fix: formatting/trailing whitespace

app.py

@@ -45,8 +45,8 @@ _EXAMPLES_FOOTER_EN = (
     "**Example 1 — Gentle & Melancholic Girl**  \n"
     '`Control Instruction`: *"A young girl with a soft, sweet voice. '
     'Speaks slowly with a melancholic, slightly tsundere tone."*  \n'
-    '`Target Text`: *"I never asked you to stay… It\'s not like I care or anything. '
+    "`Target Text`: *\"I never asked you to stay… It's not like I care or anything. "
-    'But… why does it still hurt so much now that you\'re gone?"*  \n\n'
+    "But… why does it still hurt so much now that you're gone?\"*  \n\n"
     "**Example 2 — Laid-Back Surfer Dude**  \n"
     '`Control Instruction`: *"Relaxed young male voice, slightly nasal, '
     'lazy drawl, very casual and chill."*  \n'
@@ -147,7 +147,7 @@ _I18N_TRANSLATIONS = {
         "examples_footer": _EXAMPLES_FOOTER_ZH,
     },
     "zh-Hans": None,  # alias, filled below
-    "zh": None,       # alias, filled below
+    "zh": None,  # alias, filled below
 }
 _I18N_TRANSLATIONS["zh-Hans"] = _I18N_TRANSLATIONS["zh-CN"]
 _I18N_TRANSLATIONS["zh"] = _I18N_TRANSLATIONS["zh-CN"]
@@ -160,8 +160,7 @@ for _d in _I18N_TRANSLATIONS.values():
 I18N = gr.I18n(**_I18N_TRANSLATIONS)
 
 DEFAULT_TARGET_TEXT = (
-    "VoxCPM2 is a creative multilingual TTS model from ModelBest, "
+    "VoxCPM2 is a creative multilingual TTS model from ModelBest, " "designed to generate highly realistic speech."
-    "designed to generate highly realistic speech."
 )
 
 _CUSTOM_CSS = """
@@ -224,6 +223,7 @@ _APP_THEME = gr.themes.Soft(
 
 # ---------- Model ----------
 
+
 class VoxCPMDemo:
     def __init__(self, model_id: str = "openbmb/VoxCPM2", device: str = "auto") -> None:
         self.device = resolve_runtime_device(device, "cuda")
@@ -252,9 +252,7 @@ class VoxCPMDemo:
     def get_or_load_asr_model(self) -> AutoModel:
         if self.asr_model is not None:
             return self.asr_model
-        logger.info(
+        logger.info(f"Loading ASR model: {self.asr_model_id} on device: {self.asr_device}")
-            f"Loading ASR model: {self.asr_model_id} on device: {self.asr_device}"
-        )
         self.asr_model = AutoModel(
             model=self.asr_model_id,
             disable_update=True,
@@ -352,6 +350,7 @@ class VoxCPMDemo:
 
 # ---------- UI ----------
 
+
 def create_demo_interface(demo: VoxCPMDemo):
     gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"])
 
@@ -554,6 +553,7 @@ def create_demo_interface(demo: VoxCPMDemo):
 
     return interface
 
+
 def run_demo(
     server_name: str = "0.0.0.0",
     server_port: int = 8808,
@@ -575,9 +575,12 @@ def run_demo(
 
 if __name__ == "__main__":
     import argparse
+
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--model-id", type=str, default="openbmb/VoxCPM2",
+        "--model-id",
+        type=str,
+        default="openbmb/VoxCPM2",
         help="Local path or HuggingFace repo ID (default: openbmb/VoxCPM2)",
     )
     parser.add_argument("--port", type=int, default=8808, help="Server port")

app_old.py

@@ -6,6 +6,7 @@ import gradio as gr
 from typing import Optional, Tuple
 from funasr import AutoModel
 from pathlib import Path
+
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 if os.environ.get("HF_REPO_ID", "").strip() == "":
     os.environ["HF_REPO_ID"] = "openbmb/VoxCPM1.5"
@@ -23,7 +24,7 @@ class VoxCPMDemo:
         self.asr_model: Optional[AutoModel] = AutoModel(
             model=self.asr_model_id,
             disable_update=True,
-            log_level='DEBUG',
+            log_level="DEBUG",
             device="cuda:0" if self.device == "cuda" else "cpu",
         )
 
