Initial upload: OmniVoice + singing + emotion finetune

.gitattributes

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 *.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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+---
+language:
+- en
+- zh
+- ja
+- ko
+- es
+- fr
+- de
+- it
+- ru
+- hi
+- gu
+library_name: transformers
+pipeline_tag: text-to-speech
+license: apache-2.0
+base_model: k2-fsa/OmniVoice
+tags:
+- text-to-speech
+- tts
+- singing
+- emotion
+- expressive-tts
+- multilingual
+- voice-cloning
+- omnivoice
+---
+
+# OmniVoice — Singing + Emotion Finetune
+
+A finetune of [`k2-fsa/OmniVoice`](https://huggingface.co/k2-fsa/OmniVoice) that adds:
+
+- **`[singing]` tag** — sung speech / nursery-style melodic vocals
+- **Emotion tags** — `[happy]`, `[sad]`, `[angry]`, `[excited]`, `[calm]`, `[nervous]`, `[whisper]`
+- **Combined tags** — e.g. `[singing] [happy] ...` or `[singing] [sad] ...`
+
+Original OmniVoice capabilities (multilingual zero-shot TTS, voice cloning, voice design, 600+ languages) are **preserved** — the base speech head was protected during finetuning with a continuity mix of plain speech and singing.
+
+## Drop-in replacement
+
+This checkpoint is fully compatible with the upstream [k2-fsa/OmniVoice](https://github.com/k2-fsa/OmniVoice) code — same architecture (Qwen3-0.6B LM + HiggsAudioV2 audio tokenizer at 24 kHz), same inference API. Replace the model id:
+
+```python
+from omnivoice.models.omnivoice import OmniVoice
+
+model = OmniVoice.from_pretrained("ModelsLab/omnivoice-singing").to("cuda").eval()
+
+# Normal speech (unchanged behavior)
+audios = model.generate(
+    text="The quick brown fox jumps over the lazy dog.",
+    language="English",
+)
+
+# Singing
+audios = model.generate(
+    text="[singing] Twinkle twinkle little star, how I wonder what you are.",
+    language="English",
+)
+
+# Emotional speech
+audios = model.generate(
+    text="[happy] I just got the best news of my entire year!",
+    language="English",
+)
+
+# Combined
+audios = model.generate(
+    text="[singing] [sad] Quiet rain falls on the stone, memories of days now gone.",
+    language="English",
+)
+
+import soundfile as sf
+sf.write("out.wav", audios[0], model.sampling_rate)
+```
+
+CLI works the same way:
+
+```bash
+omnivoice-infer --model ModelsLab/omnivoice-singing \
+    --text "[happy] Hello there, how wonderful to see you today!" \
+    --language English \
+    --output out.wav
+```
+
+## Supported tags
+
+| Tag | Source data | Strength |
+|---|---|---|
+| `[singing]` | GTSinger English (6,755 clips, ~8 h) | strong |
+| `[happy]` | CREMA-D + RAVDESS + Expresso (~2900 clips) | strong |
+| `[sad]` | CREMA-D + RAVDESS + Expresso (~2900 clips) | strong |
+| `[angry]` | CREMA-D + RAVDESS (~1500 clips) | strong |
+| `[nervous]` | CREMA-D fear + RAVDESS fearful (~1400 clips) | strong |
+| `[whisper]` | Expresso whisper (~1500 clips) | strong |
+| `[calm]` | RAVDESS calm (~190 clips) | weak — limited data |
+| `[excited]` | RAVDESS surprised (~190 clips) | weak — limited data |
+
+Guidance scale of **3.0** (up from default 2.0) is recommended to make tag behavior more pronounced:
+
+```python
+audios = model.generate(
+    text="[happy] Welcome!",
+    language="English",
+    guidance_scale=3.0,
+)
+```
+
+## What's preserved from the base
+
+- Multilingual TTS (English, Chinese, Japanese, Korean, Spanish, French, German, Italian, Russian, Hindi, Gujarati, etc.)
+- Voice cloning from reference audio (`ref_audio` / `ref_text` args)
+- Voice design via `instruct` parameter (pitch / gender / age / accent attributes)
+- Fine-grained pronunciation control (pinyin / CMU phoneme overrides)
+- Speed and duration control (`speed` / `duration` args)
+- Built-in non-verbal symbols (`[laughter]`, `[sigh]`, etc.)
+
+## Training
+
+Two-stage finetune from `k2-fsa/OmniVoice`:
+
+**Stage 1 — Singing** (2500 steps):
+- GTSinger English (6.8k clips, tagged `[singing] {lyrics}`)
+- LibriTTS-R dev+test clean (10k clips, plain text — speech preservation)
+- LR 3e-5 cosine, bf16, 2 GPUs, batch_tokens=8192
+- Final eval loss: **4.74**
+
+**Stage 2 — Emotion** (2500 steps, forked from singing/checkpoint-2500):
+- CREMA-D + RAVDESS + Expresso read config (10.8k emotion clips)
+- 1.5k singing + 1.5k speech continuity samples
+- LR 3e-5 cosine, bf16, 2 GPUs, batch_tokens=8192
+- Best eval loss: **4.72** (at step 750) / final **4.88** (step 2500 — this checkpoint, found to sound better subjectively)
+
+This published checkpoint is the **final emotion step 2500**, which subjectively produces the cleanest emotional tag behavior while preserving speech/singing quality.
+
+## Known limitations
+
+- `[calm]` and `[excited]` had only ~190 training samples each (only one dataset contributed) — behavior is weaker than the other emotion tags.
+- Cross-language singing (sung Hindi, Gujarati, etc.) is extrapolation — works but quality varies.
+- Like the base model, output quality is bounded by the **HiggsAudioV2 tokenizer** (24 kHz, ~2 kbps, speech-domain tuned). Music / drum content is not supported by design.
+
+## License
+
+Apache 2.0. Downstream users must also comply with the individual licenses of the training datasets:
+- GTSinger: CC BY-NC-SA 4.0 (research use)
+- CREMA-D: ODbL
+- RAVDESS: CC BY-NC-SA 4.0
+- Expresso: CC BY-NC 4.0
+- LibriTTS-R: CC BY 4.0
+
+## Acknowledgements
+
+- [k2-fsa/OmniVoice](https://github.com/k2-fsa/OmniVoice) — base model & training framework
+- [HiggsAudioV2](https://huggingface.co/bosonai/higgs-audio-v2-tokenizer) — discrete audio tokenizer
+- Qwen team — Qwen3-0.6B backbone
+- Dataset authors: GTSinger, CREMA-D, RAVDESS, Expresso, LibriTTS-R teams

audio_tokenizer/.gitattributes

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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]
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+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
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+
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+
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audio_tokenizer/config.json

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+{
+  "acoustic_model_config": {
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+    "codebook_loss_weight": 1.0,
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+    "sampling_rate": 16000,
+    "upsampling_ratios": [
+      8,
+      5,
+      4,
+      2,
+      3
+    ]
+  },
+  "architectures": [
+    "HiggsAudioV2TokenizerModel"
+  ],
+  "block_dilations": [
+    1,
+    1
+  ],
+  "channel_ratios": [
+    1,
+    1
+  ],
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+  "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,
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+      10,
+      3,
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+    "feat_extract_activation": "gelu",
+    "feat_extract_norm": "group",
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+    "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
+}

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+size 805665628

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 %}

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": "5.5.4"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1141123172e28971fc97a59f0dfbb5356574c2730b141e2486ee01da089f98b6
+size 2450344144

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
+}