Shortcutting a baseline dataset for VALL-E

README.md

@@ -0,0 +1,14 @@
+# LibriSpeech-Finetuning for VALL-E
+
+Included is a dataset I've prepared for training with [my fork of a VALL-E implementation](https://git.ecker.tech/mrq/vall-e), sourced from [LibriSpeech-Finetuning](https://dl.fbaipublicfiles.com/librilight/data/librispeech_finetuning.tgz).
+
+>\> What makes this different?
+
+I've trimmed them down to better train against them, as too large of a piece of data will increase VRAM use drastically:
+* I re-transcribed using [m-bain/WhisperX](https://github.com/m-bain/whisperX/)'s large-v2 model and using the VAD filter to get near-perfect timestamps.
+* I then bias the start by -0.05 seconds, and the ends by 0.05 seconds).
+* very short segments are merged with preceding ones to avoid fragmenting too much
+* the source audio is then sliced according to each segment, and each segment gets phonemized using [bootphon/phonemizer](https://github.com/bootphon/phonemizer/) (espeak backend).
+* finally, the sliced audio is quantized using Encodec, for VALL-E's use.
+
+This will help alleviate problems from the default `max_phoneme` length ignoring a large chunk of the dataset, and relatively evenly distributing lengths.

config.yaml

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+data_dirs: [./training/libritts-small/valle/]
+spkr_name_getter: "lambda p: p.parts[-1].split('-')[0]"
+
+models: [ 'ar-half', 'nar-half' ]
+
+optimizer: Adamw
+
+max_phones: 256
+
+batch_size: 16
+gradient_accumulation_steps: 2
+eval_batch_size: 1
+save_ckpt_every: 100
+eval_every: 500
+max_iter: 100000
+
+warmup_max_lr: 2.e-4
+warmup_num_steps: 3000
+

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