@@ -48,6 +49,7 @@ class VoxCPMDemo:
             if not os.path.isdir(target_dir):
                 try:
                     from huggingface_hub import snapshot_download  # type: ignore
+
                     os.makedirs(target_dir, exist_ok=True)
                     print(f"Downloading model from HF repo '{repo_id}' to '{target_dir}' ...", file=sys.stderr)
                     snapshot_download(repo_id=repo_id, local_dir=target_dir, local_dir_use_symlinks=False)
@@ -72,7 +74,7 @@ class VoxCPMDemo:
         if prompt_wav is None:
             return ""
         res = self.asr_model.generate(input=prompt_wav, language="auto", use_itn=True)
-        text = res[0]["text"].split('|>')[-1]
+        text = res[0]["text"].split("|>")[-1]
         return text
 
     def generate_tts_audio(
@@ -149,11 +151,13 @@ _CUSTOM_CSS = """
 
 def create_demo_interface(demo: VoxCPMDemo):
     """Build the Gradio UI for VoxCPM demo."""
-    gr.set_static_paths(paths=[Path.cwd().absolute()/"assets"])
+    gr.set_static_paths(paths=[Path.cwd().absolute() / "assets"])
 
     with gr.Blocks() as interface:
         # Header logo
-        gr.HTML('<div class="logo-container"><img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo"></div>')
+        gr.HTML(
+            '<div class="logo-container"><img src="/gradio_api/file=assets/voxcpm_logo.png" alt="VoxCPM Logo"></div>'
+        )
 
         # Quick Start
         with gr.Accordion("📋 Quick Start Guide ｜快速入门", open=False, elem_id="acc_quick"):
@@ -201,7 +205,7 @@ def create_demo_interface(demo: VoxCPMDemo):
         with gr.Row():
             with gr.Column():
                 prompt_wav = gr.Audio(
-                    sources=["upload", 'microphone'],
+                    sources=["upload", "microphone"],
                     type="filepath",
                     label="Prompt Speech (Optional, or let VoxCPM improvise)",
                     value="./examples/example.wav",
@@ -210,13 +214,13 @@ def create_demo_interface(demo: VoxCPMDemo):
                     value=False,
                     label="Prompt Speech Enhancement",
                     elem_id="chk_denoise",
-                    info="We use ZipEnhancer model to denoise the prompt audio."
+                    info="We use ZipEnhancer model to denoise the prompt audio.",
                 )
                 with gr.Row():
                     prompt_text = gr.Textbox(
                         value="Just by listening a few minutes a day, you'll be able to eliminate negative thoughts by conditioning your mind to be more positive.",
                         label="Prompt Text",
-                        placeholder="Please enter the prompt text. Automatic recognition is supported, and you can correct the results yourself..."
+                        placeholder="Please enter the prompt text. Automatic recognition is supported, and you can correct the results yourself...",
                     )
                 run_btn = gr.Button("Generate Speech", variant="primary")
 
@@ -227,7 +231,7 @@ def create_demo_interface(demo: VoxCPMDemo):
                     value=2.0,
                     step=0.1,
                     label="CFG Value (Guidance Scale)",
-                    info="Higher values increase adherence to prompt, lower values allow more creativity"
+                    info="Higher values increase adherence to prompt, lower values allow more creativity",
                 )
                 inference_timesteps = gr.Slider(
                     minimum=4,
@@ -235,7 +239,7 @@ def create_demo_interface(demo: VoxCPMDemo):
                     value=10,
                     step=1,
                     label="Inference Timesteps",
-                    info="Number of inference timesteps for generation (higher values may improve quality but slower)"
+                    info="Number of inference timesteps for generation (higher values may improve quality but slower)",
                 )
                 with gr.Row():
                     text = gr.Textbox(
@@ -247,14 +251,22 @@ def create_demo_interface(demo: VoxCPMDemo):
                         value=False,
                         label="Text Normalization",
                         elem_id="chk_normalize",
-                        info="We use wetext library to normalize the input text."
+                        info="We use wetext library to normalize the input text.",
                     )
                 audio_output = gr.Audio(label="Output Audio")
 
