diff --git a/README.md b/README.md
index 75106d1..ca7942d 100755
--- a/README.md
+++ b/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).
-
-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.
+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.
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 `` and `` 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 --ar-ckpt ./models/ar.pt --nar-ckpt ./models/nar.pt` or `python -m vall_e yaml=`
+To synthesize speech, invoke either (if exported the models): `python -m vall_e --model-ckpt ./data/ckpt/ar+nar-retnet-8/fp32.pth` or `python -m vall_e yaml=`
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
diff --git a/vall_e/__main__.py b/vall_e/__main__.py
index 27faaef..5ccb1cc 100755
--- a/vall_e/__main__.py
+++ b/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("--nar-ckpt", type=Path, default=None)
+ parser.add_argument("--model-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,
diff --git a/vall_e/config.py b/vall_e/config.py
index cef0bec..aa8d075 100755
--- a/vall_e/config.py
+++ b/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)
diff --git a/vall_e/data.py b/vall_e/data.py
index 4472afe..8984a94 100755
--- a/vall_e/data.py
+++ b/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 = {'': 1, '': 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
+ return {'': 1, '': 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}
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 {
"": 0,
"": 1,
"": 2,
@@ -54,7 +59,6 @@ def get_task_symmap():
"": 6,
"": 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]
- resps = resps[:, :cfg.models.prom_levels]
+ proms = proms[:, :cfg.model.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
diff --git a/vall_e/emb/qnt.py b/vall_e/emb/qnt.py
index a86af5e..242d852 100755
--- a/vall_e/emb/qnt.py
+++ b/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 ]
diff --git a/vall_e/engines/__init__.py b/vall_e/engines/__init__.py
index 2ae2fef..31e2551 100755
--- a/vall_e/engines/__init__.py
+++ b/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:
- embeddings_path = cfg.relpath / cfg.models._embeddings
+ if cfg.model._embeddings is not None:
+ embeddings_path = cfg.relpath / cfg.model._embeddings
if embeddings_path.exists():
embeddings = torch.load(embeddings_path, map_location=torch.device(cfg.device))
diff --git a/vall_e/inference.py b/vall_e/inference.py
index 0b72024..e7c32b2 100755
--- a/vall_e/inference.py
+++ b/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
-
- 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
+ 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
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.nar = self.nar.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.ar.eval()
- self.nar.eval()
+ self.model.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,
diff --git a/vall_e/models/__init__.py b/vall_e/models/__init__.py
index 0e4566e..5979e44 100755
--- a/vall_e/models/__init__.py
+++ b/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,
diff --git a/vall_e/models/ar.py b/vall_e/models/ar.py
deleted file mode 100755
index d58ef26..0000000
--- a/vall_e/models/ar.py
+++ /dev/null
@@ -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 = {'': 1, '': 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""] + [ " " if not p else p for p in split ] + [f""]
- 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()
diff --git a/vall_e/models/ar_nar.py b/vall_e/models/ar_nar.py
index 0ad074e..fe0fa5e 100644
--- a/vall_e/models/ar_nar.py
+++ b/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 = {'': 1, '': 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""] + [ " " if not p else p for p in split ] + [f""]
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:
- embeddings_path = cfg.relpath / cfg.models._embeddings
+ if cfg.model._embeddings is not None:
+ embeddings_path = cfg.relpath / cfg.model._embeddings
if embeddings_path.exists():
embeddings = torch.load(embeddings_path, map_location=torch.device(cfg.device))
diff --git a/vall_e/models/base.py b/vall_e/models/base.py
index cb514a2..1103e7f 100755
--- a/vall_e/models/base.py
+++ b/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],
- proms_list: list[Tensor],
- resps_list: list[Tensor],
- targ_list: list[Tensor] | None = None,
-
- lang_list: list[Tensor] | None = None,
-
- quant_levels: Tensor | None = None,
- state: dict | list | None = None,
+ inputs,
+ mask = None,
+ state = None,
):
- batch_size = len(text_list)
-
- 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)
+ x = inputs
+ m = mask.squeeze(-1).int()
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
+ ': 1, '': 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),
diff --git a/vall_e/models/nar.py b/vall_e/models/nar.py
deleted file mode 100755
index bb18f3a..0000000
--- a/vall_e/models/nar.py
+++ /dev/null
@@ -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 = {'': 1, '': 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""] + [ " " if not p else p for p in split ] + [f""]
- 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()
diff --git a/vall_e/plot.py b/vall_e/plot.py
index 294a256..0c1b61c 100644
--- a/vall_e/plot.py
+++ b/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"]
diff --git a/vall_e/webui.py b/vall_e/webui.py
index a613ef3..580c331 100644
--- a/vall_e/webui.py
+++ b/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("--nar-ckpt", type=Path, default=None)
+ parser.add_argument("--model-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())