Sad-tts-v1: Vocence OmniVoice miner + weights

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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

.gitignore

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+.cache/
+__pycache__/
+*.pyc
+*.bak
+*.bak-*
+
+# Large checkpoints — add from https://huggingface.co/k2-fsa/OmniVoice (or your fork) before Hub push
+/model.safetensors
+/audio_tokenizer/model.safetensors

README.md

@@ -0,0 +1,58 @@
+---
+license: apache-2.0
+pipeline_tag: text-to-speech
+language:
+  - en
+  - zh
+  - multilingual
+tags:
+  - vocence
+  - bittensor
+  - prompt-tts
+  - omnivoice
+  - sad-tts-v1
+---
+
+# Sad-tts-v1 (Vocence PromptTTS)
+
+**Sad-tts-v1** is a **[Vocence SN78](https://github.com/Vocence-bt/vocence)** miner bundle (OmniVoice backbone). Hub: **`KGSS/Sad-tts-v1`** — natural-language **instruction** plus **text** in, **mono WAV** out via [`miner.py`](./miner.py).
+
+Base weights align with **[k2-fsa/OmniVoice](https://huggingface.co/k2-fsa/OmniVoice)** · Apache-2.0 · [`omnivoice`](https://pypi.org/project/omnivoice/) runtime. Pin commits in `miner_deploy_sad_tts_v1.py` and [VOCENCE_HF.md](./VOCENCE_HF.md).
+
+## Vocence contract
+
+| Method | Role |
+|--------|------|
+| `Miner(path_hf_repo)` | Load checkpoint from a directory (or HF snapshot) containing `config.json`, `model.safetensors`, tokenizers, and `audio_tokenizer/`. |
+| `warmup()` | One short synthesis to prime the stack. |
+| `generate_wav(instruction, text)` | Returns `(float32 mono ndarray, sample_rate)`; typically **24 kHz**. |
+
+Validators send **free-form** `instruction`. OmniVoice **voice-design** only accepts [whitelisted attribute tags](https://huggingface.co/k2-fsa/OmniVoice); **`miner.py` maps** keywords (gender, age, pitch, whisper, accent, Chinese dialects) to those tags. Unmatched instructions fall back to `runtime.default_instruct` in [`vocence_config.yaml`](./vocence_config.yaml).
+
+## Repo layout
+
+| File | Role |
+|------|------|
+| `miner.py` | Engine + NL → `instruct` mapping |
+| `chute_config.yml` | Chutes image (PyTorch cu128 + `omnivoice`) |
+| `vocence_config.yaml` | Limits, default voice tags, `num_step` / `guidance_scale` |
+| Weight files | Shipped in this Hub repo (`model.safetensors`, `audio_tokenizer/`); see [VOCENCE_HF.md](./VOCENCE_HF.md) |
+
+## Local quick check (GPU)
+
+```bash
+pip install omnivoice torch torchaudio  # match CUDA index from chute_config.yml
+# Copy snapshot: huggingface-cli download k2-fsa/OmniVoice --local-dir ./OmniVoice_weights
+python -c "
+from pathlib import Path
+from miner import Miner
+m = Miner(Path('./OmniVoice_weights'))
+m.warmup()
+w, sr = m.generate_wav('Calm female voice, British accent.', 'Hello from OmniVoice on Vocence.')
+print(w.shape, sr)
+"
+```
+
+## License
+
+**Apache-2.0** for this packaging layout and miner glue; **OmniVoice** weights and upstream code remain under their stated licenses on the [model card](https://huggingface.co/k2-fsa/OmniVoice).

VOCENCE_HF.md

@@ -0,0 +1,21 @@
+# Publishing **Sad-tts-v1** (`KGSS/Sad-tts-v1`)
+
+1. **Weights** — Full OmniVoice-style snapshot (main `model.safetensors` + `audio_tokenizer/`). Upstream reference: **[k2-fsa/OmniVoice](https://huggingface.co/k2-fsa/OmniVoice)**.
+
+   - `model.safetensors`
+   - `config.json`, `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`, etc.
+   - `audio_tokenizer/` (directory with its `model.safetensors` and configs)
+
+   You can sync without Git LFS clutter locally:
+
+   ```bash
+   huggingface-cli download k2-fsa/OmniVoice --local-dir ./mysnap
+   cp -r miner.py chute_config.yml vocence_config.yaml README.md ./mysnap/
+   # then push ./mysnap to your Hub repo
+   ```
+
+2. **Revision** — Pin the commit SHA in your deploy script as `VOCENCE_REVISION` and use the same value in `vocence miner commit --model-revision`.
+
+3. **`chute_config.yml` on SN78** — Subnet Chutes integrations require TEE + supported GPU class (see your working `rhy-TTS-v1` deploy); this folder’s `chute_config.yml` matches that pattern.
+
+4. **Safetensors metadata** — OmniVoice checkpoints from the official Hub use standard PyTorch safetensors metadata; no `pt-sn78v`-style patching should be needed (unlike some custom Qwen repacks).

