added option to specify frames per second for the given audio representation (Encodec is 75Hz, DAC is 41Hz (at 24K sources))
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c494894261
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@ -156,6 +156,14 @@ class Dataset:
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sample_type: str = "path" # path | speaker
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tasks_list: list[str] = field(default_factory=lambda: ["tts"])
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_frames_per_second: int = 0 # in encodec, each frame is 75 codes, in dac, each frame is 41
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@cached_property
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def frames_per_second(self):
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if self._frames_per_second > 0:
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return self._frames_per_second
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return 41 if cfg.inference.audio_backend == "dac" else 75
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@property
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def min_phones(self):
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@ -63,10 +63,10 @@ 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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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"
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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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@ -371,10 +371,10 @@ class Dataset(_Dataset):
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# shuffle it up a bit
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prom_length = 0
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if cfg.experimental:
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trim_length = random.randint(75 * 3, 75 * 6) # [3 seconds, 6 seconds]
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#trim_length = max(2, int(np.random.normal(loc=5, scale=1.25) * 75))
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trim_length = random.randint(cfg.dataset.frames_per_second * 3, cfg.dataset.frames_per_second * 6) # [3 seconds, 6 seconds]
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#trim_length = max(2, int(np.random.normal(loc=5, scale=1.25) * cfg.dataset.frames_per_second))
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else:
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trim_length = int(cfg.dataset.prompt_duration * 75) + random.randint(-75, 75)
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trim_length = int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second) + random.randint(-cfg.dataset.frames_per_second, cfg.dataset.frames_per_second)
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for _ in range(cfg.dataset.max_prompts):
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path = random.choice(choices)
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@ -470,7 +470,7 @@ class Dataset(_Dataset):
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resps = torch.concat([ resps, qnt ])
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task = "tts"
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trim_length = int(cfg.dataset.prompt_duration * 75)
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trim_length = int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second)
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proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
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@ -484,7 +484,7 @@ class Dataset(_Dataset):
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task = "tts"
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noise_scale = 0.25
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if task == "tts" or task == "tts-c":
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trim_length = int(cfg.dataset.prompt_duration * 75)
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trim_length = int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second)
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# demote if the target is too short
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if task == "tts-c" and trim_length * 2 >= resps.shape[0]:
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task = "tts"
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@ -805,7 +805,7 @@ def create_dataset_metadata( skip_existing=True ):
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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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duration = qnt.shape[0] / cfg.dataset.frames_per_second
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metadata[id]["duration"] = duration
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else:
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@ -912,7 +912,7 @@ def create_dataset_hdf5( skip_existing=True ):
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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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duration = qnt.shape[0] / cfg.dataset.frames_per_second
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qnt = qnt.numpy().astype(np.int16)
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@ -115,7 +115,7 @@ class TTS():
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res = torch.cat([qnt.encode_from_file(path)[0][:, :].t().to(torch.int16) for path in paths])
