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Model: hypaai/hypaai_orpheus_v5
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+library_name: transformers
+tags:
+- unsloth
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- 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.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
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+- **Model type:** [More Information Needed]
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+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
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+
+## 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. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
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+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
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+
+### Results
+
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+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
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+
+## 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]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
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adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "okezieowen/hypaai_orpheus",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 1024,
+  "lora_bias": false,
+  "lora_dropout": 0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 1024,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "gate_proj",
+    "k_proj",
+    "up_proj",
+    "down_proj",
+    "o_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a66ec20e5333ab49676a7e337a14bb82cad65debb66559f48dc6b98a2846a03b
+size 6224401560

chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "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",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "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,
+  "transformers_version": "4.57.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.12.9",
+  "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": "4.57.3"
+}

handler.py

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+import os
+import torch
+import numpy as np
+import librosa
+import soundfile as sf
+import traceback
+import base64
+import io
+import wave
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from snac import SNAC
+from vllm import LLM, SamplingParams
+
+class EndpointHandler:
+  def __init__(self, path=""):
+
+    # Delimiter tokens as defined in Orpheus' vocabulary
+    self.START_OF_HUMAN =      128259
+    self.START_OF_TEXT =       128000
+    self.END_OF_TEXT =         128009
+    self.END_OF_HUMAN =        128260
+    self.START_OF_AI =         128261
+    self.START_OF_SPEECH =     128257
+    self.END_OF_SPEECH =       128258
+    self.END_OF_AI =           128262
+    self.AUDIO_TOKENS_START =  128266
+
+    # Load the models and tokenizer
+    self.model = LLM(path, max_model_len = 4096, gpu_memory_utilization = 0.3)
+    self.tokenizer = AutoTokenizer.from_pretrained(path)
+
+    # Move to devices
+    self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    # Load SNAC model for audio decoding
+    try:
+      self.snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
+      self.snac_model.to(self.device)
+    except Exception as e:
+      raise RuntimeError(f"Failed to load SNAC model: {e}")
+
+  # Set up functions to format and encode text/audio
+  def encode_text(self, text):
+    return self.tokenizer.encode(text, return_tensors="pt", add_special_tokens=False)
+
+  def encode_audio(self, base64_audio_str):
+    audio_bytes = base64.b64decode(base64_audio_str)
+    audio_buffer = io.BytesIO(audio_bytes)
+    waveform, sr = sf.read(audio_buffer, dtype='float32')
+
+    if waveform.ndim > 1:
+      waveform = np.mean(waveform, axis=1)
+    if sr != 24000:
+      waveform = librosa.resample(waveform, orig_sr=sr, target_sr=24000)
+    return self.tokenize_audio(waveform)
+
+  def format_text_block(self, text_ids):
+    return [
+        torch.tensor([[self.START_OF_HUMAN]], dtype=torch.int64),
+        torch.tensor([[self.START_OF_TEXT]], dtype=torch.int64),
+        text_ids,
+        torch.tensor([[self.END_OF_TEXT]], dtype=torch.int64),
+        torch.tensor([[self.END_OF_HUMAN]], dtype=torch.int64)
+    ]
+
+  def format_audio_block(self, audio_codes):
+    return [
+        torch.tensor([[self.START_OF_AI]], dtype=torch.int64),
+        torch.tensor([[self.START_OF_SPEECH]], dtype=torch.int64),
+        torch.tensor([audio_codes], dtype=torch.int64),
+        torch.tensor([[self.END_OF_SPEECH]], dtype=torch.int64),
+        torch.tensor([[self.END_OF_AI]], dtype=torch.int64)
+    ]
+
+  def enroll_user(self, enrollment_pairs):
+    """
+    Parameters:
+    - enrollment_pairs: List of tuples (text, audio_data), where audio_data is
+    base64-encoded audio data
+    Returns:
+    - cloning_features (str): serialized enrollment data
+    """
+    enrollment_data = []
+
+    for text, base64_audio in enrollment_pairs:
+      text_ids = self.encode_text(text).cpu()
