deprecate sole AR/NAR model by only keeping the AR+NAR (the beauty of no one using this is that I can break compat as much as I want), add tone token for when I classify my dataset with tone/emotion in the future, some other things

README.md

@@ -6,16 +6,10 @@
 
 An unofficial PyTorch implementation of [VALL-E](https://valle-demo.github.io/), utilizing the [EnCodec](https://github.com/facebookresearch/encodec) encoder/decoder.
 
-[Main Repo](https://git.ecker.tech/mrq/vall-e) | [GitHub Mirror](https://github.com/e-c-k-e-r/vall-e/)
-
 > **Note** Development on this is very sporadic. Gomen.
 
 ## Requirements
 
-* [`DeepSpeed`](https://github.com/microsoft/DeepSpeed#requirements):
-  - DeepSpeed training is Linux only. Installation under Windows should ignore trying to install DeepSpeed.
-  - If your config YAML has the training backend set to `deepspeed`, you will need to have a GPU that DeepSpeed has developed and tested against, as well as a CUDA or ROCm compiler pre-installed to install this package.
-
 * [`espeak-ng`](https://github.com/espeak-ng/espeak-ng/):
   - For phonemizing text, this repo requires `espeak`/`espeak-ng` installed.
   - Linux users can consult their package managers on installing `espeak`/`espeak-ng`.
@@ -24,7 +18,7 @@ An unofficial PyTorch implementation of [VALL-E](https://valle-demo.github.io/),
 
 ## Install
 
-Simply run `pip install git+https://git.ecker.tech/mrq/vall-e`.
+Simply run `pip install git+https://git.ecker.tech/mrq/vall-e` or `pip install git+https://github.com/e-c-k-e-r/vall-e`.
 
 I've tested this repo under Python versions `3.10.9` and `3.11.3`.
 
@@ -68,7 +62,7 @@ A script to setup a proper environment and train can be invoked with `./scripts/
 
 If you're interested in creating an HDF5 copy of your dataset, simply invoke: `python -m vall_e.data --action='hdf5' yaml='./data/config.yaml'`
 
-5. Train the AR and NAR models using the following scripts: `python -m vall_e.train yaml=./data/config.yaml`
+5. Train the model using the following scripts: `python -m vall_e.train yaml=./data/config.yaml`
 * If distributing your training (for example, multi-GPU), use `deepspeed --module vall_e.train yaml="./data/config.yaml"`
 
 You may quit your training any time by just entering `quit` in your CLI. The latest checkpoint will be automatically saved.
@@ -93,18 +87,23 @@ You can specify what X and Y labels you want to plot against by passing `--xs to
 
 #### Training Under Windows
 
-As training under `deepspeed` and Windows is not supported, under your `config.yaml`, simply change `trainer.backend` to `local` to use the local training backend.
+As training under `deepspeed` and Windows is not (easily) supported, under your `config.yaml`, simply change `trainer.backend` to `local` to use the local training backend.
 
-Keep in mind that creature comforts like distributed training or `float16` training cannot be verified as working at the moment.
+Keep in mind that creature comforts like distributed training or `float16` training cannot be verified as working at the moment with the local trainer.
 
 #### Training on Low-VRAM Cards
 
-During experimentation, I've found I can comfortably train on a 4070Ti (12GiB VRAM) with `trainer.deepspeed.compression_training` enabled with both the AR and NAR at a batch size of 16, albeit I feel this is mostly snakeoil. Better VRAM savings can be had with use of BitsAndBytes and their respective flags (specifically its AdamW implementation).
+During experimentation, I've found I can comfortably train on a 4070Ti (12GiB VRAM). Howver, VRAM use is predicated on your dataset; a mix of large and small utterances will cause VRAM usage to spike and can trigger OOM conditions during the backwards pass if you are not careful.
-
-VRAM use is also predicated on your dataset; a mix of large and small utterances will cause VRAM usage to spike and can trigger OOM conditions during the backwards pass if you are not careful.
 
 Additionally, under Windows, I managed to finetune the AR on my 2060 (6GiB VRAM) with a batch size of 8 (although, with the card as a secondary GPU).
 
+#### Training Caveats
+
+Unfortunately, efforts to train a *good* foundational model seems entirely predicated on a good dataset. My dataset might be too fouled with:
+* too short utterances: trying to extrapolate longer contexts seems to utterly fall apart from just the `text` being too long.
+* too tightly trimmed utterances: there being little to no space at the start and end might harm associating `<s>` and `</s>` tokens with empty utterances.
+* a poorly mapped phoneme mapping: I naively crafted my own phoneme mapping, where a HuggingFace tokenizer might supply a better token mapping.
+
 #### Backend Architectures
 
 As the core of VALL-E makes use of a language model, various LLM architectures can be supported and slotted in. Currently supported:
@@ -112,6 +111,8 @@ As the core of VALL-E makes use of a language model, various LLM architectures c
 * `transformer`: a basic attention-based transformer implementation, with attention heads + feed forwards.
 * `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.
   - Its implementation for MoE can also be utilized.
+* `retnet-hf`: using [syncdoth/RetNet/](https://github.com/syncdoth/RetNet/) with a HuggingFace-compatible RetNet model
+  - inferencing cost is about 0.5x, and MoE is not implemented.
 * `llama`: using HF transformer's LLaMa implementation for its attention-based transformer, boasting RoPE and other improvements.
 * `mixtral`: using HF transformer's Mixtral implementation for its attention-based transformer, also utilizing its MoE implementation.
 * `bitnet`: using [this](https://github.com/kyegomez/BitNet/) implementation of BitNet's transformer.
@@ -121,11 +122,11 @@ As the core of VALL-E makes use of a language model, various LLM architectures c
 
 To export the models, run: `python -m vall_e.export yaml=./data/config.yaml`.
 
-This will export the latest checkpoints, for example, under `./data/ckpt/ar-retnet-2/fp32.pth` and `./data/ckpt/nar-retnet-2/fp32.pth`, to be loaded on any system with PyTorch, and will include additional metadata, such as the symmap used, and training stats.
+This will export the latest checkpoints, for example, under `./data/ckpt/ar+nar-retnet-8/fp32.pth`, to be loaded on any system with PyTorch, and will include additional metadata, such as the symmap used, and training stats.
 
 ## Synthesis
 
-To synthesize speech, invoke either (if exported the models): `python -m vall_e <text> <ref_path> <out_path> --ar-ckpt ./models/ar.pt --nar-ckpt ./models/nar.pt` or `python -m vall_e <text> <ref_path> <out_path> yaml=<yaml_path>`
+To synthesize speech, invoke either (if exported the models): `python -m vall_e <text> <ref_path> <out_path> --model-ckpt ./data/ckpt/ar+nar-retnet-8/fp32.pth` or `python -m vall_e <text> <ref_path> <out_path> yaml=<yaml_path>`
 
 Some additional flags you can pass are:
 * `--language`: specifies the language for phonemizing the text, and helps guide inferencing when the model is trained against that language.
@@ -154,7 +155,6 @@ And some experimental sampling flags you can use too (your mileage will ***defin
 ## To-Do
 
 * train and release a ***good*** model.
-  - the current model seems to require a ***long*** time of training at a very small LR rate to try and cover a wide variety of speakers of varying acoustics.
 * clean up the README, and document, document, document onto the wiki.
 * extend to ~~multiple languages ([VALL-E X](https://arxiv.org/abs/2303.03926)) and~~ addditional tasks ([SpeechX](https://arxiv.org/abs/2308.06873)).
   - training additional tasks needs the SpeechX implementation to be reworked.
@@ -164,7 +164,7 @@ And some experimental sampling flags you can use too (your mileage will ***defin
     + this requires a properly trained AR, however.
 * work around issues with extending context past what's trained (despite RetNet's retention allegedly being able to defeat this):
   - "sliding" AR input, such as have the context a fixed length.
-    + the model may need to be trained for this with a fancy positional embedding injected. Naively sliding the context window while making use of the RetNet implementation's positional embedding doesn't seem fruitful.
+    + the model may need to be trained for this with a fancy positional embedding injected OR already trained with a sliding context window in mind. Naively sliding the context window while making use of the RetNet implementation's positional embedding doesn't seem fruitful.
 
