Update README.md

README.md

@@ -8,4 +8,156 @@ base_model:
 - Qwen/Qwen3-0.6B
 pipeline_tag: text-to-speech
 library_name: transformers
----
+---
+## Overview
+VyvoTTS-v0-Qwen3-0.6B is a Text-to-Speech model based on Qwen3-0.6B, trained to produce natural-sounding English speech.
+
+- **Type:** Text-to-Speech  
+- **Language:** English  
+- **License:** MIT  
+- **Params:** ~810M  
+
+> **Note:** This model has a high Word Error Rate (WER) as it was trained on a 10,000-hour dataset. To improve the model's accuracy, you should use it as a pretrained base.
+> I can recommend the Emilia dataset for this purpose. After the pretraining process is complete, you should perform fine-tuning for single-speaker speech.
+
+## Usage
+Below is an example of using the model with `unsloth` and `SNAC` for speech generation:
+
+```python
+from unsloth import FastLanguageModel
+import torch
+from snac import SNAC
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/orpheus-3b-0.1-ft",
+    max_seq_length= 2048,
+    dtype = None,
+    load_in_4bit = False,
+)
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+tokeniser_length = 151669
+start_of_text = 151643
+end_of_text = 151645
+
+start_of_speech = tokeniser_length + 1
+end_of_speech = tokeniser_length + 2
+start_of_human = tokeniser_length + 3
+end_of_human = tokeniser_length + 4
+pad_token = tokeniser_length + 7
+
+audio_tokens_start = tokeniser_length + 10
+prompts = ["Hey there my name is Elise, and I'm a speech generation model that can sound like a person."]
+chosen_voice = None
+
+FastLanguageModel.for_inference(model)
+snac_model.to("cpu")
+prompts_ = [(f"{chosen_voice}: " + p) if chosen_voice else p for p in prompts]
+
+all_input_ids = []
+for prompt in prompts_:
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    all_input_ids.append(input_ids)
+
+start_token = torch.tensor([[start_of_human]], dtype=torch.int64)
+end_tokens = torch.tensor([[end_of_text, end_of_human]], dtype=torch.int64)
+
+all_modified_input_ids = []
+for input_ids in all_input_ids:
+    modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1)
+    all_modified_input_ids.append(modified_input_ids)
+
+all_padded_tensors, all_attention_masks = [], []
+max_length = max([m.shape[1] for m in all_modified_input_ids])
+for m in all_modified_input_ids:
+    padding = max_length - m.shape[1]
+    padded_tensor = torch.cat([torch.full((1, padding), pad_token, dtype=torch.int64), m], dim=1)
+    attention_mask = torch.cat([torch.zeros((1, padding), dtype=torch.int64), torch.ones((1, m.shape[1]), dtype=torch.int64)], dim=1)
+    all_padded_tensors.append(padded_tensor)
+    all_attention_masks.append(attention_mask)
+
+input_ids = torch.cat(all_padded_tensors, dim=0).to("cuda")
+attention_mask = torch.cat(all_attention_masks, dim=0).to("cuda")
+
+generated_ids = model.generate(
+    input_ids=input_ids,
+    attention_mask=attention_mask,
+    max_new_tokens=1200,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.95,
+    repetition_penalty=1.1,
+    num_return_sequences=1,
+    eos_token_id=end_of_speech,
+    use_cache=True
+)
+
+token_to_find = start_of_speech
+token_to_remove = end_of_speech
+token_indices = (generated_ids == token_to_find).nonzero(as_tuple=True)
+
+if len(token_indices[1]) > 0:
+    last_occurrence_idx = token_indices[1][-1].item()
+    cropped_tensor = generated_ids[:, last_occurrence_idx+1:]
+else:
+    cropped_tensor = generated_ids
+
+processed_rows = []
+for row in cropped_tensor:
+    masked_row = row[row != token_to_remove]
+    processed_rows.append(masked_row)
+
+code_lists = []
+for row in processed_rows:
+    row_length = row.size(0)
+    new_length = (row_length // 7) * 7
+    trimmed_row = row[:new_length]
+    trimmed_row = [t - audio_tokens_start for t in trimmed_row]
+    code_lists.append(trimmed_row)
+
+def redistribute_codes(code_list):
+    layer_1, layer_2, layer_3 = [], [], []
+    for i in range((len(code_list)+1)//7):
+        layer_1.append(code_list[7*i])
+        layer_2.append(code_list[7*i+1]-4096)
+        layer_3.append(code_list[7*i+2]-(2*4096))
+        layer_3.append(code_list[7*i+3]-(3*4096))
+        layer_2.append(code_list[7*i+4]-(4*4096))
+        layer_3.append(code_list[7*i+5]-(5*4096))
+        layer_3.append(code_list[7*i+6]-(6*4096))
+    codes = [
+        torch.tensor(layer_1).unsqueeze(0),
+        torch.tensor(layer_2).unsqueeze(0),
+        torch.tensor(layer_3).unsqueeze(0)
+    ]
+    audio_hat = snac_model.decode(codes)
+    return audio_hat
+
+my_samples = []
+for code_list in code_lists:
+    samples = redistribute_codes(code_list)
+    my_samples.append(samples)
+
+from IPython.display import display, Audio
+if len(prompts) != len(my_samples):
+    raise Exception("Number of prompts and samples do not match")
+else:
+    for i in range(len(my_samples)):
+        print(prompts[i])
+        samples = my_samples[i]
+        display(Audio(samples.detach().squeeze().to("cpu").numpy(), rate=24000))
+
+del my_samples, samples
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{VyvoTTS-v0-Qwen3-0.6B,
+  title={VyvoTTS-v0-Qwen3-0.6B},
+  author={Vyvo},
+  year={2025},
+  howpublished={\url{https://huggingface.co/Vyvo/VyvoTTS-v0-Qwen3-0.6B}}
+}
+```