add JVS and VCTK models

models/tts/tungnaa_117_jvs.ckpt

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models/tts/tungnaa_117_jvs.md

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+---
+block_size: 2048
+sample_rate: 44100
+latent_size: 12
+vocoder: "042-jvs-100m-xfermulti_0abe2b072b_streaming_norm.ts"
+dataset: "John Van Stan (LibriTTS)"
+vocoder_type: "RAVE"
+alignment_type: "DCA"
+likelihood_type: "NSF"
+text_encoder_type: "CANINE"
+---
+
+# tungnaa_116_jvs
+
+### dimensions
+
+block size: 2048
+
+sample rate: 44100
+
+latent size: 12
+
+### dataset
+
+JVS (Hi-Fi TTS speaker 9017)
+
+### vocoder
+
+`models/vocoder/042-jvs-100m-xfermulti_0abe2b072b_streaming_norm.ts`
+
+### training
+
+tungnaa commit  `09ecdcd532eac3d454a8b4e28e896bca5bccbf9f`
+
+```bash
+tungnaa trainer --experiment 117-jvs-e2emulti-mask-ends --model-dir /data/users/victor/ivoice-models --log-dir /data/users/victor/ivoice-logs --manifest /data/users/victor/tmp/ivoice_prep_100m_0abe_multi/9017_manifest_clean_train.json --rave-model /data/users/victor/rave-v2/runs/042-jvs-100m-xfermulti_0abe2b072b/version_0/checkpoints/042-jvs-100m-xfermulti_0abe2b072b_streaming_norm.ts --lr 3e-4 --lr-text 3e-5 --epoch-size 200 --save-epochs 20 --device cuda:0 train 
+```
+
+### notes
+
+trained with full JVS dataset, no annotations.
+
+uses a 12-dimensional vocoder trained with a subset of JVS, fine tuned from a multivoice model.
+
+this model uses a neural spline flow likelihood.

models/tts/tungnaa_119_vctk.ckpt

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models/tts/tungnaa_119_vctk.md

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+---
+block_size: 2048
+sample_rate: 48000
+latent_size: 11
+vocoder: "046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc_streaming_norm.ts"
+dataset: "VCTK"
+vocoder_type: "RAVE"
+alignment_type: "DCA"
+likelihood_type: "NSF"
+text_encoder_type: "CANINE"
+---
+
+# tungnaa_119_vctk
+
+### dimensions
+
+block size: 2048
+
+sample rate: 48000
+
+latent size: 11
+
+### dataset
+
+VCTK
+
+### vocoder
+
+`models/vocoder/046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc_streaming_norm.ts`
+
+
+### training
+
+```bash
+tungnaa prep --datasets '{kind:"vctk", path:"/data/datasets/VCTK"}' --rave-path /data/users/victor/rave-v2/runs/046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc/version_0/checkpoints/046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc_streaming_norm.ts --out-path /data/users/victor/tmp/ivoice_prep_824a/
+
+tungnaa trainer --experiment 119-vctk --model-dir /data/users/victor/ivoice-models --log-dir /data/users/victor/ivoice-logs --manifest /data/users/victor/tmp/ivoice_prep_824a/vctk.json --concat-speakers 2 --speaker-annotate --device cuda:1 --batch-size 32 --rave-model /data/users/victor/rave-v2/runs/046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc/version_0/checkpoints/046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc_streaming_norm.ts --lr 3e-4 --lr-text 3e-5 --epoch-size 200 --save-epochs 20 train
+```
+
+### notes
+
+trained with concatation of utterance pairs plus speaker annotations. example syntax: `[p225] this is an utterance. [p330] this is another.`
+
+uses a multi-dataset vocoder which was *not* fine tuned to only VCTK, so it should have a lot of play in the latent biases.
+
+this model uses a neural spline flow likelihood.

models/vocoder/042-jvs-100m-xfermulti_0abe2b072b_streaming_norm.ts

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models/vocoder/046-multivoice-2048-48k-vlobeta-specdis-noise_824a15d4dc_streaming_norm.ts

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