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https://github.com/storytold/storyteller-ml.git
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@@ -290,6 +290,7 @@ class Svc:
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absolute_thresh: bool = False,
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absolute_thresh: bool = False,
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# fade_seconds: float = 0.0,
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# fade_seconds: float = 0.0,
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) -> np.ndarray[Any, np.dtype[np.float32]]:
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) -> np.ndarray[Any, np.dtype[np.float32]]:
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LOG.info(f"infer_silence(): f0_method = {f0_method}")
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sr = self.target_sample
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sr = self.target_sample
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result_audio = np.array([], dtype=np.float32)
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result_audio = np.array([], dtype=np.float32)
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chunk_length_min = chunk_length_min = (
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chunk_length_min = chunk_length_min = (
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@@ -38,6 +38,8 @@ def infer(
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absolute_thresh: bool = False,
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absolute_thresh: bool = False,
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device: str | torch.device = get_optimal_device(),
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device: str | torch.device = get_optimal_device(),
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):
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):
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LOG.info("infer()")
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model_path = Path(model_path)
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model_path = Path(model_path)
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output_path = Path(output_path)
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output_path = Path(output_path)
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input_path = Path(input_path)
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input_path = Path(input_path)
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@@ -52,7 +54,12 @@ def infer(
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device=device,
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device=device,
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)
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)
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LOG.info("infer() librosa.load()")
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audio, _ = librosa.load(input_path, sr=svc_model.target_sample)
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audio, _ = librosa.load(input_path, sr=svc_model.target_sample)
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LOG.info("infer() svc_model.infer_silence()")
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audio = svc_model.infer_silence(
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audio = svc_model.infer_silence(
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audio.astype(np.float32),
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audio.astype(np.float32),
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speaker=speaker,
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speaker=speaker,
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@@ -67,6 +74,8 @@ def infer(
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absolute_thresh=absolute_thresh,
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absolute_thresh=absolute_thresh,
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)
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)
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LOG.info("infer() soundfile.write()")
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soundfile.write(output_path, audio, svc_model.target_sample)
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soundfile.write(output_path, audio, svc_model.target_sample)
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