Format code (#1169)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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commit
c7bbf0bfd0
@ -19,6 +19,7 @@ version_config_list = [
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"v2/32k.json",
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]
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def singleton_variable(func):
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def wrapper(*args, **kwargs):
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if not wrapper.instance:
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@ -101,7 +102,7 @@ class Config:
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return True
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except Exception:
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return False
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def use_fp32_config(self):
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for config_file in version_config_list:
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self.json_config[config_file]["train"]["fp16_run"] = False
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33
gui_v1.py
33
gui_v1.py
@ -455,14 +455,17 @@ if __name__ == "__main__":
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inp_q,
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opt_q,
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device,
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self.rvc if hasattr(self, "rvc") else None
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self.rvc if hasattr(self, "rvc") else None,
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)
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self.config.samplerate = self.rvc.tgt_sr
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self.config.crossfade_time = min(
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self.config.crossfade_time, self.config.block_time
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)
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self.zc = self.rvc.tgt_sr // 100
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self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc
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self.block_frame = (
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int(np.round(self.config.block_time * self.config.samplerate / self.zc))
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* self.zc
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)
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self.block_frame_16k = 160 * self.block_frame // self.zc
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self.crossfade_frame = int(
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self.config.crossfade_time * self.config.samplerate
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@ -484,7 +487,9 @@ if __name__ == "__main__":
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),
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dtype="float32",
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)
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self.input_wav_res: torch.Tensor= torch.zeros(160 * len(self.input_wav) // self.zc)
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self.input_wav_res: torch.Tensor = torch.zeros(
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160 * len(self.input_wav) // self.zc
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)
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self.output_wav_cache: torch.Tensor = torch.zeros(
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int(
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np.ceil(
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@ -577,12 +582,18 @@ if __name__ == "__main__":
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if db_threhold[i]:
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indata[i * hop_length : (i + 1) * hop_length] = 0
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self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :]
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self.input_wav[-self.block_frame: ] = indata
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self.input_wav[-self.block_frame :] = indata
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# infer
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inp = torch.from_numpy(self.input_wav[-self.block_frame-2*self.zc :]).to(device)
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self.input_wav_res[ : -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
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self.input_wav_res[-self.block_frame_16k-160 :] = self.resampler(inp)[160 :]
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inp = torch.from_numpy(
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self.input_wav[-self.block_frame - 2 * self.zc :]
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).to(device)
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self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[
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self.block_frame_16k :
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].clone()
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self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(inp)[
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160:
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]
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rate = (
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self.crossfade_frame + self.sola_search_frame + self.block_frame
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) / (
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@ -592,11 +603,11 @@ if __name__ == "__main__":
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+ self.block_frame
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)
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f0_extractor_frame = self.block_frame_16k + 800
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if self.config.f0method == 'rmvpe':
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if self.config.f0method == "rmvpe":
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f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
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res2 = self.rvc.infer(
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self.input_wav_res,
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self.input_wav_res[-f0_extractor_frame :].cpu().numpy(),
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self.input_wav_res[-f0_extractor_frame:].cpu().numpy(),
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self.block_frame_16k,
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rate,
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self.pitch,
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@ -720,9 +731,7 @@ if __name__ == "__main__":
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sd.default.device[1] = output_device_indices[
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output_devices.index(output_device)
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]
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logger.info(
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"Input device: %s:%d", str(sd.default.device[0]), input_device
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)
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logger.info("Input device: %s:%d", str(sd.default.device[0]), input_device)
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logger.info(
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"Output device: %s:%d", str(sd.default.device[1]), output_device
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)
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105
infer-web.py
105
infer-web.py
@ -208,9 +208,15 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
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f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
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f.close()
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per = 3.0 if config.is_half else 3.7
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cmd = (
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'"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f'
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% (config.python_cmd, trainset_dir, sr, n_p, now_dir, exp_dir, config.noparallel, per)
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cmd = '"%s" infer/modules/train/preprocess.py "%s" %s %s "%s/logs/%s" %s %.1f' % (
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config.python_cmd,
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trainset_dir,
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sr,
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n_p,
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now_dir,
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exp_dir,
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config.noparallel,
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per,
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)
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logger.info(cmd)
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p = Popen(cmd, shell=True) # , stdin=PIPE, stdout=PIPE,stderr=PIPE,cwd=now_dir
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@ -272,14 +278,17 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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leng = len(gpus_rmvpe)
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ps = []
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for idx, n_g in enumerate(gpus_rmvpe):
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cmd = '"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s ' % (
