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| import onnxruntime | |
| import librosa | |
| import numpy as np | |
| import soundfile | |
| class ContentVec: | |
| def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None): | |
| print("load model(s) from {}".format(vec_path)) | |
| if device == "cpu" or device is None: | |
| providers = ["CPUExecutionProvider"] | |
| elif device == "cuda": | |
| providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] | |
| elif device == "dml": | |
| providers = ["DmlExecutionProvider"] | |
| else: | |
| raise RuntimeError("Unsportted Device") | |
| self.model = onnxruntime.InferenceSession(vec_path, providers=providers) | |
| def __call__(self, wav): | |
| return self.forward(wav) | |
| def forward(self, wav): | |
| feats = wav | |
| if feats.ndim == 2: # double channels | |
| feats = feats.mean(-1) | |
| assert feats.ndim == 1, feats.ndim | |
| feats = np.expand_dims(np.expand_dims(feats, 0), 0) | |
| onnx_input = {self.model.get_inputs()[0].name: feats} | |
| logits = self.model.run(None, onnx_input)[0] | |
| return logits.transpose(0, 2, 1) | |
| def get_f0_predictor(f0_predictor, hop_length, sampling_rate, **kargs): | |
| if f0_predictor == "pm": | |
| from lib.infer_pack.modules.F0Predictor.PMF0Predictor import PMF0Predictor | |
| f0_predictor_object = PMF0Predictor( | |
| hop_length=hop_length, sampling_rate=sampling_rate | |
| ) | |
| elif f0_predictor == "harvest": | |
| from lib.infer_pack.modules.F0Predictor.HarvestF0Predictor import HarvestF0Predictor | |
| f0_predictor_object = HarvestF0Predictor( | |
| hop_length=hop_length, sampling_rate=sampling_rate | |
| ) | |
| elif f0_predictor == "dio": | |
| from lib.infer_pack.modules.F0Predictor.DioF0Predictor import DioF0Predictor | |
| f0_predictor_object = DioF0Predictor( | |
| hop_length=hop_length, sampling_rate=sampling_rate | |
| ) | |
| else: | |
| raise Exception("Unknown f0 predictor") | |
| return f0_predictor_object | |
| class OnnxRVC: | |
| def __init__( | |
| self, | |
| model_path, | |
| sr=40000, | |
| hop_size=512, | |
| vec_path="vec-768-layer-12", | |
| device="cpu", | |
| ): | |
| vec_path = f"pretrained/{vec_path}.onnx" | |
| self.vec_model = ContentVec(vec_path, device) | |
| if device == "cpu" or device is None: | |
| providers = ["CPUExecutionProvider"] | |
| elif device == "cuda": | |
| providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] | |
| elif device == "dml": | |
| providers = ["DmlExecutionProvider"] | |
| else: | |
| raise RuntimeError("Unsportted Device") | |
| self.model = onnxruntime.InferenceSession(model_path, providers=providers) | |
| self.sampling_rate = sr | |
| self.hop_size = hop_size | |
| def forward(self, hubert, hubert_length, pitch, pitchf, ds, rnd): | |
| onnx_input = { | |
| self.model.get_inputs()[0].name: hubert, | |
| self.model.get_inputs()[1].name: hubert_length, | |
| self.model.get_inputs()[2].name: pitch, | |
| self.model.get_inputs()[3].name: pitchf, | |
| self.model.get_inputs()[4].name: ds, | |
| self.model.get_inputs()[5].name: rnd, | |
| } | |
| return (self.model.run(None, onnx_input)[0] * 32767).astype(np.int16) | |
| def inference( | |
| self, | |
| raw_path, | |
| sid, | |
| f0_method="dio", | |
| f0_up_key=0, | |
| pad_time=0.5, | |
| cr_threshold=0.02, | |
| ): | |
| f0_min = 50 | |
| f0_max = 1100 | |
| f0_mel_min = 1127 * np.log(1 + f0_min / 700) | |
| f0_mel_max = 1127 * np.log(1 + f0_max / 700) | |
| f0_predictor = get_f0_predictor( | |
| f0_method, | |
| hop_length=self.hop_size, | |
| sampling_rate=self.sampling_rate, | |
| threshold=cr_threshold, | |
| ) | |
| wav, sr = librosa.load(raw_path, sr=self.sampling_rate) | |
| org_length = len(wav) | |
| if org_length / sr > 50.0: | |
| raise RuntimeError("Reached Max Length") | |
| wav16k = librosa.resample(wav, orig_sr=self.sampling_rate, target_sr=16000) | |
| wav16k = wav16k | |
| hubert = self.vec_model(wav16k) | |
| hubert = np.repeat(hubert, 2, axis=2).transpose(0, 2, 1).astype(np.float32) | |
| hubert_length = hubert.shape[1] | |
| pitchf = f0_predictor.compute_f0(wav, hubert_length) | |
| pitchf = pitchf * 2 ** (f0_up_key / 12) | |
| pitch = pitchf.copy() | |
| f0_mel = 1127 * np.log(1 + pitch / 700) | |
| f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( | |
| f0_mel_max - f0_mel_min | |
| ) + 1 | |
| f0_mel[f0_mel <= 1] = 1 | |
| f0_mel[f0_mel > 255] = 255 | |
| pitch = np.rint(f0_mel).astype(np.int64) | |
| pitchf = pitchf.reshape(1, len(pitchf)).astype(np.float32) | |
| pitch = pitch.reshape(1, len(pitch)) | |
| ds = np.array([sid]).astype(np.int64) | |
| 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 = np.pad(out_wav, (0, 2 * self.hop_size), "constant") | |
| return out_wav[0:org_length] | |