audio_tokenizer/.gitattributes

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+*.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
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+*.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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+*.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

audio_tokenizer/LICENSE

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+BOSON HIGGS AUDIO 2 COMMUNITY LICENSE AGREEMENT
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audio_tokenizer/README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# 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]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **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]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## 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 -->
+
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+
+### 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. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## 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]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
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+## Model Card Contact
+
+[More Information Needed]

audio_tokenizer/config.json

@@ -0,0 +1,129 @@
+{
+  "acoustic_model_config": {
+    "codebook_dim": 8,
+    "codebook_loss_weight": 1.0,
+    "codebook_size": 1024,
+    "commitment_loss_weight": 0.25,
+    "decoder_hidden_size": 1024,
+    "downsampling_ratios": [
+      8,
+      5,
+      4,
+      2,
+      3
+    ],
+    "encoder_hidden_size": 64,
+    "hidden_size": 256,
+    "hop_length": 960,
+    "model_type": "dac",
+    "n_codebooks": 9,
+    "quantizer_dropout": 0,
+    "sampling_rate": 16000,
+    "upsampling_ratios": [
+      8,
+      5,
+      4,
+      2,
+      3
+    ]
+  },
+  "architectures": [
+    "HiggsAudioV2TokenizerModel"
+  ],
+  "block_dilations": [
+    1,
+    1
+  ],
+  "channel_ratios": [
+    1,
+    1
+  ],
+  "codebook_dim": 64,
+  "codebook_size": 1024,
+  "downsample_factor": 320,
+  "dtype": "float32",
+  "initializer_range": 0.02,
+  "kernel_size": 3,
+  "model_type": "higgs_audio_v2_tokenizer",
+  "sample_rate": 24000,
+  "semantic_model_config": {
+    "activation_dropout": 0.1,
+    "apply_spec_augment": true,
+    "attention_dropout": 0.1,
+    "bos_token_id": 1,
+    "classifier_proj_size": 256,
+    "conv_bias": false,
+    "conv_dim": [
+      512,
+      512,
+      512,
+      512,
+      512,
+      512,
+      512
+    ],
+    "conv_kernel": [
+      10,
+      3,
+      3,
+      3,
+      3,
+      2,
+      2
+    ],
+    "conv_pos_batch_norm": false,
+    "conv_stride": [
+      5,
+      2,
+      2,
+      2,
+      2,
+      2,
+      2
+    ],
+    "ctc_loss_reduction": "sum",
+    "ctc_zero_infinity": false,
+    "do_stable_layer_norm": false,
+    "eos_token_id": 2,
+    "feat_extract_activation": "gelu",
+    "feat_extract_norm": "group",
+    "feat_proj_dropout": 0.0,
+    "feat_proj_layer_norm": true,
+    "final_dropout": 0.1,
+    "hidden_act": "gelu",
+    "hidden_dropout": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-05,
+    "layerdrop": 0.1,
+    "mask_feature_length": 10,
+    "mask_feature_min_masks": 0,
+    "mask_feature_prob": 0.0,
+    "mask_time_length": 10,
+    "mask_time_min_masks": 2,
+    "mask_time_prob": 0.0,
+    "model_type": "hubert",
+    "num_attention_heads": 12,
+    "num_conv_pos_embedding_groups": 16,
+    "num_conv_pos_embeddings": 128,
+    "num_feat_extract_layers": 7,
+    "num_hidden_layers": 12,
+    "pad_token_id": 0,
+    "use_weighted_layer_sum": false,
+    "vocab_size": 32
+  },
+  "semantic_sample_rate": 16000,
+  "strides": [
+    1,
+    1
+  ],
+  "target_bandwidths": [
+    0.5,
+    1,
+    1.5,
+    2
+  ],
+  "transformers_version": "5.3.0.dev0",
+  "unit_kernel_size": 3
+}

audio_tokenizer/preprocessor_config.json

@@ -0,0 +1,9 @@
+{
+  "feature_extractor_type": "DacFeatureExtractor",
+  "feature_size": 1,
+  "hop_length": 960,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 24000
+}