         # Wiring
         run_btn.click(
             fn=demo.generate_tts_audio,
-            inputs=[text, prompt_wav, prompt_text, cfg_value, inference_timesteps, DoNormalizeText, DoDenoisePromptAudio],
+            inputs=[
+                text,
+                prompt_wav,
+                prompt_text,
+                cfg_value,
+                inference_timesteps,
+                DoNormalizeText,
+                DoDenoisePromptAudio,
+            ],
             outputs=[audio_output],
             show_progress=True,
             api_name="generate",
@@ -277,4 +289,4 @@ def run_demo(server_name: str = "localhost", server_port: int = 7860, show_error
 
 
 if __name__ == "__main__":
-    run_demo()
+    run_demo()

lora_ft_webui.py

@@ -308,7 +308,8 @@ def run_inference(text, prompt_wav, prompt_text, lora_selection, cfg_scale, step
             if new_r is not None and current_r is not None and new_r != current_r:
                 print(f"LoRA rank mismatch (model r={current_r}, checkpoint r={new_r}), reloading...", file=sys.stderr)
                 reload_base = (
-                    new_base_model if new_base_model and os.path.exists(new_base_model)
+                    new_base_model
+                    if new_base_model and os.path.exists(new_base_model)
                     else (pretrained_path if pretrained_path and pretrained_path.strip() else default_pretrained_path)
                 )
                 try:
@@ -982,9 +983,7 @@ with gr.Blocks(title="VoxCPM LoRA WebUI", theme=gr.themes.Soft(), css=custom_css
 
                         gr.Markdown("#### 分发选项 (Distribution)")
                         with gr.Row():
-                            hf_model_id = gr.Textbox(
+                            hf_model_id = gr.Textbox(label="HuggingFace Model ID (e.g., openbmb/VoxCPM2)", value="")
-                                label="HuggingFace Model ID (e.g., openbmb/VoxCPM2)", value=""
-                            )
                             distribute = gr.Checkbox(label="分发模式 (distribute)", value=False)
 
                 with gr.Column(scale=2, elem_classes="form-section"):

scripts/test_pick_runtime_dtype.py

@@ -3,6 +3,7 @@
 Loads src/voxcpm/model/utils.py directly to avoid the heavy voxcpm package
 init. Run with: `python scripts/test_pick_runtime_dtype.py`.
 """
+
 import importlib.util
 import os
 import pathlib

scripts/test_voxcpm_ft_infer.py

@@ -143,4 +143,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

scripts/test_voxcpm_lora_infer.py

@@ -273,4 +273,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

scripts/train_voxcpm_finetune.py

@@ -599,7 +599,9 @@ def generate_sample_audio(
             )
             with torch.no_grad():
                 with autocast_ctx:
-                    generated = unwrapped_model.generate(target_text=text, inference_timesteps=10, cfg_value=2.0, seed=42 )
+                    generated = unwrapped_model.generate(
+                        target_text=text, inference_timesteps=10, cfg_value=2.0, seed=42
+                    )
 
             # Restore training setup
             # unwrapped_model.to(torch.float32)

src/voxcpm/cli.py

@@ -86,9 +86,7 @@ def resolve_prompt_text(args, parser) -> str | None:
 
 
 def detect_model_architecture(args) -> str | None:
-    model_location = getattr(args, "model_path", None) or getattr(
+    model_location = getattr(args, "model_path", None) or getattr(args, "hf_model_id", None)
-        args, "hf_model_id", None
-    )
     if not model_location:
         return None
 
@@ -103,11 +101,7 @@ def detect_model_architecture(args) -> str | None:
     model_hint = str(model_location).lower()
     if "voxcpm2" in model_hint:
         return "voxcpm2"
-    if (
+    if "voxcpm1.5" in model_hint or "voxcpm-1.5" in model_hint or "voxcpm_1.5" in model_hint:
-        "voxcpm1.5" in model_hint
-        or "voxcpm-1.5" in model_hint
-        or "voxcpm_1.5" in model_hint
-    ):
         return "voxcpm"
 
     return None
@@ -121,9 +115,7 @@ def validate_prompt_related_args(args, parser, prompt_text: str | None):
         parser.error("--prompt-audio requires --prompt-text or --prompt-file.")
 
