a bunch of shit to salvage my old encodec-quantized audio because dac-encoded audio just does not want to converge
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@ -5,15 +5,21 @@ import torchaudio
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from tqdm.auto import tqdm
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from pathlib import Path
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from vall_e.config import cfg
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from vall_e.emb.g2p import encode as valle_phonemize
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from vall_e.emb.qnt import encode as valle_quantize, _replace_file_extension
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# to-do: use argparser
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# things that could be args
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cfg.sample_rate = 24_000
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cfg.inference.audio_backend = "encodec"
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input_audio = "voices"
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input_metadata = "training/metadata"
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output_dataset = "training/data"
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input_metadata = "./training/metadata"
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output_dataset = f"./training/data-{'2' if cfg.sample_rate else '4'}4KHz-{cfg.inference.audio_backend}"
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device = "cuda"
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audio_extension = ".dac" if cfg.inference.audio_backend == "dac" else ".enc"
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slice = "auto"
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missing = {
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"transcription": [],
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@ -28,6 +34,9 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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if not os.path.isdir(f'./{input_audio}/{dataset_name}/'):
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print("Is not dir:", f'./{input_audio}/{dataset_name}/')
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continue
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if dataset_name in ["LibriVox", "Audiobooks"]:
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continue
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for speaker_id in tqdm(sorted(os.listdir(f'./{input_audio}/{dataset_name}/')), desc=f"Processing speaker in {dataset_name}"):
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if not os.path.isdir(f'./{input_audio}/{dataset_name}/{speaker_id}'):
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@ -35,6 +44,23 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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continue
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os.makedirs(f'./{output_dataset}/{dataset_name}/{speaker_id}/', exist_ok=True)
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if speaker_id == "Noise":
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for filename in sorted(os.listdir(f'./{input_audio}/{dataset_name}/{speaker_id}/')):
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inpath = Path(f'./{input_audio}/{dataset_name}/{speaker_id}/{filename}')
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outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{filename}')
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if _replace_file_extension(outpath, audio_extension).exists():
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continue
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waveform, sample_rate = torchaudio.load(inpath)
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qnt = valle_quantize(waveform, sr=sample_rate, device=device)
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if cfg.inference.audio_backend == "dac":
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qnt.save(_replace_file_extension(outpath, audio_extension))
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else:
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torch.save( qnt, _replace_file_extension(outpath, audio_extension) )
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continue
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metadata_path = Path(f'./{input_metadata}/{dataset_name}/{speaker_id}/whisper.json')
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if not metadata_path.exists():
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@ -47,6 +73,9 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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missing["transcription"].append(str(metadata_path))
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continue
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if f'{dataset_name}/{speaker_id}' not in dataset:
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dataset.append(f'{dataset_name}/{speaker_id}')
