Update README.md

README.md

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 ---
 license: mit
+datasets:
+- doof-ferb/infore1_25hours
 ---
+<div align="center">
+  <div>&nbsp;</div>
+  <img src="logo.png" width="300"/> <br>
+  <a href="https://trendshift.io/repositories/8133" target="_blank"><img src="https://trendshift.io/api/badge/repositories/8133" alt="myshell-ai%2FMeloTTS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+</div>
+
+## Introduction
+MeloTTS Vietnamese is a version of MeloTTS optimized for the Vietnamese language. This version inherits the high-quality characteristics of the original model but has been specially adjusted to work well with the Vietnamese language.
+
+## Technical Features
+- Uses [underthesea](https://github.com/undertheseanlp/underthesea) for Vietnamese text segmentation
+- Integrates [PhoBert](https://github.com/VinAIResearch/PhoBERT) (vinai/phobert-base-v2) to extract Vietnamese language features
+- Fully supports Vietnamese language characteristics:
+  - 45 symbols (phonemes)
+  - 8 tones (7 tonal marks and 1 unmarked tone)
+  - All defined in `melo/text/symbols.py`
+- Text-to-phoneme conversion source:
+  - Based on [Text2PhonemeSequence](https://github.com/thelinhbkhn2014/Text2PhonemeSequence) library
+  - An improved version with higher performance has been developed at [Text2PhonemeFast](https://github.com/manhcuong02/Text2PhonemeFast)
+
+## Fine-tuning from Base Model
+This model was fine-tuned from the base MeloTTS model by:
+- Replacing phonemes not found in English and Vietnamese with Vietnamese phonemes
+- Specifically replacing Korean phonemes with corresponding Vietnamese phonemes
+- Adjusting parameters to match Vietnamese phonetic characteristics
+
+## Training Data
+- The model was trained on the Infore dataset, consisting of approximately 25 hours of speech
+- Note on data quality: This dataset has several limitations including poor voice quality, lack of punctuation, and inaccurate phonetic transcriptions. However, when trained on internal data, the results were much better.
+
+## Downloading the Model
+The pre-trained model can be downloaded from Hugging Face:
+- [MeloTTS Vietnamese on Hugging Face](https://huggingface.co/nmcuong/MeloTTS_Vietnamese)
+
+## Usage Guide
+
+### Data Preparation
+The data preparation process is detailed in `docs/training.md`. Basically, you need:
+- Audio files (recommended to use 44100Hz format)
+- Metadata file with the format:
+  ```
+  path/to/audio_001.wav |<speaker_name>|<language_code>|<text_001>
+  path/to/audio_002.wav |<speaker_name>|<language_code>|<text_002>
+  ```
+
+### Data Preprocessing
+To process data, use the command:
+```bash
+python melo/preprocess_text.py --metadata /path/to/text_training.list --config_path /path/to/config.json --device cuda:0 --val-per-spk 10 --max-val-total 500
+```
+or use the script `melo/preprocess_text.sh` with appropriate parameters.
+
+### Using the Model
+Refer to the notebook `test_infer.ipynb` to learn how to use the model:
+```python
+# colab_infer.py
+from melo.api import TTS
+
+# Speed is adjustable
+speed = 1.0
+
+# CPU is sufficient for real-time inference.
+# You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps'
+device = "cuda:0"  # Will automatically use GPU if available
+
+# English
+model = TTS(
+    language="VI",
+    device=device,
+    config_path="/path/to/config.json",
+    ckpt_path="/path/to/G_model.pth",
+)
+speaker_ids = model.hps.data.spk2id
+
+# Convert text to speech
+text = "Nhập văn bản tại đây"
+speaker_ids = model.hps.data.spk2id
+output_path = "output.wav"
+model.tts_to_file(text, speaker_ids["speaker_name"], output_path, speed=1.0, quiet=True)
+```
+
+## Audio Examples
+Listen to sample outputs from the model:
+
+### Sample Audio
+<audio controls>
+  <source src="samples/sample.wav" type="audio/wav">
+  Your browser does not support the audio element.
+</audio>
+
+## License
+This project follows the MIT License, like the original MeloTTS project, allowing use for both commercial and non-commercial purposes.
+
+## Acknowledgements
+
+This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.