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README.md

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----
-license: other
-license_name: vpl
-license_link: https://viralpubliclicense.org/
----
+---
+language: en
+license: other
+tags:
+  - milady
+  - orpheus
+  - text-to-speech
+  - tts
+library_name: transformers
+base_model: unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit
+pipeline_tag: text-to-speech
+---
+
+# Volady
+
+This model provides Milady-styled speech synthesis, capturing the distinctive voice patterns and personality of Milady NFT characters in an audio format.
+
+## Intended Use
+
+The Milady Talks model is designed to:
+- Generate speech in the unique Milady voice style
+- Create playful and creative speech responses to text prompts
+- Emulate Milady's distinctive personality through speech
+
+## Model Information
+
+This model is a fine-tuned version of the Orpheus 3B TTS model, specialized for Milady-style speech generation. It uses SNAC (Speech Neural Audio Codec) for high-quality audio synthesis.
+
+The model was fine-tuned on a curated dataset of Milady-style speech examples from [FakeYou](https://fakeyou.com/) under `Pikachu` voice by `Vegito1089`. 
+
+
+## Installation
+
+First, install the required packages using uv (recommended for faster installation):
+
+```bash
+uv pip install torch torchaudio snac transformers
+```
+
+Or using standard pip:
+
+```bash
+pip install torch torchaudio snac transformers
+```
+
+## Usage
+
+### Sample Code
+
+Here's a complete script to generate speech with the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import torchaudio
+from snac import SNAC
+import os
+
+# Load the model and tokenizer
+print("Loading model...")
+model = AutoModelForCausalLM.from_pretrained(
+    "theycallmeloki/volady",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("theycallmeloki/volady")
+
+# Load SNAC model for audio decoding
+print("Loading SNAC model...")
+snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+snac_model = snac_model.to("cpu")  # CPU is more reliable for audio processing
+
+# Define your prompt
+test_prompt = "Hello there, my name is milady. I am a neural network trained to generate speech."
+print(f"Generating speech for prompt: '{test_prompt}'")
+
+# Prepare input for generation with special tokens
+input_ids = tokenizer(test_prompt, return_tensors="pt").input_ids
+start_token = torch.tensor([[128259]], dtype=torch.int64)  # Start of human
+end_tokens = torch.tensor([[128009, 128260]], dtype=torch.int64)  # End of text, End of human
+
+# Concatenate tokens
+modified_input_ids = torch.cat([start_token, input_ids, end_tokens], dim=1)
+attention_mask = torch.ones_like(modified_input_ids)
+
+# Move to appropriate device
+device = model.device
+modified_input_ids = modified_input_ids.to(device)
+attention_mask = attention_mask.to(device)
+
+# Generate speech tokens
+print("Generating tokens...")
+generated_ids = model.generate(
+    input_ids=modified_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=128258,
+    use_cache=True
+)
+
+# Process the generated tokens
+token_to_find = 128257  # Start of speech token
+token_to_remove = 128258  # End of speech token
+
+# Find the speech section
+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:]
+    print(f"Found speech section starting at position {last_occurrence_idx}")
+else:
+    cropped_tensor = generated_ids
+    print("Warning: No speech section found")
+
+# Remove end of speech token
+row = cropped_tensor[0]
+masked_row = row[row != token_to_remove]
+
+# Ensure length is divisible by 7
+row_length = masked_row.size(0)
+new_length = (row_length // 7) * 7
+trimmed_row = masked_row[:new_length]
+print(f"Processed audio tokens: length={new_length}")
+
+# Adjust token values
+code_list = [t.item() - 128266 for t in trimmed_row]
+
+# Redistribute codes for audio synthesis
+def redistribute_codes(code_list):
+    if len(code_list) == 0:
+        print("No audio codes generated")
+        return None
+        
+    print(f"Redistributing {len(code_list)} codes")
+    layer_1 = []
+    layer_2 = []
+    layer_3 = []
+    
+    for i in range(len(code_list) // 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)
+    ]
+    
+    print("Decoding audio with SNAC...")
+    audio_hat = snac_model.decode(codes)
+    return audio_hat
+
+# Generate audio
+audio_samples = redistribute_codes(code_list)
+
+if audio_samples is not None:
+    # Save the audio to a WAV file
+    output_path = "milady_speech.wav"
+    audio_numpy = audio_samples.detach().squeeze().numpy()
+    # The sampling rate of 24000 Hz is crucial for correct playback speed
+    torchaudio.save(output_path, torch.tensor(audio_numpy).unsqueeze(0), 24000)
+    print(f"Audio saved to {output_path}")
+else:
+    print("Failed to generate audio")
+```
+
+Save this script as `generate_speech.py` and run:
+
+```bash
+python generate_speech.py
+```
+
+This will generate a speech file named `milady_speech.wav` in the current directory.
+
+### Customizing Prompts
+
+To use your own text prompt, modify the `test_prompt` variable:
+
+```python
+test_prompt = "Your custom text here"
+```
+
+### Technical Details
+
+The model processes text input through the following steps:
+1. Tokenization of the input text
+2. Addition of special tokens for human/AI interaction
+3. Generation of audio token sequences
+4. Conversion of tokens to audio using SNAC
+
+## Model Training
+
+This model was fine-tuned on a curated dataset of Milady-style speech examples. Training was performed using LoRA (Low-Rank Adaptation) to efficiently adapt the Orpheus 3B base model.
+
+## Additional Information
+
+This model is part of a greater collection of models in the Milady Instrumentality Project. The speech synthesis capabilities complement the text generation models
+
+### Future Development
+
+Future plans include:
+- Integration with the Milady Language Model for end-to-end text-to-speech generation
+- Improved speech naturalness and emotion expression
+
+## Acknowledgments
+
+- Based on the Orpheus 3B Text-to-Speech model
+- Uses the SNAC audio codec for high-quality audio synthesis
+- Training framework provided by Unsloth for efficient fine-tuning

config.json

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+{
+  "_name_or_path": "unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
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+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.3.6",
+  "use_cache": true,
+  "vocab_size": 156940
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128009,
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.49.0"
+}

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