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txts = []
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wavs = []
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@ -64,9 +93,6 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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waveform, sample_rate = None, None
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language = metadata[filename]["language"] if "language" in metadata[filename] else "english"
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if f'{dataset_name}/{speaker_id}' not in dataset:
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dataset.append(f'{dataset_name}/{speaker_id}')
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if len(metadata[filename]["segments"]) == 0 or not use_slices:
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outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{fname}.{extension}')
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text = metadata[filename]["text"]
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@ -74,7 +100,7 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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if len(text) == 0:
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continue
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if _replace_file_extension(outpath, ".json").exists() and _replace_file_extension(outpath, ".dac").exists():
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if _replace_file_extension(outpath, ".json").exists() and _replace_file_extension(outpath, audio_extension).exists():
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continue
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if not _replace_file_extension(outpath, ".json").exists():
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@ -84,7 +110,7 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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language,
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))
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if not _replace_file_extension(outpath, ".dac").exists():
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if not _replace_file_extension(outpath, audio_extension).exists():
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if waveform is None:
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waveform, sample_rate = torchaudio.load(inpath)
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@ -100,7 +126,7 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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i = i + 1
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outpath = Path(f'./{output_dataset}/{dataset_name}/{speaker_id}/{fname}_{id}.{extension}')
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if _replace_file_extension(outpath, ".json").exists() and _replace_file_extension(outpath, ".dac").exists():
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if _replace_file_extension(outpath, ".json").exists() and _replace_file_extension(outpath, audio_extension).exists():
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continue
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if not _replace_file_extension(outpath, ".json").exists():
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@ -110,7 +136,7 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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language,
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))
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if not _replace_file_extension(outpath, ".dac").exists():
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if not _replace_file_extension(outpath, audio_extension).exists():
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if waveform is None:
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waveform, sample_rate = torchaudio.load(inpath)
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@ -132,7 +158,7 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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))
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if len(txts) > 0:
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for job in tqdm(txts, desc=f"Phonemizing: {speaker_id}"):
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for job in tqdm(txts, desc=f"Phonemizing: {speaker_id}", disable=True):
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outpath, text, language = job
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phones = valle_phonemize(text)
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data = {
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@ -147,10 +173,13 @@ for dataset_name in sorted(os.listdir(f'./{input_audio}/')):
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try:
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outpath, waveform, sample_rate = job
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qnt = valle_quantize(waveform, sr=sample_rate, device=device)
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qnt.save(_replace_file_extension(outpath, ".dac"))
