set default repo id

README.md

@@ -1,7 +1,7 @@
 ## 🎙️ VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning
 
 
-[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-blue)](https://github.com/OpenBMB/VoxCPM/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OpenBMB-yellow)](hhttps://huggingface.co/openbmb/VoxCPM-0.5B) [![Live Playground](https://img.shields.io/badge/Live%20PlayGround-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [![Samples](https://img.shields.io/badge/Page-Samples-red)](https://thuhcsi.github.io/VoxCPM/)
+[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-blue)](https://github.com/OpenBMB/VoxCPM/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OpenBMB-yellow)](https://huggingface.co/openbmb/VoxCPM-0.5B) [![Live Playground](https://img.shields.io/badge/Live%20PlayGround-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [![Samples](https://img.shields.io/badge/Page-Samples-red)](https://thuhcsi.github.io/VoxCPM/)
 
 
 <div align="center">
@@ -9,17 +9,17 @@
 </div>
 
 ## News 
-* [2025.09.16] 🔥 🔥 🔥  We Open Source the VoxCPM-0.5B weights!
+* [2025.09.16] 🔥 🔥 🔥  We Open Source the VoxCPM-0.5B [weights](https://huggingface.co/openbmb/VoxCPM-0.5B)!
 * [2025.09.16] 🎉 🎉 🎉  We Provide the [Gradio PlayGround](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) for VoxCPM-0.5B, try it now! 
 
 ## Overview
 
 VoxCPM is a novel tokenizer-free Text-to-Speech (TTS) system that redefines realism in speech synthesis. By modeling speech in a continuous space, it overcomes the limitations of discrete tokenization and enables two flagship capabilities: context-aware speech generation and true-to-life zero-shot voice cloning.
 
-Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses an end-to-end diffusion autoregressive architecture that directly generates continuous speech representations from text. Built on [MiniCPM-4](https://huggingface.co/openbmb/MiniCPM4-0.5B), it achieves implicit semantic-acoustic decoupling through hierachical language modeling and FSQ constraints, greatly enhancing both expressiveness and generation stability.
+Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses an end-to-end diffusion autoregressive architecture that directly generates continuous speech representations from text. Built on [MiniCPM-4](https://huggingface.co/openbmb/MiniCPM4-0.5B) backbone, it achieves implicit semantic-acoustic decoupling through hierachical language modeling and FSQ constraints, greatly enhancing both expressiveness and generation stability.
 
 <div align="center">
-  <img src="assets/voxcpm_model.png" alt="VoxCPM Model Architecture" width="500">
+  <img src="assets/voxcpm_model.png" alt="VoxCPM Model Architecture" width="90%">
 </div>
 
 
@@ -30,6 +30,13 @@ Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses
 
 
 
+
+
+
+
+
+
+
 ##  Quick Start
 
 ### 🔧 Install from PyPI
@@ -61,13 +68,13 @@ wav = model.generate(
     text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.",
     prompt_wav_path=None,      # optional: path to a prompt speech for voice cloning
     prompt_text=None,          # optional: reference text
-    cfg_value=2.0,             
-    inference_timesteps=10,
-    normalize=True, 
-    denoise=True,
-    retry_badcase=True,        # optional: enable retrying mode
-    retry_badcase_max_times=3,
-    retry_badcase_ratio_threshold=6.0,
+    cfg_value=2.0,             # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
+    inference_timesteps=10,   # LocDiT inference timesteps, higher for better result, lower for fast speed
+    normalize=True,           # enable external TN tool
+    denoise=True,             # enable external Denoise tool
+    retry_badcase=True,        # enable retrying mode for some bad cases (unstoppable)
+    retry_badcase_max_times=3,  # maximum retrying times
+    retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
 )
 