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config.python_cmd,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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config.is_half,
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cmd = (
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'"%s" infer/modules/train/extract/extract_f0_rmvpe.py %s %s %s "%s/logs/%s" %s '
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% (
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config.python_cmd,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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config.is_half,
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)
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)
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logger.info(cmd)
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p = Popen(
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@ -333,15 +342,18 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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leng = len(gpus)
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ps = []
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for idx, n_g in enumerate(gpus):
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cmd = '"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s' % (
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config.python_cmd,
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config.device,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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version19,
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cmd = (
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'"%s" infer/modules/train/extract_feature_print.py %s %s %s %s "%s/logs/%s" %s'
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% (
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config.python_cmd,
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config.device,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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version19,
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)
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)
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logger.info(cmd)
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p = Popen(
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@ -379,12 +391,16 @@ def get_pretrained_models(path_str, f0_str, sr2):
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if not if_pretrained_generator_exist:
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logger.warn(
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"assets/pretrained%s/%sG%s.pth not exist, will not use pretrained model",
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path_str, f0_str, sr2
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path_str,
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f0_str,
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sr2,
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)
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if not if_pretrained_discriminator_exist:
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logger.warn(
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"assets/pretrained%s/%sD%s.pth not exist, will not use pretrained model",
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path_str, f0_str, sr2
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path_str,
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f0_str,
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sr2,
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)
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return (
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"assets/pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2)
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@ -421,9 +437,11 @@ def change_version19(sr2, if_f0_3, version19):
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def change_f0(if_f0_3, sr2, version19): # f0method8,pretrained_G14,pretrained_D15
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path_str = "" if version19 == "v1" else "_v2"
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return (
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{"visible": if_f0_3, "__type__": "update"}, *get_pretrained_models(path_str, "f0", sr2)
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{"visible": if_f0_3, "__type__": "update"},
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*get_pretrained_models(path_str, "f0", sr2),
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)
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# but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
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def click_train(
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exp_dir1,
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@ -522,24 +540,33 @@ def click_train(
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config_save_path = os.path.join(exp_dir, "config.json")
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if not pathlib.Path(config_save_path).exists():
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with open(config_save_path, "w", encoding="utf-8") as f:
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json.dump(config.json_config[config_path], f, ensure_ascii=False, indent=4, sort_keys=True)
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json.dump(
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config.json_config[config_path],
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f,
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ensure_ascii=False,
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indent=4,
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sort_keys=True,
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)
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f.write("\n")
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if gpus16:
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cmd = '"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' % (
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config.python_cmd,
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exp_dir1,
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sr2,
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1 if if_f0_3 else 0,
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batch_size12,
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gpus16,
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total_epoch11,
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save_epoch10,
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"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
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"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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version19,
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cmd = (
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'"%s" infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s'
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% (
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config.python_cmd,
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exp_dir1,
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sr2,
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1 if if_f0_3 else 0,
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batch_size12,
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gpus16,
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total_epoch11,
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save_epoch10,
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"-pg %s" % pretrained_G14 if pretrained_G14 != "" else "",
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"-pd %s" % pretrained_D15 if pretrained_D15 != "" else "",
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1 if if_save_latest13 == i18n("是") else 0,
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1 if if_cache_gpu17 == i18n("是") else 0,
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1 if if_save_every_weights18 == i18n("是") else 0,
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version19,
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)
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)
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else:
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cmd = (
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@ -617,7 +617,10 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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)
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self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
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logger.debug(
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"gin_channels: " + str(gin_channels) + ", self.spk_embed_dim: " + str(self.spk_embed_dim)
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"gin_channels: "
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+ str(gin_channels)
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+ ", self.spk_embed_dim: "
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+ str(self.spk_embed_dim)
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)
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def remove_weight_norm(self):
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@ -735,7 +738,10 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
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)
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self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
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logger.debug(
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"gin_channels: " + str(gin_channels) + ", self.spk_embed_dim: " + str(self.spk_embed_dim)
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"gin_channels: "
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+ str(gin_channels)
