metadata only path (might drop HDF5 since its giving file sizes twice as large as my actual unpacked dataset)
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140
vall_e/data.py
140
vall_e/data.py
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@ -753,56 +753,98 @@ def create_train_val_dataloader():
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return train_dl, subtrain_dl, val_dl
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# parse dataset into better to sample metadata
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def create_dataset_metadata():
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# need to fix
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if True:
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return
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cfg.dataset.validate = False
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cfg.dataset.use_hdf5 = False
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paths_by_spkr_name = {}
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paths_by_spkr_name |= _load_paths(cfg.dataset.training, "training")
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paths_by_spkr_name |= _load_paths(cfg.dataset.validation, "validation")
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paths_by_spkr_name |= _load_paths(cfg.dataset.noise, "noise")
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paths = list(itertools.chain.from_iterable(paths_by_spkr_name.values()))
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metadata = {}
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for path in tqdm(paths, desc="Parsing paths"):
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if isinstance(path, str):
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print("str:", path)
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path = Path(path)
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speaker = cfg.get_spkr(path)
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if speaker not in metadata:
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metadata[speaker] = {}
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if cfg.dataset.use_hdf5:
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phones = cfg.hdf5[_get_hdf5_path(path)].attrs['phonemes']
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duration = cfg.hdf5[_get_hdf5_path(path)].attrs['duration']
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else:
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phns_path = _get_phone_path(path)
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qnts_path = _get_quant_path(path)
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phones = len(_get_phones(phns_path)) if phns_path.exists() else 0
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duration = _load_quants(qnts_path).shape[0] / 75 if qnts_path.exists() else 0
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metadata[speaker][path.name.split(".")[0]] = {
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"phones": phones,
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"duration": duration
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}
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for speaker, paths in tqdm(paths_by_spkr_name.items(), desc="Writing metadata"):
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if len(paths) == 0:
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continue
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with open(paths[0].parent / "metadata.json", "w", encoding="utf-8") as f:
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f.write( json.dumps( metadata[speaker] ) )
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def create_dataset_metadata( skip_existing=False ):
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symmap = get_phone_symmap()
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with open(cfg.relpath / "metadata.json", "w", encoding="utf-8") as f:
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f.write( json.dumps( metadata ) )
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root = str(cfg.data_dir)
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metadata_root = str(cfg.metadata_dir)
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cfg.metadata_dir.mkdir(parents=True, exist_ok=True)
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def add( dir, type="training", audios=True, texts=True ):
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name = str(dir)
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name = name.replace(root, "")
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metadata_path = Path(f"{metadata_root}/{name}.json")
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metadata = {} if not metadata_path.exists() else json.loads(open(str(metadata_path), "r", encoding="utf-8").read())
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if not os.path.isdir(f'{root}/{name}/'):
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return
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# tqdm.write(f'{root}/{name}')
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files = os.listdir(f'{root}/{name}/')
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# grab IDs for every file
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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}{_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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key = f'{type}/{name}/{id}'
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if skip_existing and key in metadata:
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continue
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if id not in metadata:
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metadata[id] = {}
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# audio
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if audios:
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if _get_quant_extension() == ".dac":
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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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duration = dac["metadata"]["original_length"] / dac["metadata"]["sample_rate"]
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metadata[id]["metadata"] = {
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"original_length": dac["metadata"]["original_length"],
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"sample_rate": dac["metadata"]["sample_rate"],
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}
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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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metadata[id]["duration"] = duration
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else:
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metadata[id]["duration"] = 0
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# text
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if texts:
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if _get_phone_extension() == ".json":
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json_metadata = json.loads(open(f'{root}/{name}/{id}{_get_phone_extension()}', "r", encoding="utf-8").read())
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content = json_metadata["phonemes"]
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txt = json_metadata["text"]
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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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txt = ""
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phn = cfg.tokenizer.encode("".join(content))
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phn = np.array(phn).astype(np.uint8)
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metadata[id]["phones"] = len(phn)
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metadata[id]["transcription"] = txt
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except Exception as e:
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raise e
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#pass
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with open(str(metadata_path), "w", encoding="utf-8") as f:
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f.write( json.dumps( metadata ) )
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# training
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for data_dir in tqdm(sorted(cfg.dataset.training), desc="Processing Training"):
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add( data_dir, type="training" )
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# validation
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for data_dir in tqdm(sorted(cfg.dataset.validation), desc='Processing Validation'):
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add( data_dir, type="validation" )
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# noise
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for data_dir in tqdm(sorted(cfg.dataset.noise), desc='Processing Noise'):
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add( data_dir, type="noise", texts=False )
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# parse yaml to create an hdf5 file
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def create_dataset_hdf5( skip_existing=True ):
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