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if cfg.inference.audio_backend == "dac":
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qnt.save(_replace_file_extension(outpath, audio_extension))
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else:
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torch.save( qnt, _replace_file_extension(outpath, audio_extension) )
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except Exception as e:
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print(f"Failed to quantize: {outpath}:", e)
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continue
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open("./training/missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
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open("./training/dataset_list.json", 'w', encoding='utf-8').write(json.dumps(dataset))
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open("./missing.json", 'w', encoding='utf-8').write(json.dumps(missing))
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open("./dataset_list.json", 'w', encoding='utf-8').write(json.dumps(dataset))
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145
vall_e/data.py
145
vall_e/data.py
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@ -145,7 +145,7 @@ def _get_hdf5_paths( data_dir, type="training", validate=False ):
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return cfg.dataset.min_duration <= duration and duration <= cfg.dataset.max_duration and cfg.dataset.min_phones <= phones and phones <= cfg.dataset.max_phones
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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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return [ Path(f"{key}/{child}") for child in cfg.hdf5[key].keys() if not validate or _validate(child) ] if key in cfg.hdf5 else []
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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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@ -1003,6 +1003,145 @@ def create_dataset_hdf5( skip_existing=True ):
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hf.create_dataset('symmap', data=json.dumps(symmap))
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hf.close()
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def extract_dataset_hdf5( skip_existing=True ):
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cfg.dataset.use_hdf5 = True
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cfg.load_hdf5(write=False)
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hf = cfg.hdf5
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symmap = get_phone_symmap()
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reverse_symmap = {"1":"<s>","2":"</s>","3":" ","4":".","5":",","6":"!","7":"p","8":"iː","9":"ɚ","10":"ˌ","11":"dˌ","12":"mˌ","13":"d","14":"ɹ","15":"tˈ","16":"pˌ","17":"uː","18":"l","19":"æ","20":"ɛ","21":"ɪ","22":"j","23":"ʊ","24":"t","25":"n","26":"v","27":"a","28":"o","29":"ŋ","30":"w","31":"ʌ","32":"hˈ","33":"ɡˈ","34":"ə","35":"θˈ","36":"dˈ","37":"wˌ","38":"h","39":"z","40":"k","41":"ð","42":"ɡˌ","43":"ˈ","44":"fˈ","45":"i","46":"s","47":"ʃ","48":"wˈ","49":"ðˈ","50":"ɹˈ","51":"lˈ","52":"ɡ","53":"oː","54":"mˈ","55":"e","56":"ɑː","57":"nˈ","58":"m","59":"θˌ","60":"sˈ","61":"f","62":"ɔː","63":"hˌ","64":"b","65":"jˈ","66":"ɐ","67":"ʒˈ","68":"θ","69":"bˈ","70":"ɾ","71":"ɜː","72":"ʌˈ","73":"ʃˌ","74":"bˌ","75":"kˈ","76":"ɔ","77":"zˈ","78":"ᵻ","79":"kˌ","80":"vˈ","81":"fˌ","82":"ʒ","83":"ʃˈ","84":"ɹˌ","85":"tˌ","86":"pˈ","87":"ðˌ","88":"sˌ","89":"nˌ","90":"lˌ","91":"̩","92":"ʔ","93":"vˌ","94":"ɪˈ","95":"\"","96":"ɪˌ","97":"ʒˌ","98":"uːˌ","99":"ʊˈ","100":"jˌ","101":"uːˈ","102":"iːˈ","103":"zˌ","104":".ˈ","105":"…","106":"ŋˌ","107":"ɐˌ","108":"—ˈ","109":"iˌ","110":"iːˌ","111":"ɛː","112":")","113":")ˈ","114":"(","115":"u","116":"-","117":"ɖˈ","118":"iˈ","119":"ʰˈ","120":"ɟˈ","121":"̃","122":"eː","123":"ɾˈ","124":"r","125":"ʰ","126":"-ˌ","127":"ɫ","128":"q","129":"—","130":"ʊˌ","131":"aː","132":"cˈ","133":"…ˈ","134":"c","135":"ɳ","136":"ɐˈ","137":"x","138":"ʔˌ","139":".ˌ","140":"ɑ","141":"?ˈ","142":"̩ˈ","143":"\"ˈ","144":",ˈ","145":"ŋˈ","146":"əˌ","147":"!ˈ","148":"\"ˌ","149":"?ˌ","150":",ˌ","151":"—ˌ","152":"̩ˌ","153":"əˈ","154":"!ˌ","155":"ɬ","156":"ʲ","157":"¡","158":"ɯ","159":"qˌ","160":"ʑ","161":"ʑˈ","162":"¿","163":"ɑːˈ","164":"iːː","165":"ɛˈ","166":"¡ˈ","167":"æˈ","168":"ç","169":"ɾˌ","170":"ᵻˈ","171":"xˈ","172":"ɔːˈ","173":";","174":"ɬˌ","175":":","176":"ʔˈ","177":"ɑːˌ","178":"ɬˈ","179":"”","180":"“","181":"“ˈ","182":"“ˌ","183":";ˈ","184":";ˌ","185":":ˈ","186":"1","187":"rˈ","188":"qˈ","189":"ᵻˌ","190":"ä","191":"̞ˌ","192":"̞","193":"ũˌ","194":"ʑˌ","195":"ᵝ","196":"ɽ","197":"ʲˌ","198":"ᵝˌ","199":"ũ","200":"ũˈ","201":"äˌ","202":"ɕ","203":"ɕˌ","204":"ɽˌ","205":"çˌ","206":"…ˌ","207":"̞ˈ","208":"äˈ","209":"ɽˈ","210":"ɸˌ","211":"ɴ","212":"ɸˈ","213":"ɕˈ","214":"ɸ","215":"ᵝˈ","216":"ʲˈ","217":"ĩ","218":"çˈ","219":"ĩˌ","220":"oˌ","221":"eˈ","222":"ʍ","223":"eˌ","224":"uˌ","225":"ʍˌ","226":"uˈ","227":"oˈ","228":"aˈ"}
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root = str(cfg.data_dir)
