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README.md

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+# Usage
+Clone repo
+```bash
+git clone https://github.com/nguyenhoanganh2002/XTTSv2-Finetuning-for-New-Languages.git
+cd XTTSv2-Finetuning-for-New-Languages
+pip install -r requirements.txt
+```
+
+Pull model's weights
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(repo_id="anhnh2002/vnTTS",
+                  repo_type="model",
+                  local_dir="model/")
+```
+
+Load model
+```python
+from pprint import pprint
+import torch
+import torchaudio
+from tqdm import tqdm
+from underthesea import sent_tokenize
+from vinorm import TTSnorm
+from TTS.tts.configs.xtts_config import XttsConfig
+from TTS.tts.models.xtts import Xtts
+
+device = "cuda:0"
+
+xtts_checkpoint = "model/model.pth"
+xtts_config = "model/config.json"
+xtts_vocab = "model/vocab.json"
+
+config = XttsConfig()
+config.load_json(xtts_config)
+XTTS_MODEL = Xtts.init_from_config(config)
+XTTS_MODEL.load_checkpoint(config,
+                            checkpoint_path=xtts_checkpoint,
+                            vocab_path=xtts_vocab,
+                            use_deepspeed=False)
+XTTS_MODEL.to(device)
+```
+
+Preprocessing and chunking
+```python
+def preprocess_text(text, language="vi"):
+    if language == "vi":
+        text = TTSnorm(text)
+    
+    # split text into sentences
+    if language in ["ja", "zh-cn"]:
+        sentences = text.split("。")
+    else:
+        sentences = sent_tokenize(text)
+
+    chunks = []
+    chunk_i = ""
+    len_chunk_i = 0
+    for sentence in sentences:
+        chunk_i += " " + sentence
+        len_chunk_i += len(sentence.split())
+        if len_chunk_i > 30:
+            chunks.append(chunk_i.strip())
+            chunk_i = ""
+            len_chunk_i = 0
+
+    if (len(chunks) > 0) and (len_chunk_i < 15):
+        chunks[-1] += chunk_i
+    else:
+        chunks.append(chunk_i)
+
+    return chunks
+```
+
+Generate latent embeddings for the speaker
+```python
+speaker_audio_file = "model/vi_man.wav"
+
+gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(
+    audio_path=speaker_audio_file,
+    gpt_cond_len=XTTS_MODEL.config.gpt_cond_len,
+    max_ref_length=XTTS_MODEL.config.max_ref_len,
+    sound_norm_refs=XTTS_MODEL.config.sound_norm_refs,
+)
+```
+
+Inference
+```python
+def tts(
+    model: Xtts,
+    text: str,
+    language: str,
+    gpt_cond_latent: torch.Tensor,
+    speaker_embedding: torch.Tensor,
+    verbose: bool = False,
+):
+    # preprocess text
+    chunks = preprocess_text(text, language)
+
+    wav_chunks = []
+    for text in tqdm(chunks):
+        if text.strip() == "":
+            continue
+        wav_chunk = model.inference(
+            text=text,
+            language=language,
+            gpt_cond_latent=gpt_cond_latent,
+            speaker_embedding=speaker_embedding,
+            length_penalty=1.0,
+            repetition_penalty=10.0,
+            top_k=10,
+            top_p=0.5,
+        )
+
+        wav_chunk["wav"] = torch.tensor(wav_chunk["wav"])
+
+        wav_chunks.append(wav_chunk["wav"])
+
+    out_wav = torch.cat(wav_chunks, dim=0).unsqueeze(0).cpu()
+
+    return out_wav
+
+from IPython.display import Audio
+
+audio = tts(
+    model=XTTS_MODEL,
+    text="Xin chào, tôi là một hệ thống chuyển đổi văn bản tiếng Việt thành giọng nói.", #Hello, I am a Vietnamese text to speech conversion system.
+    language="vi",
+    gpt_cond_latent=gpt_cond_latent,
+    speaker_embedding=speaker_embedding,
+    verbose=True,
+)
+
+Audio(audio, rate=24000)
+```
+
+# License
+This project uses a model licensed under the Coqui Public Model License 1.0.0, which permits non-commercial use only. This includes personal research, testing, and charitable purposes. Commercial entities may use it for non-commercial research and evaluation. Revenue-generating activities are prohibited. Users must include the license terms when distributing the model or its outputs. For full details, please refer to: https://coqui.ai/cpml