Duplicate from openbmb/VoxCPM2

Co-authored-by: Yixuan Zhou <zhouyx1998@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+language:
+- zh
+- en
+- ar
+- my
+- da
+- nl
+- fi
+- fr
+- de
+- el
+- he
+- hi
+- id
+- it
+- ja
+- km
+- ko
+- lo
+- ms
+- no
+- pl
+- pt
+- ru
+- es
+- sw
+- sv
+- tl
+- th
+- tr
+- vi
+license: apache-2.0
+library_name: voxcpm
+tags:
+- text-to-speech
+- tts
+- multilingual
+- voice-cloning
+- voice-design
+- diffusion
+- audio
+pipeline_tag: text-to-speech
+---
+
+# VoxCPM2
+
+**VoxCPM2** is a tokenizer-free, diffusion autoregressive Text-to-Speech model — **2B parameters**, **30 languages**, **48kHz** audio output, trained on over **2 million hours** of multilingual speech data.
+
+[![GitHub](https://img.shields.io/badge/GitHub-VoxCPM-blue?logo=github)](https://github.com/OpenBMB/VoxCPM)
+[![Docs](https://img.shields.io/badge/Docs-ReadTheDocs-8CA1AF)](https://voxcpm.readthedocs.io/en/latest/)
+[![Demo](https://img.shields.io/badge/Live%20Playground-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo)
+[![Audio Samples](https://img.shields.io/badge/Audio%20Samples-Demo%20Page-green)](https://openbmb.github.io/voxcpm2-demopage)
+[![Discord](https://img.shields.io/badge/Discord-VoxCPM-5865F2?logo=discord&logoColor=white)](https://discord.gg/KZUx7tVNwz)
+[![Lark](https://img.shields.io/badge/飞书群-VoxCPM-00D6B9?logo=lark&logoColor=white)](https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=acds0b9d-23d8-4d7e-b696-d200f3e22a7f)
+
+## Highlights
+
+- 🌍 **30-Language Multilingual** — No language tag needed; input text in any supported language directly
+- 🎨 **Voice Design** — Generate a novel voice from a natural-language description alone (gender, age, tone, emotion, pace…); no reference audio required
+- 🎛️ **Controllable Cloning** — Clone any voice from a short clip, with optional style guidance to steer emotion, pace, and expression while preserving timbre
+- 🎙️ **Ultimate Cloning** — Provide reference audio + its transcript for audio-continuation cloning; every vocal nuance faithfully reproduced
+- 🔊 **48kHz Studio-Quality Output** — Accepts 16kHz reference; outputs 48kHz via AudioVAE V2's built-in super-resolution, no external upsampler needed
+- 🧠 **Context-Aware Synthesis** — Automatically infers appropriate prosody and expressiveness from text content
+- ⚡ **Real-Time Streaming** — RTF as low as ~0.3 on NVIDIA RTX 4090, and ~0.13  accelerated by [Nano-VLLM](https://github.com/a710128/nanovllm-voxcpm)
+- 📜 **Fully Open-Source & Commercial-Ready** — Apache-2.0 license, free for commercial use
+
+
+<summary><b>Supported Languages (30)</b></summary>
+
+Arabic, Burmese, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Vietnamese
+
+Chinese Dialects: 四川话, 粤语, 吴语, 东北话, 河南话, 陕西话, 山东话, 天津话, 闽南话
+
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install voxcpm
+```
+
+**Requirements:** Python ≥ 3.10, PyTorch ≥ 2.5.0, CUDA ≥ 12.0 · [Full Quick Start →](https://voxcpm.readthedocs.io/en/latest/quickstart.html)
+
+### Text-to-Speech
+
+```python
+from voxcpm import VoxCPM
+import soundfile as sf
+
+model = VoxCPM.from_pretrained("openbmb/VoxCPM2", load_denoiser=False)
+
+wav = model.generate(
+    text="VoxCPM2 brings multilingual support, creative voice design, and controllable voice cloning.",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("output.wav", wav, model.tts_model.sample_rate)
+```
+
+### Voice Design
+
+Put the voice description in parentheses at the start of `text`, followed by the content to synthesize:
+
+```python
+wav = model.generate(
+    text="(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM2!",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("voice_design.wav", wav, model.tts_model.sample_rate)
+```
+
+### Controllable Voice Cloning
+
+```python
+# Basic cloning
+wav = model.generate(
