index int64 0 50.7k | project_path stringclasses 279
values | completion_path stringlengths 57 199 | signature_postion stringlengths 3 11 | class_position stringlengths 3 11 | function_name stringlengths 1 62 | class_name stringlengths 0 55 | body_position stringlengths 3 11 | body_position_with_comments stringlengths 3 11 | masked_class stringlengths 14 8.32k | masked_class_with_comment stringlengths 14 8.32k | func_body stringlengths 9 6.72k | func_body_with_comment stringlengths 9 6.72k | len_func_body int64 1 643 | len_input int64 10 2.02k | len_output int64 4 1.9k | total int64 20 2.05k | file_code stringlengths 14 442k | file_code_with_comment stringlengths 14 442k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
25,126 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/llm/ollama_client.py | 19,20 | 8,26 | inference | Ollama | 20,26 | 20,26 | class Ollama:
def __init__(self):
try:
self.client = ollama.Client(Config().get_ollama_api_endpoint())
self.models = self.client.list()["models"]
log.info("Ollama available")
except:
self.client = None
log.warning("Ollama not available")
... | class Ollama:
def __init__(self):
try:
self.client = ollama.Client(Config().get_ollama_api_endpoint())
self.models = self.client.list()["models"]
log.info("Ollama available")
except:
self.client = None
log.warning("Ollama not available")
... | response = self.client.generate(
model=model_id,
prompt=prompt.strip(),
options={"temperature": 0}
)
return response['response']
| response = self.client.generate(
model=model_id,
prompt=prompt.strip(),
options={"temperature": 0}
)
return response['response']
| 10 | 145 | 53 | 198 | import ollama
from src.logger import Logger
from src.config import Config
log = Logger()
class Ollama:
def __init__(self):
try:
self.client = ollama.Client(Config().get_ollama_api_endpoint())
self.models = self.client.list()["models"]
log.info("Ollama available")
... | import ollama
from src.logger import Logger
from src.config import Config
log = Logger()
class Ollama:
def __init__(self):
try:
self.client = ollama.Client(Config().get_ollama_api_endpoint())
self.models = self.client.list()["models"]
log.info("Ollama available")
... |
25,127 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/llm/openai_client.py | 13,14 | 6,25 | inference | OpenAi | 14,25 | 14,25 | class OpenAi:
def __init__(self):
config = Config()
api_key = config.get_openai_api_key()
base_url = config.get_openai_api_base_url()
self.client = OpenAI(api_key=api_key, base_url=base_url)
def inference(self, model_id: str, prompt: str) -> str:
FILL_FUNC_BODY | class OpenAi:
def __init__(self):
config = Config()
api_key = config.get_openai_api_key()
base_url = config.get_openai_api_base_url()
self.client = OpenAI(api_key=api_key, base_url=base_url)
def inference(self, model_id: str, prompt: str) -> str:
FILL_FUNC_BODY | chat_completion = self.client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt.strip(),
}
],
model=model_id,
temperature=0
)
return chat_completion.choic... | chat_completion = self.client.chat.completions.create(
messages=[
{
"role": "user",
"content": prompt.strip(),
}
],
model=model_id,
temperature=0
)
return chat_completion.choic... | 16 | 113 | 93 | 206 | from openai import OpenAI
from src.config import Config
class OpenAi:
def __init__(self):
config = Config()
api_key = config.get_openai_api_key()
base_url = config.get_openai_api_base_url()
self.client = OpenAI(api_key=api_key, base_url=base_url)
def inference(self, model_id:... | from openai import OpenAI
from src.config import Config
class OpenAi:
def __init__(self):
config = Config()
api_key = config.get_openai_api_key()
base_url = config.get_openai_api_base_url()
self.client = OpenAI(api_key=api_key, base_url=base_url)
def inference(self, model_id:... |
25,128 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/logger.py | 40,41 | 1,79 | route_logger | 47,79 | 41,79 | from functools import wraps
from fastlogging import LogInit
from flask import request
from src.config import Config
class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, ... | from functools import wraps
from fastlogging import LogInit
from flask import request
from src.config import Config
class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, ... |
log_enabled = Config().get_logging_rest_api()
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
# Log entry point
if log_enabled:
logger.info(f"{request.path} {request.method}")
# Call the actual route function
res... | """
Decorator factory that creates a decorator to log route entry and exit points.
The decorator uses the provided logger to log the information.
:param logger: The logger instance to use for logging.
"""
log_enabled = Config().get_logging_rest_api()
def decorator(func):
@wraps(f... | 89 | 381 | 330 | 711 | from functools import wraps
from fastlogging import LogInit
from flask import request
from src.config import Config
class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, ... | from functools import wraps
from fastlogging import LogInit
from flask import request
from src.config import Config
class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, ... | |
25,129 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/logger.py | 15,16 | 9,40 | read_log_file | Logger | 16,19 | 16,19 | class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, console=True, colors=True, encoding="utf-8")
def read_log_file(self) -> str:
FILL_FUNC_BODY def info(self, messa... | class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, console=True, colors=True, encoding="utf-8")
def read_log_file(self) -> str:
FILL_FUNC_BODY def info(self, messa... | with open(self.logger.pathName, "r") as file:
return file.read()
| with open(self.logger.pathName, "r") as file:
return file.read()
| 7 | 259 | 29 | 288 | from functools import wraps
from fastlogging import LogInit
from flask import request
from src.config import Config
class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, ... | from functools import wraps
from fastlogging import LogInit
from flask import request
from src.config import Config
class Logger:
def __init__(self, filename="devika_agent.log"):
config = Config()
logs_dir = config.get_logs_dir()
self.logger = LogInit(pathName=logs_dir + "/" + filename, ... |
25,130 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/memory/knowledge_base.py | 22,23 | 15,34 | add_knowledge | KnowledgeBase | 23,28 | 23,28 | class KnowledgeBase:
def __init__(self):
config = Config()
sqlite_path = config.get_sqlite_db()
self.engine = create_engine(f"sqlite:///{sqlite_path}")
SQLModel.metadata.create_all(self.engine)
def add_knowledge(self, tag: str, contents: str):
FILL_FUNC_BODY def get_knowledge... | class KnowledgeBase:
def __init__(self):
config = Config()
sqlite_path = config.get_sqlite_db()
self.engine = create_engine(f"sqlite:///{sqlite_path}")
SQLModel.metadata.create_all(self.engine)
def add_knowledge(self, tag: str, contents: str):
FILL_FUNC_BODY def get_knowledge... | knowledge = Knowledge(tag=tag, contents=contents)
with Session(self.engine) as session:
session.add(knowledge)
session.commit()
| knowledge = Knowledge(tag=tag, contents=contents)
with Session(self.engine) as session:
session.add(knowledge)
session.commit()
| 10 | 168 | 48 | 216 | from typing import Optional
from sqlmodel import Field, Session, SQLModel, create_engine
from src.config import Config
"""
TODO: The tag check should be a BM25 search, it's just a simple equality check now.
"""
class Knowledge(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
t... | from typing import Optional
from sqlmodel import Field, Session, SQLModel, create_engine
from src.config import Config
"""
TODO: The tag check should be a BM25 search, it's just a simple equality check now.
"""
class Knowledge(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary_key=True)
t... |
25,131 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/project.py | 40,41 | 17,147 | delete_project | ProjectManager | 41,47 | 41,47 | class ProjectManager:
def __init__(self):
config = Config()
sqlite_path = config.get_sqlite_db()
self.project_path = config.get_projects_dir()
self.engine = create_engine(f"sqlite:///{sqlite_path}")
SQLModel.metadata.create_all(self.engine)
def new_message(self):
... | class ProjectManager:
def __init__(self):
config = Config()
sqlite_path = config.get_sqlite_db()
self.project_path = config.get_projects_dir()
self.engine = create_engine(f"sqlite:///{sqlite_path}")
SQLModel.metadata.create_all(self.engine)
def new_message(self):
... | with Session(self.engine) as session:
project_state = session.query(Projects).filter(Projects.project == project).first()
if project_state:
session.delete(project_state)
session.commit()
| with Session(self.engine) as session:
project_state = session.query(Projects).filter(Projects.project == project).first()
if project_state:
session.delete(project_state)
session.commit()
| 13 | 1,480 | 66 | 1,546 | import os
import json
import zipfile
from datetime import datetime
from typing import Optional
from src.socket_instance import emit_agent
from sqlmodel import Field, Session, SQLModel, create_engine
from src.config import Config
class Projects(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary... | import os
import json
import zipfile
from datetime import datetime
from typing import Optional
from src.socket_instance import emit_agent
from sqlmodel import Field, Session, SQLModel, create_engine
from src.config import Config
class Projects(SQLModel, table=True):
id: Optional[int] = Field(default=None, primary... |
25,132 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/services/git.py | 13,14 | 3,21 | get_commits | Git | 14,16 | 14,16 | class Git:
def __init__(self, path):
self.repo = GitPython.Repo(path)
def clone(self, url, path):
return GitPython.Repo.clone_from(url, path)
def get_branches(self):
return self.repo.branches
def get_commits(self, branch):
FILL_FUNC_BODY def get_commit(self, commit):
... | class Git:
def __init__(self, path):
self.repo = GitPython.Repo(path)
def clone(self, url, path):
return GitPython.Repo.clone_from(url, path)
def get_branches(self):
return self.repo.branches
def get_commits(self, branch):
FILL_FUNC_BODY def get_commit(self, commit):
... | return self.repo.iter_commits(branch)
| return self.repo.iter_commits(branch)
| 2 | 157 | 17 | 174 | import git as GitPython
class Git:
def __init__(self, path):
self.repo = GitPython.Repo(path)
def clone(self, url, path):
return GitPython.Repo.clone_from(url, path)
def get_branches(self):
return self.repo.branches
def get_commits(self, branch):
FILL_FUNC_BODY def get_com... | import git as GitPython
class Git:
def __init__(self, path):
self.repo = GitPython.Repo(path)
def clone(self, url, path):
return GitPython.Repo.clone_from(url, path)
def get_branches(self):
return self.repo.branches
def get_commits(self, branch):
FILL_FUNC_BODY def get_com... |
25,133 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/services/github.py | 9,10 | 5,16 | get_repositories | GitHub | 10,16 | 10,16 | class GitHub:
def __init__(self, token: str) -> None:
self.token = token
def get_repositories(self) -> List[str]:
FILL_FUNC_BODY | class GitHub:
def __init__(self, token: str) -> None:
self.token = token
def get_repositories(self) -> List[str]:
FILL_FUNC_BODY | headers = {"Authorization": f"token {self.token}"}
response = requests.get(
"https://api.github.com/user/repos", headers=headers
)
response.raise_for_status()
return [repo["full_name"] for repo in response.json()] | headers = {"Authorization": f"token {self.token}"}
response = requests.get(
"https://api.github.com/user/repos", headers=headers
)
response.raise_for_status()
return [repo["full_name"] for repo in response.json()] | 18 | 56 | 80 | 136 | import requests
from typing import List
class GitHub:
def __init__(self, token: str) -> None:
self.token = token
def get_repositories(self) -> List[str]:
FILL_FUNC_BODY | import requests
from typing import List
class GitHub:
def __init__(self, token: str) -> None:
self.token = token
def get_repositories(self) -> List[str]:
FILL_FUNC_BODY |
25,134 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/services/utils.py | 32,33 | 1,91 | validate_responses | 33,91 | 33,91 | # create wrapper function that will has retry logic of 5 times
import sys
import time
from functools import wraps
import json
from src.socket_instance import emit_agent
def retry_wrapper(func):
def wrapper(*args, **kwargs):
max_tries = 5
tries = 0
while tries < max_tries:
resul... | # create wrapper function that will has retry logic of 5 times
import sys
import time
from functools import wraps
import json
from src.socket_instance import emit_agent
def retry_wrapper(func):
def wrapper(*args, **kwargs):
max_tries = 5
tries = 0
while tries < max_tries:
resul... | @wraps(func)
def wrapper(*args, **kwargs):
args = list(args)
response = args[1]
response = response.strip()
try:
response = json.loads(response)
print("first", type(response))
args[1] = response
return func(*args, **kwargs)
... | @wraps(func)
def wrapper(*args, **kwargs):
args = list(args)
response = args[1]
response = response.strip()
try:
response = json.loads(response)
print("first", type(response))
args[1] = response
return func(*args, **kwargs)
... | 134 | 259 | 509 | 768 | # create wrapper function that will has retry logic of 5 times
import sys
import time
from functools import wraps
import json
from src.socket_instance import emit_agent
def retry_wrapper(func):
def wrapper(*args, **kwargs):
max_tries = 5
tries = 0
while tries < max_tries:
resul... | # create wrapper function that will has retry logic of 5 times
import sys
import time
from functools import wraps
import json
from src.socket_instance import emit_agent
def retry_wrapper(func):
def wrapper(*args, **kwargs):
max_tries = 5
tries = 0
while tries < max_tries:
resul... | |
25,135 | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika | /home/hieuvd/dung_repo/raw/stars_01/stitionai_devika/devika/src/socket_instance.py | 9,10 | 1,18 | emit_agent | 10,18 | 10,18 | # socketio_instance.py
from flask_socketio import SocketIO
from src.logger import Logger
socketio = SocketIO(cors_allowed_origins="*", async_mode="gevent")
logger = Logger()
def emit_agent(channel, content, log=True):
FILL_FUNC_BODY | # socketio_instance.py
from flask_socketio import SocketIO
from src.logger import Logger
socketio = SocketIO(cors_allowed_origins="*", async_mode="gevent")
logger = Logger()
def emit_agent(channel, content, log=True):
FILL_FUNC_BODY | try:
socketio.emit(channel, content)
if log:
logger.info(f"SOCKET {channel} MESSAGE: {content}")
return True
except Exception as e:
logger.error(f"SOCKET {channel} ERROR: {str(e)}")
return False
| try:
socketio.emit(channel, content)
if log:
logger.info(f"SOCKET {channel} MESSAGE: {content}")
return True
except Exception as e:
logger.error(f"SOCKET {channel} ERROR: {str(e)}")
return False
| 21 | 87 | 87 | 174 | # socketio_instance.py
from flask_socketio import SocketIO
from src.logger import Logger
socketio = SocketIO(cors_allowed_origins="*", async_mode="gevent")
logger = Logger()
def emit_agent(channel, content, log=True):
FILL_FUNC_BODY | # socketio_instance.py
from flask_socketio import SocketIO
from src.logger import Logger
socketio = SocketIO(cors_allowed_origins="*", async_mode="gevent")
logger = Logger()
def emit_agent(channel, content, log=True):
FILL_FUNC_BODY | |
25,137 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/data_utils.py | 94,95 | 18,131 | random_slice | TextAudioSpeakerLoader | 95,121 | 95,121 | class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
1) loads audio, speaker_id, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths, hparams, all_in_mem: bool = False, vol_aug: ... | class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
1) loads audio, speaker_id, text pairs
2) normalizes text and converts them to sequences of integers
3) computes spectrograms from audio files.