 ## Notices and Citations
 

vall_e/__main__.py

@@ -13,8 +13,7 @@ def main():
 	parser.add_argument("--out-path", type=Path, default=None)
 
 	parser.add_argument("--yaml", type=Path, default=None)
-	parser.add_argument("--ar-ckpt", type=Path, default=None)
+	parser.add_argument("--model-ckpt", type=Path, default=None)
-	parser.add_argument("--nar-ckpt", type=Path, default=None)
 
 	parser.add_argument("--max-ar-steps", type=int, default=6 * 75)
 	parser.add_argument("--max-nar-levels", type=int, default=7)
@@ -41,7 +40,7 @@ def main():
 	parser.add_argument("--dtype", type=str, default=None)
 	args = parser.parse_args()
 
-	tts = TTS( config=args.yaml, ar_ckpt=args.ar_ckpt, nar_ckpt=args.nar_ckpt, device=args.device, dtype=args.dtype, amp=args.amp )
+	tts = TTS( config=args.yaml, model_ckpt=args.model_ckpt, device=args.device, dtype=args.dtype, amp=args.amp )
 	tts.inference(
 		text=args.text,
 		references=args.references,

vall_e/config.py

@@ -162,6 +162,9 @@ class Dataset:
 
 @dataclass()
 class Model:
+	_max_levels: int = 0
+	_embeddings: str | None = None
+
 	name: str = "" # vanity name for the model
 	version: int = 1 # 1 = old with MultiEmbedding, 2 = new with AudioEmbedding
 	size: str | dict = "full" # preset string or explicitly defined dimensionality
@@ -169,6 +172,7 @@ class Model:
 	prom_levels: int = 8 # RVQ-bin levels this model accepts as an input prompt
 	tasks: int = 8 # ["tts", "ns", "sr", "tse", "cse", "nse"] and leaves two more for anything else I want (like "svc")
 	langs: int = 1 # defined languages
+	tones: int = 1 # defined tones
 	experts: int = 1
 	arch_type: str = "retnet" # or "transformer""
 	training: bool = True # unneeded now
@@ -176,6 +180,13 @@ class Model:
 	p_ar_level: float | str = "auto" # determines odds of selecting the AR (level 0) when training, "auto" for default behavior
 	frozen_params: list[str] = field(default_factory=lambda: []) # frozen parameters that are not updated when training
 
+	def get(self, name=None):
+		return [ self ] if not name or self.name == name else []
+
+	@property
+	def max_levels(self):
+		return self._max_levels if self._max_levels > 0 else self.prom_levels
+
 	@property
 	# required for fp8 as the lengths needs to be divisible by 8
 	def input_alignment(self):
@@ -203,7 +214,7 @@ class Model:
 		if self.interleave:
 			name.append("interleaved")
 		else:
-			name.append(f'{cfg.models.prom_levels}')
+			name.append(f'{cfg.model.prom_levels}')
 
 
 		return "-".join(name)
@@ -256,58 +267,6 @@ class Model:
 	def activation_checkpointing(self):
 		return cfg.trainer.activation_checkpointing
 	
-
-@dataclass()
-class Models:
-	_max_levels: int = 0
-	_prom_levels: int = 1
-	_embeddings: str | None = None
-
-	_models: list[Model] = field(default_factory=lambda: [
-		Model(name="ar", resp_levels=1, prom_levels=8, tasks=8, langs=1, experts=1, training=True, interleave=False),
-		Model(name="nar", resp_levels=7, prom_levels=8, tasks=8, langs=1, experts=1, training=True, interleave=False),
-	])
-
-	def get(self, name=None):
-		if not name:
-			return [ Model(**model) for model in self._models ]
-
-		for model in self._models:
-			if model.name == name:
-				return model
-
-		raise ValueError
-
-	@property
-	def ar(self):
-		return self.get("ar")
-
-	@property
-	def ar_nar(self):
-		return self.get("ar+nar")
-
-	@property
-	def nar(self):
-		return self.get("nar")
-
-	@property
-	def prom_levels(self):
-		prom_levels = self._prom_levels
-		for model in self._models:
-			prom_levels = max(prom_levels, model.prom_levels)
-		return prom_levels
-
-	@property
-	def tasks(self):
-		tasks = 1
-		for model in self._models:
-			tasks = max(tasks, model.tasks)
-		return tasks
-
-	@property
-	def max_levels(self):
-		return self._max_levels if self._max_levels > 0 else self.prom_levels
-	
 @dataclass()
 class Hyperparameters:
 	batch_size: int = 8
@@ -568,7 +527,7 @@ class Config(_Config):
 	experimental: bool = False # So I can stop commenting out things when committing
 
 	dataset: Dataset = field(default_factory=lambda: Dataset)
-	models: Models = field(default_factory=lambda: Models)
+	model: Model = field(default_factory=lambda: Model)
 	hyperparameters: Hyperparameters = field(default_factory=lambda: Hyperparameters)
 	evaluation: Evaluation = field(default_factory=lambda: Evaluation)
 	trainer: Trainer = field(default_factory=lambda: Trainer)
@@ -617,7 +576,7 @@ class Config(_Config):
 
 	def format( self ):
 		self.dataset = Dataset(**self.dataset)
-		self.models = Models(**self.models)
+		self.model = Model(**self.model)
 		self.hyperparameters = Hyperparameters(**self.hyperparameters)
 		self.evaluation = Evaluation(**self.evaluation)
 		self.trainer = Trainer(**self.trainer)

vall_e/data.py

@@ -29,22 +29,27 @@ from tqdm.auto import tqdm
 
 _logger = logging.getLogger(__name__)
 
+# to-do: clean up this symmap mess
 def get_phone_symmap():
 	if cfg.dataset.use_hdf5 and 'symmap' in cfg.hdf5:
 		return json.loads( cfg.hdf5['symmap'].asstr()[()] )
 