     if args.control and prompt_text:
-        parser.error(
+        parser.error("--control cannot be used together with --prompt-text or --prompt-file.")
-            "--control cannot be used together with --prompt-text or --prompt-file."
-        )
 
 
 def validate_reference_support(args, parser):
@@ -138,9 +130,7 @@ def validate_reference_support(args, parser):
 def validate_design_args(args, parser):
     prompt_text = resolve_prompt_text(args, parser)
     if args.prompt_audio or args.reference_audio or prompt_text:
-        parser.error(
+        parser.error("`design` does not accept prompt/reference audio. Use `clone` instead.")
-            "`design` does not accept prompt/reference audio. Use `clone` instead."
-        )
 
 
 def validate_clone_args(args, parser):
@@ -149,9 +139,7 @@ def validate_clone_args(args, parser):
     validate_reference_support(args, parser)
 
     if not args.prompt_audio and not args.reference_audio:
-        parser.error(
+        parser.error("`clone` requires --reference-audio, or --prompt-audio with --prompt-text/--prompt-file.")
-            "`clone` requires --reference-audio, or --prompt-audio with --prompt-text/--prompt-file."
-        )
 
     return prompt_text
 
@@ -173,9 +161,7 @@ def load_model(args):
 
     print("Loading VoxCPM model...", file=sys.stderr)
 
-    zipenhancer_path = getattr(args, "zipenhancer_path", None) or os.environ.get(
+    zipenhancer_path = getattr(args, "zipenhancer_path", None) or os.environ.get("ZIPENHANCER_MODEL_PATH", None)
-        "ZIPENHANCER_MODEL_PATH", None
-    )
 
     # Build LoRA config if provided
     lora_config = None
@@ -259,8 +245,7 @@ def _run_single(args, parser, *, text: str, output: str, prompt_text: str | None
         cfg_value=args.cfg_value,
         inference_timesteps=args.inference_timesteps,
         normalize=args.normalize,
-        denoise=args.denoise
+        denoise=args.denoise and (args.prompt_audio is not None or args.reference_audio is not None),
-        and (args.prompt_audio is not None or args.reference_audio is not None),
         seed=args.seed,
     )
 
@@ -275,17 +260,13 @@ def _run_single(args, parser, *, text: str, output: str, prompt_text: str | None
 def cmd_design(args, parser):
     validate_design_args(args, parser)
     final_text = build_final_text(args.text, args.control)
-    return _run_single(
+    return _run_single(args, parser, text=final_text, output=args.output, prompt_text=None)
-        args, parser, text=final_text, output=args.output, prompt_text=None
-    )
 
 
 def cmd_clone(args, parser):
     prompt_text = validate_clone_args(args, parser)
     final_text = build_final_text(args.text, args.control)
-    return _run_single(
+    return _run_single(args, parser, text=final_text, output=args.output, prompt_text=prompt_text)
-        args, parser, text=final_text, output=args.output, prompt_text=prompt_text
-    )
 
 
 def cmd_validate(args, parser):
@@ -324,15 +305,11 @@ def cmd_batch(args, parser):
 
     prompt_audio_path = None
     if args.prompt_audio:
-        prompt_audio_path = str(
+        prompt_audio_path = str(require_file_exists(args.prompt_audio, parser, "prompt audio file"))
-            require_file_exists(args.prompt_audio, parser, "prompt audio file")
-        )
 
     reference_audio_path = None
     if args.reference_audio:
-        reference_audio_path = str(
+        reference_audio_path = str(require_file_exists(args.reference_audio, parser, "reference audio file"))
-            require_file_exists(args.reference_audio, parser, "reference audio file")
-        )
 
     success_count = 0
 
@@ -347,8 +324,7 @@ def cmd_batch(args, parser):
                 cfg_value=args.cfg_value,
                 inference_timesteps=args.inference_timesteps,
                 normalize=args.normalize,
-                denoise=args.denoise
+                denoise=args.denoise and (prompt_audio_path is not None or reference_audio_path is not None),
-                and (prompt_audio_path is not None or reference_audio_path is not None),
                 seed=args.seed,
             )
 