"""
def __init__(self, audiopaths, hparams, all_in_mem: bool = False, vol_aug: ... | # if spec.shape[1] < 30:
# print("skip too short audio:", filename)
# return None
if random.choice([True, False]) and self.vol_aug and volume is not None:
max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
max_shift = min(1, np.log10(1/max_amp))
... | # if spec.shape[1] < 30:
# print("skip too short audio:", filename)
# return None
if random.choice([True, False]) and self.vol_aug and volume is not None:
max_amp = float(torch.max(torch.abs(audio_norm))) + 1e-5
max_shift = min(1, np.log10(1/max_amp))
... | 108 | 1,256 | 420 | 1,676 | import os
import random
import numpy as np
import torch
import torch.utils.data
import utils
from modules.mel_processing import spectrogram_torch
from utils import load_filepaths_and_text, load_wav_to_torch
# import h5py
"""Multi speaker version"""
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
... | import os
import random
import numpy as np
import torch
import torch.utils.data
import utils
from modules.mel_processing import spectrogram_torch
from utils import load_filepaths_and_text, load_wav_to_torch
# import h5py
"""Multi speaker version"""
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
"""
... |
25,138 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/diffusion/infer_gt_mel.py | 58,60 | 7,75 | infer | DiffGtMel | 60,75 | 60,75 | class DiffGtMel:
def __init__(self, project_path=None, device=None):
self.project_path = project_path
if device is not None:
self.device = device
else:
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = None
self.vocoder = None
... | class DiffGtMel:
def __init__(self, project_path=None, device=None):
self.project_path = project_path
if device is not None:
self.device = device
else:
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = None
self.vocoder = None
... | start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size)
if use_silence:
audio = audio[:, start_frame * self.vocoder.vocoder_hop_size:]
f0 = f0[:, start_frame:, :]
hubert = hubert[:, start_frame:, :]
volume = volu... | start_frame = int(silence_front * self.vocoder.vocoder_sample_rate / self.vocoder.vocoder_hop_size)
if use_silence:
audio = audio[:, start_frame * self.vocoder.vocoder_hop_size:]
f0 = f0[:, start_frame:, :]
hubert = hubert[:, start_frame:, :]
volume = volu... | 64 | 734 | 279 | 1,013 | import torch
import torch.nn.functional as F
from diffusion.unit2mel import load_model_vocoder
class DiffGtMel:
def __init__(self, project_path=None, device=None):
self.project_path = project_path
if device is not None:
self.device = device
else:
self.device = 'cud... | import torch
import torch.nn.functional as F
from diffusion.unit2mel import load_model_vocoder
class DiffGtMel:
def __init__(self, project_path=None, device=None):
self.project_path = project_path
if device is not None:
self.device = device
else:
self.device = 'cud... |
25,139 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/diffusion/logger/saver.py | 47,48 | 15,146 | log_info | Saver | 49,70 | 48,70 | class Saver(object):
def __init__(
self,
args,
initial_global_step=-1):
self.expdir = args.env.expdir
self.sample_rate = args.data.sampling_rate
# cold start
self.global_step = initial_global_step
self.init_time = time.time()
... | class Saver(object):
def __init__(
self,
args,
initial_global_step=-1):
self.expdir = args.env.expdir
self.sample_rate = args.data.sampling_rate
# cold start
self.global_step = initial_global_step
self.init_time = time.time()
... | if isinstance(msg, dict):
msg_list = []
for k, v in msg.items():
tmp_str = ''
if isinstance(v, int):
tmp_str = '{}: {:,}'.format(k, v)
else:
tmp_str = '{}: {}'.format(k, v)
msg_list.a... | '''log method'''
if isinstance(msg, dict):
msg_list = []
for k, v in msg.items():
tmp_str = ''
if isinstance(v, int):
tmp_str = '{}: {:,}'.format(k, v)
else:
tmp_str = '{}: {}'.format(k, v)
... | 47 | 1,092 | 201 | 1,293 | '''
author: wayn391@mastertones
'''
import datetime
import os
import time
import matplotlib.pyplot as plt
import torch
import yaml
from torch.utils.tensorboard import SummaryWriter
class Saver(object):
def __init__(
self,
args,
initial_global_step=-1):
self.expdir =... | '''
author: wayn391@mastertones
'''
import datetime
import os
import time
import matplotlib.pyplot as plt
import torch
import yaml
from torch.utils.tensorboard import SummaryWriter
class Saver(object):
def __init__(
self,
args,
initial_global_step=-1):
self.expdir =... |
25,141 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/diffusion/wavenet.py | 46,47 | 31,64 | forward | ResidualBlock | 47,64 | 47,64 | class ResidualBlock(nn.Module):
def __init__(self, encoder_hidden, residual_channels, dilation):
super().__init__()
self.residual_channels = residual_channels
self.dilated_conv = nn.Conv1d(
residual_channels,
2 * residual_channels,
kernel_size=3,
... | class ResidualBlock(nn.Module):
def __init__(self, encoder_hidden, residual_channels, dilation):
super().__init__()
self.residual_channels = residual_channels
self.dilated_conv = nn.Conv1d(
residual_channels,
2 * residual_channels,
kernel_size=3,
... | diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
conditioner = self.conditioner_projection(conditioner)
y = x + diffusion_step
y = self.dilated_conv(y) + conditioner
# Using torch.split instead of torch.chunk to avoid using onnx::Slice
gate, filt... | diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
conditioner = self.conditioner_projection(conditioner)
y = x + diffusion_step
y = self.dilated_conv(y) + conditioner
# Using torch.split instead of torch.chunk to avoid using onnx::Slice
gate, filt... | 65 | 232 | 260 | 492 | import math
from math import sqrt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Mish
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class SinusoidalPosEmb(nn.Mod... | import math
from math import sqrt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Mish
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class SinusoidalPosEmb(nn.Mod... |
25,142 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/diffusion/wavenet.py | 21,22 | 16,31 | forward | SinusoidalPosEmb | 22,31 | 22,31 | class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
FILL_FUNC_BODY | class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
FILL_FUNC_BODY | device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
| device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :]
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
| 35 | 58 | 122 | 180 | import math
from math import sqrt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Mish
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class SinusoidalPosEmb(nn.Mod... | import math
from math import sqrt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Mish
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class SinusoidalPosEmb(nn.Mod... |
25,143 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/diffusion/wavenet.py | 86,87 | 64,109 | forward | WaveNet | 93,109 | 87,109 | class WaveNet(nn.Module):
def __init__(self, in_dims=128, n_layers=20, n_chans=384, n_hidden=256):
super().__init__()
self.input_projection = Conv1d(in_dims, n_chans, 1)
self.diffusion_embedding = SinusoidalPosEmb(n_chans)
self.mlp = nn.Sequential(
nn.Linear(n_chans, n_ch... | class WaveNet(nn.Module):
def __init__(self, in_dims=128, n_layers=20, n_chans=384, n_hidden=256):
super().__init__()
self.input_projection = Conv1d(in_dims, n_chans, 1)
self.diffusion_embedding = SinusoidalPosEmb(n_chans)
self.mlp = nn.Sequential(
nn.Linear(n_chans, n_ch... | x = spec.squeeze(1)
x = self.input_projection(x) # [B, residual_channel, T]
x = F.relu(x)
diffusion_step = self.diffusion_embedding(diffusion_step)
diffusion_step = self.mlp(diffusion_step)
skip = []
for layer in self.residual_layers:
x, skip_connect... | """
:param spec: [B, 1, M, T]
:param diffusion_step: [B, 1]
:param cond: [B, M, T]
:return:
"""
x = spec.squeeze(1)
x = self.input_projection(x) # [B, residual_channel, T]
x = F.relu(x)
diffusion_step = self.diffusion_embedding(diffusion_... | 57 | 390 | 239 | 629 | import math
from math import sqrt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Mish
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class SinusoidalPosEmb(nn.Mod... | import math
from math import sqrt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Mish
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
nn.init.kaiming_normal_(self.weight)
class SinusoidalPosEmb(nn.Mod... |
25,144 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/edgetts/tts.py | 21,22 | 1,48 | _main | 22,38 | 22,38 | import asyncio
import random
import sys
import edge_tts
from edge_tts import VoicesManager
from langdetect import DetectorFactory, detect
DetectorFactory.seed = 0
TEXT = sys.argv[1]
LANG = detect(TEXT) if sys.argv[2] == "Auto" else sys.argv[2]
RATE = sys.argv[3]
VOLUME = sys.argv[4]
GENDER = sys.argv[5] if len(sys.a... | import asyncio
import random
import sys
import edge_tts
from edge_tts import VoicesManager
from langdetect import DetectorFactory, detect
DetectorFactory.seed = 0
TEXT = sys.argv[1]
LANG = detect(TEXT) if sys.argv[2] == "Auto" else sys.argv[2]
RATE = sys.argv[3]
VOLUME = sys.argv[4]
GENDER = sys.argv[5] if len(sys.a... | voices = await VoicesManager.create()
if GENDER is not None:
# From "zh-cn" to "zh-CN" etc.
if LANG == "zh-cn" or LANG == "zh-tw":
LOCALE = LANG[:-2] + LANG[-2:].upper()
voice = voices.find(Gender=GENDER, Locale=LOCALE)
else:
voice = voices.find(Gender... | voices = await VoicesManager.create()
if GENDER is not None:
# From "zh-cn" to "zh-CN" etc.
if LANG == "zh-cn" or LANG == "zh-tw":
LOCALE = LANG[:-2] + LANG[-2:].upper()
voice = voices.find(Gender=GENDER, Locale=LOCALE)
else:
voice = voices.find(Gender... | 65 | 323 | 242 | 565 | import asyncio
import random
import sys
import edge_tts
from edge_tts import VoicesManager
from langdetect import DetectorFactory, detect
DetectorFactory.seed = 0
TEXT = sys.argv[1]
LANG = detect(TEXT) if sys.argv[2] == "Auto" else sys.argv[2]
RATE = sys.argv[3]
VOLUME = sys.argv[4]
GENDER = sys.argv[5] if len(sys.a... | import asyncio
import random
import sys
import edge_tts
from edge_tts import VoicesManager
from langdetect import DetectorFactory, detect
DetectorFactory.seed = 0
TEXT = sys.argv[1]
LANG = detect(TEXT) if sys.argv[2] == "Auto" else sys.argv[2]
RATE = sys.argv[3]
VOLUME = sys.argv[4]
GENDER = sys.argv[5] if len(sys.a... | |
25,146 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/inference/slicer.py | 26,27 | 6,120 | _apply_slice | Slicer | 27,33 | 27,33 | class Slicer:
def __init__(self,
sr: int,
threshold: float = -40.,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000):
if not min_length >= min_interval >= hop_si... | class Slicer:
def __init__(self,
sr: int,
threshold: float = -40.,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000):
if not min_length >= min_interval >= hop_si... | if len(waveform.shape) > 1:
return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
else:
return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
# @timeit
| if len(waveform.shape) > 1:
return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
else:
return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
# @timeit
| 24 | 1,748 | 93 | 1,841 | import librosa
import torch
import torchaudio
class Slicer:
def __init__(self,
sr: int,
threshold: float = -40.,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000):
... | import librosa
import torch
import torchaudio
class Slicer:
def __init__(self,
sr: int,
threshold: float = -40.,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000):
... |
25,148 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/DSConv.py | 65,66 | 1,77 | weight_norm_modules | 66,72 | 66,72 | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... | if isinstance(module,Depthwise_Separable_Conv1D) or isinstance(module,Depthwise_Separable_TransposeConv1D):
module.weight_norm()
return module
else:
return weight_norm(module,name,dim)
| if isinstance(module,Depthwise_Separable_Conv1D) or isinstance(module,Depthwise_Separable_TransposeConv1D):
module.weight_norm()
return module
else:
return weight_norm(module,name,dim)
| 10 | 971 | 74 | 1,045 | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... | |
25,149 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/DSConv.py | 30,31 | 5,34 | remove_weight_norm | Depthwise_Separable_Conv1D | 31,34 | 31,34 | class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padding_mode = 'zeros', # TODO: refine this type
device=None,
dtype=No... | class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padding_mode = 'zeros', # TODO: refine this type
device=None,
dtype=No... | self.depth_conv = remove_weight_norm(self.depth_conv, name = 'weight')
self.point_conv = remove_weight_norm(self.point_conv, name = 'weight')
| self.depth_conv = remove_weight_norm(self.depth_conv, name = 'weight')
self.point_conv = remove_weight_norm(self.point_conv, name = 'weight')
| 12 | 370 | 54 | 424 | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... |
25,150 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/DSConv.py | 56,57 | 34,65 | weight_norm | Depthwise_Separable_TransposeConv1D | 57,60 | 57,60 | class Depthwise_Separable_TransposeConv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
output_padding = 0,
bias = True,
dilation = 1,
padding_mode = 'zeros', # TODO: refine this type... | class Depthwise_Separable_TransposeConv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
output_padding = 0,
bias = True,
dilation = 1,
padding_mode = 'zeros', # TODO: refine this type... | self.depth_conv = weight_norm(self.depth_conv, name = 'weight')
self.point_conv = weight_norm(self.point_conv, name = 'weight')
| self.depth_conv = weight_norm(self.depth_conv, name = 'weight')
self.point_conv = weight_norm(self.point_conv, name = 'weight')
| 12 | 384 | 50 | 434 | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... | import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm
class Depthwise_Separable_Conv1D(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride = 1,
padding = 0,
dilation = 1,
bias = True,
padd... |
25,151 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/PMF0Predictor.py | 40,41 | 7,73 | compute_f0 | PMF0Predictor | 41,57 | 41,57 | class PMF0Predictor(F0Predictor):
def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.sampling_rate = sampling_rate
self.name = "pm"
def interpolate_f0(self,f0):
... | class PMF0Predictor(F0Predictor):
def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.sampling_rate = sampling_rate
self.name = "pm"
def interpolate_f0(self,f0):
... | x = wav
if p_len is None:
p_len = x.shape[0]//self.hop_length
else:
assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
time_step = self.hop_length / self.sampling_rate * 1000
f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
... | x = wav
if p_len is None:
p_len = x.shape[0]//self.hop_length
else:
assert abs(p_len-x.shape[0]//self.hop_length) < 4, "pad length error"
time_step = self.hop_length / self.sampling_rate * 1000
f0 = parselmouth.Sound(x, self.sampling_rate).to_pitch_ac(
... | 62 | 785 | 302 | 1,087 | import numpy as np
import parselmouth
from modules.F0Predictor.F0Predictor import F0Predictor
class PMF0Predictor(F0Predictor):
def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.... | import numpy as np
import parselmouth
from modules.F0Predictor.F0Predictor import F0Predictor
class PMF0Predictor(F0Predictor):
def __init__(self,hop_length=512,f0_min=50,f0_max=1100,sampling_rate=44100):
self.hop_length = hop_length
self.f0_min = f0_min
self.f0_max = f0_max
self.... |
25,152 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/fcpe/nvSTFT.py | 13,14 | 1,134 | load_wav_to_torch | 14,45 | 14,45 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 48000
... | sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 48000
... | 176 | 1,487 | 556 | 2,043 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | |
25,153 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/fcpe/nvSTFT.py | 71,72 | 57,133 | get_mel | STFT | 72,128 | 72,128 | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
factor = 2 ** (keyshift / 12) ... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
factor = 2 ** (keyshift / 12) ... | 212 | 308 | 942 | 1,250 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampl... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampl... |
25,154 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 35,36 | 7,42 | forward | ConvBlockRes | 36,42 | 36,42 | class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
... | class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
... | if self.is_shortcut:
return self.conv(x) + self.shortcut(x)
else:
return self.conv(x) + x
| if self.is_shortcut:
return self.conv(x) + self.shortcut(x)
else:
return self.conv(x) + x
| 11 | 357 | 50 | 407 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,155 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 142,143 | 132,148 | forward | Decoder | 143,148 | 143,148 | class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
self.layers = nn.ModuleList()
self.n_decoders = n_decoders
for i in range(self.n_decoders):
out_channels = in_channels // 2
... | class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
self.layers = nn.ModuleList()
self.n_decoders = n_decoders
for i in range(self.n_decoders):
out_channels = in_channels // 2
... | for i in range(self.n_decoders):
x = self.layers[i](x, concat_tensors[-1-i])
return x
| for i in range(self.n_decoders):
x = self.layers[i](x, concat_tensors[-1-i])
return x
| 10 | 181 | 49 | 230 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,156 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 170,171 | 162,178 | forward | DeepUnet | 171,178 | 171,178 | class DeepUnet(nn.Module):
def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
super(DeepUnet, self).__init__()
self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels)
self.intermediate = Inte... | class DeepUnet(nn.Module):
def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
super(DeepUnet, self).__init__()
self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels)
self.intermediate = Inte... | x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
concat_tensors = self.tf(concat_tensors)
x = self.decoder(x, concat_tensors)
return x
| x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
concat_tensors = self.tf(concat_tensors)
x = self.decoder(x, concat_tensors)
return x
| 16 | 227 | 80 | 307 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,157 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 186,187 | 178,191 | forward | DeepUnet0 | 187,191 | 187,191 | class DeepUnet0(nn.Module):
def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
super(DeepUnet0, self).__init__()
self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels)
self.intermediate = In... | class DeepUnet0(nn.Module):
def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
super(DeepUnet0, self).__init__()
self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels)
self.intermediate = In... | x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
x = self.decoder(x, concat_tensors)
return x
| x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
x = self.decoder(x, concat_tensors)
return x
| 13 | 229 | 55 | 284 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,158 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 108,109 | 92,117 | forward | Encoder | 109,117 | 109,117 | class Encoder(nn.Module):
def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
... | class Encoder(nn.Module):
def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
... | concat_tensors = []
x = self.bn(x)
for i in range(self.n_encoders):
_, x = self.layers[i](x)
concat_tensors.append(_)
return x, concat_tensors
| concat_tensors = []
x = self.bn(x)
for i in range(self.n_encoders):
_, x = self.layers[i](x)
concat_tensors.append(_)
return x, concat_tensors
| 18 | 273 | 81 | 354 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,159 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 126,127 | 117,132 | forward | Intermediate | 127,132 | 127,132 | class Intermediate(nn.Module):
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.n_inters = n_inters
self.layers = nn.ModuleList()
self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, m... | class Intermediate(nn.Module):
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.n_inters = n_inters
self.layers = nn.ModuleList()
self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, m... | for i in range(self.n_inters):
x = self.layers[i](x)
return x
| for i in range(self.n_inters):
x = self.layers[i](x)
return x
| 9 | 179 | 37 | 216 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,160 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 84,85 | 63,92 | forward | ResDecoderBlock | 85,92 | 85,92 | class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.n_blocks = n_blocks
self.conv1 = nn.Sequential(
nn.ConvTra... | class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.n_blocks = n_blocks
self.conv1 = nn.Sequential(
nn.ConvTra... | x = self.conv1(x)
x = torch.cat((x, concat_tensor), dim=1)
for i in range(self.n_blocks):
x = self.conv2[i](x)
return x
| x = self.conv1(x)
x = torch.cat((x, concat_tensor), dim=1)
for i in range(self.n_blocks):
x = self.conv2[i](x)
return x
| 17 | 332 | 70 | 402 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,161 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 54,55 | 42,63 | forward | ResEncoderBlock | 55,63 | 55,63 | class ResEncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.conv = nn.ModuleList()
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
... | class ResEncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.conv = nn.ModuleList()
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
... | for i in range(self.n_blocks):
x = self.conv[i](x)
if self.kernel_size is not None:
return x, self.pool(x)
else:
return x
| for i in range(self.n_blocks):
x = self.conv[i](x)
if self.kernel_size is not None:
return x, self.pool(x)
else:
return x
| 18 | 214 | 66 | 280 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,162 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/deepunet.py | 155,156 | 148,162 | forward | TimbreFilter | 156,162 | 156,162 | class TimbreFilter(nn.Module):
def __init__(self, latent_rep_channels):
super(TimbreFilter, self).__init__()
self.layers = nn.ModuleList()
for latent_rep in latent_rep_channels:
self.layers.append(ConvBlockRes(latent_rep[0], latent_rep[0]))
def forward(self, x_tensors):
FILL... | class TimbreFilter(nn.Module):
def __init__(self, latent_rep_channels):
super(TimbreFilter, self).__init__()
self.layers = nn.ModuleList()
for latent_rep in latent_rep_channels:
self.layers.append(ConvBlockRes(latent_rep[0], latent_rep[0]))
def forward(self, x_tensors):
FILL... | out_tensors = []
for i, layer in enumerate(self.layers):
out_tensors.append(layer(x_tensors[i]))
return out_tensors
| out_tensors = []
for i, layer in enumerate(self.layers):
out_tensors.append(layer(x_tensors[i]))
return out_tensors
| 11 | 115 | 59 | 174 | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... | import torch
import torch.nn as nn
from .constants import N_MELS
