt_language=lang_pr,
text_language=langs if accent == "no-accent" else lang,
best_of=5,
)
# Decode with Vocos
frames = encoded_frames.permute(2,0,1)
features = vocos.codes_to_features(frames)
samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device))
# offload model
model.to('cpu')
torch.cuda.empty_cache()
message = f"text prompt: {text_pr}\nsythesized text: {text}"
return message, (24000, samples.squeeze(0).cpu().numpy())
@torch.no_grad()
def infer_from_prompt(text, language, accent, preset_prompt, prompt_file):
clear_prompts()
model.to(device)
# text to synthesize
if language == 'auto-detect':
lang_token = lang2token[langid.classify(text)[0]]
else:
lang_token = langdropdown2token[language]
lang = token2lang[lang_token]
text = lang_token + text + lang_token
# load prompt
if prompt_file is not None:
prompt_data = np.load(prompt_file.name)
else:
prompt_data = np.load(os.path.join("./presets/", f"{preset_prompt}.npz"))
audio_prompts = prompt_data['audio_tokens']
text_prompts = prompt_data['text_tokens']
lang_pr = prompt_data['lang_code']
lang_pr = code2lang[int(lang_pr)]
# numpy to tensor
audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device)
text_prompts = torch.tensor(text_prompts).type(torch.int32)
enroll_x_lens = text_prompts.shape[-1]
logging.info(f"synthesize text: {text}")
phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
text_tokens, text_tokens_lens = text_collater(
[
phone_tokens
]
)
text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
text_tokens_lens += enroll_x_lens
# accent control
lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
encoded_frames = model.inference(
text_tokens.to(device),
text_tokens_lens.to(device),
audio_prompts,
enroll_x_lens=enroll_x_lens,
top_k=-100,
temperature=1,
prompt_language=lang_pr,
text_language=langs if accent == "no-accent" else lang,
best_of=5,
)
# Decode with Vocos
frames = encoded_frames.permute(2,0,1)
features = vocos.codes_to_features(frames)
samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device))
model.to('cpu')
torch.cuda.empty_cache()
message = f"sythesized text: {text}"
return message, (24000, samples.squeeze(0).cpu().numpy())
from utils.sentence_cutter import split_text_into_sentences
@torch.no_grad()
def infer_long_text(text, preset_prompt, prompt=None, language='auto', accent='no-accent'):
"""
For long audio generation, two modes are available.
fixed-prompt: This mode will keep using the same prompt the user has provided, and generate audio sentence by sentence.
sliding-window: This mode will use the last sentence as the prompt for the next sentence, but has some concern on speaker maintenance.
"""
mode = 'fixed-prompt'
global model, audio_tokenizer, text_tokenizer, text_collater
model.to(device)
if (prompt is None or prompt == "") and preset_prompt == "":
mode = 'sliding-window' # If no prompt is given, use sliding-window mode
sentences = split_text_into_sentences(text)
# detect language
if language == "auto-detect":
language = langid.classify(text)[0]
else:
language = token2lang[langdropdown2token[language]]
# if initial prompt is given, encode it
if prompt is not None and prompt != "":
# load prompt
prompt_data = np.load(prompt.name)
audio_prompts = prompt_data['audio_tokens']
text_prompts = prompt_data['text_tokens']
lang_pr = prompt_data['lang_code']
lang_pr = code2lang[int(lang_pr)]
# numpy to tensor
audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device)
text_prompts = torch.tensor(text_prompts).type(torch.int32)
elif preset_prompt is not None and preset_prompt != "":
prompt_data = np.load(os.path.join("./presets/", f"{preset_prompt}.npz"))
audio_prompts = prompt_data['audio_tokens']
text_prompts = prompt_data['text_tokens']
lang_pr = prompt_data['lang_code']
lang_pr = code2lang[int(lang_pr)]
# numpy to tensor
audio_prompts = torch.tensor(audio_prompts).type(torch.int32).to(device)
text_prompts = torch.tensor(text_prompts).type(torch.int32)
else:
audio_prompts = torch.zeros([1, 0, NUM_QUANTIZERS]).type(torch.int32).to(device)
text_prompts = torch.zeros([1, 0]).type(torch.int32)
lang_pr = language if language != 'mix' else 'en'
if mode == 'fixed-prompt':
complete_tokens = torch.zeros([1, NUM_QUANTIZERS, 0]).type(torch.LongTensor).to(device)