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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, "data/")
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Path(f'{cfg.relpath}/{name}/').mkdir(parents=True, exist_ok=True)
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if f'{type}/{name}' not in hf:
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return
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ids = [ key for key in hf[f'{type}/{name}'].keys() ]
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for id in tqdm(ids, desc=f"Processing {name}"):
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try:
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key = f'{type}/{name}/{id}'
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if key not in hf:
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tqdm.write(f'Missing key: {key}')
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continue
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group = hf[key]
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audio_exists = "audio" in group
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text_exists = "text" in group
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if not audio_exists or not text_exists:
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tqdm.write(f'Missing audio/text: {key}')
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continue
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audio_path = Path(f'{cfg.relpath}/{name}/{id}.enc')
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text_path = Path(f'{cfg.relpath}/{name}/{id}.json')
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# audio
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if audios and audio_exists and not audio_path.exists():
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qnt = group["audio"][:, :]
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torch.save( qnt, f'{cfg.relpath}/{name}/{id}.enc' )
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# text
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if texts and text_exists and not text_path.exists():
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tokens = group["text"][:][1:-1]
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phones = [ reverse_symmap[f'{token}'] for token in tokens ]
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phones = list("".join(phones).replace(" ", " "))
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j = {
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"text": "",
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"phonemes": phones,
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"language": "en"
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}
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with open(text_path, "w", encoding="utf-8") as f:
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f.write( json.dumps( j ) )
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except Exception as e:
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raise e
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# training
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for data_dir in tqdm(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(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(cfg.dataset.noise, desc='Processing Noise'):
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add( data_dir, type="noise", texts=False )
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hf.close()
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def retokenize_dataset_hdf5( skip_existing=True ):
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cfg.dataset.use_hdf5 = True
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cfg.load_hdf5(write=True)
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hf = cfg.hdf5
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symmap = get_phone_symmap()
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reverse_symmap = {"1":"<s>","2":"</s>","3":" ","4":".","5":",","6":"!","7":"p","8":"iː","9":"ɚ","10":"ˌ","11":"dˌ","12":"mˌ","13":"d","14":"ɹ","15":"tˈ","16":"pˌ","17":"uː","18":"l","19":"æ","20":"ɛ","21":"ɪ","22":"j","23":"ʊ","24":"t","25":"n","26":"v","27":"a","28":"o","29":"ŋ","30":"w","31":"ʌ","32":"hˈ","33":"ɡˈ","34":"ə","35":"θˈ","36":"dˈ","37":"wˌ","38":"h","39":"z","40":"k","41":"ð","42":"ɡˌ","43":"ˈ","44":"fˈ","45":"i","46":"s","47":"ʃ","48":"wˈ","49":"ðˈ","50":"ɹˈ","51":"lˈ","52":"ɡ","53":"oː","54":"mˈ","55":"e","56":"ɑː","57":"nˈ","58":"m","59":"θˌ","60":"sˈ","61":"f","62":"ɔː","63":"hˌ","64":"b","65":"jˈ","66":"ɐ","67":"ʒˈ","68":"θ","69":"bˈ","70":"ɾ","71":"ɜː","72":"ʌˈ","73":"ʃˌ","74":"bˌ","75":"kˈ","76":"ɔ","77":"zˈ","78":"ᵻ","79":"kˌ","80":"vˈ","81":"fˌ","82":"ʒ","83":"ʃˈ","84":"ɹˌ","85":"tˌ","86":"pˈ","87":"ðˌ","88":"sˌ","89":"nˌ","90":"lˌ","91":"̩","92":"ʔ","93":"vˌ","94":"ɪˈ","95":"\"","96":"ɪˌ","97":"ʒˌ","98":"uːˌ","99":"ʊˈ","100":"jˌ","101":"uːˈ","102":"iːˈ","103":"zˌ","104":".