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_channels=in_channels,
out_ch... |
25,163 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/inference.py | 28,29 | 11,58 | mel2hidden | RMVPE | 29,35 | 29,35 | class RMVPE:
def __init__(self, model_path, device=None, dtype = torch.float32, hop_length=160):
self.resample_kernel = {}
if device is None:
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
else:
self.device = device
model = E2E0(4, 1, (2, 2))
... | class RMVPE:
def __init__(self, model_path, device=None, dtype = torch.float32, hop_length=160):
self.resample_kernel = {}
if device is None:
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
else:
self.device = device
model = E2E0(4, 1, (2, 2))
... | with torch.no_grad():
n_frames = mel.shape[-1]
mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='constant')
hidden = self.model(mel)
return hidden[:, :n_frames]
| with torch.no_grad():
n_frames = mel.shape[-1]
mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='constant')
hidden = self.model(mel)
return hidden[:, :n_frames]
| 27 | 742 | 92 | 834 | import torch
import torch.nn.functional as F
from torchaudio.transforms import Resample
from .constants import * # noqa: F403
from .model import E2E0
from .spec import MelSpectrogram
from .utils import to_local_average_cents, to_viterbi_cents
class RMVPE:
def __init__(self, model_path, device=None, dtype = torc... | import torch
import torch.nn.functional as F
from torchaudio.transforms import Resample
from .constants import * # noqa: F403
from .model import E2E0
from .spec import MelSpectrogram
from .utils import to_local_average_cents, to_viterbi_cents
class RMVPE:
def __init__(self, model_path, device=None, dtype = torc... |
25,164 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/model.py | 30,31 | 9,43 | forward | E2E | 31,43 | 31,43 | class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
super(E2E, self).__init__()
self.mel = MelSpectrogram(N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX) # n... | class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
super(E2E, self).__init__()
self.mel = MelSpectrogram(N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX) # n... | mel = self.mel(x.reshape(-1, x.shape[-1])).transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
# x = self.fc(x)
hidden_vec = 0
if len(self.fc) == 4:
for i in range(len(self.fc)):
x = self.fc[i](x)
if ... | mel = self.mel(x.reshape(-1, x.shape[-1])).transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
# x = self.fc(x)
hidden_vec = 0
if len(self.fc) == 4:
for i in range(len(self.fc)):
x = self.fc[i](x)
if ... | 37 | 418 | 158 | 576 | from torch import nn
from .constants import * # noqa: F403
from .deepunet import DeepUnet, DeepUnet0
from .seq import BiGRU
from .spec import MelSpectrogram
class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_c... | from torch import nn
from .constants import * # noqa: F403
from .deepunet import DeepUnet, DeepUnet0
from .seq import BiGRU
from .spec import MelSpectrogram
class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_c... |
25,165 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/model.py | 63,64 | 43,68 | forward | E2E0 | 64,68 | 64,68 | class E2E0(nn.Module):
def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
super(E2E0, self).__init__()
self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels)
sel... | class E2E0(nn.Module):
def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_channels=16):
super(E2E0, self).__init__()
self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels)
sel... | mel = mel.transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
return x
| mel = mel.transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
return x
| 13 | 357 | 68 | 425 | from torch import nn
from .constants import * # noqa: F403
from .deepunet import DeepUnet, DeepUnet0
from .seq import BiGRU
from .spec import MelSpectrogram
class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_c... | from torch import nn
from .constants import * # noqa: F403
from .deepunet import DeepUnet, DeepUnet0
from .seq import BiGRU
from .spec import MelSpectrogram
class E2E(nn.Module):
def __init__(self, hop_length, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
en_out_c... |
25,166 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/seq.py | 9,10 | 4,13 | forward | BiGRU | 10,13 | 10,13 | class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
FILL_FUNC_BODY | class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
FILL_FUNC_BODY | return self.gru(x)[0]
| return self.gru(x)[0]
| 2 | 105 | 17 | 122 | import torch.nn as nn
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
FILL_FUNC_BODYclas... | import torch.nn as nn
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
FILL_FUNC_BODYclas... |
25,167 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/seq.py | 18,19 | 13,21 | forward | BiLSTM | 19,21 | 19,21 | class BiLSTM(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiLSTM, self).__init__()
self.lstm = nn.LSTM(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
FILL_FUNC_BODY | class BiLSTM(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiLSTM, self).__init__()
self.lstm = nn.LSTM(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
FILL_FUNC_BODY | return self.lstm(x)[0]
| return self.lstm(x)[0]
| 2 | 106 | 16 | 122 | import torch.nn as nn
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
return sel... | import torch.nn as nn
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
def forward(self, x):
return sel... |
25,168 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/spec.py | 38,39 | 7,68 | forward | MelSpectrogram | 39,68 | 39,68 | class MelSpectrogram(torch.nn.Module):
def __init__(
self,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp = 1e-5
):
super().__init__()
n_fft = win_length if n_fft is None ... | class MelSpectrogram(torch.nn.Module):
def __init__(
self,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp = 1e-5
):
super().__init__()
n_fft = win_length if n_fft is None ... | factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.n_fft * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
keyshift_key = str(keyshift)+'_'+str(audio.device)
if keyshift_key... | factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.n_fft * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
keyshift_key = str(keyshift)+'_'+str(audio.device)
if keyshift_key... | 92 | 366 | 460 | 826 | import numpy as np
import torch
import torch.nn.functional as F
from librosa.filters import mel
class MelSpectrogram(torch.nn.Module):
def __init__(
self,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
... | import numpy as np
import torch
import torch.nn.functional as F
from librosa.filters import mel
class MelSpectrogram(torch.nn.Module):
def __init__(
self,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
... |
25,169 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/F0Predictor/rmvpe/utils.py | 18,19 | 1,108 | summary | 19,64 | 19,64 | import sys
from functools import reduce
import librosa
import numpy as np
import torch
from torch.nn.modules.module import _addindent
from .constants import * # noqa: F403
def cycle(iterable):
while True:
for item in iterable:
yield item
def summary(model, file=sys.stdout):
FILL_FUNC_BODY... | import sys
from functools import reduce
import librosa
import numpy as np
import torch
from torch.nn.modules.module import _addindent
from .constants import * # noqa: F403
def cycle(iterable):
while True:
for item in iterable:
yield item
def summary(model, file=sys.stdout):
FILL_FUNC_BODY... | def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split('\n')
child_lines = []
tota... | def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty string will be split into list ['']
if extra_repr:
extra_lines = extra_repr.split('\n')
child_lines = []
tota... | 166 | 765 | 520 | 1,285 | import sys
from functools import reduce
import librosa
import numpy as np
import torch
from torch.nn.modules.module import _addindent
from .constants import * # noqa: F403
def cycle(iterable):
while True:
for item in iterable:
yield item
def summary(model, file=sys.stdout):
FILL_FUNC_BODY... | import sys
from functools import reduce
import librosa
import numpy as np
import torch
from torch.nn.modules.module import _addindent
from .constants import * # noqa: F403
def cycle(iterable):
while True:
for item in iterable:
yield item
def summary(model, file=sys.stdout):
FILL_FUNC_BODY... | |
25,171 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/losses.py | 4,5 | 1,59 | feature_loss | 5,15 | 5,15 | import torch
def feature_loss(fmap_r, fmap_g):
FILL_FUNC_BODYdef discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1-dr)**2)
g_lo... | import torch
def feature_loss(fmap_r, fmap_g):
FILL_FUNC_BODYdef discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1-dr)**2)
g_lo... | loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
| loss = 0
for dr, dg in zip(fmap_r, fmap_g):
for rl, gl in zip(dr, dg):
rl = rl.float().detach()
gl = gl.float()
loss += torch.mean(torch.abs(rl - gl))
return loss * 2
| 30 | 529 | 100 | 629 | import torch
def feature_loss(fmap_r, fmap_g):
FILL_FUNC_BODYdef discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1-dr)**2)
g_lo... | import torch
def feature_loss(fmap_r, fmap_g):
FILL_FUNC_BODYdef discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
dr = dr.float()
dg = dg.float()
r_loss = torch.mean((1-dr)**2)
g_lo... | |
25,172 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/mel_processing.py | 40,41 | 1,84 | spectrogram_torch | 41,67 | 41,67 | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompr... | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompr... | if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global hann_window
dtype_device = str(y.dtype) + '_' + str(y.device)
wnsize_dtype_device = str(win_size) + '_' + dtype_device
if wnsize_dtype_device not in hann... | if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global hann_window
dtype_device = str(y.dtype) + '_' + str(y.device)
wnsize_dtype_device = str(win_size) + '_' + dtype_device
if wnsize_dtype_device not in hann... | 84 | 677 | 446 | 1,123 | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompr... | import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompr... | |
25,174 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 63,64 | 38,73 | forward | ConvReluNorm | 64,73 | 64,73 | class ConvReluNorm(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.n_layer... | class ConvReluNorm(nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
super().__init__()
self.in_channels = in_channels
self.hidden_channels = hidden_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.n_layer... | x_org = x
for i in range(self.n_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x)
x = self.relu_drop(x)
x = x_org + self.proj(x)
return x * x_mask
| x_org = x
for i in range(self.n_layers):
x = self.conv_layers[i](x * x_mask)
x = self.norm_layers[i](x)
x = self.relu_drop(x)
x = x_org + self.proj(x)
return x * x_mask
| 27 | 420 | 98 | 518 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,175 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 249,250 | 242,260 | forward | ElementwiseAffine | 250,260 | 250,260 | class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels,1))
self.logs = nn.Parameter(torch.zeros(channels,1))
def forward(self, x, x_mask, reverse=False, **kwargs):
FILL_FUNC_BODY | class ElementwiseAffine(nn.Module):
def __init__(self, channels):
super().__init__()
self.channels = channels
self.m = nn.Parameter(torch.zeros(channels,1))
self.logs = nn.Parameter(torch.zeros(channels,1))
def forward(self, x, x_mask, reverse=False, **kwargs):
FILL_FUNC_BODY | if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1,2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
| if not reverse:
y = self.m + torch.exp(self.logs) * x
y = y * x_mask
logdet = torch.sum(self.logs * x_mask, [1,2])
return y, logdet
else:
x = (x - self.m) * torch.exp(-self.logs) * x_mask
return x
| 36 | 114 | 112 | 226 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,176 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 233,234 | 232,242 | forward | Flip | 234,242 | 234,242 | class Flip(nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
FILL_FUNC_BODY | class Flip(nn.Module):
def forward(self, x, *args, reverse=False, **kwargs):
FILL_FUNC_BODY | x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x
| x = torch.flip(x, [1])
if not reverse:
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
return x, logdet
else:
return x
| 17 | 38 | 79 | 117 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,177 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 32,33 | 23,38 | forward | LayerNorm | 33,38 | 33,38 | class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
FILL_FUNC_BODY | class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-5):
super().__init__()
self.channels = channels
self.eps = eps
self.gamma = nn.Parameter(torch.ones(channels))
self.beta = nn.Parameter(torch.zeros(channels))
def forward(self, x):
FILL_FUNC_BODY | x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
| x = x.transpose(1, -1)
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
return x.transpose(1, -1)
| 14 | 112 | 66 | 178 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,178 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 222,223 | 221,232 | forward | Log | 223,232 | 223,232 | class Log(nn.Module):
def forward(self, x, x_mask, reverse=False, **kwargs):
FILL_FUNC_BODY | class Log(nn.Module):
def forward(self, x, x_mask, reverse=False, **kwargs):
FILL_FUNC_BODY | if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
| if not reverse:
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
logdet = torch.sum(-y, [1, 2])
return y, logdet
else:
x = torch.exp(x) * x_mask
return x
