small cleanups

README.md

@@ -44,7 +44,7 @@ Training is very dependent on:
 
 ### Pre-Processed Dataset
 
-A "libre" dataset can be found [here](https://huggingface.co/ecker/vall-e) under `data.tar.gz`.
+A "libre" dataset utilizing EnCodec quantized audio can be found [here](https://huggingface.co/ecker/vall-e) under `data.tar.gz`.
 
 A script to setup a proper environment and train can be invoked with `./scripts/setup-training.sh`
 
@@ -52,6 +52,8 @@ A script to setup a proper environment and train can be invoked with `./scripts/
 
 > **Note** Preparing a dataset is a bit messy.
 
+If you already have a dataset you want, for example your own large corpus, or for finetuning, you can use your own dataset instead.
+
 0. Set up a `venv` with `https://github.com/m-bain/whisperX/`.
   + At the moment only WhisperX is utilized. Using other variants like `faster-whisper` is an exercise left to the user at the moment.
   + It's recommended to use a dedicated virtualenv specifically for transcribing, as WhisperX will break a few dependencies.
@@ -84,7 +86,7 @@ Two dataset formats are supported:
   - this will shove everything into a single HDF5 file and store some metadata alongside (for now, the symbol map generated, and text/audio lengths)
   - be sure to also define `use_hdf5` in your config YAML.
 
-### Initializing Training
+### Training
 
 For single GPUs, simply running `python3 -m vall_e.train yaml="./training/config.yaml`.
 
@@ -114,6 +116,7 @@ Keep in mind that creature comforts like distributed training or `float16` train
 
 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.
+  + It might help to, instead, initially train with smaller utterances, train for two epochs, then increase the each sample length.
 * 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.
 
@@ -129,7 +132,7 @@ As the core of VALL-E makes use of a language model, various LLM architectures c
 * `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.
-  - Setting `bitsandbytes.bitnet=True` will make use of BitNet's linear implementation.
+  - Setting `cfg.optimizers.bitnet=True` will make use of BitNet's linear implementation.
 
 If you're training a true foundational model, consider which backend you want to use the most. `llama` backends can benefit from all the additional tech with it, while exotic ones like `retnet` or `bitnet` can't at the moment, but may leverage experimental gains.
 
@@ -173,6 +176,7 @@ And some experimental sampling flags you can use too (your mileage will ***defin
 * 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.
+  - this requires a good foundational model before extending it to transfer tasks onto.
 * improve throughput (despite peaking at 120it/s):
   - properly utilize RetNet's recurrent forward / chunkwise forward passes (does not seem to want to work no matter how the model is trained).
   - utilize an approach similar to [FasterDecoding/Medusa](https://github.com/FasterDecoding/Medusa/) with additional heads for decoding N+1, N+2, N+3 AR tokens

setup.py

@@ -44,7 +44,7 @@ setup(
         "encodec>=0.1.1",
         "phonemizer>=2.1.0",
         "matplotlib>=3.6.0",
-        "numpy==1.23.0",
+        "numpy",
         "omegaconf==2.0.6",
         "tqdm>=4.64.1",
         "humanize>=4.4.0",
@@ -60,6 +60,8 @@ setup(
         "h5py",
         "torchscale @ git+https://git.ecker.tech/mrq/torchscale",
         "prodigyopt @ git+https://github.com/konstmish/prodigy",
+
+        "descript-audio-codec",
     ],
     url="https://git.ecker.tech/mrq/vall-e",
 )

vall_e/config.py

@@ -512,10 +512,10 @@ class Inference:
 	use_encodec: bool = True
 	use_dac: bool = True
 
+	# shit that doesn't work
 	recurrent_chunk_size: int = 0
 	recurrent_forward: bool = False
 
-
 	@cached_property
 	def dtype(self):
 		if self.weight_dtype == "float16":
@@ -562,7 +562,7 @@ class Config(_Config):
 	tokenizer: str = "./tokenizer.json"
 
 	sample_rate: int = 24_000
-	variable_sample_rate: bool = True
+	variable_sample_rate: bool = True # for DAC, this will override the model automatically resampling to 44KHz.
 
 	@property
 	def distributed(self):

vall_e/data.py

@@ -370,9 +370,8 @@ class Dataset(_Dataset):
 
 		# shuffle it up a bit
 		prom_length = 0
-		if cfg.experimental:
-			trim_length = random.randint(cfg.dataset.frames_per_second * 3, cfg.dataset.frames_per_second * 6) # [3 seconds, 6 seconds]
-			#trim_length =  max(2, int(np.random.normal(loc=5, scale=1.25) * cfg.dataset.frames_per_second))
+		if cfg.experimental and False:
+			trim_length =  max(2, int(np.random.normal(loc=5, scale=1.25) * cfg.dataset.frames_per_second))
 		else:
 			trim_length =  int(cfg.dataset.prompt_duration * cfg.dataset.frames_per_second) + random.randint(-cfg.dataset.frames_per_second, cfg.dataset.frames_per_second)
 

vall_e/models/ar_nar.py

@@ -125,7 +125,7 @@ class AR_NAR(Base):
 			if n_levels == self.n_resp_levels:
 				# might be better to have this decided on the dataloader level
 
-				if cfg.experimental and False:
+				if cfg.experimental:
 					# makes higher levels less likely
 					def generate( lo=0, hi=8 ):
 						index = lo

vall_e/models/base.py

@@ -197,7 +197,7 @@ try:
 except Exception as e:
 	print("Error creating `LLamaXformersAttention`:", e)
 
-def replace_attention( model, impl, verbose=Valse ):
+def replace_attention( model, impl, verbose=False ):
 	device = next(model.parameters()).device
 	dtype = next(model.parameters()).dtype
 	attentions = [k.split('.') for k, m in model.named_modules() if isinstance(m, LlamaAttention)]