ryota-komatsu
/

SylReg-Decoder-Base

@@ -1,77 +1,75 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ## Training Details
@@ -79,121 +77,14 @@ Use the code below to get started with the model.
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 ---
 library_name: transformers
+language:
+- en
+license: mit
 ---
+# SylReg-Decoder Base
 <!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
 <!-- Provide a longer summary of what this model is. -->
+- **Model type:** Flow-matching-based Diffusion Transformer (DiT) with BigVGAN-v2
+- **Language(s) (NLP):** English
+- **License:** MIT
+### Model Sources
 <!-- Provide the basic links for the model. -->
+- **Repository:** [Code](https://github.com/ryota-komatsu/speaker_disentangled_hubert)
+- **Demo:** [Project page](https://ryota-komatsu.github.io/speaker_disentangled_hubert)
+## How to Get Started with the Model
+Use the code below to get started with the model.
+```sh
+git clone https://github.com/ryota-komatsu/speaker_disentangled_hubert.git
+cd speaker_disentangled_hubert
+sudo apt install git-lfs  # for UTMOS
+conda create -y -n py310 -c pytorch -c nvidia -c conda-forge python=3.10.19 pip=24.0 faiss-gpu=1.12.0
+conda activate py310
+pip install -r requirements/requirements.txt
+sh scripts/setup.sh
+```
+```python
+import re
+import torch
+import torchaudio
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from src.flow_matching import FlowMatchingWithBigVGan
+from src.s5hubert.models.sylreg import SylRegForSyllableDiscovery
+wav_path = "/path/to/wav"
+# download pretrained models from hugging face hub
+encoder = SylRegForSyllableDiscovery.from_pretrained("ryota-komatsu/SylReg-Distill", device_map="cuda")
+decoder = FlowMatchingWithBigVGan.from_pretrained("ryota-komatsu/SylReg-Decoder-Base", device_map="cuda")
+# load a waveform
+waveform, sr = torchaudio.load(wav_path)
+waveform = torchaudio.functional.resample(waveform, sr, 16000)
+# encode a waveform into syllabic units
+outputs = encoder(waveform.to(encoder.device))
+units = outputs[0]["units"]  # [3950, 67, ..., 503]
+# unit-to-speech synthesis
+generated_speech = decoder(units.unsqueeze(0)).waveform.cpu()
+```
 ## Training Details
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+|  | License | Provider |
+| --- | --- | --- |
+| [LibriTTS-R](https://www.openslr.org/141/) | CC BY 4.0 | Y. Koizumi *et al.* |
+### Training Hyperparameters
+- **Training regime:** fp16 mixed precision
+## Hardware
+2 x A6000