@@ -389,9 +365,7 @@ def _add_common_generation_args(parser):
         default=10,
         help="Inference steps (int, recommended 4–30, default: 10)",
     )
-    parser.add_argument(
+    parser.add_argument("--normalize", action="store_true", help="Enable text normalization")
-        "--normalize", action="store_true", help="Enable text normalization"
-    )
     parser.add_argument(
         "--seed",
         type=int,
@@ -406,12 +380,8 @@ def _add_prompt_reference_args(parser):
         "-pa",
         help="Prompt audio file path (continuation mode, requires --prompt-text or --prompt-file)",
     )
-    parser.add_argument(
+    parser.add_argument("--prompt-text", "-pt", help="Text corresponding to the prompt audio")
-        "--prompt-text", "-pt", help="Text corresponding to the prompt audio"
+    parser.add_argument("--prompt-file", type=str, help="Text file corresponding to the prompt audio")
-    )
-    parser.add_argument(
-        "--prompt-file", type=str, help="Text file corresponding to the prompt audio"
-    )
     parser.add_argument(
         "--reference-audio",
         "-ra",
@@ -438,15 +408,9 @@ def _add_model_args(parser):
         default="auto",
         help="Runtime device: auto, cpu, mps, cuda, or cuda:N (default: auto)",
     )
-    parser.add_argument(
+    parser.add_argument("--cache-dir", type=str, help="Cache directory for Hub downloads")
-        "--cache-dir", type=str, help="Cache directory for Hub downloads"
+    parser.add_argument("--local-files-only", action="store_true", help="Disable network access")
-    )
+    parser.add_argument("--no-denoiser", action="store_true", help="Disable denoiser model loading")
-    parser.add_argument(
-        "--local-files-only", action="store_true", help="Disable network access"
-    )
-    parser.add_argument(
-        "--no-denoiser", action="store_true", help="Disable denoiser model loading"
-    )
     parser.add_argument(
         "--no-optimize",
         action="store_true",
@@ -461,9 +425,7 @@ def _add_model_args(parser):
 
 def _add_lora_args(parser):
     parser.add_argument("--lora-path", type=str, help="Path to LoRA weights")
-    parser.add_argument(
+    parser.add_argument("--lora-r", type=int, default=32, help="LoRA rank (positive int, default: 32)")
-        "--lora-r", type=int, default=32, help="LoRA rank (positive int, default: 32)"
-    )
     parser.add_argument(
         "--lora-alpha",
         type=int,
@@ -476,12 +438,8 @@ def _add_lora_args(parser):
         default=0.0,
         help="LoRA dropout rate (0.0–1.0, default: 0.0)",
     )
-    parser.add_argument(
+    parser.add_argument("--lora-disable-lm", action="store_true", help="Disable LoRA on LM layers")
-        "--lora-disable-lm", action="store_true", help="Disable LoRA on LM layers"
+    parser.add_argument("--lora-disable-dit", action="store_true", help="Disable LoRA on DiT layers")
-    )
-    parser.add_argument(
-        "--lora-disable-dit", action="store_true", help="Disable LoRA on DiT layers"
-    )
     parser.add_argument(
         "--lora-enable-proj",
         action="store_true",
@@ -504,37 +462,23 @@ Examples:
 
     subparsers = parser.add_subparsers(dest="command")
 
-    design_parser = subparsers.add_parser(
+    design_parser = subparsers.add_parser("design", help="Generate speech with VoxCPM2-first voice design")
-        "design", help="Generate speech with VoxCPM2-first voice design"
-    )
     _add_common_generation_args(design_parser)
     _add_prompt_reference_args(design_parser)
     _add_model_args(design_parser)
     _add_lora_args(design_parser)
-    design_parser.add_argument(
+    design_parser.add_argument("--output", "-o", required=True, help="Output audio file path")
-        "--output", "-o", required=True, help="Output audio file path"
-    )
 
