fix loading without needing an hdf5 dataset already prepped (and some other incidental speedups during dataloader prep)
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d88a5ca183
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@ -63,7 +63,7 @@ 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" if cfg.inference.audio_backend == "dac" else ".qnt.pt"
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return ".dac" if cfg.inference.audio_backend == "dac" else ".enc"
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def _get_phone_extension():
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return ".json" # if cfg.inference.audio_backend == "dac" else ".phn.txt"
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@ -161,25 +161,28 @@ def _get_paths_of_extensions( path, extensions=_get_quant_extension(), validate=
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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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def _load_quants(path, return_metadata=False) -> Tensor:
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qnt = np.load(_get_quant_path(path), allow_pickle=True)[()]
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if return_metadata:
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return torch.from_numpy(qnt["codes"].astype(int))[0][:, :].t().to(torch.int16), qnt["metadata"]
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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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def _cleanup_phones( phones, targets=[" "]):
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return [ p for i, p in enumerate(phones) if p not in targets or ( p in targets and p != phones[i-1] ) ]
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@cache
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def _get_phones(path, language="en"):
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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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def _get_phones(path):
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phone_path = _get_phone_path(path)
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quant_path = _get_quant_path(path)
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if phone_path.exists():
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metadata = json.loads(open(phone_path, "r", encoding="utf-8").read())
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elif quant_path.exists():
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_, metadata = _load_quants( path, return_metadata=True )
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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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raise Exception(f"Could not load phonemes: {path}")
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content = metadata["phonemes"]
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return "".join(content)
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def _interleaved_reorder(l, fn):
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@ -269,9 +272,11 @@ class Dataset(_Dataset):
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#self.duration = _total_durations[self.dataset_type] if self.dataset_type in _total_durations else 0
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self.duration = _calculate_durations(self.dataset_type)
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"""
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@cached_property
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def phones(self):
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return sorted(set().union(*[_get_phones(path) for path in self.paths]))
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"""
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def get_speaker(self, path):
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if isinstance(path, str):
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@ -350,7 +355,7 @@ class Dataset(_Dataset):
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key = _get_hdf5_path(path)
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qnt = torch.from_numpy(cfg.hdf5[key]["audio"][:, :]).to(torch.int16)
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else:
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qnt = _load_quants(path)
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qnt = _load_quants(path, return_metadata=False)
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return qnt
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def sample_speakers(self, ignore=[]):
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@ -386,7 +391,7 @@ class Dataset(_Dataset):
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qnt = torch.from_numpy(cfg.hdf5[key]["audio"][:, :]).to(torch.int16)
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else:
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qnt = _load_quants(path)
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qnt = _load_quants(path, return_metadata=False)
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if 0 < trim_length and trim_length < qnt.shape[0]:
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qnt = trim( qnt, trim_length )
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@ -438,8 +443,9 @@ class Dataset(_Dataset):
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text = torch.from_numpy(text).to(self.text_dtype)
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resps = torch.from_numpy(resps).to(torch.int16)
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else:
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text = torch.tensor(tokenize( _get_phones( path ) )).to(self.text_dtype)
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resps = _load_quants(path)
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resps, metadata = _load_quants(path, return_metadata=True)
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text = torch.tensor(tokenize( metadata["phonemes"] )).to(self.text_dtype)
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#text = torch.tensor(tokenize( _get_phones( path ) )).to(self.text_dtype)
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lang = torch.tensor([ self.lang_symmap[ self.get_language(spkr_group) ]]).to(torch.uint8)
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@ -462,8 +468,9 @@ class Dataset(_Dataset):
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qnt = torch.from_numpy(qnt).to(torch.int16)
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else:
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#txt = torch.tensor([*map(self.phone_symmap.get, _get_phones(sampled_path))]).to(self.text_dtype)
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txt = torch.tensor(tokenize(_get_phones(sampled_path))).to(self.text_dtype)
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qnt = _load_quants(sampled_path)
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#txt = torch.tensor(tokenize(_get_phones(sampled_path))).to(self.text_dtype)
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qnt, metadata = _load_quants(sampled_path, return_metadata=True)
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txt = torch.tensor(tokenize( metadata["phonemes"] )).to(self.text_dtype)
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# <s>[original text] [new text]</s>
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# removes the original text's </s>, includes a space, and remove the new text's <s>
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@ -788,10 +795,10 @@ def create_dataset_metadata( skip_existing=True ):
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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}{_get_quant_extension()}') if audios else True
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quant_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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if not quant_exists:
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continue
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key = f'{type}/{speaker_name}/{id}'
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@ -817,8 +824,7 @@ def create_dataset_metadata( skip_existing=True ):
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if "original_length" in dac["metadata"] and "sample_rate" in dac["metadata"]:
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utterance_metadata["duration"] = dac["metadata"]["original_length"] / dac["metadata"]["sample_rate"]
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# text
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if texts:
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if not utterance_metadata:
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if texts and text_exists and not utterance_metadata:
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utterance_metadata = json.loads(open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read())
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for k, v in utterance_metadata.items():
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@ -878,27 +884,19 @@ def create_dataset_hdf5( skip_existing=True ):
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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}{_get_quant_extension()}')
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text_exists = os.path.exists(f'{root}/{name}/{id}{_get_phone_extension()}') if type != "Noise" else True
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quant_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:
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if not quant_exists:
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continue
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key = f'{type}/{speaker_name}/{id}'
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"""
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if skip_existing and key in hf:
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continue
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"""
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group = hf.create_group(key) if key not in hf else hf[key]
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"""
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group.attrs['id'] = id
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group.attrs['type'] = type
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group.attrs['speaker'] = speaker_name
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"""
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if id not in metadata:
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metadata[id] = {}
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@ -906,7 +904,6 @@ def create_dataset_hdf5( skip_existing=True ):
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# audio
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if audios:
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# ideally we'll encode Encodec-based audio in a similar manner because np has smaller files than pt
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dac = np.load(f'{root}/{name}/{id}{_get_quant_extension()}', allow_pickle=True)[()]
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qnt = torch.from_numpy(dac["codes"].astype(int))[0].t().to(dtype=torch.int16)
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