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

chute_config.yml

@@ -0,0 +1,26 @@
+# Vocence PromptTTS — k2-fsa/OmniVoice (diffusion LM TTS, voice design via `instruct`).
+# Weights + tokenizer ship in the HF repo; miner maps free-form `instruction` → OmniVoice tags.
+
+Image:
+  from_base: parachutes/base-python:3.12.9
+  run_command:
+    - pip install --no-cache-dir torch==2.10.0 torchaudio==2.10.0 --extra-index-url https://download.pytorch.org/whl/cu128
+    - pip install --no-cache-dir transformers accelerate safetensors soundfile scipy einops
+    - pip install --no-cache-dir omnivoice
+    - pip install --no-cache-dir numpy pyyaml huggingface_hub
+
+NodeSelector:
+  gpu_count: 1
+  min_vram_gb_per_gpu: 48
+  include: ["pro_6000"]
+  exclude: []
+
+Chute:
+  tagline: Sad-tts-v1 OmniVoice TTS (PromptTTS voice design, Vocence)
+  readme: NL instruction + text to WAV; Vocence SN78 Sad-tts-v1 miner.
+  boost: 1.32
+  shutdown_after_seconds: 86400
+  concurrency: 1
+  max_instances: 1
+  scaling_threshold: 0.5
+  tee: true

config.json

@@ -0,0 +1,101 @@
+{
+  "architectures": [
+    "OmniVoice"
+  ],
+  "audio_codebook_weights": [
+    8,
+    8,
+    6,
+    6,
+    4,
+    4,
+    2,
+    2
+  ],
+  "audio_mask_id": 1024,
+  "audio_vocab_size": 1025,
+  "bos_token_id": null,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "llm_config": {
+    "_name_or_path": "",
+    "architectures": [
+      "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "chunk_size_feed_forward": 0,
+    "dtype": "float32",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_types": [
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 40960,
+    "max_window_layers": 28,
+    "model_type": "qwen3",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "pad_token_id": null,
+    "problem_type": null,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 1000000,
+      "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151676
+  },
+  "model_type": "omnivoice",
+  "num_audio_codebook": 8,
+  "pad_token_id": 151643,
+  "transformers_version": "5.3.0"
+}