| 25 | 38 | 95 | 133 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,179 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 172,173 | 149,194 | forward | ResBlock1 | 173,187 | 173,187 | class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList([
weight_norm_modules(Conv1dModel(channels, channels, kernel_size, 1, dilation=dilation[0],
padd... | class ResBlock1(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
super(ResBlock1, self).__init__()
self.convs1 = nn.ModuleList([
weight_norm_modules(Conv1dModel(channels, channels, kernel_size, 1, dilation=dilation[0],
padd... | for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt... | for c1, c2 in zip(self.convs1, self.convs2):
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c1(xt)
xt = F.leaky_relu(xt, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt... | 57 | 504 | 179 | 683 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,180 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 205,206 | 194,221 | forward | ResBlock2 | 206,216 | 206,216 | class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList([
weight_norm_modules(Conv1dModel(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=... | class ResBlock2(torch.nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
super(ResBlock2, self).__init__()
self.convs = nn.ModuleList([
weight_norm_modules(Conv1dModel(channels, channels, kernel_size, 1, dilation=dilation[0],
padding=... | for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
| for c in self.convs:
xt = F.leaky_relu(x, LRELU_SLOPE)
if x_mask is not None:
xt = xt * x_mask
xt = c(xt)
x = xt + x
if x_mask is not None:
x = x * x_mask
return x
| 38 | 249 | 106 | 355 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,181 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 288,289 | 260,309 | forward | ResidualCouplingLayer | 289,309 | 289,309 | class ResidualCouplingLayer(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
wn_sharing_parameter=None
):
assert channels % 2 == 0, "channels should be divisible... | class ResidualCouplingLayer(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
p_dropout=0,
gin_channels=0,
mean_only=False,
wn_sharing_parameter=None
):
assert channels % 2 == 0, "channels should be divisible... | x0, x1 = torch.split(x, [self.half_channels]*2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels]*2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:... | x0, x1 = torch.split(x, [self.half_channels]*2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels]*2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:... | 78 | 393 | 279 | 672 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,182 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 337,338 | 309,357 | forward | TransformerCouplingLayer | 338,357 | 338,357 | class TransformerCouplingLayer(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
n_layers,
n_heads,
p_dropout=0,
filter_channels=0,
mean_only=False,
wn_sharing_parameter=None,
gin_channels = 0
):
assert channels % 2 == 0, "chan... | class TransformerCouplingLayer(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
n_layers,
n_heads,
p_dropout=0,
filter_channels=0,
mean_only=False,
wn_sharing_parameter=None,
gin_channels = 0
):
assert channels % 2 == 0, "chan... | x0, x1 = torch.split(x, [self.half_channels]*2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels]*2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:... | x0, x1 = torch.split(x, [self.half_channels]*2, 1)
h = self.pre(x0) * x_mask
h = self.enc(h, x_mask, g=g)
stats = self.post(h) * x_mask
if not self.mean_only:
m, logs = torch.split(stats, [self.half_channels]*2, 1)
else:
m = stats
logs = torch.zeros_like(m)
if not reverse:... | 78 | 398 | 277 | 675 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,183 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/modules/modules.py | 110,111 | 73,149 | forward | WN | 111,140 | 111,140 | class WN(torch.nn.Module):
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
super(WN, self).__init__()
assert(kernel_size % 2 == 1)
self.hidden_channels =hidden_channels
self.kernel_size = kernel_size,
self.dilation_rate = dilation_rate
se... | class WN(torch.nn.Module):
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
super(WN, self).__init__()
assert(kernel_size % 2 == 1)
self.hidden_channels =hidden_channels
self.kernel_size = kernel_size,
self.dilation_rate = dilation_rate
se... | output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None:
g = self.cond_layer(g)
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
if g is not None:
cond_offset = i * 2 * self.hidden_channels
g_l = g[:,cond_off... | output = torch.zeros_like(x)
n_channels_tensor = torch.IntTensor([self.hidden_channels])
if g is not None:
g = self.cond_layer(g)
for i in range(self.n_layers):
x_in = self.in_layers[i](x)
if g is not None:
cond_offset = i * 2 * self.hidden_channels
g_l = g[:,cond_off... | 83 | 649 | 372 | 1,021 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
from modules.commons import get_padding, init_weights
from modules.DSConv import (
Depthwise_Separable_Conv1D,
remove_weight_norm_modules,
weight_norm_modules,
)
... |
25,184 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 135,136 | 120,157 | forward | DiscriminatorP | 136,157 | 136,157 | class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm is False else spectral... | class DiscriminatorP(torch.nn.Module):
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
super(DiscriminatorP, self).__init__()
self.period = period
self.use_spectral_norm = use_spectral_norm
norm_f = weight_norm if use_spectral_norm is False else spectral... | fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs... | fmap = []
# 1d to 2d
b, c, t = x.shape
if t % self.period != 0: # pad first
n_pad = self.period - (t % self.period)
x = F.pad(x, (0, n_pad), "reflect")
t = t + n_pad
x = x.view(b, c, t // self.period, self.period)
for l in self.convs... | 71 | 446 | 235 | 681 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,185 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 171,172 | 157,185 | forward | DiscriminatorS | 172,185 | 172,185 | class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_... | class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = weight_norm if use_spectral_norm is False else spectral_norm
self.convs = nn.ModuleList([
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
norm_... | fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
| fmap = []
for l in self.convs:
x = l(x)
x = F.leaky_relu(x, modules.LRELU_SLOPE)
fmap.append(x)
x = self.conv_post(x)
fmap.append(x)
x = torch.flatten(x, 1, -1)
return x, fmap
| 27 | 350 | 121 | 471 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,186 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 73,74 | 51,84 | forward | Encoder | 74,84 | 74,84 | class Encoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
super().__init__()
self.in_channels =... | class Encoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
super().__init__()
self.in_channels =... | # print(x.shape,x_lengths.shape)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m ... | # print(x.shape,x_lengths.shape)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m ... | 42 | 290 | 181 | 471 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,187 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 217,218 | 185,228 | forward | F0Decoder | 218,228 | 218,228 | class F0Decoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=0):
super().__init__(... | class F0Decoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=0):
super().__init__(... | x = torch.detach(x)
if spk_emb is not None:
x = x + self.cond(spk_emb)
x += self.f0_prenet(norm_f0)
x = self.prenet(x) * x_mask
x = self.decoder(x * x_mask, x_mask)
x = self.proj(x) * x_mask
return x
| x = torch.detach(x)
if spk_emb is not None:
x = x + self.cond(spk_emb)
x += self.f0_prenet(norm_f0)
x = self.prenet(x) * x_mask
x = self.decoder(x * x_mask, x_mask)
x = self.proj(x) * x_mask
return x
| 34 | 397 | 121 | 518 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,188 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 41,42 | 16,51 | forward | ResidualCouplingBlock | 42,51 | 42,51 | class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0):
super().__init__()
self.chann... | class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0):
super().__init__()
self.chann... | if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
| if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
| 27 | 294 | 92 | 386 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,189 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 312,313 | 228,334 | forward | SynthesizerTrn | 313,334 | 313,334 | class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(self,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
... | class SynthesizerTrn(nn.Module):
"""
Synthesizer for Training
"""
def __init__(self,
spec_channels,
segment_size,
inter_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
... |
decoder_inp = F.pad(c, [0, 0, 1, 0])
mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
g = g.unsqueeze(0)
g = self.emb_g(g).trans... |
decoder_inp = F.pad(c, [0, 0, 1, 0])
mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
g = g.unsqueeze(0)
g = self.emb_g(g).trans... | 108 | 970 | 530 | 1,500 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,190 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx.py | 111,112 | 84,120 | forward | TextEncoder | 112,120 | 112,120 | class TextEncoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None):
... | class TextEncoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None):
... | x = x + self.f0_emb(f0).transpose(1, 2)
x = self.enc_(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + z * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
| x = x + self.f0_emb(f0).transpose(1, 2)
x = self.enc_(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + z * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
| 37 | 293 | 123 | 416 | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... | import torch
from torch import nn
from torch.nn import Conv1d, Conv2d
from torch.nn import functional as F
from torch.nn.utils import spectral_norm, weight_norm
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from modules.commons import get_paddi... |
25,191 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx_speaker_mix.py | 114,115 | 92,125 | forward | Encoder | 115,125 | 115,125 | class Encoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
super().__init__()
self.in_channels =... | class Encoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
gin_channels=0):
super().__init__()
self.in_channels =... | # print(x.shape,x_lengths.shape)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m ... | # print(x.shape,x_lengths.shape)
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
x = self.pre(x) * x_mask
x = self.enc(x, x_mask, g=g)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m ... | 42 | 290 | 181 | 471 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... |
25,192 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx_speaker_mix.py | 194,195 | 162,205 | forward | F0Decoder | 195,205 | 195,205 | class F0Decoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=0):
super().__init__(... | class F0Decoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
spk_channels=0):
super().__init__(... | x = torch.detach(x)
if (spk_emb is not None):
x = x + self.cond(spk_emb)
x += self.f0_prenet(norm_f0)
x = self.prenet(x) * x_mask
x = self.decoder(x * x_mask, x_mask)
x = self.proj(x) * x_mask
return x
| x = torch.detach(x)
if (spk_emb is not None):
x = x + self.cond(spk_emb)
x += self.f0_prenet(norm_f0)
x = self.prenet(x) * x_mask
x = self.decoder(x * x_mask, x_mask)
x = self.proj(x) * x_mask
return x
| 34 | 397 | 122 | 519 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... |
25,193 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx_speaker_mix.py | 42,43 | 12,51 | forward | ResidualCouplingBlock | 43,51 | 43,51 | class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
share_parameter=False
... | class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
kernel_size,
dilation_rate,
n_layers,
n_flows=4,
gin_channels=0,
share_parameter=False
... | if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
| if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
| 27 | 372 | 90 | 462 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... |
25,195 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx_speaker_mix.py | 152,153 | 125,162 | forward | TextEncoder | 153,162 | 153,162 | class TextEncoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None):
... | class TextEncoder(nn.Module):
def __init__(self,
out_channels,
hidden_channels,
kernel_size,
n_layers,
gin_channels=0,
filter_channels=None,
n_heads=None,
p_dropout=None):
... | x = x + self.f0_emb(f0).transpose(1, 2)
x = self.enc_(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + z * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
| x = x + self.f0_emb(f0).transpose(1, 2)
x = self.enc_(x * x_mask, x_mask)
stats = self.proj(x) * x_mask
m, logs = torch.split(stats, self.out_channels, dim=1)
z = (m + z * torch.exp(logs)) * x_mask
return z, m, logs, x_mask
| 37 | 293 | 125 | 418 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... |
25,196 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/onnxexport/model_onnx_speaker_mix.py | 82,83 | 51,92 | forward | TransformerCouplingBlock | 83,92 | 83,92 | class TransformerCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
n_flows=4,
gin... | class TransformerCouplingBlock(nn.Module):
def __init__(self,
channels,
hidden_channels,
filter_channels,
n_heads,
n_layers,
kernel_size,
p_dropout,
n_flows=4,
gin... | if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
| if not reverse:
for flow in self.flows:
x, _ = flow(x, x_mask, g=g, reverse=reverse)
else:
for flow in reversed(self.flows):
x = flow(x, x_mask, g=g, reverse=reverse)
return x