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if trim_length:
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res = trim( res, int( 75 * trim_length ) )
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res = trim( res, int( cfg.dataset.frames_per_second * trim_length ) )
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return res
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@ -125,7 +125,7 @@ class TTS():
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text,
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references,
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language="en",
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max_ar_steps=6 * 75,
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max_ar_steps=6 * cfg.dataset.frames_per_second,
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max_ar_context=-1,
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max_nar_levels=7,
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input_prompt_length=0.0,
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@ -150,7 +150,7 @@ class AR_NAR(Base):
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"""
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if cfg.experimental:
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proms_list = [ r if l == 0 else trim(r, 75 * 3) for r, l in zip(proms_list, quant_levels) ] # trim input prompt to 3 seconds
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proms_list = [ r if l == 0 else trim(r, cfg.dataset.frames_per_second * 3) for r, l in zip(proms_list, quant_levels) ] # trim input prompt to 3 seconds
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"""
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# append stop tokens for AR
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@ -350,7 +350,7 @@ def example_usage():
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tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
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]
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proms_list = [
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qnt[:75, :].to(device),
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qnt[:cfg.dataset.frames_per_second, :].to(device),
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]
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resps_list = [
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qnt.to(device),
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@ -873,98 +873,4 @@ class Base(nn.Module):
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return res, scores
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# and sample
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return [ Categorical(logits=logit).sample() for logit in logits ]
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def example_usage():
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from ..config import cfg
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cfg.trainer.backend = "local"
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cfg.trainer.check_for_oom = False
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from functools import partial
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from einops import repeat
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from ..emb.qnt import decode_to_file
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from ..engines import Engine, Engines
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from tqdm import tqdm, trange
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from ..utils import wrapper as ml
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from .ar import AR
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from .nar import NAR
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device = "cuda"
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x8 = partial(repeat, pattern="t -> t l", l=cfg.model.prom_levels)
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symmap = {'<s>': 1, '</s>': 2, ' ': 3, '.': 4, ',': 5, '!': 6, '?': 7, 'p': 7, 'iː': 8, 'ɚ': 9, 'ˌ': 10, 'dˌ': 11, 'mˌ': 12, 'd': 13, 'ɹ': 14, 'tˈ': 15, 'pˌ': 16, 'uː': 17, 'l': 18, 'æ': 19, 'ɛ': 20, 'ɪ': 21, 'j': 22, 'ʊ': 23, 't': 24, 'n': 25, 'v': 26, 'a': 27, 'o': 28, 'ŋ': 29, 'w': 30, 'ʌ': 31, 'hˈ': 32, 'ɡˈ': 33, 'ə': 34, 'θˈ': 35, 'dˈ': 36, 'wˌ': 37, 'h': 38, 'z': 39, 'k': 40, 'ð': 41, 'ɡˌ': 42, 'ˈ': 43, 'fˈ': 44, 'i': 45, 's': 46, 'ʃ': 47, 'wˈ': 48, 'ðˈ': 49, 'ɹˈ': 50, 'lˈ': 51, 'ɡ': 52, 'oː': 53, 'mˈ': 54, 'e': 55, 'ɑː': 56, 'nˈ': 57, 'm': 58, 'θˌ': 59, 'sˈ': 60, 'f': 61, 'ɔː': 62, 'hˌ': 63, 'b': 64, 'jˈ': 65, 'ɐ': 66, 'ʒˈ': 67, 'θ': 68, 'bˈ': 69, 'ɾ': 70, 'ɜː': 71, 'ʌˈ': 72, 'ʃˌ': 73, 'bˌ': 74, 'kˈ': 75, 'ɔ': 76, 'zˈ': 77, 'ᵻ': 78, 'kˌ': 79, 'vˈ': 80, 'fˌ': 81, 'ʒ': 82, 'ʃˈ': 83, 'ɹˌ': 84, 'tˌ': 85, 'pˈ': 86, 'ðˌ': 87, 'sˌ': 88, 'nˌ': 89, 'lˌ': 90, '̩': 91, 'ʔ': 92, 'vˌ': 93, 'ɪˈ': 94, '"': 95, 'ɪˌ': 96, 'ʒˌ': 97, 'uːˌ': 98, 'ʊˈ': 99, 'jˌ': 100, 'uːˈ': 101, 'iːˈ': 102, 'zˌ': 103, '.ˈ': 104, '…': 105, 'ŋˌ': 106, 'ɐˌ': 107, '—ˈ': 108, 'iˌ': 109, 'iːˌ': 110, 'ɛː': 111, ')': 112, ')ˈ': 113, '(': 114, 'u': 115, '-': 116, 'ɖˈ': 117, 'iˈ': 118, 'ʰˈ': 119, 'ɟˈ': 120, '̃': 121, 'eː': 122, 'ɾˈ': 123, 'r': 124, 'ʰ': 125, '-ˌ': 126, 'ɫ': 127, 'q': 128, '—': 129, 'ʊˌ': 130, 'aː': 131, 'cˈ': 132, '…ˈ': 133, 'c': 134, 'ɳ': 135, 'ɐˈ': 136, 'x': 137, 'ʔˌ': 138, '.ˌ': 139, 'ɑ': 140, '?ˈ': 141, '̩ˈ': 142, '"ˈ': 143, ',ˈ': 144, 'ŋˈ': 145, 'əˌ': 146, '!ˈ': 147, '"ˌ': 148, '?ˌ': 149, ',ˌ': 150, '—ˌ': 151, '̩ˌ': 152, 'əˈ': 153, '!ˌ': 154, 'ɬ': 155, 'ʲ': 156, '¡': 157, 'ɯ': 158, 'qˌ': 159, 'ʑ': 160, 'ʑˈ': 161, '¿': 162, 'ɑːˈ': 163, 'iːː': 164, 'ɛˈ': 165, '¡ˈ': 166, 'æˈ': 167, 'ç': 168, 'ɾˌ': 169, 'ᵻˈ': 170, 'xˈ': 171, 'ɔːˈ': 172, ';': 173, 'ɬˌ': 174, ':': 175, 'ʔˈ': 176, 'ɑːˌ': 177, 'ɬˈ': 178}