ˈ","105":"…","106":"ŋˌ","107":"ɐˌ","108":"—ˈ","109":"iˌ","110":"iːˌ","111":"ɛː","112":")","113":")ˈ","114":"(","115":"u","116":"-","117":"ɖˈ","118":"iˈ","119":"ʰˈ","120":"ɟˈ","121":"̃","122":"eː","123":"ɾˈ","124":"r","125":"ʰ","126":"-ˌ","127":"ɫ","128":"q","129":"—","130":"ʊˌ","131":"aː","132":"cˈ","133":"…ˈ","134":"c","135":"ɳ","136":"ɐˈ","137":"x","138":"ʔˌ","139":".ˌ","140":"ɑ","141":"?ˈ","142":"̩ˈ","143":"\"ˈ","144":",ˈ","145":"ŋˈ","146":"əˌ","147":"!ˈ","148":"\"ˌ","149":"?ˌ","150":",ˌ","151":"—ˌ","152":"̩ˌ","153":"əˈ","154":"!ˌ","155":"ɬ","156":"ʲ","157":"¡","158":"ɯ","159":"qˌ","160":"ʑ","161":"ʑˈ","162":"¿","163":"ɑːˈ","164":"iːː","165":"ɛˈ","166":"¡ˈ","167":"æˈ","168":"ç","169":"ɾˌ","170":"ᵻˈ","171":"xˈ","172":"ɔːˈ","173":";","174":"ɬˌ","175":":","176":"ʔˈ","177":"ɑːˌ","178":"ɬˈ","179":"”","180":"“","181":"“ˈ","182":"“ˌ","183":";ˈ","184":";ˌ","185":":ˈ","186":"1","187":"rˈ","188":"qˈ","189":"ᵻˌ","190":"ä","191":"̞ˌ","192":"̞","193":"ũˌ","194":"ʑˌ","195":"ᵝ","196":"ɽ","197":"ʲˌ","198":"ᵝˌ","199":"ũ","200":"ũˈ","201":"äˌ","202":"ɕ","203":"ɕˌ","204":"ɽˌ","205":"çˌ","206":"…ˌ","207":"̞ˈ","208":"äˈ","209":"ɽˈ","210":"ɸˌ","211":"ɴ","212":"ɸˈ","213":"ɕˈ","214":"ɸ","215":"ᵝˈ","216":"ʲˈ","217":"ĩ","218":"çˈ","219":"ĩˌ","220":"oˌ","221":"eˈ","222":"ʍ","223":"eˌ","224":"uˌ","225":"ʍˌ","226":"uˈ","227":"oˈ","228":"aˈ"}
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root = str(cfg.data_dir)
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def add( dir, type="training" ):
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name = str(dir)
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name = name.replace(root, "data/")
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Path(f'{cfg.relpath}/{name}/').mkdir(parents=True, exist_ok=True)
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if f'{type}/{name}' not in hf:
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return
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ids = [ key for key in hf[f'{type}/{name}'].keys() ]
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for id in tqdm(ids, desc=f"Processing {name}"):
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try:
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key = f'{type}/{name}/{id}'
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if key not in hf:
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tqdm.write(f'Missing key: {key}')
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continue
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group = hf[key]
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if not "text" in group:
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tqdm.write(f'Missing text: {key}')
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continue
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tokens = group["text"][:][1:-1]
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content = list("".join([ reverse_symmap[f'{token}'] for token in tokens ]).replace(" ", " "))
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tokens = cfg.tokenizer.encode("".join(content))
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tokens = np.array(tokens).astype(np.uint8)
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del group['text']
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group.create_dataset('text', data=tokens, compression='lzf')
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except Exception as e:
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||||
raise e
|
||||
|
||||
# training
|
||||
for data_dir in tqdm(cfg.dataset.training, desc="Processing Training"):
|
||||
add( data_dir, type="training" )
|
||||
|
||||
# validation
|
||||
for data_dir in tqdm(cfg.dataset.validation, desc='Processing Validation'):
|
||||
add( data_dir, type="validation" )
|
||||
|
||||
# write symmap
|
||||
if "symmap" in hf:
|
||||
del hf['symmap']
|
||||
|
||||
hf.create_dataset('symmap', data=json.dumps(symmap))
|
||||
hf.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
import argparse
|
||||
|
||||
|
|
@ -1023,6 +1162,10 @@ if __name__ == "__main__":
|
|||
|
||||
if args.action == "hdf5":
|
||||
create_dataset_hdf5()
|
||||
if args.action == "extract-hdf5":
|
||||
extract_dataset_hdf5()
|
||||
if args.action == "retokenize-hdf5":
|
||||
retokenize_dataset_hdf5()
|
||||
elif args.action == "metadata":
|
||||
create_dataset_metadata()
|
||||
elif args.action == "sample":
|
||||
|
|
|
|||
|
|
@ -144,7 +144,6 @@ def _load_vocos_model(device="cuda", levels=cfg.model.max_levels):
|
|||
@cache
|
||||
def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
|
||||
kwargs = dict(model_type="24khz",model_bitrate="8kbps",tag="latest")
|
||||
"""
|
||||
if not cfg.variable_sample_rate:
|
||||
# yes there's a better way, something like f'{cfg.sample.rate//1000}hz'
|
||||
if cfg.sample_rate == 44_000:
|
||||
|
|
@ -155,7 +154,6 @@ def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
|
|||
kwargs["model_type"] = "16khz"
|
||||
else:
|
||||
raise Exception(f'unsupported sample rate: {cfg.sample_rate}')
|
||||
"""
|
||||
|
||||
model = __load_dac_model(**kwargs)
|
||||
model = model.to(device)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user