for text in sentences:
text = text.replace("\n", "").strip(" ")
if text == "":
continue
lang_token = lang2token[language]
lang = token2lang[lang_token]
text = lang_token + text + lang_token
enroll_x_lens = text_prompts.shape[-1]
logging.info(f"synthesize text: {text}")
phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
text_tokens, text_tokens_lens = text_collater(
[
phone_tokens
]
)
text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
text_tokens_lens += enroll_x_lens
# accent control
lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
encoded_frames = model.inference(
text_tokens.to(device),
text_tokens_lens.to(device),
audio_prompts,
enroll_x_lens=enroll_x_lens,
top_k=-100,
temperature=1,
prompt_language=lang_pr,
text_language=langs if accent == "no-accent" else lang,
best_of=5,
)
complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1)
# Decode with Vocos
frames = complete_tokens.permute(1, 0, 2)
features = vocos.codes_to_features(frames)
samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device))
model.to('cpu')
message = f"Cut into {len(sentences)} sentences"
return message, (24000, samples.squeeze(0).cpu().numpy())
elif mode == "sliding-window":
complete_tokens = torch.zeros([1, NUM_QUANTIZERS, 0]).type(torch.LongTensor).to(device)
original_audio_prompts = audio_prompts
original_text_prompts = text_prompts
for text in sentences:
text = text.replace("\n", "").strip(" ")
if text == "":
continue
lang_token = lang2token[language]
lang = token2lang[lang_token]
text = lang_token + text + lang_token
enroll_x_lens = text_prompts.shape[-1]
logging.info(f"synthesize text: {text}")
phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
text_tokens, text_tokens_lens = text_collater(
[
phone_tokens
]
)
text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
text_tokens_lens += enroll_x_lens
# accent control
lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
encoded_frames = model.inference(
text_tokens.to(device),
text_tokens_lens.to(device),
audio_prompts,
enroll_x_lens=enroll_x_lens,
top_k=-100,
temperature=1,
prompt_language=lang_pr,
text_language=langs if accent == "no-accent" else lang,
best_of=5,
)
complete_tokens = torch.cat([complete_tokens, encoded_frames.transpose(2, 1)], dim=-1)
if torch.rand(1) < 1.0:
audio_prompts = encoded_frames[:, :, -NUM_QUANTIZERS:]
text_prompts = text_tokens[:, enroll_x_lens:]
else:
audio_prompts = original_audio_prompts
text_prompts = original_text_prompts
# Decode with Vocos
frames = complete_tokens.permute(1, 0, 2)
features = vocos.codes_to_features(frames)
samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device))
model.to('cpu')
message = f"Cut into {len(sentences)} sentences"
return message, (24000, samples.squeeze(0).cpu().numpy())
else:
raise ValueError(f"No such mode {mode}")
def main():
app = gr.Blocks(title="VALL-E X")
with app:
gr.Markdown(top_md)
with gr.Tab("Infer from audio"):
gr.Markdown(infer_from_audio_md)
with gr.Row():
with gr.Column():
textbox = gr.TextArea(label="Text",
placeholder="Type your sentence here",
value="Welcome back, Master. What can I do for you today?", elem_id=f"tts-input")
language_dropdown = gr.Dropdown(choices=['auto-detect', 'English', '中文', '日本語'], value='auto-detect', label='language')
accent_dropdown = gr.Dropdown(choices=['no-accent', 'English', '中文', '日本語'], value='no-accent', label='accent')
textbox_transcript = gr.TextArea(label="Transcript",
placeholder="Write transcript here. (leave empty to use whisper)",
value="", elem_id=f"prompt-name")
upload_audio_prompt = gr.Audio(label='uploaded audio prompt', source='upload', interactive=True)
record_audio_prompt = gr.Audio(label='recorded audio prompt', source='microphone', interactive=True)
with gr.Column():
text_output = gr.Textbox(label="Message")
audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
btn = gr.Button("Generate!")
btn.click(infer_from_audio,
inputs=[textbox, language_dropdown, accent_dropdown, upload_audio_prompt, record_audio_prompt, textbox_transcript],
outputs=[text_output, audio_output])
textbox_mp = gr.TextArea(label="Prompt name",
placeholder="Name your prompt here",
value="prompt_1", elem_id=f"prompt-name")
btn_mp = gr.Button("Make prompt!")