-    clone_parser = subparsers.add_parser(
+    clone_parser = subparsers.add_parser("clone", help="Clone a voice with reference/prompt audio")
-        "clone", help="Clone a voice with reference/prompt audio"
-    )
     _add_common_generation_args(clone_parser)
     _add_prompt_reference_args(clone_parser)
     _add_model_args(clone_parser)
     _add_lora_args(clone_parser)
-    clone_parser.add_argument(
+    clone_parser.add_argument("--output", "-o", required=True, help="Output audio file path")
-        "--output", "-o", required=True, help="Output audio file path"
-    )
 
-    batch_parser = subparsers.add_parser(
+    batch_parser = subparsers.add_parser("batch", help="Batch-generate one line per output file")
-        "batch", help="Batch-generate one line per output file"
+    batch_parser.add_argument("--input", "-i", required=True, help="Input text file (one text per line)")
-    )
+    batch_parser.add_argument("--output-dir", "-od", required=True, help="Output directory")
-    batch_parser.add_argument(
-        "--input", "-i", required=True, help="Input text file (one text per line)"
-    )
-    batch_parser.add_argument(
-        "--output-dir", "-od", required=True, help="Output directory"
-    )
     batch_parser.add_argument(
         "--control",
         type=str,
@@ -553,9 +497,7 @@ Examples:
         default=10,
         help="Inference steps (int, recommended 4–30, default: 10)",
     )
-    batch_parser.add_argument(
+    batch_parser.add_argument("--normalize", action="store_true", help="Enable text normalization")
-        "--normalize", action="store_true", help="Enable text normalization"
-    )
     batch_parser.add_argument(
         "--seed",
         type=int,
@@ -570,9 +512,7 @@ Examples:
         "validate",
         help="Validate a training data manifest (JSONL) before fine-tuning",
     )
-    validate_parser.add_argument(
+    validate_parser.add_argument("--manifest", "-m", required=True, help="Path to JSONL training manifest")
-        "--manifest", "-m", required=True, help="Path to JSONL training manifest"
-    )
     validate_parser.add_argument(
         "--sample-rate",
         type=int,
@@ -585,19 +525,13 @@ Examples:
         default=0,
         help="Maximum number of samples to validate (0 = all, default: 0)",
     )
-    validate_parser.add_argument(
+    validate_parser.add_argument("--verbose", "-v", action="store_true", help="Print per-sample progress")
-        "--verbose", "-v", action="store_true", help="Print per-sample progress"
-    )
 
     # Legacy root arguments
     parser.add_argument("--input", "-i", help="Input text file (batch mode only)")
-    parser.add_argument(
+    parser.add_argument("--output-dir", "-od", help="Output directory (batch mode only)")
-        "--output-dir", "-od", help="Output directory (batch mode only)"
-    )
     _add_common_generation_args(parser)
-    parser.add_argument(
+    parser.add_argument("--output", "-o", help="Output audio file path (single or clone mode)")
-        "--output", "-o", help="Output audio file path (single or clone mode)"
-    )
     _add_prompt_reference_args(parser)
     _add_model_args(parser)
     _add_lora_args(parser)
@@ -609,9 +543,7 @@ def _dispatch_legacy(args, parser):
     warn_legacy_mode()
 
     if args.input and args.text:
-        parser.error(
+        parser.error("Use either batch mode (--input) or single mode (--text), not both.")
-            "Use either batch mode (--input) or single mode (--text), not both."
-        )
 
     if args.input:
         if not args.output_dir:
@@ -621,12 +553,7 @@ def _dispatch_legacy(args, parser):
     if not args.text or not args.output:
         parser.error("Single-sample legacy mode requires --text and --output")
 