| 27 | 395 | 92 | 487 | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... | import torch
from torch import nn
from torch.nn import functional as F
import modules.attentions as attentions
import modules.commons as commons
import modules.modules as modules
import utils
from utils import f0_to_coarse
class ResidualCouplingBlock(nn.Module):
def __init__(self,
channels,
... |
25,197 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/preprocess_flist_config.py | 15,16 | 1,120 | get_wav_duration | 16,28 | 16,28 | import argparse
import json
import os
import re
import wave
from random import shuffle
from loguru import logger
from tqdm import tqdm
import diffusion.logger.utils as du
pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
def get_wav_duration(file_path):
FILL_FUNC_BODYif __name__ == "__main__":
parser = argparse.Argu... | import argparse
import json
import os
import re
import wave
from random import shuffle
from loguru import logger
from tqdm import tqdm
import diffusion.logger.utils as du
pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
def get_wav_duration(file_path):
FILL_FUNC_BODYif __name__ == "__main__":
parser = argparse.Argu... | try:
with wave.open(file_path, 'rb') as wav_file:
# 获取音频帧数
n_frames = wav_file.getnframes()
# 获取采样率
framerate = wav_file.getframerate()
# 计算时长（秒）
return n_frames / float(framerate)
except Exception as e:
logger.error(f"Readi... | try:
with wave.open(file_path, 'rb') as wav_file:
# 获取音频帧数
n_frames = wav_file.getnframes()
# 获取采样率
framerate = wav_file.getframerate()
# 计算时长（秒）
return n_frames / float(framerate)
except Exception as e:
logger.error(f"Readi... | 30 | 1,581 | 137 | 1,718 | import argparse
import json
import os
import re
import wave
from random import shuffle
from loguru import logger
from tqdm import tqdm
import diffusion.logger.utils as du
pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
def get_wav_duration(file_path):
FILL_FUNC_BODYif __name__ == "__main__":
parser = argparse.Argu... | import argparse
import json
import os
import re
import wave
from random import shuffle
from loguru import logger
from tqdm import tqdm
import diffusion.logger.utils as du
pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
def get_wav_duration(file_path):
FILL_FUNC_BODYif __name__ == "__main__":
parser = argparse.Argu... | |
25,198 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/pretrain/meta.py | 30,31 | 1,40 | get_speech_encoder | 31,40 | 31,40 | def download_dict():
return {
"vec768l12": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
"output": "./pretrain/checkpoint_best_legacy_500.pt"
},
"vec256l9": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffo... | def download_dict():
return {
"vec768l12": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
"output": "./pretrain/checkpoint_best_legacy_500.pt"
},
"vec256l9": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffo... | import json
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
speech_encoder = config["model"]["speech_encoder"]
dict = download_dict()
return dict[speech_encoder]["url"], dict[speech_encoder]["output"]
| import json
with open(config_path, "r") as f:
data = f.read()
config = json.loads(data)
speech_encoder = config["model"]["speech_encoder"]
dict = download_dict()
return dict[speech_encoder]["url"], dict[speech_encoder]["output"]
| 22 | 665 | 100 | 765 | def download_dict():
return {
"vec768l12": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
"output": "./pretrain/checkpoint_best_legacy_500.pt"
},
"vec256l9": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffo... | def download_dict():
return {
"vec768l12": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffooyatzdwsqn2psd9lrr",
"output": "./pretrain/checkpoint_best_legacy_500.pt"
},
"vec256l9": {
"url": "https://ibm.ent.box.com/shared/static/z1wgl1stco8ffo... | |
25,199 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/train_diff.py | 14,15 | 1,78 | parse_args | 16,26 | 15,26 | import argparse
import torch
from loguru import logger
from torch.optim import lr_scheduler
from diffusion.data_loaders import get_data_loaders
from diffusion.logger import utils
from diffusion.solver import train
from diffusion.unit2mel import Unit2Mel
from diffusion.vocoder import Vocoder
def parse_args(args=None... | import argparse
import torch
from loguru import logger
from torch.optim import lr_scheduler
from diffusion.data_loaders import get_data_loaders
from diffusion.logger import utils
from diffusion.solver import train
from diffusion.unit2mel import Unit2Mel
from diffusion.vocoder import Vocoder
def parse_args(args=None... | parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
required=True,
help="path to the config file")
return parser.parse_args(args=args, namespace=namespace)
| """Parse command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--config",
type=str,
required=True,
help="path to the config file")
return parser.parse_args(args=args, namespace=namespace)
| 16 | 774 | 81 | 855 | import argparse
import torch
from loguru import logger
from torch.optim import lr_scheduler
from diffusion.data_loaders import get_data_loaders
from diffusion.logger import utils
from diffusion.solver import train
from diffusion.unit2mel import Unit2Mel
from diffusion.vocoder import Vocoder
def parse_args(args=None... | import argparse
import torch
from loguru import logger
from torch.optim import lr_scheduler
from diffusion.data_loaders import get_data_loaders
from diffusion.logger import utils
from diffusion.solver import train
from diffusion.unit2mel import Unit2Mel
from diffusion.vocoder import Vocoder
def parse_args(args=None... | |
25,200 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifigan/env.py | 11,12 | 1,16 | build_env | 12,16 | 12,16 | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
| t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
| 13 | 85 | 70 | 155 | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | |
25,201 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifigan/nvSTFT.py | 12,13 | 1,110 | load_wav_to_torch | 13,44 | 13,44 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC_BODYdef dynamic_range_compressi... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC_BODYdef dynamic_range_compressi... | sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 32000
... | sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 32000
... | 176 | 1,152 | 556 | 1,708 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC_BODYdef dynamic_range_compressi... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC_BODYdef dynamic_range_compressi... | |
25,202 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifigan/nvSTFT.py | 70,71 | 56,109 | get_mel | STFT | 71,104 | 71,104 | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
if torch.min(y) < -1.:
... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
if torch.min(y) < -1.:
... | 101 | 294 | 609 | 903 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... |
25,203 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifigan/utils.py | 46,47 | 1,69 | save_checkpoint | 47,52 | 47,52 | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | print("Saving checkpoint to {}".format(filepath))
torch.save(obj, filepath)
print("Complete.")
| print("Saving checkpoint to {}".format(filepath))
torch.save(obj, filepath)
print("Complete.")
| 7 | 632 | 41 | 673 | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | |
25,204 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/alias/filter.py | 29,30 | 1,111 | kaiser_sinc_filter1d | 30,61 | 30,61 | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
if 'sinc' in dir(torch):
sinc = torch.sinc
else:
# This code is adopted from adefossez's julius.... | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
if 'sinc' in dir(torch):
sinc = torch.sinc
else:
# This code is adopted from adefossez's julius.... | even = (kernel_size % 2 == 0)
half_size = kernel_size // 2
#For kaiser window
delta_f = 4 * half_width
A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
if A > 50.:
beta = 0.1102 * (A - 8.7)
elif A >= 21.:
beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
else:
... | even = (kernel_size % 2 == 0)
half_size = kernel_size // 2
#For kaiser window
delta_f = 4 * half_width
A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
if A > 50.:
beta = 0.1102 * (A - 8.7)
elif A >= 21.:
beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
else:
... | 151 | 1,068 | 403 | 1,471 | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
if 'sinc' in dir(torch):
sinc = torch.sinc
else:
# This code is adopted from adefossez's julius.... | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
if 'sinc' in dir(torch):
sinc = torch.sinc
else:
# This code is adopted from adefossez's julius.... | |
25,205 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/alias/filter.py | 93,94 | 61,111 | forward | LowPassFilter1d | 94,111 | 94,111 | class LowPassFilter1d(nn.Module):
def __init__(self,
cutoff=0.5,
half_width=0.6,
stride: int = 1,
padding: bool = True,
padding_mode: str = 'replicate',
kernel_size: int = 12,
C=None):
# ke... | class LowPassFilter1d(nn.Module):
def __init__(self,
cutoff=0.5,
half_width=0.6,
stride: int = 1,
padding: bool = True,
padding_mode: str = 'replicate',
kernel_size: int = 12,
C=None):
# ke... | if self.conv1d_block[0].weight.device != x.device:
self.conv1d_block[0] = self.conv1d_block[0].to(x.device)
if self.conv1d_block is None:
_, C, _ = x.shape
if self.padding:
x = F.pad(x, (self.pad_left, self.pad_right),
mode... | if self.conv1d_block[0].weight.device != x.device:
self.conv1d_block[0] = self.conv1d_block[0].to(x.device)
if self.conv1d_block is None:
_, C, _ = x.shape
if self.padding:
x = F.pad(x, (self.pad_left, self.pad_right),
mode... | 46 | 449 | 236 | 685 | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
if 'sinc' in dir(torch):
sinc = torch.sinc
else:
# This code is adopted from adefossez's julius.... | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
if 'sinc' in dir(torch):
sinc = torch.sinc
else:
# This code is adopted from adefossez's julius.... |
25,206 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/alias/resample.py | 69,70 | 57,73 | forward | DownSample1d | 70,73 | 70,73 | class DownSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=None, C=None):
super().__init__()
self.ratio = ratio
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
... | class DownSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=None, C=None):
super().__init__()
self.ratio = ratio
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
... | xx = self.lowpass(x)
return xx | xx = self.lowpass(x)
return xx | 5 | 158 | 18 | 176 | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import torch.nn as nn
from torch.nn import functional as F
from .filter import LowPassFilter1d, kaiser_sinc_filter1d
class UpSample1d(nn.Module):
def __init__(self, ratio=2, kern... | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import torch.nn as nn
from torch.nn import functional as F
from .filter import LowPassFilter1d, kaiser_sinc_filter1d
class UpSample1d(nn.Module):
def __init__(self, ratio=2, kern... |
25,207 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/alias/resample.py | 38,39 | 10,57 | forward | UpSample1d | 39,57 | 39,57 | class UpSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=None, C=None):
super().__init__()
self.ratio = ratio
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
self.stride = ratio
self.pad = self.kernel_size // ratio - 1
se... | class UpSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=None, C=None):
super().__init__()
self.ratio = ratio
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
self.stride = ratio
self.pad = self.kernel_size // ratio - 1
se... | if self.conv_transpose1d_block[0].weight.device != x.device:
self.conv_transpose1d_block[0] = self.conv_transpose1d_block[0].to(x.device)
if self.conv_transpose1d_block is None:
if C is None:
_, C, _ = x.shape
# print("snake.conv_t.in:",x.shape)
... | if self.conv_transpose1d_block[0].weight.device != x.device:
self.conv_transpose1d_block[0] = self.conv_transpose1d_block[0].to(x.device)
if self.conv_transpose1d_block is None:
if C is None:
_, C, _ = x.shape
# print("snake.conv_t.in:",x.shape)
... | 63 | 424 | 321 | 745 | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import torch.nn as nn
from torch.nn import functional as F
from .filter import LowPassFilter1d, kaiser_sinc_filter1d
class UpSample1d(nn.Module):
def __init__(self, ratio=2, kern... | # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import torch.nn as nn
from torch.nn import functional as F
from .filter import LowPassFilter1d, kaiser_sinc_filter1d
class UpSample1d(nn.Module):
def __init__(self, ratio=2, kern... |
25,208 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/env.py | 11,12 | 1,16 | build_env | 12,16 | 12,16 | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
| t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
| 13 | 85 | 70 | 155 | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | |
25,209 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/nvSTFT.py | 53,54 | 1,110 | dynamic_range_decompression_torch | 54,56 | 54,56 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... | return torch.exp(x) / C
| return torch.exp(x) / C
| 4 | 1,664 | 15 | 1,679 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... | |
25,210 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/nvSTFT.py | 70,71 | 56,109 | get_mel | STFT | 71,104 | 71,104 | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
if torch.min(y) < -1.:
... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
if torch.min(y) < -1.:
... | 101 | 294 | 609 | 903 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampling_rate = None
try:
... |
25,211 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/hifiganwithsnake/utils.py | 52,53 | 1,69 | del_old_checkpoints | 53,62 | 53,62 | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | pattern = os.path.join(cp_dir, prefix + '????????')