+      audio_codes = self.encode_audio(base64_audio)
+      enrollment_data.append({
+          "text_ids": text_ids,
+          "audio_codes": audio_codes
+      })
+
+    # Serialize enrollment data
+    buffer = io.BytesIO()
+    torch.save(enrollment_data, buffer)
+    buffer.seek(0)
+
+    # Encode as base64 string and assign to attribute
+    cloning_features = base64.b64encode(buffer.read()).decode('utf-8')
+    return cloning_features
+
+  def prepare_audio_tokens_for_decoder(self, audio_codes_list):
+    """
+    Given a list containing sequences of generated audio codes, do the following:
+    1. Trim length to a multiple of 7 (SNAC decoder requires 7 tokens per audio frame)
+    2. Adjust token values to SNAC decoder's expected range
+    """
+    modified_audio_codes_list = []
+    for audio_codes in audio_codes_list:
+
+      # Trim length to a multiple of 7
+      length = (audio_codes.size(0) // 7) * 7
+      trimmed = audio_codes[:length]
+
+      # Adjust token values to SNAC decoder's expected range
+      audio_codes = trimmed - self.AUDIO_TOKENS_START
+
+      # Add modified audio codes to list
+      modified_audio_codes_list.append(audio_codes)
+
+    return modified_audio_codes_list
+
+  # Convert audio sample to codes and reconstruct
+  def tokenize_audio(self, waveform):
+    waveform = torch.from_numpy(waveform).unsqueeze(0).unsqueeze(0).to(self.device)
+
+    with torch.inference_mode():
+      codes = self.snac_model.encode(waveform)
+
+    all_codes = []
+    for i in range(codes[0].shape[1]):
+
+      all_codes.append(codes[0][0][(1 * i) + 0].item() + self.AUDIO_TOKENS_START + (0 * 4096))
+      all_codes.append(codes[1][0][(2 * i) + 0].item() + self.AUDIO_TOKENS_START + (1 * 4096))
+      all_codes.append(codes[2][0][(4 * i) + 0].item() + self.AUDIO_TOKENS_START + (2 * 4096))
+      all_codes.append(codes[2][0][(4 * i) + 1].item() + self.AUDIO_TOKENS_START + (3 * 4096))
+      all_codes.append(codes[1][0][(2 * i) + 1].item() + self.AUDIO_TOKENS_START + (4 * 4096))
+      all_codes.append(codes[2][0][(4 * i) + 2].item() + self.AUDIO_TOKENS_START + (5 * 4096))
+      all_codes.append(codes[2][0][(4 * i) + 3].item() + self.AUDIO_TOKENS_START + (6 * 4096))
+
+    return all_codes
+
+  def preprocess(self, data):
+
+    # Preprocess input data before inference
+
+    self.voice_cloning = data.get("clone", False)
+    clone_on_the_fly = data.get("clone_on_the_fly", False)
+
+    # Extract parameters from request
+    target_text = data["inputs"]
+    parameters = data.get("parameters", {})
+    cloning_features = data.get("cloning_features", None)
+
+    temperature = float(parameters.get("temperature", 0.6))
+    top_p = float(parameters.get("top_p", 0.95))
+    max_new_tokens = int(parameters.get("max_new_tokens", 1200))
+    repetition_penalty = float(parameters.get("repetition_penalty", 1.1))
+
+    if self.voice_cloning:
+      if clone_on_the_fly:
+        # Clone using text-audio enrollment pair
+        enrollment_pairs = data.get("enrollments", [])
+        enrollment_data = []
+
+        # Raise error if no enrollment is provided
+        if not enrollment_pairs:
+          raise ValueError("No enrollment pairs provided")
+        
+        for text, base64_audio in enrollment_pairs:
+          text_ids = self.encode_text(text).cpu()
+          audio_codes = self.encode_audio(base64_audio)
+          enrollment_data.append({
+              "text_ids": text_ids,
+              "audio_codes": audio_codes
+          })
+
+      elif not cloning_features:
+        raise ValueError("No cloning features were provided")
+      else:
+        # Clone using enrollment features gotten earlier
+        enrollment_data = torch.load(io.BytesIO(base64.b64decode(cloning_features)))
+
+      # Process pre-tokenized enrollment_data
+      input_sequence = []
+      for item in enrollment_data:
+        text_ids = item["text_ids"]
+        audio_codes = item["audio_codes"]
+        input_sequence.extend(self.format_text_block(text_ids))
+        input_sequence.extend(self.format_audio_block(audio_codes))
+
+      # Append target text whose audio we want
+      target_text_ids = self.encode_text(target_text)
+      input_sequence.extend(self.format_text_block(target_text_ids))
+
+      # Start of target audio - audio codes to be completed by model
+      input_sequence.extend([
+          torch.tensor([[self.START_OF_AI]], dtype=torch.int64),
+          torch.tensor([[self.START_OF_SPEECH]], dtype=torch.int64)
+      ])
+
+      # Final input tensor
+      input_ids = torch.cat(input_sequence, dim=1)
+
+      # Create attention mask and move tensors to device
+      attention_mask = torch.ones_like(input_ids)
+      input_ids = input_ids.to(self.device)
+      attention_mask = attention_mask.to(self.device)
+
+    else:
+      # Handle standard text-to-speech
+
+      # Extract parameters from request
+      voice = parameters.get("voice", "Eniola")
+      prompt = f"{voice}: {target_text}"
+      input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
+
+      # Add special tokens