-	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, '”': 179, '“': 180, '“ˈ': 181, '“ˌ': 182, ';ˈ': 183, ';ˌ': 184, ':ˈ': 185, '1': 186, 'rˈ': 187, 'qˈ': 188, 'ᵻˌ': 189, 'ä': 190, '̞ˌ': 191, '̞': 192, 'ũˌ': 193, 'ʑˌ': 194, 'ᵝ': 195, 'ɽ': 196, 'ʲˌ': 197, 'ᵝˌ': 198, 'ũ': 199, 'ũˈ': 200, 'äˌ': 201, 'ɕ': 202, 'ɕˌ': 203, 'ɽˌ': 204, 'çˌ': 205, '…ˌ': 206, '̞ˈ': 207, 'äˈ': 208, 'ɽˈ': 209, 'ɸˌ': 210, 'ɴ': 211, 'ɸˈ': 212, 'ɕˈ': 213, 'ɸ': 214, 'ᵝˈ': 215, 'ʲˈ': 216, 'ĩ': 217, 'çˈ': 218, 'ĩˌ': 219, 'oˌ': 220, 'eˈ': 221, 'ʍ': 222, 'eˌ': 223, 'uˌ': 224, 'ʍˌ': 225, 'uˈ': 226, 'oˈ': 227, 'aˈ': 228}
+	return {'<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, '”': 179, '“': 180, '“ˈ': 181, '“ˌ': 182, ';ˈ': 183, ';ˌ': 184, ':ˈ': 185, '1': 186, 'rˈ': 187, 'qˈ': 188, 'ᵻˌ': 189, 'ä': 190, '̞ˌ': 191, '̞': 192, 'ũˌ': 193, 'ʑˌ': 194, 'ᵝ': 195, 'ɽ': 196, 'ʲˌ': 197, 'ᵝˌ': 198, 'ũ': 199, 'ũˈ': 200, 'äˌ': 201, 'ɕ': 202, 'ɕˌ': 203, 'ɽˌ': 204, 'çˌ': 205, '…ˌ': 206, '̞ˈ': 207, 'äˈ': 208, 'ɽˈ': 209, 'ɸˌ': 210, 'ɴ': 211, 'ɸˈ': 212, 'ɕˈ': 213, 'ɸ': 214, 'ᵝˈ': 215, 'ʲˈ': 216, 'ĩ': 217, 'çˈ': 218, 'ĩˌ': 219, 'oˌ': 220, 'eˈ': 221, 'ʍ': 222, 'eˌ': 223, 'uˌ': 224, 'ʍˌ': 225, 'uˈ': 226, 'oˈ': 227, 'aˈ': 228}
-	return symmap
 
 def get_lang_symmap():
-	symmap = {
+	return {
 		"en": 0,
 		"ja": 1,
 	}
+
+def get_tone_symmap():
+	return {
+		"neutral": 0,
+	}
 	return symmap
 
 def get_task_symmap():
-	symmap = {
+	return {
 		"<tts>": 0,
 		"<tts-c>": 1,
 		"<ns>": 2,
@@ -54,7 +59,6 @@ def get_task_symmap():
 		"<mask>": 6,
 		"<eoe>": 7,
 	}
-	return symmap
 
 def _replace_file_extension(path, suffix):
 	return (path.parent / path.name.split(".")[0]).with_suffix(suffix)
@@ -237,6 +241,7 @@ class Dataset(_Dataset):
 		self.spkr_symmap = self._get_spkr_symmap()
 		self.spkr_group_symmap = self._get_spkr_group_symmap()
 		self.lang_symmap = self._get_lang_symmap()
+		self.tone_symmap = self._get_tone_symmap()
 		self.task_symmap = self._get_task_symmap()
 
 		# assert len(self.phone_symmap) < 256, "Unique token count should be [0,255] to fit within uint8"
@@ -309,11 +314,14 @@ class Dataset(_Dataset):
 	def _get_lang_symmap(self):
 		return get_lang_symmap()
 
+	def _get_tone_symmap(self):
+		return get_tone_symmap()
+
 	def _get_task_symmap(self):
 		return get_task_symmap()
 
 	"""
-	def get_task_token( self, token, levels=cfg.models.max_levels ):
+	def get_task_token( self, token, levels=cfg.model.max_levels ):
 		if not hasattr(self, "task_symmap"):
 			self.task_symmap = self._get_task_symmap()
 		return torch.Tensor([[ self.task_symmap[f'<{token}>'] for _ in range(levels) ]]).to(dtype=torch.int16)
@@ -339,7 +347,7 @@ class Dataset(_Dataset):
 		choices = set(self.paths_by_spkr_name[spkr_name]) - {ignore}
 		choices = [*choices]
 
-		# no other utterances, it'd make more sense to prune speakers with only one utterance in the validatoin step
+		# no other utterances, it'd make more sense to prune speakers with only one utterance in the validation step
 		if len(choices) == 0:
 			choices = [*set(self.paths_by_spkr_name[spkr_name])]
 			"""
@@ -622,8 +630,8 @@ class Dataset(_Dataset):
 		"""
 
 		# trim to fit to requested prom/resps levels
-		proms = proms[:, :cfg.models.prom_levels]
+		proms = proms[:, :cfg.model.prom_levels]
-		resps = resps[:, :cfg.models.prom_levels]
+		resps = resps[:, :cfg.model.prom_levels]
 
 
 		return dict(
@@ -928,7 +936,7 @@ if __name__ == "__main__":
 			if task not in cfg.dataset.tasks_list:
 				continue
 
-			print(text, task, cfg.models.prom_levels)
+			print(text, task, cfg.model.prom_levels)
 			print( proms.shape, resps.shape )
 
 			tokens = 0

vall_e/emb/qnt.py

@@ -21,7 +21,7 @@ except Exception as e:
 	cfg.inference.use_vocos = False
 
 @cache
-def _load_encodec_model(device="cuda", levels=cfg.models.max_levels):
+def _load_encodec_model(device="cuda", levels=cfg.model.max_levels):
 	# Instantiate a pretrained EnCodec model
 	assert cfg.sample_rate == 24_000
 
@@ -44,7 +44,7 @@ def _load_encodec_model(device="cuda", levels=cfg.models.max_levels):
 	return model
 
 @cache
-def _load_vocos_model(device="cuda", levels=cfg.models.max_levels):
+def _load_vocos_model(device="cuda", levels=cfg.model.max_levels):
 	assert cfg.sample_rate == 24_000
 
 	model = Vocos.from_pretrained("charactr/vocos-encodec-24khz")
@@ -66,7 +66,7 @@ def _load_vocos_model(device="cuda", levels=cfg.models.max_levels):
 	return model
 
 @cache
-def _load_model(device="cuda", vocos=cfg.inference.use_vocos, levels=cfg.models.max_levels):
+def _load_model(device="cuda", vocos=cfg.inference.use_vocos, levels=cfg.model.max_levels):
 	if vocos:
 		model = _load_vocos_model(device, levels=levels)
 	else:
@@ -80,7 +80,7 @@ def unload_model():
 
 
 @torch.inference_mode()
-def decode(codes: Tensor, device="cuda", levels=cfg.models.max_levels):
+def decode(codes: Tensor, device="cuda", levels=cfg.model.max_levels):
 	"""
 	Args:
 		codes: (b q t)
@@ -117,7 +117,7 @@ def decode(codes: Tensor, device="cuda", levels=cfg.models.max_levels):
 	return wav, model.sample_rate
 
 # huh
-def decode_to_wave(resps: Tensor, device="cuda", levels=cfg.models.max_levels):
+def decode_to_wave(resps: Tensor, device="cuda", levels=cfg.model.max_levels):
 	return decode(resps, device=device, levels=levels)
 
 def decode_to_file(resps: Tensor, path: Path, device="cuda"):
@@ -131,7 +131,7 @@ def _replace_file_extension(path, suffix):
 