cp_list = glob.glob(pattern) # get checkpoint paths
cp_list = sorted(cp_list)# sort by iter
if len(cp_list) > n_models: # if more than n_models models are found
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_model... | pattern = os.path.join(cp_dir, prefix + '????????')
cp_list = glob.glob(pattern) # get checkpoint paths
cp_list = sorted(cp_list)# sort by iter
if len(cp_list) > n_models: # if more than n_models models are found
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_model... | 57 | 527 | 150 | 677 | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | import glob
import os
# matplotlib.use("Agg")
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
p... | |
25,212 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/nsf_hifigan/env.py | 11,12 | 1,16 | build_env | 12,16 | 12,16 | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
| t_path = os.path.join(path, config_name)
if config != t_path:
os.makedirs(path, exist_ok=True)
shutil.copyfile(config, os.path.join(path, config_name))
| 13 | 85 | 70 | 155 | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | import os
import shutil
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def build_env(config, config_name, path):
FILL_FUNC_BODY | |
25,213 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/nsf_hifigan/nvSTFT.py | 13,14 | 1,133 | load_wav_to_torch | 14,45 | 14,45 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 48000
... | sampling_rate = None
try:
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
except Exception as ex:
print(f"'{full_path}' failed to load.\nException:")
print(ex)
if return_empty_on_exception:
return [], sampling_rate or target_sr or 48000
... | 176 | 1,480 | 556 | 2,036 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
FILL_FUNC... | |
25,214 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/nsf_hifigan/nvSTFT.py | 71,72 | 57,132 | get_mel | STFT | 72,127 | 72,127 | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | class STFT():
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
self.target_sr = sr
self.n_mels = n_mels
self.n_fft = n_fft
self.win_size = win_size
self.hop_length = hop_length
... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
factor = 2 ** (keyshift / 12) ... | sampling_rate = self.target_sr
n_mels = self.n_mels
n_fft = self.n_fft
win_size = self.win_size
hop_length = self.hop_length
fmin = self.fmin
fmax = self.fmax
clip_val = self.clip_val
factor = 2 ** (keyshift / 12) ... | 200 | 304 | 938 | 1,242 | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampl... | import os
import librosa
import numpy as np
import soundfile as sf
import torch
import torch.nn.functional as F
import torch.utils.data
from librosa.filters import mel as librosa_mel_fn
os.environ["LRU_CACHE_CAPACITY"] = "3"
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
sampl... |
25,215 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vdecoder/nsf_hifigan/utils.py | 54,55 | 1,71 | del_old_checkpoints | 55,64 | 55,64 | import glob
import os
import matplotlib
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
matplotlib.use("Agg")
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolat... | import glob
import os
import matplotlib
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
matplotlib.use("Agg")
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolat... | pattern = os.path.join(cp_dir, prefix + '????????')
cp_list = glob.glob(pattern) # get checkpoint paths
cp_list = sorted(cp_list)# sort by iter
if len(cp_list) > n_models: # if more than n_models models are found
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_model... | pattern = os.path.join(cp_dir, prefix + '????????')
cp_list = glob.glob(pattern) # get checkpoint paths
cp_list = sorted(cp_list)# sort by iter
if len(cp_list) > n_models: # if more than n_models models are found
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_model... | 57 | 532 | 150 | 682 | import glob
import os
import matplotlib
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
matplotlib.use("Agg")
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolat... | import glob
import os
import matplotlib
import matplotlib.pylab as plt
import torch
from torch.nn.utils import weight_norm
matplotlib.use("Agg")
def plot_spectrogram(spectrogram):
fig, ax = plt.subplots(figsize=(10, 2))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolat... | |
25,216 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/CNHubertLarge.py | 23,24 | 7,37 | encoder | CNHubertLarge | 24,37 | 24,37 | class CNHubertLarge(SpeechEncoder):
def __init__(self, vec_path="pretrain/chinese-hubert-large-fairseq-ckpt.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.hidden_dim = 1024
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_... | class CNHubertLarge(SpeechEncoder):
def __init__(self, vec_path="pretrain/chinese-hubert-large-fairseq-ckpt.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.hidden_dim = 1024
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav.device),
"... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav.device),
"... | 41 | 230 | 184 | 414 | import torch
from fairseq import checkpoint_utils
from vencoder.encoder import SpeechEncoder
class CNHubertLarge(SpeechEncoder):
def __init__(self, vec_path="pretrain/chinese-hubert-large-fairseq-ckpt.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
se... | import torch
from fairseq import checkpoint_utils
from vencoder.encoder import SpeechEncoder
class CNHubertLarge(SpeechEncoder):
def __init__(self, vec_path="pretrain/chinese-hubert-large-fairseq-ckpt.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
se... |
25,217 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/ContentVec256L9.py | 23,24 | 7,39 | encoder | ContentVec256L9 | 24,39 | 24,39 | class ContentVec256L9(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[vec_path],
... | class ContentVec256L9(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
[vec_path],
... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav.device),
"... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav.device),
"... | 49 | 229 | 218 | 447 | import torch
from fairseq import checkpoint_utils
from vencoder.encoder import SpeechEncoder
class ContentVec256L9(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
models,... | import torch
from fairseq import checkpoint_utils
from vencoder.encoder import SpeechEncoder
class ContentVec256L9(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
models,... |
25,218 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/ContentVec768L12.py | 23,24 | 7,38 | encoder | ContentVec768L12 | 24,38 | 24,38 | class ContentVec768L12(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.hidden_dim = 768
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(... | class ContentVec768L12(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.hidden_dim = 768
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav.device),
"... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats.view(1, -1)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.to(wav.device),
"... | 46 | 230 | 203 | 433 | import torch
from fairseq import checkpoint_utils
from vencoder.encoder import SpeechEncoder
class ContentVec768L12(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.h... | import torch
from fairseq import checkpoint_utils
from vencoder.encoder import SpeechEncoder
class ContentVec768L12(SpeechEncoder):
def __init__(self, vec_path="pretrain/checkpoint_best_legacy_500.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
self.h... |
25,219 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/DPHubert.py | 20,21 | 7,30 | encoder | DPHubert | 21,30 | 21,30 | class DPHubert(SpeechEncoder):
def __init__(self, vec_path="pretrain/DPHuBERT-sp0.75.pth", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
if device is None:
self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
... | class DPHubert(SpeechEncoder):
def __init__(self, vec_path="pretrain/DPHuBERT-sp0.75.pth", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
if device is None:
self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
else:
... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats[None, :]
with torch.no_grad():
with torch.inference_mode():
units = self.model(feats)[0]
return un... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats[None, :]
with torch.no_grad():
with torch.inference_mode():
units = self.model(feats)[0]
return un... | 31 | 224 | 117 | 341 | import torch
from vencoder.dphubert.model import wav2vec2_model
from vencoder.encoder import SpeechEncoder
class DPHubert(SpeechEncoder):
def __init__(self, vec_path="pretrain/DPHuBERT-sp0.75.pth", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
if device ... | import torch
from vencoder.dphubert.model import wav2vec2_model
from vencoder.encoder import SpeechEncoder
class DPHubert(SpeechEncoder):
def __init__(self, vec_path="pretrain/DPHuBERT-sp0.75.pth", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
if device ... |
25,220 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/HubertSoft.py | 19,20 | 7,29 | encoder | HubertSoft | 20,29 | 20,29 | class HubertSoft(SpeechEncoder):
def __init__(self, vec_path="pretrain/hubert-soft-0d54a1f4.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
hubert_soft = hubert_model.hubert_soft(vec_path)
if device is None:
self.dev = torch.device("c... | class HubertSoft(SpeechEncoder):
def __init__(self, vec_path="pretrain/hubert-soft-0d54a1f4.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
hubert_soft = hubert_model.hubert_soft(vec_path)
if device is None:
self.dev = torch.device("c... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats[None,None,:]
with torch.no_grad():
with torch.inference_mode():
units = self.model.units(feats)
... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
feats = feats[None,None,:]
with torch.no_grad():
with torch.inference_mode():
units = self.model.units(feats)
... | 30 | 199 | 120 | 319 | import torch
from vencoder.encoder import SpeechEncoder
from vencoder.hubert import hubert_model
class HubertSoft(SpeechEncoder):
def __init__(self, vec_path="pretrain/hubert-soft-0d54a1f4.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
hubert_soft = ... | import torch
from vencoder.encoder import SpeechEncoder
from vencoder.hubert import hubert_model
class HubertSoft(SpeechEncoder):
def __init__(self, vec_path="pretrain/hubert-soft-0d54a1f4.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
hubert_soft = ... |
25,221 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/WavLMBasePlus.py | 22,23 | 7,33 | encoder | WavLMBasePlus | 23,33 | 23,33 | class WavLMBasePlus(SpeechEncoder):
def __init__(self, vec_path="pretrain/WavLM-Base+.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
checkpoint = torch.load(vec_path)
self.cfg = WavLMConfig(checkpoint['cfg'])
if device is None:
... | class WavLMBasePlus(SpeechEncoder):
def __init__(self, vec_path="pretrain/WavLM-Base+.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
checkpoint = torch.load(vec_path)
self.cfg = WavLMConfig(checkpoint['cfg'])
if device is None:
... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
if self.cfg.normalize:
feats = torch.nn.functional.layer_norm(feats, feats.shape)
with torch.no_grad():
with torch.inference_mode():... | feats = wav
if feats.dim() == 2: # double channels
feats = feats.mean(-1)
assert feats.dim() == 1, feats.dim()
if self.cfg.normalize:
feats = torch.nn.functional.layer_norm(feats, feats.shape)
with torch.no_grad():
with torch.inference_mode():... | 35 | 236 | 152 | 388 | import torch
from vencoder.encoder import SpeechEncoder
from vencoder.wavlm.WavLM import WavLM, WavLMConfig
class WavLMBasePlus(SpeechEncoder):
def __init__(self, vec_path="pretrain/WavLM-Base+.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
checkpoin... | import torch
from vencoder.encoder import SpeechEncoder
from vencoder.wavlm.WavLM import WavLM, WavLMConfig
class WavLMBasePlus(SpeechEncoder):
def __init__(self, vec_path="pretrain/WavLM-Base+.pt", device=None):
super().__init__()
print("load model(s) from {}".format(vec_path))
checkpoin... |
25,223 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 94,99 | 64,137 | forward | ConvLayerBlock | 107,122 | 99,122 | class ConvLayerBlock(Module):
"""Convolution unit of FeatureExtractor"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
bias: bool,
layer_norm: Optional[Module],
prune_conv_channels: bool = False,
):
... | class ConvLayerBlock(Module):
"""Convolution unit of FeatureExtractor"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
bias: bool,
layer_norm: Optional[Module],
prune_conv_channels: bool = False,
):
... | x = self.conv(x)
if self.layer_norm is not None:
x = self.layer_norm(x)
x = nn.functional.gelu(x)
if self.hard_concrete is not None:
channel_mask = self.hard_concrete() # hard concrete mask, (out_channels,)
x = x * channel_mask.unsqueeze(-1)
... | """
Args:
x (Tensor): Shape: ``[batch, in_channels, in_frame]``.
length (Tensor or None, optional): Shape ``[batch, ]``.
