Spaces:
Sleeping
Sleeping
File size: 2,501 Bytes
b5a064f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import os
from typing import *
import ffmpeg
import numpy as np
import requests
import torch
from tqdm import tqdm
from lib.rvc.config import TrainConfig
from modules.shared import ROOT_DIR
def load_audio(file: str, sr):
try:
# https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
# This launches a subprocess to decode audio while down-mixing and resampling as necessary.
# Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
file = (
file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
) # Prevent small white copy path head and tail with spaces and " and return
out, _ = (
ffmpeg.input(file, threads=0)
.output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
)
except Exception as e:
raise RuntimeError(f"Failed to load audio: {e}")
return np.frombuffer(out, np.float32).flatten()
def get_gpus():
num_gpus = torch.cuda.device_count()
return [torch.device(f"cuda:{i}") for i in range(num_gpus)]
def download_file(url: str, out: str, position: int = 0, show: bool = True):
req = requests.get(url, stream=True, allow_redirects=True)
content_length = req.headers.get("content-length")
if show:
progress_bar = tqdm(
total=int(content_length) if content_length is not None else None,
leave=False,
unit="B",
unit_scale=True,
unit_divisor=1024,
position=position,
)
# with tqdm
with open(out, "wb") as f:
for chunk in req.iter_content(chunk_size=1024):
if chunk:
if show:
progress_bar.update(len(chunk))
f.write(chunk)
def load_config(
version: Literal["v1", "v2"],
training_dir: str,
sample_rate: str,
emb_channels: int,
fp16: bool,
):
if emb_channels == 256:
config_path = os.path.join(ROOT_DIR, "configs", f"{sample_rate}.json")
else:
config_path = os.path.join(
ROOT_DIR, "configs", f"{sample_rate}-{emb_channels}.json"
)
config = TrainConfig.parse_file(config_path)
config.version = version
config.train.fp16_run = fp16
config_save_path = os.path.join(training_dir, "config.json")
with open(config_save_path, "w") as f:
f.write(config.json())
return config
|