 sf.write("output.wav", wav, 16000)
@@ -80,10 +87,10 @@ After installation, the entry point is `voxcpm` (or use `python -m voxcpm.cli`).
 
 ```bash
 # 1) Direct synthesis (single text)
-voxcpm --text "Hello VoxCPM" --output out.wav
+voxcpm --text "VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech." --output out.wav
 
 # 2) Voice cloning (reference audio + transcript)
-voxcpm --text "Hello" \
+voxcpm --text "VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech." \
   --prompt-audio path/to/voice.wav \
   --prompt-text "reference transcript" \
   --output out.wav \
@@ -175,41 +182,41 @@ VoxCPM achieves competitive results on public zero-shot TTS benchmarks:
 | Model | Parameters | Open-Source | test-EN | | test-ZH | | test-Hard | |
 |------|------|------|:------------:|:--:|:------------:|:--:|:-------------:|:--:|
 | | | | WER/%⬇ | SIM/%⬆| CER/%⬇| SIM/%⬆ | CER/%⬇ | SIM/%⬆ |
+| MegaTTS3 | 0.5B | ❌ | 2.79 | 77.1 | 1.52 | 79.0 | - | - |
+| DiTAR | 0.6B | ❌ | 1.69 | 73.5 | 1.02 | 75.3 | - | - |
+| CosyVoice3 | 0.5B | ❌ | 2.02 | 71.8 | 1.16 | 78.0 | 6.08 | 75.8 |
+| CosyVoice3 | 1.5B | ❌ | 2.22 | 72.0 | 1.12 | 78.1 | 5.83 | 75.8 |
+| Seed-TTS | - | ❌ | 2.25 | 76.2 | 1.12 | 79.6 | 7.59 | 77.6 |
+| MiniMax-Speech | - | ❌ | 1.65 | 69.2 | 0.83 | 78.3 | - | - |
 | CosyVoice | 0.3B | ✅ | 4.29 | 60.9 | 3.63 | 72.3 | 11.75 | 70.9 |
-| CosyVoice2 | 0.5B | ✅ | 3.09 | 65.9 | 1.38 | 75.7 | 6.83 | 72.4 |
+| CosyVoice2 | 0.5B | ✅ | 3.09 | 65.9 | 1.38 | 75.7 | **6.83** | 72.4 |
 | F5-TTS | 0.3B | ✅ | 2.00 | 67.0 | 1.53 | 76.0 | 8.67 | 71.3 |
 | SparkTTS | 0.5B | ✅ | 3.14 | 57.3 | 1.54 | 66.0 | - | - |
 | FireRedTTS | 0.5B | ✅ | 3.82 | 46.0 | 1.51 | 63.5 | 17.45 | 62.1 |
 | FireRedTTS-2 | 1.5B | ✅ | 1.95 | 66.5 | 1.14 | 73.6 | - | - |
-| Qwen2.5-Omni | 7B | ✅ | 2.72 | 63.2 | 1.70 | 75.2 | 7.97 | 74.7 |
+| Qwen2.5-Omni | 7B | ✅ | 2.72 | 63.2 | 1.70 | 75.2 | 7.97 | **74.7** |
 | OpenAudio-s1-mini | 0.5B | ✅ | 1.94 | 55.0 | 1.18 | 68.5 | - | - |
 | IndexTTS2 | 1.5B | ✅ | 2.23 | 70.6 | 1.03 | 76.5 | - | - |
 | VibeVoice | 1.5B | ✅ | 3.04 | 68.9 | 1.16 | 74.4 | - | - |
 | HiggsAudio-v2 | 3B | ✅ | 2.44 | 67.7 | 1.50 | 74.0 | - | - |
-| CosyVoice3 | 0.5B | ❌ | 2.02 | 71.8 | 1.16 | 78.0 | 6.08 | 75.8 |
-| CosyVoice3 | 1.5B | ❌ | 2.22 | 72.0 | 1.12 | 78.1 | 5.83 | 75.8 |
-| MegaTTS3 | 0.5B | ❌ | 2.79 | 77.1 | 1.52 | 79.0 | - | - |
-| DiTAR | 0.6B | ❌ | 1.69 | 73.5 | 1.02 | 75.3 | - | - |
-| Seed-TTS | - | ❌ | 2.25 | 76.2 | 1.12 | 79.6 | 7.59 | 77.6 |
-| MiniMax-Speech | - | ❌ | 1.65 | 69.2 | 0.83 | 78.3 | - | - |
-| **VoxCPM** | **0.5B** | **✅** | **1.85** | **72.9** | **0.93** | **77.2** | 8.87 | 73.0 |
+| **VoxCPM** | 0.5B | ✅ | **1.85** | **72.9** | **0.93** | **77.2** | 8.87 | 73.0 |
 
 
 ###  CV3-eval Benchmark
 
-| Model | zh | en | hard-zh | | | hard-en | | | |
-|-------|:--:|:--:|:-------:|:--:|:--:|:-------:|:--:|:--:|:--:|
-| | CER/%⬇ | WER/%⬇ | CER/%⬇ | SIM/%⬆ | DNSMOS⬆ | WER/%⬇ | SIM/%⬆ | DNSMOS⬆ | |
-| F5-TTS | 5.47 | 8.90 | - | - | - | - | - | - | |
-| SparkTTS | 5.15 | 11.0 | - | - | - | - | - | - | |