miner.py

@@ -0,0 +1,391 @@
+"""
+Vocence SN78 PromptTTS engine for OmniVoice (k2-fsa/OmniVoice).
+
+OmniVoice voice-design mode only accepts whitelisted comma-separated attributes
+in ``instruct``; free-form validator ``instruction`` is mapped to those tags.
+"""
+
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from typing import Any, Mapping
+
+import numpy as np
+
+_VOCENCE_YAML = "vocence_config.yaml"
+_ZH_RE = re.compile(r"[\u4e00-\u9fff]")
+_WORD_RE = re.compile(r"\w+", re.UNICODE)
+
+
+def _read_vocence_yaml(repo: Path) -> dict[str, Any]:
+    path = repo / _VOCENCE_YAML
+    if not path.is_file():
+        return {}
+    from yaml import safe_load
+
+    with path.open("r", encoding="utf-8") as fh:
+        data = safe_load(fh)
+    return data if isinstance(data, Mapping) else {}
+
+
+def _has_cjk(s: str) -> bool:
+    return bool(re.search(r"[\u3040-\u30ff\u3100-\u312f\u3400-\u9fff\uac00-\ud7af]", s))
+
+
+def _pick_language(text: str, instruction_lower: str, default_lang: str = "English") -> str | None:
+    t, ins = text or "", instruction_lower or ""
+    if "japanese" in ins or "日本" in t or re.search(r"[\u3040-\u30ff]", t):
+        return "Japanese"
+    if "korean" in ins or "한글" in t or re.search(r"[\uac00-\ud7af]", t):
+        return "Korean"
+    if _has_cjk(t) or "chinese" in ins or "mandarin" in ins or "cantonese" in ins:
+        return "Chinese"
+    if any(
+        w in ins
+        for w in (
+            "french",
+            "german",
+            "spanish",
+            "italian",
+            "portuguese",
+            "russian",
+            "arabic",
+            "hindi",
+        )
+    ):
+        for name in (
+            "French",
+            "German",
+            "Spanish",
+            "Italian",
+            "Portuguese",
+            "Russian",
+            "Arabic",
+            "Hindi",
+        ):
+            if name.lower() in ins:
+                return name
+    return default_lang if default_lang else None
+
+
+def _inject_natural_punctuation(text: str) -> str:
+    """Light pacing hints for long unpunctuated Latin text (OmniVoice benefits from boundaries)."""
+    if not text or not text.strip():
+        return text
+    if _has_cjk(text):
+        return text
+    words = _WORD_RE.findall(text)
+    if len(words) < 22:
+        return text
+    if len(re.findall(r"[,.;:!?]", text)) >= max(2, len(words) // 14):
+        return text
+    parts: list[str] = []
+    breaks = ("and", "but", "or", "yet", "so", "while", "however", "therefore")
+    wix = 0
+    i = 0
+    n = len(text)
+    last_break_at = -10
+    while i < n:
+        ch = text[i]
+        if ch.isalnum():
+            j = i
+            while j < n and (text[j].isalnum() or text[j] == "'"):
+                j += 1
+            token = text[i:j]
+            low = token.lower()
+            if (
+                low in breaks
+                and wix > 0
+                and (wix - last_break_at) >= 8
+            ):
+                while parts and parts[-1] == " ":
+                    parts.pop()
+                parts.append(", ")
+                last_break_at = wix
+            parts.append(token)
+            wix += 1
+            i = j
+        else:
+            parts.append(ch)
+            i += 1
+    return "".join(parts)
+
+
+def _nl_to_omnivoice_instruct(
+    instruction: str,
+    text: str,
+    *,
+    default_instruct: str,
+) -> str:
+    """Map free-form NL to OmniVoice voice-design tags (English or Chinese dialect tokens)."""
+    raw = (instruction or "").strip()
+    t = text or ""
+
+    gender: str | None = None
+    age: str | None = None
+    pitch: str | None = None
+    style: str | None = None
+    accent: str | None = None
+    dialect_zh: str | None = None
+
+    s = raw.lower()
+    # Gender
+    female = any(
+        k in s
+        for k in (
+            "female",
+            "woman",
+            "girl",
+            "lady",
+            "mother",
+            "soprano",
+            "feminine",
+            "herself",
+        )
+    ) or bool(re.search(r"\b(she|her)\b", s))
+    male = any(
+        k in s
+        for k in (
+            "male",
+            "man",
+            "boy",
+            "gentleman",
+            "father",
+            "baritone",
+            "masculine",
+            "himself",
+        )
+    ) or bool(re.search(r"\b(he|his|him)\b", s))
+    if female and not male:
+        gender = "female"
+    elif male and not female:
+        gender = "male"
+
+    # Age
+    if any(k in s for k in ("child", "kid", "toddler")):
+        age = "child"
