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Model: RL-gang/vocence-orpheus-amir
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
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.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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+---
+base_model: unsloth/orpheus-3b-0.1-ft
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+license: apache-2.0
+language:
+- en
+---
+
+# Uploaded finetuned  model
+
+- **Developed by:** xtz999
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
+
+This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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 %}

chute_config.yml

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+Image:
+  from_base: parachutes/base-python:3.12.9
+  run_command:
+    - pip install torch torchaudio transformers accelerate huggingface_hub pyyaml soundfile snac
+  set_workdir: /app
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 16
+  include:
+    - pro_6000
+  exclude: []
+
+Chute:
+  tagline: vocence Orpheus Amir TTS
+  readme: Vocence PromptTTS — American English Amir (Orpheus 3B)
+  shutdown_after_seconds: 86400
+  concurrency: 1
+  max_instances: 1
+  scaling_threshold: 0.5
+  tee: true

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "torch_dtype": "float16",
+  "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": "4.56.2",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.11.4",
+  "use_cache": true,
+  "vocab_size": 156940
+}

miner.py

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+from __future__ import annotations
+
+import threading
+from functools import cached_property
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+
+SOH_ID = 128259
+EOH_ID = 128260
+CODE_START_TOKEN_ID = 128257
+CODE_END_TOKEN_ID = 128258
+TEXT_EOT_ID = 128009
+CODE_TOKEN_OFFSET = 128266
+SNAC_MIN_ID = 128266
+SNAC_MAX_ID = 156937
+SNAC_TOKENS_PER_FRAME = 7
+SNAC_MODEL_NAME = "hubertsiuzdak/snac_24khz"
+
+VOCENCE_EMOTION_HINTS = {
+    "happy": "cheerfully",
+    "sad": "sadly",
+    "angry": "firmly",
+    "excited": "excitedly",
+    "calm": "calmly",
+    "neutral": "",
+}
+
+
+def _parse_vocence_instruction(instruction: str) -> Dict[str, str]:
+    parts: Dict[str, str] = {}
+    for segment in instruction.split("|"):
+        segment = segment.strip()
+        if ":" in segment:
+            key, val = segment.split(":", 1)
+            parts[key.strip().lower()] = val.strip().lower()
+    return parts
+
+
+def _build_orpheus_prompt(voice: str, instruction: str, text: str) -> str:
+    traits = _parse_vocence_instruction(instruction)
+    hints: List[str] = []
+    emotion = traits.get("emotion", "")
+    if emotion in VOCENCE_EMOTION_HINTS and VOCENCE_EMOTION_HINTS[emotion]:
+        hints.append(VOCENCE_EMOTION_HINTS[emotion])
+    speed = traits.get("speed", "")
+    if speed in ("slow", "very_slow"):
+        hints.append("slowly")
+    elif speed in ("fast", "very_fast"):
+        hints.append("quickly")
+    if instruction.strip() and not traits:
+        hints.append(instruction.strip()[:120])
+
+    body = text.strip() or "Hello."
+    if hints:
+        body = f"{' '.join(hints)} {body}".strip()
+    return f"{voice}: {body}"
+
+
+def _redistribute_codes(code_list: List[int]) -> List[Any]:
+    import torch
+
+    layer_1: List[int] = []
+    layer_2: List[int] = []
+    layer_3: List[int] = []
+    frames = len(code_list) // 7
+    for i in range(frames):
+        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))
+    return [
+        torch.tensor(layer_1).unsqueeze(0),
+        torch.tensor(layer_2).unsqueeze(0),
+        torch.tensor(layer_3).unsqueeze(0),
+    ]
+
+
+class Miner:
+
+    REPO_SENTINEL = "config.json"
+    SETTINGS_FILE = "vocence_config.yaml"
+    WARMUP_TIMEOUT = 600.0
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        self.root = Path(path_hf_repo).resolve()
+        if not (self.root / self.REPO_SENTINEL).is_file():
+            raise FileNotFoundError(f"{self.REPO_SENTINEL} not present in {self.root}")
+        _ = self.settings
+        _ = self.model
+
+    def __repr__(self) -> str:
+        return f"<Miner root={self.root.name} engine=orpheus-amir>"
+
+    @cached_property
+    def settings(self) -> SimpleNamespace:
+        raw = self._load_yaml(self.root / self.SETTINGS_FILE)
+        rt = raw.get("runtime") or {}
+        gen = raw.get("generation") or {}
+        lim = raw.get("limits") or {}
+        return SimpleNamespace(
+            voice=str(rt.get("voice", "Amir")),
+            sample_rate=int(gen.get("sample_rate", 24000)),
+            max_instruction_chars=int(lim.get("max_instruction_chars", 600)),
+            max_text_chars=int(lim.get("max_text_chars", 2000)),
+            max_new_tokens=int(gen.get("max_new_tokens", 1200)),