 
 @torch.inference_mode()
-def encode(wav: Tensor, sr: int = 24_000, device="cuda", levels=cfg.models.max_levels):
+def encode(wav: Tensor, sr: int = 24_000, device="cuda", levels=cfg.model.max_levels):
 	"""
 	Args:
 		wav: (t)
@@ -224,7 +224,7 @@ def repeat_extend_audio( qnt, target ):
 
 # merges two quantized audios together
 # I don't know if this works
-def merge_audio( *args, device="cpu", scale=[], levels=cfg.models.max_levels ):
+def merge_audio( *args, device="cpu", scale=[], levels=cfg.model.max_levels ):
 	qnts = [*args]
 	decoded = [ decode(qnt, device=device, levels=levels)[0] for qnt in qnts ]
 

vall_e/engines/__init__.py

@@ -26,7 +26,7 @@ from functools import cache
 
 @cache
 def load_engines(training=True):
-	models = get_models(cfg.models.get(), training=training)
+	models = get_models(cfg.model.get(), training=training)
 	engines = dict()
 
 	for name, model in models.items():
@@ -145,8 +145,8 @@ def load_engines(training=True):
 		engine.freeze(freeze_all=False)
 
 		# copy embeddings if requested
-		if cfg.models._embeddings is not None:
+		if cfg.model._embeddings is not None:
-			embeddings_path = cfg.relpath / cfg.models._embeddings
+			embeddings_path = cfg.relpath / cfg.model._embeddings
 			
 			if embeddings_path.exists():
 				embeddings = torch.load(embeddings_path, map_location=torch.device(cfg.device))

vall_e/inference.py

@@ -19,7 +19,7 @@ if deepspeed_available:
 	import deepspeed
 
 class TTS():
-	def __init__( self, config=None, ar_ckpt=None, nar_ckpt=None, device=None, amp=None, dtype=None ):
+	def __init__( self, config=None, model_ckpt=None, device=None, amp=None, dtype=None ):
 		self.loading = True 
 		
 		self.input_sample_rate = 24000
@@ -53,7 +53,10 @@ class TTS():
 
 		self.symmap = None
 
-		def parse( name, model, state ):
+		if model_ckpt:
+			state = torch.load(model_ckpt)
+			self.model = get_models(cfg.model.get(), training=False)[0]
+			
 			if "userdata" in state and 'symmap' in state['userdata']:
 				self.symmap = state['userdata']['symmap']
 			elif "symmap" in state:
@@ -62,55 +65,26 @@ class TTS():
 			if "module" in state:
 				state = state['module']
 			
-			model.load_state_dict(state)
+			self.model.load_state_dict(state)
 
 			if cfg.inference.backend == "local" and deepspeed_available and cfg.trainer.deepspeed.inferencing:
-				model = deepspeed.init_inference(model=model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module
+				self.model = deepspeed.init_inference(model=self.model, mp_size=1, replace_with_kernel_inject=True, dtype=dtype if not amp else torch.float32).module
-
-			return model
-
-		if ar_ckpt and nar_ckpt:
-			self.ar_ckpt = ar_ckpt
-			self.nar_ckpt = nar_ckpt
-
-			models = get_models(cfg.models.get(), training=False)
-
-			for name, model in models.items():
-				if name.startswith("ar"):
-					state = torch.load(self.ar_ckpt)
-					self.ar = parse( name, model, state )
-				elif name.startswith("nar"):
-					state = torch.load(self.nar_ckpt)
-					self.nar = parse( name, model, state )
-					
-				if name.startswith("ar+nar"):
-					self.nar = self.ar
 		else:
-			self.load_models()
+			engines = load_engines(training=False)
+			for name, engine in engines.items():
+				self.model = engine.module
+				break
 
 		if self.dtype != torch.int8:
-			self.ar = self.ar.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
+			self.model = self.model.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
-			self.nar = self.nar.to(self.device, dtype=self.dtype if not self.amp else torch.float32)
 
-		self.ar.eval()
+		self.model.eval()
-		self.nar.eval()
 
 		if self.symmap is None:
 			self.symmap = get_phone_symmap()
 
 		self.loading = False 
 
-	def load_models( self ):
-		engines = load_engines(training=False)
-		for name, engine in engines.items():
-			if name.startswith("ar"):
-				self.ar = engine.module
-			elif name.startswith("nar"):
-				self.nar = engine.module
-
-			if name.startswith("ar+nar"):
-				self.nar = self.ar
-
 	def encode_text( self, text, language="en" ):
 		# already a tensor, return it
 		if isinstance( text, Tensor ):
@@ -193,7 +167,7 @@ class TTS():
 			lang = to_device(lang, self.device).to(torch.uint8)
 
 			with torch.autocast("cuda", dtype=self.dtype, enabled=self.amp):
-				resps_list = self.ar(
+				resps_list = self.model(
 					text_list=[phns], proms_list=[prom], lang_list=[lang], max_steps=max_ar_steps, max_resp_context=max_ar_context,
 					sampling_temperature=ar_temp,
 					sampling_min_temperature=min_ar_temp,
@@ -205,7 +179,7 @@ class TTS():
 					sampling_mirostat_eta=mirostat_eta,
 				)
 				resps_list = [r.unsqueeze(-1) for r in resps_list]
-				resps_list = self.nar(
+				resps_list = self.model(
 					text_list=[phns], proms_list=[prom], lang_list=[lang], resps_list=resps_list,
 					max_levels=max_nar_levels,
 					sampling_temperature=nar_temp,

vall_e/models/__init__.py

@@ -1,19 +1,9 @@
-from .ar import AR
-from .nar import NAR
 from .ar_nar import AR_NAR
 
 def get_model(cfg, training=True):
-	if cfg.name == "ar":
-		Model = AR
-	elif cfg.name == "nar":
-		Model = NAR
-	elif cfg.name == "ar+nar":
-		Model = AR_NAR
-	else:
-		raise f"invalid model name: {cfg.name}"
 	name = cfg.name
 
-	model = Model(
+	model = AR_NAR(
 		n_tokens=cfg.tokens,
 		d_model=cfg.dim,
 		n_heads=cfg.heads,

