Merge af364358bedea07d7851613bbe9fef2a1b72bb40 into 836bfec1fbf7356d59bd2dbe3883997646554e20

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Zzzyt 2024-06-28 15:41:14 +08:00 committed by GitHub
commit 75453e82ba
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6 changed files with 33 additions and 34 deletions

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@ -25,11 +25,9 @@ import torch
infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = os.environ.get("infer_ttswebui", 9872)
infer_ttswebui = int(infer_ttswebui) infer_ttswebui = int(infer_ttswebui)
is_share = os.environ.get("is_share", "False")
is_share = eval(is_share)
if "_CUDA_VISIBLE_DEVICES" in os.environ: if "_CUDA_VISIBLE_DEVICES" in os.environ:
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available() from config import is_half,is_share
gpt_path = os.environ.get("gpt_path", None) gpt_path = os.environ.get("gpt_path", None)
sovits_path = os.environ.get("sovits_path", None) sovits_path = os.environ.get("sovits_path", None)
cnhubert_base_path = os.environ.get("cnhubert_base_path", None) cnhubert_base_path = os.environ.get("cnhubert_base_path", None)

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@ -10,7 +10,7 @@ all_parts = os.environ.get("all_parts")
os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES") os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
opt_dir = os.environ.get("opt_dir") opt_dir = os.environ.get("opt_dir")
bert_pretrained_dir = os.environ.get("bert_pretrained_dir") bert_pretrained_dir = os.environ.get("bert_pretrained_dir")
is_half = eval(os.environ.get("is_half", "True")) from config import is_half
import sys, numpy as np, traceback, pdb import sys, numpy as np, traceback, pdb
import os.path import os.path
from glob import glob from glob import glob

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@ -10,7 +10,7 @@ os.environ["CUDA_VISIBLE_DEVICES"]= os.environ.get("_CUDA_VISIBLE_DEVICES")
from feature_extractor import cnhubert from feature_extractor import cnhubert
opt_dir= os.environ.get("opt_dir") opt_dir= os.environ.get("opt_dir")
cnhubert.cnhubert_base_path= os.environ.get("cnhubert_base_dir") cnhubert.cnhubert_base_path= os.environ.get("cnhubert_base_dir")
is_half=eval(os.environ.get("is_half","True")) from config import is_half
import pdb,traceback,numpy as np,logging import pdb,traceback,numpy as np,logging
from scipy.io import wavfile from scipy.io import wavfile

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@ -8,7 +8,7 @@ os.environ["CUDA_VISIBLE_DEVICES"] = os.environ.get("_CUDA_VISIBLE_DEVICES")
opt_dir = os.environ.get("opt_dir") opt_dir = os.environ.get("opt_dir")
pretrained_s2G = os.environ.get("pretrained_s2G") pretrained_s2G = os.environ.get("pretrained_s2G")
s2config_path = os.environ.get("s2config_path") s2config_path = os.environ.get("s2config_path")
is_half = eval(os.environ.get("is_half", "True")) from config import is_half
import math, traceback import math, traceback
import multiprocessing import multiprocessing
import sys, pdb import sys, pdb

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@ -42,6 +42,7 @@ if infer_device == "cuda":
is_half=False is_half=False
if(infer_device=="cpu"):is_half=False if(infer_device=="cpu"):is_half=False
if(torch.backends.mps.is_available()):is_half=False
class Config: class Config:
def __init__(self): def __init__(self):

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@ -1,29 +1,29 @@
import os,argparse import os,argparse
from modelscope.pipelines import pipeline from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks from modelscope.utils.constant import Tasks
from tqdm import tqdm from tqdm import tqdm
path_denoise = 'tools/denoise-model/speech_frcrn_ans_cirm_16k' path_denoise = 'tools/denoise-model/speech_frcrn_ans_cirm_16k'
path_denoise = path_denoise if os.path.exists(path_denoise) else "damo/speech_frcrn_ans_cirm_16k" path_denoise = path_denoise if os.path.exists(path_denoise) else "damo/speech_frcrn_ans_cirm_16k"
ans = pipeline(Tasks.acoustic_noise_suppression,model=path_denoise) ans = pipeline(Tasks.acoustic_noise_suppression,model=path_denoise)
def execute_denoise(input_folder,output_folder): def execute_denoise(input_folder,output_folder):
os.makedirs(output_folder,exist_ok=True) os.makedirs(output_folder,exist_ok=True)
# print(input_folder) # print(input_folder)
# print(list(os.listdir(input_folder).sort())) # print(list(os.listdir(input_folder).sort()))
for name in tqdm(os.listdir(input_folder)): for name in tqdm(os.listdir(input_folder)):
ans("%s/%s"%(input_folder,name),output_path='%s/%s'%(output_folder,name)) ans("%s/%s"%(input_folder,name),output_path='%s/%s'%(output_folder,name))
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input_folder", type=str, required=True, parser.add_argument("-i", "--input_folder", type=str, required=True,
help="Path to the folder containing WAV files.") help="Path to the folder containing WAV files.")
parser.add_argument("-o", "--output_folder", type=str, required=True, parser.add_argument("-o", "--output_folder", type=str, required=True,
help="Output folder to store transcriptions.") help="Output folder to store transcriptions.")
parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32'], parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32'],
help="fp16 or fp32")#还没接入 help="fp16 or fp32")#还没接入
cmd = parser.parse_args() cmd = parser.parse_args()
execute_denoise( execute_denoise(
input_folder = cmd.input_folder, input_folder = cmd.input_folder,
output_folder = cmd.output_folder, output_folder = cmd.output_folder,
) )