-| GPT-SoVits | 7.34 | 12.5 | - | - | - | - | - | - | |
-| CosyVoice2 | 4.08 | 6.32 | 12.58 | 72.6 | 3.81 | 11.96 | 66.7 | 3.95 | |
-| OpenAudio-s1-mini | 4.00 | 5.54 | 18.1 | 58.2 | 3.77 | 12.4 | 55.7 | 3.89 | |
-| IndexTTS2 | 3.58 | 4.45 | 12.8 | 74.6 | 3.65 | fail | fail | fail | |
-| HiggsAudio-v2 | 9.54 | 7.89 | 41.0 | 60.2 | 3.39 | 10.3 | 61.8 | 3.68 | |
-| CosyVoice3-0.5B | 3.89 | 5.24 | 14.15 | 78.6 | 3.75 | 9.04 | 75.9 | 3.92 | |
-| CosyVoice3-1.5B | 3.91 | 4.99 | 9.77 | 78.5 | 3.79 | 10.55 | 76.1 | 3.95 | |
-| **VoxCPM** | **3.40** | **4.04** | 12.9 | 66.1 | 3.59 | **7.89** | 64.3 | 3.74 | |
+| Model | zh | en | hard-zh | | | hard-en | | |
+|-------|:--:|:--:|:-------:|:--:|:--:|:-------:|:--:|:--:|
+| | CER/%⬇ | WER/%⬇ | CER/%⬇ | SIM/%⬆ | DNSMOS⬆ | WER/%⬇ | SIM/%⬆ | DNSMOS⬆ |
+| F5-TTS | 5.47 | 8.90 | - | - | - | - | - | - |
+| SparkTTS | 5.15 | 11.0 | - | - | - | - | - | - |
+| GPT-SoVits | 7.34 | 12.5 | - | - | - | - | - | - |
+| CosyVoice2 | 4.08 | 6.32 | 12.58 | 72.6 | 3.81 | 11.96 | 66.7 | 3.95 |
+| OpenAudio-s1-mini | 4.00 | 5.54 | 18.1 | 58.2 | 3.77 | 12.4 | 55.7 | 3.89 |
+| IndexTTS2 | 3.58 | 4.45 | 12.8 | 74.6 | 3.65 | - | - | - |
+| HiggsAudio-v2 | 9.54 | 7.89 | 41.0 | 60.2 | 3.39 | 10.3 | 61.8 | 3.68 |
+| CosyVoice3-0.5B | 3.89 | 5.24 | 14.15 | 78.6 | 3.75 | 9.04 | 75.9 | 3.92 |
+| CosyVoice3-1.5B | 3.91 | 4.99 | 9.77 | 78.5 | 3.79 | 10.55 | 76.1 | 3.95 |
+| **VoxCPM** | **3.40** | **4.04** | 12.9 | 66.1 | 3.59 | **7.89** | 64.3 | 3.74 |
 
 
 

pyproject.toml

@@ -27,22 +27,21 @@ classifiers = [
 ]
 requires-python = ">=3.8"
 dependencies = [
-    "torch==2.5.1",
-    "torchaudio==2.5.1",
-    "transformers==4.50.1",
+    "torch>=2.5.0",
+    "torchaudio>=2.5.0",
+    "transformers>=4.36.2",
     "einops",
     "gradio",
     "inflect",
-    "WeTextProcessing",
     "addict",
-    "modelscope==1.22.0",
-    "simplejson",
-    "datasets==2.18.0",
-    "sortedcontainers",
-    "librosa",
+    "WeTextProcessing",
+    "modelscope>=1.22.0",
+    "datasets>=2,<4",
     "huggingface-hub",
     "pydantic",
     "tqdm",
+    "simplejson",
+    "sortedcontainers",
     "soundfile",
     "funasr",
     "spaces"
@@ -69,7 +68,7 @@ Documentation = "https://github.com/OpenBMB/VoxCPM#readme"
 
 [tool.setuptools.packages.find]
 where = ["src"]
-include = ["voxcpm"]
+include = ["voxcpm*"]
 
 [tool.setuptools.package-dir]
 "" = "src"

requirements.txt

@@ -1,16 +0,0 @@
-torch==2.5.1
-torchaudio==2.5.1
-transformers==4.50.1
-einops
-gradio
-inflect
-WeTextProcessing
-addicts
-modelscope==1.22.0
-simplejson 
-datasets==2.18.0 
-addicts
-sortedcontainers
-librosa
-huggingface-hub
-spaces

src/voxcpm/core.py

@@ -2,8 +2,6 @@ import torch
 import torchaudio
 import os
 import tempfile
-from modelscope.pipelines import pipeline
-from modelscope.utils.constant import Tasks
 from huggingface_hub import snapshot_download
 from .model.voxcpm import VoxCPMModel
 from .utils.text_normalize import TextNormalizer
@@ -29,9 +27,8 @@ class VoxCPM:
         self.tts_model = VoxCPMModel.from_local(voxcpm_model_path)