vall_e/models/ar.py

@@ -1,309 +0,0 @@
-from ..config import cfg
-from .base import Base, list_to_tensor, Categorical
-
-import torch
-from torch.nn.utils.rnn import pad_sequence
-
-from einops import rearrange
-from torch import Tensor
-from tqdm import trange
-
-class AR(Base):
-	@property
-	def causal(self):
-		return True
-
-	@property
-	def norm_type(self):
-		return "ln"
-
-	@property
-	def arch_type(self) -> str:
-		if hasattr(self, "config") and self.config:
-			return self.config.arch_type
-		return cfg.models.ar.arch_type
-
-	@property
-	def n_prom_levels(self) -> int:
-		return cfg.models.prom_levels
-
-	@property
-	def n_resp_levels(self) -> int:
-		if hasattr(self, "config") and self.config:
-			return self.config.resp_levels
-		return cfg.models.ar.resp_levels
-
-	@property
-	def n_max_levels(self) -> int:
-		return cfg.models.max_levels
-
-	@property
-	def n_tasks(self) -> int:
-		return cfg.models.ar.tasks
-
-	@property
-	def n_langs(self) -> int:
-		return cfg.models.ar.langs
-
-	@property
-	def recurrent_chunk_size(self) -> int:
-		if cfg.mode == "training":
-			return 0
-		return cfg.inference.recurrent_chunk_size
-
-	"""
-	@property
-	def rotary_embedding_base(self) -> float:
-		if hasattr(self, "config") and self.config:
-			return self.config.rotary_embedding_base
-		return cfg.models.ar.rotary_embedding_base
-	"""
-
-	@property
-	def interleave(self) -> bool:
-		if hasattr(self, "config") and self.config:
-			return self.config.interleave
-		return False
-
-	@property
-	def monolithic(self) -> bool:
-		return False
-
-	@property
-	def version(self) -> int:
-		if hasattr(self, "config") and self.config:
-			return self.config.version
-		return cfg.models.ar.version
-
-	def _prune(self, l: Tensor):
-		indices = (l == self.stop_token).nonzero()
-		if len(indices) == 0:
-			return l
-		return l[: indices.min().item()]
-
-	def _interleave( self, codes ):
-		if not self.interleave:
-			return codes
-
-		return codes.flatten()
-
-	def _deinterleave( self, codes, length = 0 ):
-		if not self.interleave:
-			return codes
-
-		return torch.unflatten( codes[:codes.shape[0] // self.n_prom_levels * self.n_prom_levels], 0, ( codes.shape[0] // self.n_prom_levels, self.n_prom_levels ) )
-
-	@staticmethod
-	def _unsqueeze_list(x_list, axis=-1):
-		return [x.unsqueeze(dim=axis) for x in x_list]
-
-	def forward(
-		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		resps_list: list[Tensor] | None = None,
-		lang_list: list[Tensor] | None = None,
-		max_steps: int = 1000,
-		max_resp_context: int = -1,
-
-		sampling_temperature: float = 1.0,
-		sampling_min_temperature: float = -1.0,
-		sampling_top_k: int = -100,
-		sampling_top_p: float = 1.0,
-		sampling_repetition_penalty: float = 1.0,
-		sampling_repetition_penalty_decay: float = 0.0,
-		sampling_length_penalty: float = 0.0,
-		sampling_beam_width: int = 0,
-
-		sampling_mirostat_tau: float = 0.0,
-		sampling_mirostat_eta: float = 0.1,
-	):
-		if resps_list is not None:
-			if self.interleave:
-				resps_list = [self._interleave(r) for r in resps_list]
-			else:
-				resps_list = [r[..., 0] for r in resps_list] # guarantees we only have the first levels
-
-			return super().forward(
-				text_list=text_list,
-				proms_list=proms_list,
-				resps_list=self._unsqueeze_list(resps_list),
-				targ_list=resps_list,
-				lang_list=lang_list,
-				quant_levels=None,
-			)
-
-		device = text_list[0].device
-		batch_size = len(text_list)
-
-		sequence_list = [ torch.zeros(0, device=device).to(torch.int16) for _ in text_list ]
-		stopped = torch.zeros(batch_size, device=device).bool()
-
-		recurrent_state = {} if cfg.inference.recurrent_forward else None
-		mirostat = [
-			{"n": 1024, "tau": sampling_mirostat_tau, "eta": sampling_mirostat_eta, "max_surprise": sampling_mirostat_eta * 2, "error_surprise": 0, "running_total_surprise": 0}
-		] * batch_size if sampling_mirostat_tau > 0.0 else None
-
-		sampling_beam_width_use_logs = True
-		scores = [ 1.0 ] * sampling_beam_width
-
-		if self.interleave:
-			max_steps *= self.n_prom_levels
-
-		# get next in sequence
-		for n in trange(max_steps // max(1, self.recurrent_chunk_size)):
-			if max_resp_context > 0:
-				resps_list = self._unsqueeze_list([ sequence[-max_resp_context:] for sequence in sequence_list ] )
-			else:
-				resps_list = self._unsqueeze_list(sequence_list)
-
-			logits = super().forward(
-				text_list=text_list,
-				proms_list=proms_list,
-				resps_list=resps_list,
-				
-				state=recurrent_state
-			)
-
-			r = super().sample(
-				logits=logits,
-				resps_list=resps_list,
-
-				temperature=sampling_temperature,
-				min_temperature=sampling_min_temperature,
-				top_p=sampling_top_p,
-				top_k=sampling_top_k,
-				repetition_penalty=sampling_repetition_penalty,
-				repetition_penalty_decay=sampling_repetition_penalty_decay,
-				length_penalty=sampling_length_penalty,
-				beam_width=sampling_beam_width,
-
-				mirostat=mirostat,
-			)
-
-			if mirostat is not None:
-				# r is the state
-				mirostat = r
-				# extract token from state
-				r = [ state["token"] for state in mirostat ]
-			# we do it here because the sampler will already expand our logits list
-			elif sampling_beam_width > 0:
-				# expand tuple
-				r, s = r
-				# first step, expand batch
-				if batch_size == 1:
-					batch_size *= sampling_beam_width
-					text_list = text_list * sampling_beam_width
-					proms_list = proms_list * sampling_beam_width
-					sequence_list = sequence_list * sampling_beam_width
-					stopped = torch.zeros(batch_size, device=device).bool()
-
-				# update scores
-				if sampling_beam_width_use_logs:
-					scores = [ (math.log(scores[i]) if scores[i] > 0 else 0) + math.log(score) for i, score in enumerate(s) ]
-				else:
-					scores = [ scores[i] * score for i, score in enumerate(s) ]
-
-			# append tokens
-			for i, ri in enumerate(r):
-				if self.stop_token in ri:
-					stopped[i] = True
-				sequence_list[i] = torch.cat([sequence_list[i], ri.to(device)])
-
-			# stop token found
-			stopped |= r == self.stop_token
-			if stopped.all().item():
-				break
-
-		# pick the best scoring candidate
-		# desu this is always going to be candidate 0
-		if sampling_beam_width and len(scores) > 0:
-			best_idx, best_score = (0, 0)
-			for idx, score in enumerate(scores):
-				if best_score > score:
-					best_idx, best_score = idx, score
-
-			sequence_list = [sequence_list[best_idx]]
-
-		if self.interleave:
-			sequence_list = [self._deinterleave(r) for r in sequence_list]
-		return [self._prune(r) for r in sequence_list]
-
-
-def example_usage():
-	cfg.trainer.backend = "local"
-	from functools import partial
-
-	from einops import repeat
-
-	from ..emb.qnt import decode_to_file
-	from ..engines import Engine
-	from tqdm import tqdm
-	from ..utils import wrapper as ml
-
-	device = "cuda"
-	x8 = partial(repeat, pattern="t -> t l", l=cfg.models.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()
-
-	qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
-
-	text_list = [
-		#torch.tensor([1, 2, 3], device=device),
-		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),
-	]
-	proms_list = [
-		#x8(torch.tensor([1, 2, 3], device=device)),
-		qnt.to(device),
-	]
-	resps_list = [
-		qnt.to(device),
-	]
-
-	text_list = text_list[:1]
-	proms_list = proms_list[:1]
-	resps_list = resps_list[:1]
-
-	kwargs = {
-		'n_tokens': 1024,
-		'd_model': 1024,
-		'n_heads': 16,
-		'n_layers': 24,
-	}
-
-	"""	
-	try:
-		kwargs['config'] = cfg.models.ar
-	except Exception as e:
-		pass
-	"""	
-
-	model = AR(**kwargs).to(device)
-	steps = 500
-	optimizer = ml.Prodigy(model.parameters(), lr=1.0)
-	engine = Engine(model=model, optimizer=optimizer)
-	
-	def sample( name, steps=600 ):
-		engine.eval()
-		out = engine(text_list, proms_list, max_steps=steps)
-		for i, o in enumerate(out):
-			wav, sr = decode_to_file(o, f"data/ar.{i}.{name}.wav", device=device)
-
-	def train():
-		engine.train()
-		t = trange(steps)
-		for i in t:
-			stats = {"step": i}
-			stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
-
-			tqdm.write(f"{stats}")
-
-	sample("init", 75)
-	train()
-	sample("final")
-
-if __name__ == "__main__":
-	example_usage()

vall_e/models/ar_nar.py

@@ -25,29 +25,33 @@ class AR_NAR(Base):
 	def arch_type(self) -> str:
 		if hasattr(self, "config") and self.config:
 			return self.config.arch_type
-		return cfg.models.ar_nar.arch_type
+		return cfg.model.arch_type
 
