Update README.md

README.md

@@ -1,9 +1,13 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-nc-sa-4.0
+language:
+- en
+base_model:
+- ryota-komatsu/sylreg-decoder-base
 ---
 
-# Model Card for Model ID
+# SylReg-Decoder
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -15,63 +19,62 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Model type:** Flow-matching-based Diffusion Transformer (DiT) with BigVGAN-v2
+- **Language(s) (NLP):** English
+- **License:** CC BY-NC-SA 4.0
+- **Finetuned from model:** [SylReg-Decoder Base](https://huggingface.co/ryota-komatsu/sylreg-decoder-base)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **Repository:** [Code](https://github.com/ryota-komatsu/speaker_disentangled_hubert)
+- **Demo:** [Project page](https://ryota-komatsu.github.io/speaker_disentangled_hubert)
 
-[More Information Needed]
+## How to Get Started with the Model
 
-### Downstream Use [optional]
+Use the code below to get started with the model.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```sh
+git clone https://github.com/ryota-komatsu/speaker_disentangled_hubert.git
+cd speaker_disentangled_hubert
 
-[More Information Needed]
+sudo apt install git-lfs  # for UTMOS
 
-### Out-of-Scope Use
+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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+sh scripts/setup.sh
+```
 
-[More Information Needed]
+```python
+import re
 
-## Bias, Risks, and Limitations
+import torch
+import torchaudio
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+from src.flow_matching import FlowMatchingWithBigVGan
+from src.s5hubert.models.sylreg import SylRegForSyllableDiscovery
 
-[More Information Needed]
+wav_path = "/path/to/wav"
 
-### Recommendations
+# 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", device_map="cuda")
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+# load a waveform
+waveform, sr = torchaudio.load(wav_path)
+waveform = torchaudio.functional.resample(waveform, sr, 16000)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+# encode a waveform into syllabic units
+outputs = encoder(waveform.to(encoder.device))
+units = outputs[0]["units"]  # [3950, 67, ..., 503]
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+# unit-to-speech synthesis
+generated_speech = decoder(units.unsqueeze(0)).waveform.cpu()
+```
 
 ## Training Details
 
@@ -79,121 +82,15 @@ Use the code below to get started with the model.
 
 <!-- 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. -->
 
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+|  | License | Provider |
+| --- | --- | --- |
+| [LibriTTS-R](https://www.openslr.org/141/) | CC BY 4.0 | Y. Koizumi *et al.* |
+| [Hi-Fi-CAPTAIN](https://ast-astrec.nict.go.jp/en/release/hi-fi-captain/) | CC BY-NC-SA 4.0 | T. Okamoto *et al.* |
 
-## Model Card Authors [optional]
+### Training Hyperparameters
 
-[More Information Needed]
+- **Training regime:** fp16 mixed precision
 
-## Model Card Contact
+## Hardware
 
-[More Information Needed]
+2 x A6000