converting over to a different intermediary dataset format
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4f5c9e518a
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@ -63,11 +63,17 @@ def get_task_symmap():
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def _replace_file_extension(path, suffix):
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return (path.parent / path.name.split(".")[0]).with_suffix(suffix)
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def _get_quant_extension():
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return ".dac"
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def _get_phone_extension():
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return ".json"
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def _get_quant_path(path):
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return _replace_file_extension(path, ".qnt.pt")
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return _replace_file_extension(path, _get_quant_extension())
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def _get_phone_path(path):
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return _replace_file_extension(path, ".phn.txt")
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return _replace_file_extension(path, _get_phone_extension())
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_total_durations = {}
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@ -101,7 +107,7 @@ def _load_paths_from_metadata(data_dir, type="training", validate=False):
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metadata = json.loads(open( metadata_path, "r", encoding="utf-8" ).read())
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if len(metadata) == 0:
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return _fn( data_dir, type if cfg.dataset.use_hdf5 else ".qnt.pt", validate )
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return _fn( data_dir, type if cfg.dataset.use_hdf5 else _get_quant_extension(), validate )
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def key( dir, id ):
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if not cfg.dataset.use_hdf5:
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@ -134,7 +140,7 @@ def _get_hdf5_paths( data_dir, type="training", validate=False ):
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key = f"/{type}{_get_hdf5_path(data_dir)}"
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return [ Path(f"{key}/{child.attrs['id']}") for child in cfg.hdf5[key].values() if not validate or _validate(child) ] if key in cfg.hdf5 else []
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def _get_paths_of_extensions( path, extensions=".qnt.pt", validate=False ):
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def _get_paths_of_extensions( path, extensions=_get_quant_extension(), validate=False ):
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if isinstance(path, str):
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path = Path(path)
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@ -154,6 +160,10 @@ def _get_paths_of_extensions( path, extensions=".qnt.pt", validate=False ):
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return [ p for p in list(path.iterdir()) if _validate(p) ] if path.exists() and path.is_dir() else []
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def _load_quants(path) -> Tensor:
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if _get_quant_extension() == ".dac":
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qnt = np.load(_get_quant_path(path), allow_pickle=True)[()]
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return torch.from_numpy(qnt["codes"].astype(int))[0][:, :].t().to(torch.int16)
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return torch.load(_get_quant_path(path))[0][:, :].t().to(torch.int16)
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# prune consecutive spaces
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@ -162,8 +172,12 @@ def _cleanup_phones( phones, targets=[" "]):
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@cache
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def _get_phones(path, language="en"):
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content = open(_get_phone_path(path), "r", encoding="utf-8").read().split(" ")
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content = _cleanup_phones( content )
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if _get_quant_extension() == ".json":
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metadata = json.loads(open(_get_phone_path(path), "r", encoding="utf-8").read())
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content = metadata["phonemes"]
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else:
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content = open(_get_phone_path(path), "r", encoding="utf-8").read().split(" ")
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content = _cleanup_phones( content )
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return ["<s>"] + [ " " if not p else p for p in content ] + ["</s>"]
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def _interleaved_reorder(l, fn):
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@ -807,11 +821,12 @@ def create_dataset_hdf5( skip_existing=True ):
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files = os.listdir(f'{root}/{name}/')
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# grab IDs for every file
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ids = { ".".join(file.split(".")[:-2]) for file in files }
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ids = { file.replace(_get_quant_extension(), "").replace(_get_phone_extension(), "") for file in files }
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for id in tqdm(ids, desc=f"Processing {name}"):
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try:
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audio_exists = os.path.exists(f'{root}/{name}/{id}.qnt.pt') if audios else True
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text_exists = os.path.exists(f'{root}/{name}/{id}.phn.txt') if texts else True
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audio_exists = os.path.exists(f'{root}/{name}/{id}{_get_quant_extension()}') if audios else True
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text_exists = os.path.exists(f'{root}/{name}/{id}{_get_phone_extension()}') if texts else True
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if not audio_exists or not text_exists:
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continue
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@ -831,21 +846,34 @@ def create_dataset_hdf5( skip_existing=True ):
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# audio
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if audios:
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qnt = torch.load(f'{root}/{name}/{id}.qnt.pt')[0].t()