 	@property
 	def n_prom_levels(self) -> int:
-		return cfg.models.prom_levels
+		return cfg.model.prom_levels
 
 	@property
 	def n_resp_levels(self) -> int:
 		if hasattr(self, "config") and self.config:
 			return self.config.resp_levels
-		return cfg.models.ar_nar.resp_levels
+		return cfg.model.resp_levels
 
 	@property
 	def n_max_levels(self) -> int:
-		return cfg.models.max_levels
+		return cfg.model.max_levels
 
 	@property
 	def n_tasks(self) -> int:
-		return cfg.models.ar_nar.tasks
+		return cfg.model.tasks
-
+	
 	@property
 	def n_langs(self) -> int:
-		return cfg.models.ar_nar.langs
+		return cfg.model.langs
+
+	@property
+	def n_tones(self) -> int:
+		return cfg.model.tones
 
 	@property
 	def recurrent_chunk_size(self) -> int:
@@ -58,7 +62,7 @@ class AR_NAR(Base):
 	def rotary_embedding_base(self) -> float:
 		if hasattr(self, "config") and self.config:
 			return self.config.rotary_embedding_base
-		return cfg.models.ar_nar.rotary_embedding_base
+		return cfg.model.rotary_embedding_base
 	"""
 
 	@property
@@ -73,7 +77,7 @@ class AR_NAR(Base):
 	def version(self) -> int:
 		if hasattr(self, "config") and self.config:
 			return self.config.version
-		return cfg.models.ar_nar.version
+		return cfg.model.version
 
 	def _prune(self, l: Tensor):
 		indices = (l == self.stop_token).nonzero()
@@ -92,6 +96,7 @@ class AR_NAR(Base):
 		resps_list: list[Tensor] | None = None,
 		
 		lang_list: list[Tensor] | None = None,
+		tone_list: list[Tensor] | None = None,
 
 		max_steps: int = 1000,
 		max_levels: int = 0,
@@ -134,10 +139,10 @@ class AR_NAR(Base):
 				else:
 					quant_levels = torch.randint(0, self.n_resp_levels, (batch_size,)) # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
 					"""
-					if cfg.models.ar_nar.p_ar_level == "auto" or cfg.models.ar_nar.p_ar_level is None:
+					if cfg.model.p_ar_level == "auto" or cfg.model.p_ar_level is None:
 						quant_levels = torch.randint(0, self.n_resp_levels, (batch_size,)) # randomly select a target RVQ-bin level (0 being AR, 1+ being NAR)
 					else:
-						quant_levels = torch.Tensor([ 0 if random.random() < cfg.models.ar_nar.p_ar_level else random.randint(1, self.n_resp_levels) for _ in range(batch_size) ])
+						quant_levels = torch.Tensor([ 0 if random.random() < cfg.model.p_ar_level else random.randint(1, self.n_resp_levels) for _ in range(batch_size) ])
 					"""
 
 				targ_list = [r[..., l] for r, l in zip(resps_list, quant_levels)] # ensures we only have 1 RVQ-bin (our target)
@@ -162,6 +167,7 @@ class AR_NAR(Base):
 					resps_list=resps_list,
 					targ_list=targ_list,
 					lang_list=lang_list,
+					tone_list=tone_list,
 					quant_levels=quant_levels,
 				)
 			# is NAR
@@ -182,6 +188,7 @@ class AR_NAR(Base):
 					proms_list=proms_list,
 					resps_list=prev_list,
 					lang_list=lang_list,
+					tone_list=tone_list,
 					quant_levels=quant_levels,
 				)
 
@@ -234,6 +241,7 @@ class AR_NAR(Base):
 					proms_list=proms_list,
 					resps_list=resps_list,
 					lang_list=lang_list,
+					tone_list=tone_list,
 					state=recurrent_state
 				)
 			else:
@@ -242,6 +250,7 @@ class AR_NAR(Base):
 					proms_list=proms_list,
 					resps_list=resps_list,
 					lang_list=lang_list,
+					tone_list=tone_list,
 					state=recurrent_state
 				)
 
@@ -312,14 +321,14 @@ def example_usage():
 	import re
 
 	device = "cuda"
-	x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels) 
+	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)])
 
-	qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
+	qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.model.prom_levels].to(device)
 
 	cfg.hyperparameters.gradient_accumulation_steps = 1
 
@@ -359,7 +368,7 @@ def example_usage():
 	
 	"""
 	try:
-		kwargs['config'] = cfg.models.ar_nar
+		kwargs['config'] = cfg.model
 	except Exception as e:
 		pass
 	"""
@@ -374,8 +383,8 @@ def example_usage():
 
 	# copy embeddings if requested
 	"""
-	if cfg.models._embeddings is not None:
+	if cfg.model._embeddings is not None:
-		embeddings_path = cfg.relpath / cfg.models._embeddings
+		embeddings_path = cfg.relpath / cfg.model._embeddings
 		
 		if embeddings_path.exists():
 			embeddings = torch.load(embeddings_path, map_location=torch.device(cfg.device))

vall_e/models/base.py

@@ -262,11 +262,15 @@ class Base(nn.Module):
 	@property
 	def n_langs(self) -> int:
 		raise NotImplementedError
-
+	
 	@property
 	def n_tasks(self) -> int:
 		raise NotImplementedError
 
+	@property
+	def n_tones(self) -> int:
+		raise NotImplementedError
+
 	@property
 	def recurrent_chunk_size(self) -> int:
 		raise NotImplementedError
@@ -343,6 +347,7 @@ class Base(nn.Module):
 
 		self.text_emb = Embedding(n_tokens, d_model)
 		self.langs_emb = None
+		self.tones_emb = None
 		self.tasks_emb = None
 
 		if self.version == 1: # legacy
@@ -359,6 +364,9 @@ class Base(nn.Module):
 		if self.version >= 3:
 			self.langs_emb = Embedding(self.n_langs, d_model) if self.n_langs > 0 else None
 			self.tasks_emb = Embedding(self.n_tasks, d_model) if self.n_tasks > 0 else None
+		
+		if self.version >= 4:
+			self.tones_emb = Embedding(self.n_tones, d_model) if self.n_tones > 0 else None
 
 		self.sep = nn.Parameter(torch.randn(d_model))
 
@@ -522,53 +530,15 @@ class Base(nn.Module):
 			ignore_index=self.ignore_index,
 		)
 