+            temperature=float(gen.get("temperature", 0.6)),
+            top_p=float(gen.get("top_p", 0.95)),
+            repetition_penalty=float(gen.get("repetition_penalty", 1.1)),
+            prefer_cuda=str(rt.get("device_preference", "cuda")).lower() == "cuda",
+            prefer_bf16=str(rt.get("dtype", "bfloat16")).lower() == "bfloat16",
+        )
+
+    @cached_property
+    def model(self) -> SimpleNamespace:
+        return self._instantiate_engine()
+
+    def warmup(self) -> None:
+        outcome: dict[str, Any] = {"done": False, "err": None}
+
+        def _trial() -> None:
+            try:
+                self.generate_wav(instruction="Neutral voice.", text="Warming up.")
+                outcome["done"] = True
+            except Exception as exc:
+                outcome["err"] = repr(exc)
+
+        worker = threading.Thread(target=_trial, daemon=True)
+        worker.start()
+        worker.join(timeout=self.WARMUP_TIMEOUT)
+        if not outcome["done"]:
+            raise RuntimeError(
+                f"warmup did not complete within {self.WARMUP_TIMEOUT}s: "
+                f"{outcome['err'] or 'no completion signal'}"
+            )
+
+    def generate_wav(self, instruction: str, text: str) -> Tuple[np.ndarray, int]:
+        import torch
+
+        s = self.settings
+        instruction = instruction[: s.max_instruction_chars]
+        text = text[: s.max_text_chars]
+        prompt = _build_orpheus_prompt(s.voice, instruction, text)
+
+        input_ids = self.model.tokenizer(prompt, return_tensors="pt").input_ids
+        start_token = torch.tensor([[SOH_ID]], dtype=torch.int64)
+        end_tokens = torch.tensor([[TEXT_EOT_ID, EOH_ID]], dtype=torch.int64)
+        modified = torch.cat([start_token, input_ids, end_tokens], dim=1).to(self.model.device)
+
+        with torch.inference_mode():
+            generated_ids = self.model.llm.generate(
+                modified,
+                max_new_tokens=s.max_new_tokens,
+                do_sample=True,
+                temperature=s.temperature,
+                top_p=s.top_p,
+                repetition_penalty=s.repetition_penalty,
+                num_return_sequences=1,
+                eos_token_id=CODE_END_TOKEN_ID,
+                use_cache=True,
+            )
+
+        row = generated_ids[0]
+        token_indices = (row == CODE_START_TOKEN_ID).nonzero(as_tuple=True)
+        if len(token_indices[0]) > 0:
+            last_idx = token_indices[0][-1].item()
+            cropped = row[last_idx + 1 :]
+        else:
+            cropped = row
+
+        masked = cropped[cropped != CODE_END_TOKEN_ID]
+        row_length = masked.size(0)
+        new_length = (row_length // SNAC_TOKENS_PER_FRAME) * SNAC_TOKENS_PER_FRAME
+        trimmed = masked[:new_length]
+        if trimmed.size(0) < SNAC_TOKENS_PER_FRAME:
+            raise ValueError("orpheus-amir produced insufficient SNAC tokens")
+
+        code_list = [(int(t) - CODE_TOKEN_OFFSET) for t in trimmed.tolist()]
+        codes = _redistribute_codes(code_list)
+        codes = [c.to(self.model.device) for c in codes]
+        audio_hat = self.model.snac.decode(codes)
+        wave = audio_hat.detach().squeeze().cpu().numpy().astype(np.float32)
+
+        return wave, s.sample_rate
+
+    def _instantiate_engine(self) -> SimpleNamespace:
+        import torch
+        from snac import SNAC
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+
+        s = self.settings
+        cuda_ready = torch.cuda.is_available()
+        device = "cuda:0" if (s.prefer_cuda and cuda_ready) else "cpu"
+        dtype = torch.bfloat16 if (s.prefer_bf16 and cuda_ready) else torch.float32
+
+        model_name = str(self.root)
+
+        print("[Miner] Loading Orpheus tokenizer...")
+        tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+        print(f"[Miner] Loading Orpheus model ({dtype})...")
+        llm = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=dtype,
+            device_map=device,
+            trust_remote_code=True,
+        )
+
+        print("[Miner] Loading SNAC 24kHz decoder...")
+        snac_dir = self.root / "snac"
+        if not snac_dir.is_dir():
+            raise FileNotFoundError(f"snac/ not present in {self.root} (required for no-egress deploy)")
+        model_name = str(snac_dir)
+        snac = SNAC.from_pretrained(model_name)
+        snac.to(device)
+
+        print(f"[Miner] orpheus-amir ready :: device={device} dtype={dtype}")
+        return SimpleNamespace(llm=llm, tokenizer=tokenizer, snac=snac, device=device)
+
+    @staticmethod
+    def _load_yaml(path: Path) -> dict[str, Any]:
+        if not path.is_file():
+            return {}
+        from yaml import safe_load
+
+        with path.open("r", encoding="utf-8") as fh:
+            return safe_load(fh) or {}

