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README.md
21
README.md
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@ -125,7 +125,7 @@ Unfortunately, efforts to train a *good* foundational model seems entirely predi
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#### Backend Architectures
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As the core of VALL-E makes use of a language model, various LLM architectures can be supported and slotted in. Currently supported:
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As the core of VALL-E makes use of a language model, various LLM architectures can be supported and slotted in. Currently supported LLm architectures:
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* `llama`: using HF transformer's LLaMa implementation for its attention-based transformer, boasting RoPE and other improvements.
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* `mixtral`: using HF transformer's Mixtral implementation for its attention-based transformer, also utilizing its MoE implementation.
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@ -135,9 +135,24 @@ As the core of VALL-E makes use of a language model, various LLM architectures c
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* `retnet`: using [TorchScale's RetNet](https://github.com/microsoft/torchscale/blob/main/torchscale/architecture/retnet.py) implementation, a retention-based approach can be used instead.
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- Its implementation for MoE can also be utilized.
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* `retnet-hf`: using [syncdoth/RetNet/](https://github.com/syncdoth/RetNet) with a HuggingFace-compatible RetNet model
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- inferencing cost is about 0.5x, and MoE is not implemented.
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- has an inference penality, and MoE is not implemented.
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It's recommended to use `llama` with `xformers`-based attention, as the savings are huge in comparison to even `retnet`-backed models.
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For audio backends:
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* [`encodec`](https://github.com/facebookresearch/encodec): a tried-and-tested EnCodec to encode/decode audio.
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* [`vocos`](https://huggingface.co/charactr/vocos-encodec-24khz): a higher quality EnCodec decoder.
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- encoding audio will use the `encodec` backend automagically, as there's no EnCodec encoder under `vocos`
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* [`descript-audio-codec`](https://github.com/descriptinc/descript-audio-codec): boasts better compression and quality
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- **Note** models using `descript-audio-codec` at 24KHz + 6kbps will NOT converge. Unknown if 44KHz fares any better.
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`llama`-based models also support different attention backends:
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* `math`: torch's SDPA's `math` implementation
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* `mem_efficient`: torch's SDPA's memory efficient (`xformers` adjacent) implementation
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* `flash`: torch's SDPA's flash attention implementation
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* `xformers`: [facebookresearch/xformers](https://github.com/facebookresearch/xformers/)'s memory efficient attention
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* `auto`: determine the best fit from the above
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* `sdpa`: integrated `LlamaSdpaAttention` attention model
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* `flash_attention_2`: integrated `LlamaFlashAttetion2` attention model
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## Export
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@ -15,7 +15,7 @@ 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 = f"./training/data-{'2' if cfg.sample_rate else '4'}4KHz-{cfg.inference.audio_backend}"
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output_dataset = f"./training/data-{'2' if cfg.sample_rate == 24_000 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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@ -558,7 +558,7 @@ class Inference:
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amp: bool = False
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normalize: bool = False # do NOT enable this unless you know exactly what you're doing
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audio_backend: str = "dac"
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audio_backend: str = "vocos" # encodec, vocos, dac
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# legacy / backwards compat
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use_vocos: bool = True
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@ -731,6 +731,7 @@ try:
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if cfg.dataset.use_hdf5:
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cfg.load_hdf5()
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except Exception as e:
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cfg.dataset.use_hdf5 = False
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print("Error while parsing config YAML:", e)
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pass
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100
vall_e/data.py
100
vall_e/data.py
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@ -66,7 +66,7 @@ def _get_quant_extension():
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return ".dac" if cfg.inference.audio_backend == "dac" else ".qnt.pt"
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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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return ".json" # if cfg.inference.audio_backend == "dac" else ".phn.txt"
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def _get_quant_path(path):
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return _replace_file_extension(path, _get_quant_extension())
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@ -136,7 +136,7 @@ def _get_hdf5_path(path):
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def _get_hdf5_paths( data_dir, type="training", validate=False ):
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data_dir = str(data_dir)
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def _validate(child):
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def _validate( child ):
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phones = child.attrs['phonemes']
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duration = child.attrs['duration']
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if type not in _total_durations:
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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}") for child in cfg.hdf5[key].keys() if not validate or _validate(child) ] if key in cfg.hdf5 else []
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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=_get_quant_extension(), validate=False ):
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if isinstance(path, str):
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@ -906,7 +906,6 @@ def create_dataset_hdf5( skip_existing=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}/{speaker_name}/{id}'
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if skip_existing and key in hf:
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@ -1014,44 +1013,37 @@ def extract_dataset_hdf5( skip_existing=True ):
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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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def add( type="training", audios=True, texts=True ):
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for group in tqdm( hf[f'{type}/data/'].keys(), desc=f"Processing {type}"):
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for name in tqdm( hf[f'{type}/data/{group}'].keys(), desc=f"Processing {group}"):
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(cfg.data_dir / group / name).mkdir(parents=True, exist_ok=True)
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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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for id in tqdm( hf[f'{type}/data/{group}/{name}'].keys(), desc=f"Processing {name}"):
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try:
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key = f'{type}/{name}/{id}'
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key = f'{type}/data/{group}/{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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audio_exists = "audio" in hf[key]
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text_exists = "text" in hf[key]
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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_path = Path(f'{root}/{group}/{name}/{id}.enc')
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text_path = Path(f'{root}/{group}/{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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qnt = hf[key]["audio"][:, :]
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torch.save( qnt, audio_path )
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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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tokens = hf[key]["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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@ -1067,17 +1059,9 @@ def extract_dataset_hdf5( skip_existing=True ):
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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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add( type="training" )
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add( type="validation" )
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add( type="noise", texts=False )
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hf.close()
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@ -1091,49 +1075,38 @@ def retokenize_dataset_hdf5( skip_existing=True ):
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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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def add( type="training" ):
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for group in tqdm( hf[f'{type}/data/'].keys(), desc=f"Processing {type}"):
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for name in tqdm( hf[f'{type}/data/{group}'].keys(), desc=f"Processing {group}"):
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(cfg.data_dir / group / name).mkdir(parents=True, exist_ok=True)
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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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for id in tqdm( hf[f'{type}/data/{group}/{name}'].keys(), desc=f"Processing {name}"):
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try:
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key = f'{type}/{name}/{id}'
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key = f'{type}/data/{group}/{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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if "text" not in hf[key]:
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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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# text
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tokens = hf[key]["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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del hf[key]['text']
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hf[key].create_dataset('text', data=tokens, compression='lzf')
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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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add( type="training" )
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add( type="validation" )
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# write symmap
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if "symmap" in hf:
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extract_dataset_hdf5()
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if args.action == "retokenize-hdf5":
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retokenize_dataset_hdf5()
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elif args.action == "list-dataset":
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dataset = []
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for group in os.listdir(cfg.data_dir):
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for name in os.listdir(cfg.data_dir / group):
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if len(os.listdir(cfg.data_dir / group / name)) == 0:
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continue
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dataset.append(f'{group}/{name}')
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print(dataset)
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elif args.action == "metadata":
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create_dataset_metadata()
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elif args.action == "sample":
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@ -147,7 +147,7 @@ def _load_dac_model(device="cuda", levels=cfg.model.max_levels):
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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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if cfg.sample_rate == 44_000:
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kwargs["model_type"] = "44kz"
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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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