-	def forward(
+	def _forward(
 		self,
-		text_list: list[Tensor],
+		inputs,
-		proms_list: list[Tensor],
+		mask = None,
-		resps_list: list[Tensor],
+		state = None,
-		targ_list: list[Tensor] | None = None,
-		
-		lang_list: list[Tensor] | None = None,
-
-		quant_levels: Tensor | None = None,
-		state: dict | list | None = None,
 	):
-		batch_size = len(text_list)
+		x = inputs
-
+		m = mask.squeeze(-1).int()
-		if self.langs_emb is None:
-			lang_list = None
-
-		x_list = self._samplewise_merge_tensors(
-			self.text_emb(text_list),
-			self.langs_emb(lang_list) if lang_list is not None else None,
-			self.proms_emb(proms_list),
-			self.resps_emb(resps_list, quant_levels),
-			sep=self.sep,
-		)
-
-
-		x, m = list_to_tensor(x_list)
 		aux_loss = None
-		
-		device = x.device
-		
-		# pad our input and mask, but retain the original length by doing it after
-		if self.l_padding and x.shape[1] % self.l_padding != 0:
-			# pad input
-			shape = list(x.shape)
-			shape[1] = self.l_padding - shape[1] % self.l_padding
-
-			padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
-			x = torch.cat([x, padding], dim=1)
-
-			# pad mask
-			shape[2] = 1
-			padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
-			m = torch.cat([m, padding], dim=1)
-
-		# for simplicity
-		mask  = m.squeeze(-1).int()
 
 		"""
 		# Broken
@@ -587,7 +557,7 @@ class Base(nn.Module):
 						xi = x[:, n, :].unsqueeze(1)
 
 						kwargs = dict(
-							attention_mask=mask,
+							attention_mask=m,
 							inputs_embeds=xi,
 							past_key_values=state,
 							use_cache=True,
@@ -603,9 +573,9 @@ class Base(nn.Module):
 		"""
 		
 		# HF transformer derived model
-		if self.arch_type == "llama" or self.arch_type == "mistral" or self.arch_type == "mixtral":
+		if self.arch_type in ["llama", "mistral", "mixtral"]:
 			kwargs = dict(
-				attention_mask=mask,
+				attention_mask=m,
 				inputs_embeds=x,
 				past_key_values=state,
 				use_cache=True,
@@ -632,7 +602,7 @@ class Base(nn.Module):
 			x = self.sin_emb.add_pe(x)
 			# pass our inputs through the transformer
 			for block in self.blocks:
-				x = block(x, mask, l)
+				x = block(x, m, l)
 		elif self.arch_type == "retnet":
 			# pass our inputs through the RetNet
 			x, _ = self.model(x, incremental_state=state, token_embeddings=x, features_only=True)
@@ -642,7 +612,7 @@ class Base(nn.Module):
 			first = state is None or len(state) == 0
 
 			kwargs = dict(
-				attention_mask=mask,
+				attention_mask=m,
 				inputs_embeds=x if first else x[:, -1, :].unsqueeze(1),
 				past_key_values=None if first else state,
 				use_cache=True,
@@ -659,8 +629,76 @@ class Base(nn.Module):
 			x = self.model(x)
 
 		# output projection layer with masking
+		x = self.classifier(x) * mask
 
-		x = self.classifier(x) * m
+		return x, state, aux_loss
+
+	def forward(
+		self,
+		text_list: list[Tensor],
+		proms_list: list[Tensor],
+		resps_list: list[Tensor],
+		targ_list: list[Tensor] | None = None,
+		
+		lang_list: list[Tensor] | None = None,
+		tone_list: list[Tensor] | None = None,
+
+		quant_levels: Tensor | None = None,
+		state: dict | list | None = None,
+	):
+		device = text_list[0].device
+		batch_size = len(text_list)
+
+		# silently ignore languages if model does not have it
+		if self.langs_emb is None:
+			lang_list = None
+		# inject default language 
+		elif lang_list is None:
+			lang_list = [ torch.Tensor([ 0 ]).to(dtype=torch.uint8, device=device) for _ in range(batch_size) ]
+		
+		# silently ignore tones if model does not have it
+		if self.tones_emb is None:
+			tone_list = None
+		# inject default tone 
+		elif tone_list is None:
+			tone_list = [ torch.Tensor([ 0 ]).to(dtype=torch.uint8, device=device) for _ in range(batch_size) ]
+
+		"""
+		# Typical sequence format
+		# To-do: integrate tasks again
+		<s><text></s><sep><lang><sep><prom><sep><tone><sep><resp><stop>
+		"""
+		x_list = self._samplewise_merge_tensors(
+			self.text_emb(text_list),
+			self.langs_emb(lang_list) if lang_list is not None else None,
+			self.proms_emb(proms_list),
+			self.tones_emb(tone_list) if tone_list is not None else None,
+			self.resps_emb(resps_list, quant_levels),
+			sep=self.sep,
+		)
+
+		x, m = list_to_tensor(x_list)
+		
+		# pad our input and mask, but retain the original length by doing it after
+		if self.l_padding and x.shape[1] % self.l_padding != 0:
+			# pad input
+			shape = list(x.shape)
+			shape[1] = self.l_padding - shape[1] % self.l_padding
+
+			padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
+			x = torch.cat([x, padding], dim=1)
+
+			# pad mask
+			shape[2] = 1
+			padding = torch.zeros(shape, dtype=x.dtype, device=x.device)
+			m = torch.cat([m, padding], dim=1)
+
+
+		x, state, aux_loss = self._forward(
+			inputs=x,
+			mask=m,
+			state=state,
+		)
 
 		# Remove padding
 		logits = [ hi[:li] for hi, li in zip(x, map(len, x_list)) ]
@@ -790,7 +828,7 @@ def example_usage():
 	from .nar import NAR
 
 	device = "cuda"
-	x8 = partial(repeat, pattern="t -> t l", l=cfg.models.prom_levels) 
+	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(" ")
@@ -812,7 +850,7 @@ def example_usage():
 
 	train = True
 
-	qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
+	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),