+    text="This is a cloned voice generated by VoxCPM2.",
+    reference_wav_path="speaker.wav",
+)
+sf.write("clone.wav", wav, model.tts_model.sample_rate)
+
+# Cloning with style control
+wav = model.generate(
+    text="(slightly faster, cheerful tone)This is a cloned voice with style control.",
+    reference_wav_path="speaker.wav",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("controllable_clone.wav", wav, model.tts_model.sample_rate)
+```
+
+### Ultimate Cloning
+
+Provide both the reference audio and its exact transcript for maximum fidelity. Pass the same clip to both `reference_wav_path` and `prompt_wav_path` for highest similarity:
+
+```python
+wav = model.generate(
+    text="This is an ultimate cloning demonstration using VoxCPM2.",
+    prompt_wav_path="speaker_reference.wav",
+    prompt_text="The transcript of the reference audio.",
+    reference_wav_path="speaker_reference.wav",
+)
+sf.write("hifi_clone.wav", wav, model.tts_model.sample_rate)
+```
+
+### Streaming
+
+```python
+import numpy as np
+
+chunks = []
+for chunk in model.generate_streaming(text="Streaming is easy with VoxCPM!"):
+    chunks.append(chunk)
+wav = np.concatenate(chunks)
+sf.write("streaming.wav", wav, model.tts_model.sample_rate)
+```
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Architecture | Tokenizer-free Diffusion Autoregressive (LocEnc → TSLM → RALM → LocDiT) |
+| Backbone | Based on MiniCPM-4, totally 2B parameters |
+| Audio VAE | AudioVAE V2 (asymmetric encode/decode, 16kHz in → 48kHz out) |
+| Training Data | 2M+ hours multilingual speech |
+| LM Token Rate | 6.25 Hz |
+| Max Sequence Length | 8192 tokens |
+| dtype | bfloat16 |
+| VRAM | ~8 GB |
+| RTF (RTX 4090) | ~0.30 (standard) / ~0.13 (Nano-vLLM) |
+
+## Performance
+
+VoxCPM2 achieves state-of-the-art or competitive results on major zero-shot and controllable TTS benchmarks.
+
+See the [GitHub repo](https://github.com/OpenBMB/VoxCPM#-performance) for full benchmark tables (Seed-TTS-eval, CV3-eval, InstructTTSEval, MiniMax Multilingual Test).
+
+## Fine-tuning
+
+VoxCPM2 supports both full SFT and LoRA fine-tuning with as little as 5–10 minutes of audio:
+
+```bash
+# LoRA fine-tuning (recommended)
+python scripts/train_voxcpm_finetune.py \
+    --config_path conf/voxcpm_v2/voxcpm_finetune_lora.yaml
+
+# Full fine-tuning
+python scripts/train_voxcpm_finetune.py \
+    --config_path conf/voxcpm_v2/voxcpm_finetune_all.yaml
+```
+
+See the [Fine-tuning Guide](https://voxcpm.readthedocs.io/en/latest/finetuning/finetune.html) for full instructions.
+
+## Limitations
+
+- Voice Design and Style Control results may vary between runs; generating 1–3 times is recommended to obtain the desired output.
+- Performance varies across languages depending on training data availability.
+- Occasional instability may occur with very long or highly expressive inputs.
+- **Strictly forbidden** to use for impersonation, fraud, or disinformation. AI-generated content should be clearly labeled.
+
+## Citation
+
+```bibtex
+@article{voxcpm2_2026,
+  title   = {VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning},
+  author  = {VoxCPM Team},
+  journal = {GitHub},
+  year    = {2026},
+}
+
+@article{voxcpm2025,
+  title   = {VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning},
+  author  = {Zhou, Yixuan and Zeng, Guoyang and Liu, Xin and Li, Xiang and
+             Yu, Renjie and Wang, Ziyang and Ye, Runchuan and Sun, Weiyue and
+             Gui, Jiancheng and Li, Kehan and Wu, Zhiyong and Liu, Zhiyuan},
+  journal = {arXiv preprint arXiv:2509.24650},
+  year    = {2025},
+}
+```
+
+## License
+
+Released under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) license, free for commercial use. For production deployments, we recommend thorough testing and safety evaluation tailored to your use case.