         self.text_normalizer = TextNormalizer()
         if enable_denoiser and zipenhancer_model_path is not None:
-            self.denoiser = pipeline(
-                Tasks.acoustic_noise_suppression,
-                model=zipenhancer_model_path)
+            from .zipenhancer import ZipEnhancer
+            self.denoiser = ZipEnhancer(zipenhancer_model_path)
         else:
             self.denoiser = None
         print("Warm up VoxCPMModel...")
@@ -50,7 +47,7 @@ class VoxCPM:
         """Instantiate ``VoxCPM`` from a Hugging Face Hub snapshot.
 
         Args:
-            hf_model_id: Explicit Hugging Face repository id (e.g. "org/repo").
+            hf_model_id: Explicit Hugging Face repository id (e.g. "org/repo") or local path.
             load_denoiser: Whether to initialize the denoiser pipeline.
             zipenhancer_model_id: Denoiser model id or path for ModelScope
                 acoustic noise suppression.
@@ -67,9 +64,14 @@ class VoxCPM:
                 ``hf_model_id`` is provided.
         """
         repo_id = hf_model_id
-        if not repo_id or repo_id.strip() == "":
-            raise ValueError("You must provide a valid hf_model_id")
+        if not repo_id:
+            raise ValueError("You must provide hf_model_id")
         
+        # Load from local path if provided
+        if os.path.isdir(repo_id):
+            local_path = repo_id
+        else:
+            # Otherwise, try from_pretrained (Hub); exit on failure
             local_path = snapshot_download(
                 repo_id=repo_id,
                 cache_dir=cache_dir,
@@ -82,12 +84,6 @@ class VoxCPM:
             enable_denoiser=load_denoiser,
         )
 
-    def _normalize_loudness(self, wav_path: str):
-        audio, sr = torchaudio.load(wav_path)
-        loudness = torchaudio.functional.loudness(audio, sr)
-        normalized_audio = torchaudio.functional.gain(audio, -20-loudness)
-        torchaudio.save(wav_path, normalized_audio, sr)
-
     def generate(self, 
             text : str,
             prompt_wav_path : str = None,
@@ -135,9 +131,7 @@ class VoxCPM:
                 if denoise and self.denoiser is not None:
                     with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
                         temp_prompt_wav_path = tmp_file.name
-                    
-                    self.denoiser(prompt_wav_path, output_path=temp_prompt_wav_path)
-                    self._normalize_loudness(temp_prompt_wav_path)
+                    self.denoiser.enhance(prompt_wav_path, output_path=temp_prompt_wav_path)
                     prompt_wav_path = temp_prompt_wav_path
                 fixed_prompt_cache = self.tts_model.build_prompt_cache(
                     prompt_wav_path=prompt_wav_path,

src/voxcpm/modules/locdit/unified_cfm.py

@@ -88,7 +88,7 @@ class UnifiedCFM(torch.nn.Module):
                 shape: (n_timesteps + 1,)
             mu (torch.Tensor): output of encoder
                 shape: (batch_size, n_feats)
-            cond: Not used but kept for future purposes
+            cond: condition -- prefix prompt
