added option to specify frames per second for the given audio representation (Encodec is 75Hz, DAC is 41Hz (at 24K sources))

vall_e/config.py

@@ -156,6 +156,14 @@ class Dataset:
 	sample_type: str = "path" # path | speaker
 	
 	tasks_list: list[str] = field(default_factory=lambda: ["tts"])
+	
+	_frames_per_second: int = 0 # in encodec, each frame is 75 codes, in dac, each frame is 41
+
+	@cached_property
+	def frames_per_second(self):
+		if self._frames_per_second > 0:
+			return self._frames_per_second
+		return 41 if cfg.inference.audio_backend == "dac" else 75
 
 	@property
 	def min_phones(self):

vall_e/data.py

@@ -63,10 +63,10 @@ def _replace_file_extension(path, suffix):
 	return (path.parent / path.name.split(".")[0]).with_suffix(suffix)
 
 def _get_quant_extension():
-	return ".dac"
+	return ".dac" if cfg.inference.audio_backend == "dac" else ".qnt.pt"
 
 def _get_phone_extension():
-	return ".json"
+	return ".json" if cfg.inference.audio_backend == "dac" else ".phn.txt"
 
 def _get_quant_path(path):
 	return _replace_file_extension(path, _get_quant_extension())
@@ -371,10 +371,10 @@ class Dataset(_Dataset):
 		# shuffle it up a bit
 		prom_length = 0
 		if cfg.experimental:
-			trim_length = random.randint(75 * 3, 75 * 6) # [3 seconds, 6 seconds]
+			trim_length = random.randint(cfg.dataset.frames_per_second * 3, cfg.dataset.frames_per_second * 6) # [3 seconds, 6 seconds]
-			#trim_length =  max(2, int(np.random.normal(loc=5, scale=1.25) * 75))
+			#trim_length =  max(2, int(np.random.normal(loc=5, scale=1.25) * cfg.dataset.frames_per_second))
 		else:
-			trim_length =  int(cfg.dataset.prompt_duration * 75) + random.randint(-75, 75)
+			trim_length =  int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second) + random.randint(-cfg.dataset.frames_per_second, cfg.dataset.frames_per_second)
 
 		for _ in range(cfg.dataset.max_prompts):
 			path = random.choice(choices)
@@ -470,7 +470,7 @@ class Dataset(_Dataset):
 					resps = torch.concat([ resps, qnt ])
 		
 		task = "tts"
-		trim_length = int(cfg.dataset.prompt_duration * 75)
+		trim_length = int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second)
 		proms = self.sample_prompts(spkr_name, ignore=path) if random.random() < cfg.dataset.random_utterance else resps
 
 
@@ -484,7 +484,7 @@ class Dataset(_Dataset):
 			task = "tts"
 		noise_scale = 0.25
 		if task == "tts" or task == "tts-c":
-			trim_length = int(cfg.dataset.prompt_duration * 75)
+			trim_length = int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second)
 			# demote if the target is too short
 			if task == "tts-c" and trim_length * 2 >= resps.shape[0]:
 				task = "tts"
@@ -805,7 +805,7 @@ def create_dataset_metadata( skip_existing=True ):
 						}
 					else:
 						qnt = torch.load(f'{root}/{name}/{id}{_get_quant_extension()}')[0].t()
-						duration = qnt.shape[0] / 75
+						duration = qnt.shape[0] / cfg.dataset.frames_per_second
 
 					metadata[id]["duration"] = duration
 				else:
@@ -912,7 +912,7 @@ def create_dataset_hdf5( skip_existing=True ):
 						}
 					else:
 						qnt = torch.load(f'{root}/{name}/{id}{_get_quant_extension()}')[0].t()
-						duration = qnt.shape[0] / 75
+						duration = qnt.shape[0] / cfg.dataset.frames_per_second
 					
 					qnt = qnt.numpy().astype(np.int16)
 

vall_e/inference.py

@@ -115,7 +115,7 @@ class TTS():
 		res = torch.cat([qnt.encode_from_file(path)[0][:, :].t().to(torch.int16) for path in paths])
 		
 		if trim_length:
-			res = trim( res, int( 75 * trim_length ) )
+			res = trim( res, int( cfg.dataset.frames_per_second * trim_length ) )
 		
 		return res
 
@@ -125,7 +125,7 @@ class TTS():
 		text,
 		references,
 		language="en",
-		max_ar_steps=6 * 75,
+		max_ar_steps=6 * cfg.dataset.frames_per_second,
 		max_ar_context=-1,
 		max_nar_levels=7,
 		input_prompt_length=0.0,

vall_e/models/ar_nar.py

@@ -150,7 +150,7 @@ class AR_NAR(Base):
 				