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snac/README.md

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+---
+license: mit
+tags:
+- audio
+---
+
+# SNAC 🍿
+
+Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate.
+
+👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models.
+
+🔗 GitHub repository: https://github.com/hubertsiuzdak/snac/
+
+## Overview
+
+SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently,
+covering a broader time span.
+
+This model compresses 24 kHz audio into discrete codes at a 0.98 kbps bitrate. It uses 3 RVQ levels with token rates of 12, 23, and
+47 Hz.
+
+## Pretrained models
+
+Currently, all models support only single audio channel (mono).
+
+| Model                                                                       | Bitrate   | Sample Rate | Params | Recommended use case     | 
+|-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------|
+| hubertsiuzdak/snac_24khz (this model) | 0.98 kbps | 24 kHz      | 19.8 M | 🗣️ Speech               | 
+| [hubertsiuzdak/snac_32khz](https://huggingface.co/hubertsiuzdak/snac_32khz) | 1.9 kbps  | 32 kHz      | 54.5 M | 🎸 Music / Sound Effects | 
+| [hubertsiuzdak/snac_44khz](https://huggingface.co/hubertsiuzdak/snac_44khz) | 2.6 kbps  | 44 kHz      | 54.5 M | 🎸 Music / Sound Effects |
+
+## Usage
+
+Install it using:
+
+```bash
+pip install snac
+```
+To encode (and decode) audio with SNAC in Python, use the following code:
+
+```python
+import torch
+from snac import SNAC
+
+model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
+audio = torch.randn(1, 1, 24000).cuda()  # B, 1, T
+
+with torch.inference_mode():
+    codes = model.encode(audio)
+    audio_hat = model.decode(codes)
+```
+
+You can also encode and reconstruct in a single call:
+
+```python
+with torch.inference_mode():
+    audio_hat, codes = model(audio)
+```
+
+⚠️ Note that `codes` is a list of token sequences of variable lengths, each corresponding to a different temporal
+resolution.
+
+```
+>>> [code.shape[1] for code in codes]
+[12, 24, 48]
+```
+
+## Acknowledgements
+
+Module definitions are adapted from the [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).

snac/config.json

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+{
+    "sampling_rate": 24000,
+    "encoder_dim": 48,
+    "encoder_rates": [2, 4, 8, 8],
+    "decoder_dim": 1024,
+    "decoder_rates": [8, 8, 4, 2],
+    "attn_window_size": null,
+    "codebook_size": 4096,
+    "codebook_dim": 8,
+    "vq_strides": [4, 2, 1],
+    "noise": true,
+    "depthwise": true
+}

snac/pytorch_model.bin

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+size 79488254

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|audio|>"
+  ],
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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+size 22849547

vocence_config.yaml

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+model_name: RL-gang/vocence-orpheus-amir
+
+runtime:
+  adapter: orpheus-amir
+  device_preference: cuda
+  dtype: bfloat16
+  voice: Amir
+
+generation:
+  sample_rate: 24000
+  max_new_tokens: 1200
+  temperature: 0.6
+  top_p: 0.95
+  repetition_penalty: 1.1
+
+limits:
+  max_text_chars: 2000
+  max_instruction_chars: 600