prompt_output = gr.File(interactive=False)
btn_mp.click(make_npz_prompt,
inputs=[textbox_mp, upload_audio_prompt, record_audio_prompt, textbox_transcript],
outputs=[text_output, prompt_output])
gr.Examples(examples=infer_from_audio_examples,
inputs=[textbox, language_dropdown, accent_dropdown, upload_audio_prompt, record_audio_prompt, textbox_transcript],
outputs=[text_output, audio_output],
fn=infer_from_audio,
cache_examples=False,)
with gr.Tab("Make prompt"):
gr.Markdown(make_prompt_md)
with gr.Row():
with gr.Column():
textbox2 = gr.TextArea(label="Prompt name",
placeholder="Name your prompt here",
value="prompt_1", elem_id=f"prompt-name")
# 添加选择语言和输入台本的地方
textbox_transcript2 = gr.TextArea(label="Transcript",
placeholder="Write transcript here. (leave empty to use whisper)",
value="", elem_id=f"prompt-name")
upload_audio_prompt_2 = gr.Audio(label='uploaded audio prompt', source='upload', interactive=True)
record_audio_prompt_2 = gr.Audio(label='recorded audio prompt', source='microphone', interactive=True)
with gr.Column():
text_output_2 = gr.Textbox(label="Message")
prompt_output_2 = gr.File(interactive=False)
btn_2 = gr.Button("Make!")
btn_2.click(make_npz_prompt,
inputs=[textbox2, upload_audio_prompt_2, record_audio_prompt_2, textbox_transcript2],
outputs=[text_output_2, prompt_output_2])
gr.Examples(examples=make_npz_prompt_examples,
inputs=[textbox2, upload_audio_prompt_2, record_audio_prompt_2, textbox_transcript2],
outputs=[text_output_2, prompt_output_2],
fn=make_npz_prompt,
cache_examples=False,)
with gr.Tab("Infer from prompt"):
gr.Markdown(infer_from_prompt_md)
with gr.Row():
with gr.Column():
textbox_3 = gr.TextArea(label="Text",
placeholder="Type your sentence here",
value="Welcome back, Master. What can I do for you today?", elem_id=f"tts-input")
language_dropdown_3 = gr.Dropdown(choices=['auto-detect', 'English', '中文', '日本語', 'Mix'], value='auto-detect',
label='language')
accent_dropdown_3 = gr.Dropdown(choices=['no-accent', 'English', '中文', '日本語'], value='no-accent',
label='accent')
preset_dropdown_3 = gr.Dropdown(choices=preset_list, value=None, label='Voice preset')
prompt_file = gr.File(file_count='single', file_types=['.npz'], interactive=True)
with gr.Column():
text_output_3 = gr.Textbox(label="Message")
audio_output_3 = gr.Audio(label="Output Audio", elem_id="tts-audio")
btn_3 = gr.Button("Generate!")
btn_3.click(infer_from_prompt,
inputs=[textbox_3, language_dropdown_3, accent_dropdown_3, preset_dropdown_3, prompt_file],
outputs=[text_output_3, audio_output_3])
gr.Examples(examples=infer_from_prompt_examples,
inputs=[textbox_3, language_dropdown_3, accent_dropdown_3, preset_dropdown_3, prompt_file],
outputs=[text_output_3, audio_output_3],
fn=infer_from_prompt,
cache_examples=False,)
with gr.Tab("Infer long text"):
gr.Markdown("This is a long text generation demo. You can use this to generate long audio. ")
with gr.Row():
with gr.Column():
textbox_4 = gr.TextArea(label="Text",
placeholder="Type your sentence here",
value=long_text_example, elem_id=f"tts-input")
language_dropdown_4 = gr.Dropdown(choices=['auto-detect', 'English', '中文', '日本語'], value='auto-detect',
label='language')
accent_dropdown_4 = gr.Dropdown(choices=['no-accent', 'English', '中文', '日本語'], value='no-accent',
label='accent')
preset_dropdown_4 = gr.Dropdown(choices=preset_list, value=None, label='Voice preset')
prompt_file_4 = gr.File(file_count='single', file_types=['.npz'], interactive=True)
with gr.Column():
text_output_4 = gr.TextArea(label="Message")
audio_output_4 = gr.Audio(label="Output Audio", elem_id="tts-audio")
btn_4 = gr.Button("Generate!")
btn_4.click(infer_long_text,
inputs=[textbox_4, preset_dropdown_4, prompt_file_4, language_dropdown_4, accent_dropdown_4],
outputs=[text_output_4, audio_output_4])
webbrowser.open("http://127.0.0.1:7860")
app.launch()
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
main()