just don't use DAC 24Khz, it's bad
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230da8b559
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@ -143,13 +143,11 @@ def _load_vocos_model(device="cuda", levels=cfg.model.max_levels):
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@cache
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@cache
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def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
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def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
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kwargs = dict(model_type="24khz",model_bitrate="8kbps",tag="latest")
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kwargs = dict(model_type="44khz",model_bitrate="8kbps",tag="latest")
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if not cfg.variable_sample_rate:
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if not cfg.variable_sample_rate:
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# yes there's a better way, something like f'{cfg.sample.rate//1000}hz'
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# yes there's a better way, something like f'{cfg.sample.rate//1000}hz'
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if cfg.sample_rate == 44_000:
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if cfg.sample_rate == 44_000:
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kwargs["model_type"] = "44khz"
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kwargs["model_type"] = "44khz"
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elif cfg.sample_rate == 24_000:
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kwargs["model_type"] = "24khz"
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elif cfg.sample_rate == 16_000:
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elif cfg.sample_rate == 16_000:
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kwargs["model_type"] = "16khz"
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kwargs["model_type"] = "16khz"
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else:
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else:
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@ -279,11 +277,6 @@ def encode(wav: Tensor, sr: int = cfg.sample_rate, device="cuda", levels=cfg.mod
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with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
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with torch.autocast("cuda", dtype=cfg.inference.dtype, enabled=cfg.inference.amp):
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artifact = model.compress(signal, win_duration=None, verbose=False, n_quantizers=levels)
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artifact = model.compress(signal, win_duration=None, verbose=False, n_quantizers=levels)
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# trim to 8 codebooks if 24Khz
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# probably redundant with levels, should rewrite logic eventuall
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if model.model_type == "24khz":
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artifact.codes = artifact.codes[:, :8, :]
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return artifact.codes if not return_metadata else artifact
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return artifact.codes if not return_metadata else artifact
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# vocos does not encode wavs to encodecs, so just use normal encodec
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# vocos does not encode wavs to encodecs, so just use normal encodec
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