-    if (
+    if args.prompt_audio or args.prompt_text or args.prompt_file or args.reference_audio:
-        args.prompt_audio
-        or args.prompt_text
-        or args.prompt_file
-        or args.reference_audio
-    ):
         return cmd_clone(args, parser)
 
     return cmd_design(args, parser)
@@ -663,4 +590,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

src/voxcpm/core.py

@@ -293,7 +293,7 @@ class VoxCPM:
                 retry_badcase_max_times=retry_badcase_max_times,
                 retry_badcase_ratio_threshold=retry_badcase_ratio_threshold,
                 streaming=streaming,
-                seed=seed
+                seed=seed,
             )
 
             if streaming:

src/voxcpm/model/utils.py

@@ -5,9 +5,12 @@ from transformers import PreTrainedTokenizer
 
 _LOW_PRECISION_DTYPES = {"bfloat16", "bf16", "float16", "fp16"}
 _VALID_DTYPE_OVERRIDES = {
-    "bfloat16", "bf16",
+    "bfloat16",
-    "float16", "fp16",
+    "bf16",
-    "float32", "fp32",
+    "float16",
+    "fp16",
+    "float32",
+    "fp32",
 }
 
 
@@ -173,10 +176,7 @@ def pick_runtime_dtype(device: str, configured_dtype: str) -> str:
     override = os.environ.get("VOXCPM_MPS_DTYPE", "").strip().lower()
     if override:
         if override not in _VALID_DTYPE_OVERRIDES:
-            raise ValueError(
+            raise ValueError(f"VOXCPM_MPS_DTYPE='{override}' is not one of " f"{sorted(_VALID_DTYPE_OVERRIDES)}")
-                f"VOXCPM_MPS_DTYPE='{override}' is not one of "
-                f"{sorted(_VALID_DTYPE_OVERRIDES)}"
-            )
         return override
 
     if (configured_dtype or "").lower() in _LOW_PRECISION_DTYPES:
@@ -224,15 +224,13 @@ def resolve_runtime_device(device: Optional[str], configured_device: str = "cuda
     if explicit.startswith("cuda"):
         if not torch.cuda.is_available():
             raise ValueError(
-                f"Requested device '{device}', but CUDA is not available. "
+                f"Requested device '{device}', but CUDA is not available. " "Use device='auto' for automatic fallback."
-                "Use device='auto' for automatic fallback."
             )
         return explicit
     if explicit == "mps":
         if not _has_mps():
             raise ValueError(
-                "Requested device 'mps', but MPS is not available. "
+                "Requested device 'mps', but MPS is not available. " "Use device='auto' for automatic fallback."
-                "Use device='auto' for automatic fallback."
             )
         return "mps"
     if explicit == "cpu":

src/voxcpm/model/voxcpm2.py

@@ -57,7 +57,9 @@ from .utils import (
 
 
 # A simple function to trim audio silence using VAD, not used default
-def _trim_audio_silence_vad(audio: torch.Tensor, sample_rate: int, max_silence_ms: float = 200.0, top_db: float = 35.0) -> torch.Tensor:
+def _trim_audio_silence_vad(
+    audio: torch.Tensor, sample_rate: int, max_silence_ms: float = 200.0, top_db: float = 35.0
+) -> torch.Tensor:
     if audio.numel() == 0:
         return audio
     y = audio.squeeze(0).numpy()
@@ -684,7 +686,7 @@ class VoxCPM2Model(nn.Module):
             decode_audio = self.audio_vae.decode(latent_pred.to(torch.float32))
             decode_patch_len = self.patch_size * self._decode_chunk_size
             if context_len > 0:
-                decode_audio = decode_audio[..., decode_patch_len * context_len:].squeeze(1).cpu()
+                decode_audio = decode_audio[..., decode_patch_len * context_len :].squeeze(1).cpu()
             else:
                 decode_audio = decode_audio.squeeze(1).cpu()
             yield decode_audio
@@ -979,7 +981,7 @@ class VoxCPM2Model(nn.Module):
             decode_audio = self.audio_vae.decode(latent_pred.to(torch.float32))
             decode_patch_len = self.patch_size * self._decode_chunk_size
             if context_len > 0:
-                decode_audio = decode_audio[..., decode_patch_len * context_len:].squeeze(1).cpu()
+                decode_audio = decode_audio[..., decode_patch_len * context_len :].squeeze(1).cpu()
             else:
                 decode_audio = decode_audio.squeeze(1).cpu()
             yield (decode_audio, target_text_token, pred_audio_feat)