Returns:
Tensor: Shape ``[batch, out_channels, out_frames]``.
Optional[Tensor]: Shape ``[batch, ]``.
"""
x = se... | 69 | 571 | 213 | 784 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,224 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 319,320 | 280,336 | forward | ConvolutionalPositionalEmbedding | 327,336 | 320,336 | class ConvolutionalPositionalEmbedding(Module):
"""Positional embedding which is placed at the beginning of Transformer.
Args:
embed_dim (int): Feature dimension of the input Tensor.
kernel_size (int): The number of frames to be use.
groups (int): The number of groups in feature dimensi... | class ConvolutionalPositionalEmbedding(Module):
"""Positional embedding which is placed at the beginning of Transformer.
Args:
embed_dim (int): Feature dimension of the input Tensor.
kernel_size (int): The number of frames to be use.
groups (int): The number of groups in feature dimensi... | x = x.transpose(-2, -1)
x = self.conv(x)
if self.num_remove > 0:
x = x[..., : -self.num_remove]
x = torch.nn.functional.gelu(x)
x = x.transpose(-2, -1)
return x
| """
Args:
x (Tensor): shape ``[batch, frame, feature]``.
Returns:
Tensor: The resulting feature. Shape ``[batch, frame, feature]``.
"""
x = x.transpose(-2, -1)
x = self.conv(x)
if self.num_remove > 0:
x = x[..., : -self.num_rem... | 25 | 553 | 98 | 651 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,225 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 968,973 | 958,1020 | _preprocess | Encoder | 973,986 | 973,986 | class Encoder(Module):
def __init__(
self,
feature_projection: Module,
transformer: Module,
):
super().__init__()
self.feature_projection = feature_projection
self.transformer = transformer
def _preprocess(
self,
features: Tensor,
leng... | class Encoder(Module):
def __init__(
self,
feature_projection: Module,
transformer: Module,
):
super().__init__()
self.feature_projection = feature_projection
self.transformer = transformer
def _preprocess(
self,
features: Tensor,
leng... | x = self.feature_projection(features)
mask: Optional[Tensor] = None
if lengths is not None:
batch_size, max_len, _ = x.shape
# create mask for padded elements and zero-out them
mask = torch.arange(max_len, device=lengths.device).expand(batch_size, max_len) >=... | x = self.feature_projection(features)
mask: Optional[Tensor] = None
if lengths is not None:
batch_size, max_len, _ = x.shape
# create mask for padded elements and zero-out them
mask = torch.arange(max_len, device=lengths.device).expand(batch_size, max_len) >=... | 64 | 500 | 196 | 696 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,226 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 814,821 | 794,868 | forward | EncoderLayer | 835,859 | 821,859 | class EncoderLayer(Module):
"""A layer unit in encoder. Combines multihead self attention and feed forward."""
def __init__(
self,
attention: Optional[Module], # can be None if the entire layer is pruned
dropout: float,
layer_norm_first: bool,
feed_forward: Optional[M... | class EncoderLayer(Module):
"""A layer unit in encoder. Combines multihead self attention and feed forward."""
def __init__(
self,
attention: Optional[Module], # can be None if the entire layer is pruned
dropout: float,
layer_norm_first: bool,
feed_forward: Optional[M... | if self.attention is not None:
residual = x
if self.layer_norm_first:
x = self.layer_norm(x)
x, position_bias = self.attention(
x, attention_mask=attention_mask, position_bias=position_bias, key_padding_mask=key_padding_mask
)
... | """
Args:
x (Tensor): Input of shape ``(batch, sequence_length, embed_dim)``.
attention_mask (Tensor or ``None``, optional): attention mask
of shape ``(batch, 1, sequence_length, sequence_length)``. (Default: ``None``)
position_bias (Tensor or ``None``... | 80 | 643 | 271 | 914 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,227 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 198,199 | 137,238 | prune | FeatureExtractor | 202,238 | 199,238 | class FeatureExtractor(Module):
"""Extract features from audio
Args:
conv_layers (nn.ModuleList):
convolution layers
"""
def __init__(
self,
conv_layers: nn.ModuleList,
):
super().__init__()
self.conv_layers = conv_layers
# NOTE: a dummy... | class FeatureExtractor(Module):
"""Extract features from audio
Args:
conv_layers (nn.ModuleList):
convolution layers
"""
def __init__(
self,
conv_layers: nn.ModuleList,
):
super().__init__()
self.conv_layers = conv_layers
# NOTE: a dummy... | new_config = [] # [(output_channel, kernel_size, stride), ...]
for idx, layer in enumerate(self.conv_layers):
if layer.hard_concrete is not None:
assert not layer.hard_concrete.training
mask = layer.hard_concrete() # (out_features,)
inde... | """"Prune conv layers and dummy weight based on hardconcrete parameters.
This is an in-place operation.
"""
new_config = [] # [(output_channel, kernel_size, stride), ...]
for idx, layer in enumerate(self.conv_layers):
if layer.hard_concrete is not None:
... | 109 | 637 | 487 | 1,124 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,228 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 276,277 | 238,280 | get_num_params | FeatureProjection | 277,280 | 277,280 | class FeatureProjection(Module):
"""Layer that connects FeatureExtractor and Encoder
Projects features to encoder dimension.
Args:
in_features (int): Input feature dim.
out_features (int): Output feature dim.
dropout (float): Dropout probability.
"""
def __init__(
... | class FeatureProjection(Module):
"""Layer that connects FeatureExtractor and Encoder
Projects features to encoder dimension.
Args:
in_features (int): Input feature dim.
out_features (int): Output feature dim.
dropout (float): Dropout probability.
"""
def __init__(
... | return in_features * 2 + (in_features + 1) * self.projection.out_features
| return in_features * 2 + (in_features + 1) * self.projection.out_features
| 10 | 318 | 31 | 349 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,229 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 763,764 | 696,794 | prune | FeedForward | 764,794 | 764,794 | class FeedForward(Module):
"""Layer that follows attention layer in encoder layer."""
def __init__(
self,
io_features: int,
intermediate_features: int,
intermediate_dropout: float,
output_dropout: float,
prune_intermediate: bool = False,
prune_layer: bool... | class FeedForward(Module):
"""Layer that follows attention layer in encoder layer."""
def __init__(
self,
io_features: int,
intermediate_features: int,
intermediate_dropout: float,
output_dropout: float,
prune_intermediate: bool = False,
prune_layer: bool... | new_config = {
"use_feed_forward": True,
"ff_interm_features": self.intermediate_dense.out_features
}
if self.hard_concrete_for_layer is not None:
assert not self.hard_concrete_for_layer.training
layer_mask = self.hard_concrete_for_layer()
... | new_config = {
"use_feed_forward": True,
"ff_interm_features": self.intermediate_dense.out_features
}
if self.hard_concrete_for_layer is not None:
assert not self.hard_concrete_for_layer.training
layer_mask = self.hard_concrete_for_layer()
... | 79 | 757 | 429 | 1,186 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,230 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 1408,1410 | 1401,1411 | backward | GradMultiply | 1410,1411 | 1410,1411 | class GradMultiply(torch.autograd.Function):
@staticmethod
def forward(ctx, x, scale):
ctx.scale = scale
res = x.new(x)
return res
@staticmethod
def backward(ctx, grad):
FILL_FUNC_BODY | class GradMultiply(torch.autograd.Function):
@staticmethod
def forward(ctx, x, scale):
ctx.scale = scale
res = x.new(x)
return res
@staticmethod
def backward(ctx, grad):
FILL_FUNC_BODY | return grad * ctx.scale, None
| return grad * ctx.scale, None
| 5 | 78 | 11 | 89 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,231 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 57,58 | 54,64 | forward | LayerNorm | 58,64 | 58,64 | class LayerNorm(nn.LayerNorm):
"""Layer norm with transpose"""
def forward(self, input: Tensor) -> Tensor:
FILL_FUNC_BODY | class LayerNorm(nn.LayerNorm):
"""Layer norm with transpose"""
def forward(self, input: Tensor) -> Tensor:
FILL_FUNC_BODY | x = input.transpose(-2, -1)
x = nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.transpose(-2, -1)
return x
| x = input.transpose(-2, -1)
x = nn.functional.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.transpose(-2, -1)
return x
| 17 | 48 | 76 | 124 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,233 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/components.py | 936,937 | 868,958 | prune | Transformer | 937,958 | 937,958 | class Transformer(Module):
def __init__(
self,
pos_conv_embed: Module,
dropout: float,
layers: Module,
layer_norm_first: bool,
layer_drop: float,
):
super().__init__()
self.pos_conv_embed = pos_conv_embed
self.layer_norm = nn.LayerNorm(pos_... | class Transformer(Module):
def __init__(
self,
pos_conv_embed: Module,
dropout: float,
layers: Module,
layer_norm_first: bool,
layer_drop: float,
):
super().__init__()
self.pos_conv_embed = pos_conv_embed
self.layer_norm = nn.LayerNorm(pos_... | new_config = defaultdict(list)
for layer in self.layers:
attention_config = layer.attention.prune()
new_config["use_attention"].append(attention_config["use_attention"])
if "remaining_heads" in attention_config:
new_config["remaining_heads"].append(att... | new_config = defaultdict(list)
for layer in self.layers:
attention_config = layer.attention.prune()
new_config["use_attention"].append(attention_config["use_attention"])
if "remaining_heads" in attention_config:
new_config["remaining_heads"].append(att... | 37 | 723 | 266 | 989 | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... | """Building blocks for speech SSL models supporting pruning.
Originally from:
https://github.com/pytorch/audio/blob/main/torchaudio/models/wav2vec2/components.py
"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.nn impor... |
25,235 | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc | /home/hieuvd/dung_repo/raw/stars_01/svc-develop-team_so-vits-svc/so-vits-svc/vencoder/dphubert/hardconcrete.py | 70,71 | 14,123 | reset_parameters | HardConcrete | 72,76 | 71,76 | class HardConcrete(nn.Module):
"""A HarcConcrete module.
Use this module to create a mask of size N, which you can
then use to perform L0 regularization.
To obtain a mask, simply run a forward pass through the module
with no input data. The mask is sampled in training mode, and
fixed during eva... | class HardConcrete(nn.Module):
"""A HarcConcrete module.
Use this module to create a mask of size N, which you can
then use to perform L0 regularization.
To obtain a mask, simply run a forward pass through the module
with no input data. The mask is sampled in training mode, and
fixed during eva... | self.compiled_mask = None
mean = math.log(1 - self.init_mean) - math.log(self.init_mean)
self.log_alpha.data.normal_(mean, self.init_std)
| """Reset the parameters of this module."""
self.compiled_mask = None
mean = math.log(1 - self.init_mean) - math.log(self.init_mean)
self.log_alpha.data.normal_(mean, self.init_std)
| 12 | 1,110 | 62 | 1,172 | """Implementation of the hard Concrete distribution.
Originally from:
https://github.com/asappresearch/flop/blob/master/flop/hardconcrete.py
"""
import math
import torch
import torch.nn as nn
class HardConcrete(nn.Module):
"""A HarcConcrete module.
Use this module to create a mask of size N, which you can... | """Implementation of the hard Concrete distribution.
Originally from:
https://github.com/asappresearch/flop/blob/master/flop/hardconcrete.py
"""
import math
import torch
import torch.nn as nn
class HardConcrete(nn.Module):
"""A HarcConcrete module.
Use this module to create a mask of size N, which you can... |
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