audiovae.pth

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config.json

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+{
+    "architecture": "voxcpm2",
+    "lm_config": {
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+        "eos_token_id": 2,
+        "hidden_size": 2048,
+        "intermediate_size": 6144,
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+        "kv_channels": 128,
+        "mean_mode": false,
+        "cfm_config": {
+            "sigma_min": 1e-06,
+            "solver": "euler",
+            "t_scheduler": "log-norm",
+            "inference_cfg_rate": 2.0
+        }
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+    "audio_vae_config": {
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+        "encoder_rates": [2, 5, 8, 8],
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+        "decoder_dim": 2048,
+        "decoder_rates": [8, 6, 5, 2, 2, 2],
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+        "sample_rate": 16000,
+        "out_sample_rate": 48000
+    },
+    "max_length": 8192,
+    "device": "cuda",
+    "dtype": "bfloat16"
+}

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special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
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+    {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
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+    {
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+      "rstrip": false,
+      "single_word": false
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+    {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    },
+    {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false
+    }
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_voxcpm2.py

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+"""Custom tokenizer for VoxCPM2 that splits multi-character Chinese tokens.
+
+VoxCPM2 was trained with ``mask_multichar_chinese_tokens`` which splits
+multi-character Chinese tokens (e.g. "你好" -> ["你", "好"]) into individual
+character IDs before embedding.  The base LlamaTokenizerFast produces
+multi-character Chinese tokens that the model has never seen during training,
+yielding garbled Chinese audio output in downstream inference frameworks.
+
+This module provides ``VoxCPM2Tokenizer`` which transparently applies the
+character splitting inside ``encode()`` and ``__call__()``, so any downstream
+consumer (vLLM, vLLM-Omni, Nano-vLLM, etc.) gets correct single-character
+IDs without code changes.
+"""
+
+from transformers import LlamaTokenizerFast
+
+
+class VoxCPM2Tokenizer(LlamaTokenizerFast):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._split_map = self._build_split_map()
+
+    def _build_split_map(self) -> dict[int, list[int]]:
+        vocab = self.get_vocab()
+        split_map: dict[int, list[int]] = {}
+        for token, tid in vocab.items():
+            clean = token.replace("\u2581", "")
+            if len(clean) >= 2 and all(self._is_cjk(c) for c in clean):
+                char_ids = self.convert_tokens_to_ids(list(clean))
+                if all(c != self.unk_token_id for c in char_ids):
+                    split_map[tid] = char_ids
+        return split_map
+
+    @staticmethod
+    def _is_cjk(c: str) -> bool:
+        return (
+            "\u4e00" <= c <= "\u9fff"
+            or "\u3400" <= c <= "\u4dbf"
+            or "\uf900" <= c <= "\ufaff"
+            or "\U00020000" <= c <= "\U0002a6df"
+        )
+
+    def _expand_ids(self, ids: list[int]) -> list[int]:
+        result: list[int] = []
+        for tid in ids:
+            expansion = self._split_map.get(tid)
+            if expansion is not None:
+                result.extend(expansion)
+            else:
+                result.append(tid)
+        return result
+
+    def encode(self, text, *args, **kwargs):
+        ids = super().encode(text, *args, **kwargs)
+        return self._expand_ids(ids)
+
+    def __call__(self, text, *args, **kwargs):
+        result = super().__call__(text, *args, **kwargs)
+        if hasattr(result, "input_ids"):
+            ids = result["input_ids"]
+            if isinstance(ids, list) and ids and isinstance(ids[0], list):
+                result["input_ids"] = [self._expand_ids(x) for x in ids]
+                if "attention_mask" in result:
+                    result["attention_mask"] = [
+                        [1] * len(x) for x in result["input_ids"]
+                    ]
+            elif isinstance(ids, list):
+                result["input_ids"] = self._expand_ids(ids)
+                if "attention_mask" in result:
+                    result["attention_mask"] = [1] * len(result["input_ids"])
+        return result

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "<|audio_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "<|audio_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "<|audio_prompt_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "<|audio_prompt_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "105": {
+      "content": "<|background|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "106": {
+      "content": "<|/background|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "107": {
+      "content": "<|characters|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "108": {
+      "content": "<|/characters|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "109": {
+      "content": "<|speaker_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "110": {
+      "content": "<|/speaker_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "111": {
+      "content": "<|span|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "112": {
+      "content": "<|/span|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73440": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73441": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73442": {
+      "content": "<|tool_call|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73443": {
+      "content": "<|execute_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73444": {
+      "content": "<|execute_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73445": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73446": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "73447": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_end|>",
+    "<|im_start|>",
+    "<|tool_call|>",
+    "<|execute_start|>",
+    "<|execute_end|>",
+    "<|fim_prefix|>",
+    "<|fim_middle|>",
+    "<|fim_suffix|>"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "VoxCPM2Tokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_voxcpm2.VoxCPM2Tokenizer",
+      null
+    ]
+  }
+}