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RVC-Mangio 
https://github.com/Mangio621/Mangio-RVC-Fork/releases

Google Doc for MacOS Install Instructions
https://docs.google.com/document/d/1KKKE7hoyGXMw-Lg0JWx16R8xz3OfxADjwEYJTqzDO1k/edit#heading=h.8vqd8m4fh76q

Voice Models
https://voice-models.com/

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def nextpow2(N):
    """ Function for finding the next power of 2 """
    n = 1
    while n < N: n *= 2
    return n

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from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
embedder = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')
#embedder = SentenceTransformer('all-MiniLM-L6-v2')
corpus = tfidf_sum.index.to_list()
corpus_embeddings = embedder.encode(corpus)

# Perform kmean clustering
num_clusters = 8
clustering_model = KMeans(n_clusters=num_clusters, random_state=42, init='k-means++')

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import redis
import json

# Retrieve Redis Stored Values, if any for faster processing
anc_dict_320 = {}
r = redis.Redis(host='localhost', port=6379, db=0)
json_str_320 = r.get("anc_dict_320")
if json_str_320 is not None:
    anc_dict_320 = json.loads(json_str_320)
    print(anc_dict_320)

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conda create -n  my_custom_python_39 python=3.9 -y
cd ~/SageMaker 
source activate my_custom_python_39
pip install virtualenv  
virtualenv my_custom_python_39_venv 
conda deactivate
source my_custom_python_39_venv/bin/activate
pip install -r <your_project_folder>/requirements.txt
pip install ipykernel
python -m ipykernel install --user --name=my_custom_python_39

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conda create -n  my_custom_python_39 python=3.9 -y
cd ~/SageMaker 
source activate my_custom_python_39
pip install virtualenv  
virtualenv my_custom_python_39_venv 
conda deactivate
source my_custom_python_39_venv/bin/activate
pip install ipykernel -y
python -m ipykernel install --user --name=my_custom_python_39

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# Remove URLs
text_clean = [re.sub(r'http\S+', '', t) for t in text]

# Remove new lines \n
text_clean= [t.strip().replace('\n', ' ') for t in text_clean]

# Remove emails
text_clean = [re.sub(r'[\w\.-]+@[\w\.-]+', '', t) for t in text_clean]

# Remove single quotes

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from pathlib import Path
from sklearn.model_selection import train_test_split

# IMDB Dataset can be found here
# wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
# tar -xf aclImdb_v1.tar.gz


def read_imdb_split(split_dir):
    split_dir = Path(split_dir)

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from pathlib import Path
from sklearn.model_selection import train_test_split
from transformers import DistilBertTokenizerFast
import torch
from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
import torch.nn.functional as F
from sklearn.metrics import accuracy_score, precision_recall_fscore_support

# IMDB Dataset can be found here
# wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz

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" enable syntax highlighting
syntax enable

" show line numbers
set number

" set tabs to have 4 spaces
set ts=4

" indent when moving to the next line while writing code