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def tokenize(content, lang_marker="en"):
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split = content.split(" ")
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phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
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return torch.tensor([*map(symmap.get, phones)]).to()
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kwargs = {
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'n_tokens': 1024,
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'd_model': 1024,
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'n_heads': 16,
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'n_layers': 12,
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}
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models = { "ar": AR(**kwargs).to(device), "nar": NAR(**kwargs).to(device) }
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for name, model in models.items():
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print(f"{name} parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
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engines = Engines({ name: Engine(model=model, optimizer=ml.AdamW(model.parameters(), lr=1e-4)) for name, model in models.items() })
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train = True
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qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.model.prom_levels].to(device)
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text_list = [
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tokenize("ˈ a ɪ w ɪ l nˌ ɑː t ˈ æ s k ɐ sˈ ɛ k ə n d tˈ a ɪ m").to(device),
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#tokenize("ˌ ɔ n ɡˌ o ʊ ɪ ŋ hˈ o ʊ m ð ə tˈ uː f ɹˈ ɛ n d z fˈ a ʊ n d ɐ lˈ ɛ ɾ ɚ f ɹ ʌ m ˈ æ θ o ʊ z , hˌ uː d ɪ zˈ a ɪ ɚ d ðˌ ɛ m t ə mˈ iː t hˌ ɪ m æ t ð ə ɡ ɹˈ æ n d t ʃˈ ɑː ɹ l ɪ mˌ æ ɡ n i ɔ n ð ə fˈ ɑː l o ʊ ɪ ŋ dˈ e ɪ .").to(device),
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]
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proms_list = [
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qnt.to(device),
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]
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resps_list = [
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qnt.to(device),
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]
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def sample( name, steps=600 ):
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AR = None
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NAR = None
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engines.eval()
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for name, engine in engines.items():
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if name[:2] == "ar":
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AR = engine
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elif name[:3] == "nar":
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NAR = engine
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resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
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resps_list = [r.unsqueeze(-1) for r in resps_list]
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codes = NAR( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 )
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decode_to_file(resps_list[0], f"./data/ar.{name}.wav", device=device)
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decode_to_file(codes[0], f"./data/ar+nar.{name}.wav", device=device)
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if train:
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sample("init", 15)
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engines.train()
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t = trange(500)
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for i in t:
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stats = {"step": i}
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"""
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for name, engine in engines.items():
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stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
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"""
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stats = engines.step({"text_list": text_list, "proms_list": proms_list, "resps_list": resps_list})
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tqdm.write(f"{stats}")
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else:
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for name, engine in engines.items():
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engine.module.load_state_dict(torch.load(f"./data/{name}.pth"))
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sample("final")
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if __name__ == "__main__":
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example_usage()
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return [ Categorical(logits=logit).sample() for logit in logits ]
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