---
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.