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+ ", self.spk_embed_dim: "
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+ str(self.spk_embed_dim)
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)
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def remove_weight_norm(self):
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@ -850,7 +856,10 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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)
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self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
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logger.debug(
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"gin_channels: " + str(gin_channels) + ", self.spk_embed_dim: " + str(self.spk_embed_dim)
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"gin_channels: "
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+ str(gin_channels)
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+ ", self.spk_embed_dim: "
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+ str(self.spk_embed_dim)
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)
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def remove_weight_norm(self):
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@ -958,7 +967,10 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
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)
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self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
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logger.debug(
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"gin_channels: " + str(gin_channels) + ", self.spk_embed_dim: " + str(self.spk_embed_dim)
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"gin_channels: "
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+ str(gin_channels)
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+ ", self.spk_embed_dim: "
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+ str(self.spk_embed_dim)
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)
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def remove_weight_norm(self):
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@ -621,7 +621,10 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
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self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
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self.speaker_map = None
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logger.debug(
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"gin_channels: " + gin_channels + ", self.spk_embed_dim: " + self.spk_embed_dim
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"gin_channels: "
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+ gin_channels
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+ ", self.spk_embed_dim: "
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+ self.spk_embed_dim
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)
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def remove_weight_norm(self):
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@ -34,8 +34,10 @@ def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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logger.warn(
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"shape-%s-mismatch. need: %s, get: %s"
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, k, state_dict[k].shape, saved_state_dict[k].shape
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"shape-%s-mismatch. need: %s, get: %s",
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k,
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state_dict[k].shape,
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saved_state_dict[k].shape,
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) #
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raise KeyError
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except:
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@ -110,8 +112,10 @@ def load_checkpoint(checkpoint_path, model, optimizer=None, load_opt=1):
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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logger.warn(
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"shape-%s-mismatch|need-%s|get-%s"
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, k, state_dict[k].shape, saved_state_dict[k].shape
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"shape-%s-mismatch|need-%s|get-%s",
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k,
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state_dict[k].shape,
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saved_state_dict[k].shape,
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) #
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raise KeyError
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except:
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@ -215,7 +215,8 @@ class VC:
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else "Index not used."
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)
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return (
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"Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs." % (index_info, *times),
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"Success.\n%s\nTime:\nnpy: %.2fs, f0: %.2fs, infer: %.2fs."
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% (index_info, *times),
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(self.tgt_sr, audio_opt),
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)
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except:
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@ -2,6 +2,7 @@ import os
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import sys
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import traceback
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import logging
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logger = logging.getLogger(__name__)
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from time import time as ttime
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@ -47,7 +48,16 @@ if config.dml == True:
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# config.is_half=False########强制cpu测试
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class RVC:
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def __init__(
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self, key, pth_path, index_path, index_rate, n_cpu, inp_q, opt_q, device, last_rvc=None,
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self,
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key,
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pth_path,
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index_path,
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index_rate,
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n_cpu,
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inp_q,
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opt_q,
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device,
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last_rvc=None,
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) -> None:
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"""
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初始化
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@ -74,7 +84,7 @@ class RVC:
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self.pth_path = pth_path
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self.index_path = index_path
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self.index_rate = index_rate
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if last_rvc is None:
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models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
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["assets/hubert/hubert_base.pt"],
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@ -90,7 +100,7 @@ class RVC:
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self.model = hubert_model
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else:
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self.model = last_rvc.model
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if last_rvc is None or last_rvc.pth_path != self.pth_path:
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cpt = torch.load(self.pth_path, map_location="cpu")
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self.tgt_sr = cpt["config"][-1]
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@ -126,7 +136,7 @@ class RVC:
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self.version = last_rvc.version
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self.net_g = last_rvc.net_g
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self.is_half = last_rvc.is_half
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if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
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self.model_rmvpe = last_rvc.model_rmvpe
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||||
except:
|
||||
@ -324,10 +334,10 @@ class RVC:
|
||||
if self.if_f0 == 1:
|
||||
pitch, pitchf = self.get_f0(indata, self.f0_up_key, self.n_cpu, f0method)
|
||||
start_frame = block_frame_16k // 160
|
||||
end_frame = len(cache_pitch) - (pitch.shape[0] - 4) + start_frame
|
||||
cache_pitch[:] = np.append(cache_pitch[start_frame : end_frame], pitch[3:-1])
|
||||
end_frame = len(cache_pitch) - (pitch.shape[0] - 4) + start_frame
|
||||
cache_pitch[:] = np.append(cache_pitch[start_frame:end_frame], pitch[3:-1])
|
||||
cache_pitchf[:] = np.append(
|
||||
cache_pitchf[start_frame : end_frame], pitchf[3:-1]
|
||||
cache_pitchf[start_frame:end_frame], pitchf[3:-1]
|
||||
)
|
||||
p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
|
||||
else:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user