 				"""
 				if cfg.experimental:
-					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
+					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
 				"""
 				
 				# append stop tokens for AR
@@ -350,7 +350,7 @@ def example_usage():
 		tokenize("ˈaɪ wɪl nˌɑːt ˈæsk ɐ sˈɛkənd tˈaɪm").to(device),
 	]
 	proms_list = [
-		qnt[:75, :].to(device),
+		qnt[:cfg.dataset.frames_per_second, :].to(device),
 	]
 	resps_list = [
 		qnt.to(device),

vall_e/models/base.py

@@ -873,98 +873,4 @@ class Base(nn.Module):
 			return res, scores
 
 		# and sample
-		return [ Categorical(logits=logit).sample() for logit in logits ]
+		return [ Categorical(logits=logit).sample() for logit in logits ]
-
-def example_usage():
-	from ..config import cfg
-	cfg.trainer.backend = "local"
-	cfg.trainer.check_for_oom = False
-
-	from functools import partial
-
-	from einops import repeat
-
-	from ..emb.qnt import decode_to_file
-	from ..engines import Engine, Engines
-	from tqdm import tqdm, trange
-	from ..utils import wrapper as ml
-
-	from .ar import AR
-	from .nar import NAR
-
-	device = "cuda"
-	x8 = partial(repeat, pattern="t -> t l", l=cfg.model.prom_levels) 
-	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}
-	def tokenize(content, lang_marker="en"):
-		split = content.split(" ")
-		phones = [f"<s>"] + [ " " if not p else p for p in split ] + [f"</s>"]
-		return torch.tensor([*map(symmap.get, phones)]).to()
-
-	kwargs = {
-		'n_tokens': 1024,
-		'd_model': 1024,
-		'n_heads': 16,
-		'n_layers': 12,
-	}
-	models = { "ar": AR(**kwargs).to(device), "nar": NAR(**kwargs).to(device) }
-	
-	for name, model in models.items():
-		print(f"{name} parameter count: {sum(p.numel() for p in model.parameters() if p.requires_grad)}")
-
-	engines = Engines({ name: Engine(model=model, optimizer=ml.AdamW(model.parameters(), lr=1e-4)) for name, model in models.items() })
-
-	train = True
-
-	qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.model.prom_levels].to(device)
-	text_list = [
-		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),
-		#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),
-	]
-
-	proms_list = [
-		qnt.to(device),
-	]
-	resps_list = [
-		qnt.to(device),
-	]
-	
-	def sample( name, steps=600 ):
-		AR = None
-		NAR = None
-
-		engines.eval()
-		for name, engine in engines.items():
-			if name[:2] == "ar":
-				AR = engine
-			elif name[:3] == "nar":
-				NAR = engine
-
-		resps_list = AR(text_list, proms_list, max_steps=steps, sampling_temperature=1.0)
-		resps_list = [r.unsqueeze(-1) for r in resps_list]		
-		codes = NAR( text_list, proms_list, resps_list=resps_list, sampling_temperature=0.2 ) 
-
-		decode_to_file(resps_list[0], f"./data/ar.{name}.wav", device=device)
-		decode_to_file(codes[0], f"./data/ar+nar.{name}.wav", device=device)
-	
-	if train:
-		sample("init", 15)
-
-		engines.train()
-		t = trange(500)
-		for i in t:
-			stats = {"step": i}
-			"""
-			for name, engine in engines.items():
-				stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
-			"""
-			stats = engines.step({"text_list": text_list, "proms_list": proms_list, "resps_list": resps_list})
-			tqdm.write(f"{stats}")
-	else:
-		for name, engine in engines.items():
-			engine.module.load_state_dict(torch.load(f"./data/{name}.pth"))
-
-	sample("final")
-	
-
-if __name__ == "__main__":
-	example_usage()