+      input_ids = torch.cat(self.format_text_block(input_ids), dim=1)
+
+      # No need for padding as we're processing a single sequence
+      input_ids = input_ids.to(self.device)
+
+    return {
+        "input_ids": input_ids,
+        "temperature": temperature,
+        "top_p": top_p,
+        "max_new_tokens": max_new_tokens,
+        "repetition_penalty": repetition_penalty,
+    }
+
+  def inference(self, inputs):
+    """
+    Run model inference on the preprocessed inputs
+    """
+    # Extract parameters
+    input_ids = inputs["input_ids"]
+
+    sampling_params = SamplingParams(
+        temperature = inputs["temperature"],
+        top_p = inputs["top_p"],
+        max_tokens = inputs["max_new_tokens"],        
+        repetition_penalty = inputs["repetition_penalty"],
+        stop_token_ids = [self.END_OF_SPEECH],
+        )
+    
+    prompt_string = self.tokenizer.decode(input_ids[0])
+
+    # Forward pass through the model
+    generated_ids = self.model.generate(prompt_string, sampling_params)
+
+    # return torch.tensor(generated_ids[0].outputs[0].token_ids).unsqueeze(0)
+    return {
+        "gen_ids": torch.tensor(generated_ids[0].outputs[0].token_ids).unsqueeze(0), 
+        "input_ids": input_ids
+        }
+
+  def __call__(self, data):
+    
+    # Main entry point for the handler
+
+    try:
+      enroll_user = data.get("enroll_user", False)
+
+      if enroll_user:
+        # We extract cloning features for enrollment
+        enrollment_pairs = data.get("enrollments", [])
+        cloning_features = self.enroll_user(enrollment_pairs)
+        return {"cloning_features": cloning_features}
+      else:
+        # We want to generate speech using preset cloning features
+        preprocessed_inputs = self.preprocess(data)
+        model_outputs = self.inference(preprocessed_inputs)
+        response = self.postprocess(model_outputs)
+        return response
+
+    # Catch that error, baby
+    except Exception as e:
+      traceback.print_exc()
+      return {"error": str(e)}
+
+  # Postprocess generated ids
+  def convert_codes_to_waveform(self, code_list):
+    """
+    Reorganize tokens for SNAC decoding
+    """
+    layer_1 = []  # Coarsest layer
+    layer_2 = []  # Intermediate layer
+    layer_3 = []  # Finest layer
+
+    num_groups = len(code_list) // 7
+    for i in range(num_groups):
+      idx = 7 * i
+      layer_1.append(code_list[7 * i + 0] - (0 * 4096))
+      layer_2.append(code_list[7 * i + 1] - (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).to(self.device),
+        torch.tensor(layer_2).unsqueeze(0).to(self.device),
+        torch.tensor(layer_3).unsqueeze(0).to(self.device)
+    ]
+
+    # Decode audio
+    audio_hat = self.snac_model.decode(codes)
+    return audio_hat
+
+  def postprocess(self, model_outputs):
+
+    generated_ids = model_outputs["gen_ids"]
+    input_ids = model_outputs["input_ids"]
+
+    if self.voice_cloning:
+      """
+      For cloning applications, use this postprocess function to get generated audio samples
+      """
+      # Modify audio codes to be digestible byb SNAC decoder
+      code_lists = self.prepare_audio_tokens_for_decoder(generated_ids)
+
+      # Generate audio from codes
+      temp = self.convert_codes_to_waveform(code_lists[0])
+      audio_sample = temp.detach().squeeze().to("cpu").numpy()
+
+    else:
+      """
+      Process generated tokens into audio
+      """
+      # Find Start of Audio token
+      token_indices = (generated_ids == self.START_OF_SPEECH).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
+
+      # Remove End of Audio tokens
+      processed_rows = []
+      for row in cropped_tensor:
+        masked_row = row[row != self.END_OF_SPEECH]
+        processed_rows.append(masked_row)
+
+      code_lists = self.prepare_audio_tokens_for_decoder(processed_rows)
+
+      # Generate audio from codes
+      audio_samples = []
+      for code_list in code_lists:
+        if len(code_list) > 0:
+          audio = self.convert_codes_to_waveform(code_list)
+          audio_samples.append(audio)
+        else:
+          raise ValueError("Empty code list, no audio to generate")
+
+      if not audio_samples:
+        return {"error": "No audio samples generated"}
+
+      # Return first (and only) audio sample
+      audio_sample = audio_samples[0].detach().squeeze().cpu().numpy()
+
+    # Convert float32 array to int16 for WAV format
+    audio_int16 = (audio_sample * 32767).astype(np.int16)
+
+    # Write to WAV in memory (float32 or int16 depending on your preference)
+    buffer = io.BytesIO()
+    sf.write(buffer, audio_sample, samplerate=24000, format='WAV', subtype='PCM_16')  # or PCM_32
+    buffer.seek(0)
+
+    # Encode WAV bytes as base64
+    audio_b64 = base64.b64encode(buffer.read()).decode('utf-8')
+
+    return {
+      "audio_sample": audio_sample,
+      "audio_b64": audio_b64,
+      "sample_rate": 24000,
+      "input_ids_len": input_ids.shape[1],
+      "gen_ids_len": generated_ids.shape[1]
+    }

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