From c94f3f6748cc1a113734b69bcaeb332bf6b45f5d Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 6 Jun 2024 01:03:19 +0900 Subject: [PATCH] chore(format): run black on dev (#3) Co-authored-by: github-actions[bot] --- rvc/onnx/f0predictor/dio.py | 8 ++++++-- rvc/onnx/f0predictor/f0.py | 11 ++++++++--- rvc/onnx/f0predictor/harvest.py | 8 ++++++-- rvc/onnx/f0predictor/pm.py | 8 ++++++-- rvc/onnx/infer.py | 28 +++++++++++++++++++++++----- 5 files changed, 49 insertions(+), 14 deletions(-) diff --git a/rvc/onnx/f0predictor/dio.py b/rvc/onnx/f0predictor/dio.py index 1cd2af1..438426f 100644 --- a/rvc/onnx/f0predictor/dio.py +++ b/rvc/onnx/f0predictor/dio.py @@ -9,7 +9,9 @@ class DioF0Predictor(F0Predictor): def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): super().__init__(hop_length, f0_min, f0_max, sampling_rate) - def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): + def compute_f0( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): if p_len is None: p_len = wav.shape[0] // self.hop_length f0, t = pyworld.dio( @@ -24,7 +26,9 @@ class DioF0Predictor(F0Predictor): f0[index] = round(pitch, 1) return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0] - def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): + def compute_f0_uv( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): if p_len is None: p_len = wav.shape[0] // self.hop_length f0, t = pyworld.dio( diff --git a/rvc/onnx/f0predictor/f0.py b/rvc/onnx/f0predictor/f0.py index 9c4a0ee..8c96337 100644 --- a/rvc/onnx/f0predictor/f0.py +++ b/rvc/onnx/f0predictor/f0.py @@ -1,6 +1,7 @@ import numpy as np import typing + class F0Predictor(object): def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): self.hop_length = hop_length @@ -8,9 +9,13 @@ class F0Predictor(object): self.f0_max = f0_max self.sampling_rate = sampling_rate - def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): ... + def compute_f0( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): ... - def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): ... + def compute_f0_uv( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): ... def __interpolate_f0(self, f0: np.ndarray[typing.Any, np.dtype]): """ @@ -49,7 +54,7 @@ class F0Predictor(object): last_value = data[i] return ip_data[:, 0], vuv_vector[:, 0] - + def __resize_f0(self, x: np.ndarray[typing.Any, np.dtype], target_len: int): source = np.array(x) source[source < 0.001] = np.nan diff --git a/rvc/onnx/f0predictor/harvest.py b/rvc/onnx/f0predictor/harvest.py index 77759bd..3d51ec9 100644 --- a/rvc/onnx/f0predictor/harvest.py +++ b/rvc/onnx/f0predictor/harvest.py @@ -9,7 +9,9 @@ class HarvestF0Predictor(F0Predictor): def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): super().__init__(hop_length, f0_min, f0_max, sampling_rate) - def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): + def compute_f0( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): if p_len is None: p_len = wav.shape[0] // self.hop_length f0, t = pyworld.harvest( @@ -22,7 +24,9 @@ class HarvestF0Predictor(F0Predictor): f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs) return self.__interpolate_f0(self.__resize_f0(f0, p_len))[0] - def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): + def compute_f0_uv( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): if p_len is None: p_len = wav.shape[0] // self.hop_length f0, t = pyworld.harvest( diff --git a/rvc/onnx/f0predictor/pm.py b/rvc/onnx/f0predictor/pm.py index e37216e..7101b91 100644 --- a/rvc/onnx/f0predictor/pm.py +++ b/rvc/onnx/f0predictor/pm.py @@ -9,7 +9,9 @@ class PMF0Predictor(F0Predictor): def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100): super().__init__(hop_length, f0_min, f0_max, sampling_rate) - def compute_f0(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): + def compute_f0( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): x = wav if p_len is None: p_len = x.shape[0] // self.hop_length @@ -33,7 +35,9 @@ class PMF0Predictor(F0Predictor): f0, uv = self.__interpolate_f0(f0) return f0 - def compute_f0_uv(self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None): + def compute_f0_uv( + self, wav: np.ndarray[typing.Any, np.dtype], p_len: int | None = None + ): x = wav if p_len is None: p_len = x.shape[0] // self.hop_length diff --git a/rvc/onnx/infer.py b/rvc/onnx/infer.py index 258f85d..edcaa4e 100644 --- a/rvc/onnx/infer.py +++ b/rvc/onnx/infer.py @@ -4,11 +4,20 @@ import onnxruntime import typing import os -from onnx.f0predictor import PMF0Predictor, HarvestF0Predictor, DioF0Predictor, F0Predictor +from onnx.f0predictor import ( + PMF0Predictor, + HarvestF0Predictor, + DioF0Predictor, + F0Predictor, +) class Model: - def __init__(self, path: str | bytes | os.PathLike, device: typing.Literal["cpu", "cuda", "dml"]="cpu"): + def __init__( + self, + path: str | bytes | os.PathLike, + device: typing.Literal["cpu", "cuda", "dml"] = "cpu", + ): if device == "cpu": providers = ["CPUExecutionProvider"] elif device == "cuda": @@ -19,8 +28,13 @@ class Model: raise RuntimeError("Unsportted Device") self.model = onnxruntime.InferenceSession(path, providers=providers) + class ContentVec(Model): - def __init__(self, vec_path: str | bytes | os.PathLike, device: typing.Literal["cpu", "cuda", "dml"]="cpu"): + def __init__( + self, + vec_path: str | bytes | os.PathLike, + device: typing.Literal["cpu", "cuda", "dml"] = "cpu", + ): super().__init__(vec_path, device) def __call__(self, wav: np.ndarray[typing.Any, np.dtype]): @@ -43,7 +57,9 @@ predictors: typing.Dict[str, F0Predictor] = { } -def get_f0_predictor(f0_method: str, hop_length: int, sampling_rate: int) -> F0Predictor: +def get_f0_predictor( + f0_method: str, hop_length: int, sampling_rate: int +) -> F0Predictor: return predictors[f0_method](hop_length=hop_length, sampling_rate=sampling_rate) @@ -107,7 +123,9 @@ class RVC(Model): rnd = np.random.randn(1, 192, hubert_length).astype(np.float32) hubert_length = np.array([hubert_length]).astype(np.int64) - out_wav = self.__forward(hubert, hubert_length, pitch, pitchf, ds, rnd).squeeze() + out_wav = self.__forward( + hubert, hubert_length, pitch, pitchf, ds, rnd + ).squeeze() out_wav = np.pad(out_wav, (0, 2 * self.hop_size), "constant") return out_wav[0:org_length]