             cfg_value (float, optional): cfg value for guidance. Defaults to 1.0.
         """
         t, _, dt = t_span[0], t_span[-1], t_span[0] - t_span[1]

src/voxcpm/zipenhancer.py

@@ -0,0 +1,76 @@
+"""
+ZipEnhancer Module - Audio Denoising Enhancer
+
+Provides on-demand import ZipEnhancer functionality for audio denoising processing.
+Related dependencies are imported only when denoising functionality is needed.
+"""
+
+import os
+import tempfile
+from typing import Optional, Union
+import torchaudio
+import torch
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+
+class ZipEnhancer:
+    """ZipEnhancer Audio Denoising Enhancer"""
+    def __init__(self, model_path: str = "iic/speech_zipenhancer_ans_multiloss_16k_base"):
+        """
+        Initialize ZipEnhancer
+        Args:
+            model_path: ModelScope model path or local path
+        """
+        self.model_path = model_path
+        self._pipeline = pipeline(
+                Tasks.acoustic_noise_suppression,
+                model=self.model_path
+            )
+        
+    def _normalize_loudness(self, wav_path: str):
+        """
+        Audio loudness normalization
+        
+        Args:
+            wav_path: Audio file path
+        """
+        audio, sr = torchaudio.load(wav_path)
+        loudness = torchaudio.functional.loudness(audio, sr)
+        normalized_audio = torchaudio.functional.gain(audio, -20-loudness)
+        torchaudio.save(wav_path, normalized_audio, sr)
+    
+    def enhance(self, input_path: str, output_path: Optional[str] = None, 
+                normalize_loudness: bool = True) -> str:
+        """
+        Audio denoising enhancement
+        Args:
+            input_path: Input audio file path
+            output_path: Output audio file path (optional, creates temp file by default)
+            normalize_loudness: Whether to perform loudness normalization
+        Returns:
+            str: Output audio file path
+        Raises:
+            RuntimeError: If pipeline is not initialized or processing fails
+        """
+        if not os.path.exists(input_path):
+            raise FileNotFoundError(f"Input audio file does not exist: {input_path}")
+        # Create temporary file if no output path is specified
+        if output_path is None:
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as tmp_file:
+                output_path = tmp_file.name
+        try:
+            # Perform denoising processing
+            self._pipeline(input_path, output_path=output_path)
+            # Loudness normalization
+            if normalize_loudness:
+                self._normalize_loudness(output_path)
+            return output_path
+        except Exception as e:
+            # Clean up possibly created temporary files
+            if output_path and os.path.exists(output_path):
+                try:
+                    os.unlink(output_path)
+                except OSError:
+                    pass
+            raise RuntimeError(f"Audio denoising processing failed: {e}")