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qnt = np.load(f'{root}/{name}/{id}{_get_quant_extension()}', allow_pickle=True)[()]
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codes = torch.from_numpy(qnt["codes"].astype(int))[0].t()
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if _get_quant_extension() == ".dac":
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if "audio" in group:
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del group["audio"]
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duration = qnt["metadata"]["original_length"] / qnt["metadata"]["sample_rate"]
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metadata[id]["metadata"] = qnt["metadata"]
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else:
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qnt = torch.load(f'{root}/{name}/{id}{_get_quant_extension()}')[0].t()
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duration = qnt.shape[0] / 75
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if "audio" in group:
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del group["audio"]
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group.create_dataset('audio', data=qnt.numpy(), compression='lzf')
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group.attrs['duration'] = qnt.shape[0] # / 75
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metadata[id]["duration"] = qnt.shape[0] # / 75
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group.attrs['duration'] = duration
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metadata[id]["duration"] = duration
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else:
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group.attrs['duration'] = 0
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metadata[id]["duration"] = 0
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# text
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if texts:
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"""
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content = open(f'{root}/{name}/{id}.phn.txt', "r", encoding="utf-8") .read().split(" ")
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if _get_quant_extension() == ".json":
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j_son = json.loads(open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read())
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content = j_son["phonemes"]
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else:
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content = open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read().split(" ")
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phones = [f"<s>"] + [ " " if not p else p for p in content ] + [f"</s>"]
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for s in set(phones):
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if s not in symmap:
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@ -858,7 +886,6 @@ def create_dataset_hdf5( skip_existing=True ):
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group.create_dataset('text', data=phn, compression='lzf', chunks=True)
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group.create_dataset('transcription', data=txt, compression='lzf', chunks=True)
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"""
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group.attrs['phonemes'] = len(phn)
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metadata[id]["phones"] = len(phn)
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@ -66,22 +66,22 @@ try:
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# to-do, original implementation
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"""
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resample_fn = recons.resample
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loudness_fn = recons.loudness
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# If audio is > 10 minutes long, use the ffmpeg versions
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if recons.signal_duration >= 10 * 60 * 60:
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resample_fn = recons.ffmpeg_resample
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loudness_fn = recons.ffmpeg_loudness
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recons.normalize(obj.input_db)
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resample_fn(obj.sample_rate)
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recons = recons[..., : obj.original_length]
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loudness_fn()
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recons.audio_data = recons.audio_data.reshape(
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-1, obj.channels, obj.original_length
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)
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"""
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resample_fn = recons.resample
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loudness_fn = recons.loudness
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# If audio is > 10 minutes long, use the ffmpeg versions
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if recons.signal_duration >= 10 * 60 * 60:
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resample_fn = recons.ffmpeg_resample
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loudness_fn = recons.ffmpeg_loudness
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recons.normalize(obj.input_db)
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resample_fn(obj.sample_rate)
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recons = recons[..., : obj.original_length]
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loudness_fn()
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recons.audio_data = recons.audio_data.reshape(
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-1, obj.channels, obj.original_length
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)
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self.padding = original_padding
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return recons
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@ -228,7 +228,7 @@ def decode(codes: Tensor, device="cuda", levels=cfg.model.max_levels, metadata=N
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dac_version = metadata["dac_version"] if isinstance(metadata, dict) else metadata.dac_version,
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)
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return model.decompress(artifact, verbose=False).audio_data[0], model.sample_rate
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return model.decompress(artifact, verbose=False).audio_data[0], artifact.sample_rate
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kwargs = {}
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