+    elif "teen" in s or "teenager" in s:
+        age = "teenager"
+    elif any(k in s for k in ("elderly", "old man", "old woman", "senior", "grandfather", "grandmother")):
+        age = "elderly"
+    elif any(k in s for k in ("middle-aged", "middle aged", "mature")):
+        age = "middle-aged"
+    elif any(k in s for k in ("young adult", "young woman", "young man", "adult")):
+        age = "young adult"
+    elif "young" in s and "young adult" not in s:
+        age = "young adult"
+
+    # Pitch
+    if any(k in s for k in ("very low", "very deep", "bass", "gravely low")):
+        pitch = "very low pitch"
+    elif any(k in s for k in ("low pitch", "deep voice", "low voice", "baritone")) or "deep" in s:
+        pitch = "low pitch"
+    elif any(k in s for k in ("very high", "shrill", "squeaky")):
+        pitch = "very high pitch"
+    elif any(k in s for k in ("high pitch", "high voice")) or "high-pitched" in s:
+        pitch = "high pitch"
+    elif any(k in s for k in ("moderate", "neutral", "mid", "mid-range", "normal pitch")):
+        pitch = "moderate pitch"
+
+    if "whisper" in s or "whispering" in s:
+        style = "whisper"
+
+    # English accents (mutually exclusive — first match wins)
+    accent_rules = (
+        ("british", "british accent"),
+        ("uk accent", "british accent"),
+        ("scottish", "british accent"),
+        ("american", "american accent"),
+        ("us accent", "american accent"),
+        ("australian", "australian accent"),
+        ("indian accent", "indian accent"),
+        ("indian english", "indian accent"),
+        ("canadian", "canadian accent"),
+        ("russian accent", "russian accent"),
+        ("japanese accent", "japanese accent"),
+        ("korean accent", "korean accent"),
+        ("portuguese accent", "portuguese accent"),
+        ("chinese accent", "chinese accent"),
+    )
+    for key, tag in accent_rules:
+        if key in s:
+            accent = tag
+            break
+
+    # Chinese dialects (Chinese chars / keywords)
+    zh_dialect = (
+        ("四川", "四川话"),
+        ("sichuan", "四川话"),
+        ("东北", "东北话"),
+        ("陕西", "陕西话"),
+        ("河南", "河南话"),
+        ("云南", "云南话"),
+        ("贵州", "贵州话"),
+        ("山东", "济南话"),
+        ("青岛", "青岛话"),
+        ("石家庄", "石家庄话"),
+        ("甘肃", "甘肃话"),
+        ("宁夏", "宁夏话"),
+        ("桂林", "桂林话"),
+        ("济南", "济南话"),
+    )
+    ins_or_t = raw + t
+    for key, tag in zh_dialect:
+        if key in ins_or_t:
+            dialect_zh = tag
+            break
+
+    parts: list[str] = []
+    if dialect_zh:
+        zh_bits: list[str] = []
+        if gender == "female":
+            zh_bits.append("女")
+        elif gender == "male":
+            zh_bits.append("男")
+        if age == "child":
+            zh_bits.append("儿童")
+        elif age == "teenager":
+            zh_bits.append("少年")
+        elif age in ("young adult", "middle-aged"):
+            zh_bits.append("青年" if age == "young adult" else "中年")
+        elif age == "elderly":
+            zh_bits.append("老年")
+        if pitch == "very low pitch":
+            zh_bits.append("极低音调")
+        elif pitch == "low pitch":
+            zh_bits.append("低音调")
+        elif pitch == "moderate pitch":
+            zh_bits.append("中音调")
+        elif pitch == "high pitch":
+            zh_bits.append("高音调")
+        elif pitch == "very high pitch":
+            zh_bits.append("极高音调")
+        if style == "whisper":
+            zh_bits.append("耳语")
+        zh_bits.append(dialect_zh)
+        return "，".join(zh_bits)
+
+    # English path
+    for x in (gender, age, pitch, style, accent):
+        if x:
+            parts.append(x)
+    if not parts:
+        return default_instruct.strip() or "young adult, moderate pitch, american accent"
+
+    # Omnivoice rejects mixing accent with Chinese dialect — we handled dialect above.
+    return ", ".join(parts)
+
+
+def _speed_from_instruction(s: str) -> float | None:
+    s = (s or "").lower()
+    if any(w in s for w in ("very slow", "slowly", "unhurried")):
+        return 0.85
+    if "slow" in s:
+        return 0.93
+    if any(w in s for w in ("very fast", "rapid", "quickly", "hurried")):
+        return 1.12
+    if "fast" in s:
+        return 1.06
+    return None
+
+
+def _to_mono_f32(segment: np.ndarray) -> np.ndarray:
+    arr = np.asarray(segment, dtype=np.float32)
+    if arr.ndim > 1:
+        arr = arr.mean(axis=-1)
+    return np.ascontiguousarray(arr, dtype=np.float32)
+
+
+def _peak_normalize(wav: np.ndarray, ceiling: float = 0.38) -> np.ndarray:
+    if wav.size == 0:
+        return wav
+    p = float(np.max(np.abs(wav)))
+    if p <= 0 or p <= ceiling:
+        return wav
+    return (wav * (ceiling / p)).astype(np.float32, copy=False)
+
+
+class Miner:
+    """OmniVoice engine: NL ``instruction`` → voice-design ``instruct``; ``text`` → speech."""
+
+    def __init__(self, path_hf_repo: Path) -> None:
+        import torch
+        from omnivoice import OmniVoice
+
+        self._root = Path(path_hf_repo).resolve()
+        if not (self._root / "config.json").is_file():
+            raise FileNotFoundError(f"Missing config.json under {self._root}")
+
+        cfg = _read_vocence_yaml(self._root)
+        runtime = cfg.get("runtime") or {}
+        limits = cfg.get("limits") or {}
+        gen = cfg.get("generation") or {}
+
+        self._cap_instruction = int(limits.get("max_instruction_chars", 600))
+        self._cap_text = int(limits.get("max_text_chars", 2000))
+        self._max_seconds = float(gen.get("max_seconds", 30))
+        self._default_language = str(limits.get("default_language") or runtime.get("default_language", "English"))
+        self._default_instruct = str(
+            runtime.get("default_instruct") or "young adult, moderate pitch, american accent"
+        )
+        self._num_step = int(runtime.get("num_step", 24))
+        self._guidance_scale = float(runtime.get("guidance_scale", 2.0))
+
+        prefer_cuda = torch.cuda.is_available()
+        ckpt = str(self._root)
+        dtype = torch.float16 if prefer_cuda else torch.float32
+        map_kw: dict[str, Any] = {"dtype": dtype}
+        if prefer_cuda:
+            map_kw["device_map"] = "cuda:0"
+        else:
+            map_kw["device_map"] = "cpu"
+
+        self._torch = torch
+        self._model = OmniVoice.from_pretrained(ckpt, **map_kw)
+
+    def warmup(self) -> None:
+        _ = self.generate_wav(
+            "Speak clearly in a neutral American accent.",
+            "Warm-up.",
+        )
+
+    def generate_wav(self, instruction: str, text: str) -> tuple[np.ndarray, int]:
+        if not text or not str(text).strip():
+            raise ValueError("text must be non-empty")
+
+        inst_full = (instruction or "").strip()[: self._cap_instruction]
+        tts_text = _inject_natural_punctuation(str(text).strip()[: self._cap_text])
+        lang = _pick_language(tts_text, inst_full.lower(), self._default_language)
+
+        instruct = _nl_to_omnivoice_instruct(
+            inst_full, tts_text, default_instruct=self._default_instruct
+        )
+        speed = _speed_from_instruction(inst_full)
+
+        gen_kwargs: dict[str, Any] = dict(
+            num_step=self._num_step,
+            guidance_scale=self._guidance_scale,
+        )
+
+        audios = self._model.generate(
+            text=tts_text,
+            language=lang,
+            instruct=instruct,
+            speed=speed,
+            **gen_kwargs,
+        )
+        if not audios:
+            raise ValueError("OmniVoice returned no audio")
+        first = audios[0]
+        if first is None or first.size == 0:
+            raise ValueError("OmniVoice returned empty audio")
+
+        sr = int(getattr(self._model, "sampling_rate", None) or 24000)
+        wav = _peak_normalize(_to_mono_f32(first))
+        dur = float(wav.shape[0]) / float(sr)
+        if dur > self._max_seconds + 1e-3:
+            max_samples = int(self._max_seconds * sr)
+            wav = wav[:max_samples]
+
+        return wav, sr

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:408f669b7e2b045fdf54201d815bd364e6667dbd845115da81239c40bc6dcfd1
+size 11423986

tokenizer_config.json

@@ -0,0 +1,23 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|denoise|>",
+    "<|lang_start|>",
+    "<|lang_end|>",
+    "<|instruct_start|>",
+    "<|instruct_end|>",
+    "<|text_start|>",
+    "<|text_end|>"
+  ],
+  "is_local": true,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

vocence_config.yaml

@@ -0,0 +1,21 @@
+# Vocence PromptTTS — OmniVoice (voice design). See miner.py for NL → instruct mapping.
+
+runtime:
+  adapter: "omnivoice_voice_design"
+  device_preference: "cuda"
+  dtype: "float16"
+  default_language: "English"
+  # When the NL instruction does not map to any OmniVoice attribute, use this valid instruct:
+  default_instruct: "young adult, moderate pitch, american accent"
+  # Optional generation overrides (passed to OmniVoice.generate)
+  num_step: 24
+  guidance_scale: 2.0
+
+generation:
+  sample_rate: 24000
+  max_seconds: 30
+
+limits:
+  max_text_chars: 2000
+  max_instruction_chars: 600
+  default_language: "English"