vall_e/models/nar.py

@@ -1,235 +0,0 @@
-from ..config import cfg
-from .base import Base
-
-import torch
-
-from torch import Tensor
-from tqdm import trange
-
-class NAR(Base):
-	@property
-	def causal(self):
-		return False
-
-	@property
-	def arch_type(self) -> str:
-		if hasattr(self, "config") and self.config:
-			return self.config.arch_type
-		return cfg.models.nar.arch_type
-
-	@property
-	def norm_type(self):
-		return "ln" if self.n_resp_levels == 1 else "adaln"
-
-	@property
-	def n_prom_levels(self) -> int:
-		return cfg.models.prom_levels
-
-	@property
-	def n_resp_levels(self) -> int:
-		if hasattr(self, "config") and self.config:
-			return self.config.resp_levels
-		return cfg.models.nar.resp_levels
-
-	@property
-	def n_max_levels(self) -> int:
-		return cfg.models.max_levels
-
-	@property
-	def n_tasks(self) -> int:
-		return cfg.models.nar.tasks
-
-	@property
-	def n_langs(self) -> int:
-		return cfg.models.nar.langs
-
-	@property
-	def version(self) -> int:
-		if hasattr(self, "config") and self.config:
-			return self.config.version
-		return cfg.models.nar.version
-
-	@property
-	def recurrent_chunk_size(self) -> int:
-		return 0
-
-	"""
-	@property
-	def rotary_embedding_base(self) -> float:
-		if hasattr(self, "config") and self.config:
-			return self.config.rotary_embedding_base
-		return cfg.models.nar.rotary_embedding_base
-	"""
-
-	@property
-	def interleave(self) -> bool:
-		return False
-
-	@property
-	def monolithic(self) -> bool:
-		return False
-
-	def forward(
-		self,
-		text_list: list[Tensor],
-		proms_list: list[Tensor],
-		resps_list: list[Tensor],
-		lang_list: list[Tensor] | None = None,
-		max_levels: int = 0,
-		sampling_temperature: float = 0.2,
-		sampling_min_temperature: float = -1.0,
-		sampling_top_k: int = -100,
-		sampling_top_p: float = 1.0,
-		sampling_repetition_penalty: float = 1.0,
-		sampling_repetition_penalty_decay: float = 0.0,
-		sampling_length_penalty: float = 0.0, # unused
-		sampling_beam_width: int = 0, # unused
-		sampling_mirostat_tau: float = 0.0, # unused
-	):
-		"""
-		Args:
-			text_list: [t] * b
-			proms_list: [t' l] * b, l=8
-			resps_list: [t'' l] * b, l=1 or 8, 1 for testing and 8 for training.
-		Returns:
-			[t'' l], l=8 if testing. empty list will be returned during training.
-		"""
-
-		n_levels_set = {r.shape[-1] for r in resps_list}
-
-		if len(n_levels_set) > 1:
-			raise ValueError(f"Please give only one level, got {n_levels_set}.")
-
-		n_levels = next(iter(n_levels_set))
-
-		device = text_list[0].device
-
-		if n_levels == self.n_resp_levels + 1:
-			assert resps_list is not None
-
-			quant_levels = torch.randint(0, self.n_resp_levels, (len(resps_list),))
-
-			prev_list = [o[..., : l + 1] for o, l in zip(resps_list, quant_levels)]
-			targ_list = [o[..., l + 1] for o, l in zip(resps_list, quant_levels)]
-
-			#quant_levels = quant_levels.to(device=device)
-
-			logits = super().forward(
-				text_list=text_list,
-				proms_list=proms_list,
-				resps_list=prev_list,
-				targ_list=targ_list,
-				lang_list=lang_list,
-				quant_levels=quant_levels,
-			)
-
-			prev_list = []
-		else:
-			prev_list = resps_list
-			if max_levels == 0:
-				max_levels = self.n_resp_levels
-
-			while True:
-				level = prev_list[0].shape[-1] - 1
-
-				if level >= max_levels: # min(max_levels, self.n_resp_levels): # commented out to experiment with exceeding trained levels
-					break
-
-				quant_levels = torch.full((len(text_list),), level, device=device)
-
-				logits = super().forward(
-					text_list=text_list,
-					proms_list=proms_list,
-					resps_list=prev_list,
-					lang_list=lang_list,
-					quant_levels=quant_levels,
-				)
-
-				resps_list = super().sample(
-					logits=logits,
-					resps_list=prev_list,
-					quant_levels=quant_levels,
-
-					temperature=sampling_temperature,
-					min_temperature=sampling_min_temperature,
-					top_p=sampling_top_p,
-					top_k=sampling_top_k,
-					repetition_penalty=sampling_repetition_penalty,
-					repetition_penalty_decay=sampling_repetition_penalty_decay,
-					#length_penalty=sampling_length_penalty,
-					#beam_width=sampling_beam_width,
-					#mirostat_tau=sampling_mirostat_tau,
-					#mirostat_state=mirostat_state,
-				)
-
-				prev_list = [ torch.cat([rs, r.unsqueeze(-1).to(device)], dim=-1) for rs, r in zip(prev_list, resps_list) ]
-
-		return prev_list
-
-def example_usage():
-	cfg.trainer.backend = "local"
-	from functools import partial
-
-	from einops import repeat
-
-	from ..emb.qnt import decode_to_file
-	from ..engines import Engine
-	from tqdm import tqdm
-	from ..utils import wrapper as ml
-
-	device = "cuda"
-	x8 = partial(repeat, pattern="t -> t l", l=cfg.models.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()
-
-	# to-do: unmangle this and the resp shit
-	qnt = torch.load("data/qnt.pt")[0].t()[:, :cfg.models.prom_levels].to(device)
-
-	text_list = [
-		#torch.tensor([1, 2, 3], device=device),
-		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),
-	]
-
-	proms_list = [
-		x8(torch.tensor([2, 3], device=device)),
-	]
-
-	resps_list = [
-		qnt.to(device),
-	]
-
-	kwargs = {
-		'n_tokens': 1024,
-		'd_model': 1024,
-		'n_heads': 16,
-		'n_layers': 12,
-	}
-	model = NAR(**kwargs).to(device)
-	steps = 500
-	optimizer = ml.Prodigy(model.parameters(), lr=1.0)
-	engine = Engine(model=model, optimizer=optimizer)
-
-	def sample( name ):
-		engine.eval()
-		codes = engine( text_list, proms_list, resps_list=[r[..., 0].unsqueeze(-1) for r in resps_list], sampling_temperature=0.2 )
-		decode_to_file( codes[0], f"data/nar.{name}.wav", device )
-
-	def train():
-		engine.train()
-		t = trange(steps)
-		for i in t:
-			stats = {"step": i}
-			stats |= engine.traverse(text_list=text_list, proms_list=proms_list, resps_list=resps_list)
-
-			tqdm.write(f"{stats}")
-
-	sample("init")
-	train()
-	sample("final")
-
-
-if __name__ == "__main__":
-	example_usage()

vall_e/plot.py

@@ -109,7 +109,7 @@ if __name__ == "__main__":
 	path = cfg.relpath / "logs"
 	paths = path.rglob(f"./*/{args.filename}")
 
-	args.models = [ model for model in cfg.models.get() if model.training and (args.model == "*" or model.name in args.model) ]
+	args.models = [ model for model in cfg.model.get() if model.training and (args.model == "*" or model.name in args.model) ]
 
 	if args.ys == "":
 		args.ys = ["loss"]

vall_e/webui.py

@@ -54,14 +54,13 @@ def init_tts(restart=False):
 	
 	parser = argparse.ArgumentParser(allow_abbrev=False)
 	parser.add_argument("--yaml", type=Path, default=os.environ.get('VALLE_YAML', None)) # os environ so it can be specified in a HuggingFace Space too
-	parser.add_argument("--ar-ckpt", type=Path, default=None)
+	parser.add_argument("--model-ckpt", type=Path, default=None)
-	parser.add_argument("--nar-ckpt", type=Path, default=None)
 	parser.add_argument("--device", type=str, default="cuda")
 	parser.add_argument("--amp", action="store_true")
 	parser.add_argument("--dtype", type=str, default="auto")
 	args, unknown = parser.parse_known_args()
 
-	tts = TTS( config=args.yaml, ar_ckpt=args.ar_ckpt, nar_ckpt=args.nar_ckpt, device=args.device, dtype=args.dtype if args.dtype != "auto" else None, amp=args.amp )
+	tts = TTS( config=args.yaml, model_ckpt=args.model_ckpt, device=args.device, dtype=args.dtype if args.dtype != "auto" else None, amp=args.amp )
 	return tts
 
 @gradio_wrapper(inputs=layout["inference"]["inputs"].keys())