src/voxcpm/modules/audiovae/audio_vae_v2.py

@@ -551,8 +551,7 @@ class StreamingVAEDecoder:
             if x.shape[-1] >= _p:
                 states[_k] = x[:, :, -_p:].detach()
             else:
-                prev = states.get(_k, torch.zeros(x.shape[0], x.shape[1], _p,
+                prev = states.get(_k, torch.zeros(x.shape[0], x.shape[1], _p, device=x.device, dtype=x.dtype))
-                                                  device=x.device, dtype=x.dtype))
                 states[_k] = torch.cat([prev, x], dim=-1)[:, :, -_p:].detach()
             return nn.Conv1d.forward(_m, x_pad)
 

src/voxcpm/training/packers.py

@@ -350,45 +350,80 @@ class AudioFeatureProcessingPacker:
 
         # -- text token track --
         # [103, 0×R, 104, text_ids, 101, 0×A, 102]
-        text_token_info = torch.cat([
+        text_token_info = torch.cat(
-            _tok([self.audio_prompt_start_id]),
+            [
-            torch.zeros(ref_len, dtype=torch.int32, device=device),
+                _tok([self.audio_prompt_start_id]),
-            _tok([self.audio_prompt_end_id]),
+                torch.zeros(ref_len, dtype=torch.int32, device=device),
-            text_token,
+                _tok([self.audio_prompt_end_id]),
-            _tok([self.audio_start_id]),
+                text_token,
-            torch.zeros(tgt_len, dtype=torch.int32, device=device),
+                _tok([self.audio_start_id]),
-            _tok([self.audio_end_id]),
+                torch.zeros(tgt_len, dtype=torch.int32, device=device),
-        ])
+                _tok([self.audio_end_id]),
+            ]
+        )
 
         # -- audio feature track --
         zero_1 = torch.zeros((1,) + feat_shape, dtype=torch.float32, device=device)
         zero_txt = torch.zeros((txt_len,) + feat_shape, dtype=torch.float32, device=device)
-        audio_feat_info = torch.cat([
+        audio_feat_info = torch.cat(
-            zero_1, ref_feats, zero_1,      # 103, ref, 104
+            [
-            zero_txt,                        # text
+                zero_1,
-            zero_1, tgt_feats, zero_1,       # 101, target, 102
+                ref_feats,
-        ], dim=0)
+                zero_1,  # 103, ref, 104
+                zero_txt,  # text
+                zero_1,
+                tgt_feats,
+                zero_1,  # 101, target, 102
+            ],
+            dim=0,
+        )
 
         # -- masks --
-        text_mask = torch.cat([
+        text_mask = (
-            torch.ones(1), torch.zeros(ref_len), torch.ones(1),
+            torch.cat(
-            torch.ones(txt_len),
+                [
-            torch.ones(1), torch.zeros(tgt_len), torch.ones(1),
+                    torch.ones(1),
-        ]).to(torch.int32).to(device)
+                    torch.zeros(ref_len),
+                    torch.ones(1),
+                    torch.ones(txt_len),
+                    torch.ones(1),
+                    torch.zeros(tgt_len),
+                    torch.ones(1),
+                ]
+            )
+            .to(torch.int32)
+            .to(device)
+        )
 
-        audio_mask = torch.cat([
+        audio_mask = (
-            torch.zeros(1), torch.ones(ref_len), torch.zeros(1),
+            torch.cat(
-            torch.zeros(txt_len),
+                [
-            torch.zeros(1), torch.ones(tgt_len), torch.zeros(1),
+                    torch.zeros(1),
-        ]).to(torch.int32).to(device)
+                    torch.ones(ref_len),
+                    torch.zeros(1),
+                    torch.zeros(txt_len),
+                    torch.zeros(1),
+                    torch.ones(tgt_len),
+                    torch.zeros(1),
+                ]
+            )
+            .to(torch.int32)
+            .to(device)
+        )
 
-        loss_mask = torch.cat([
+        loss_mask = (
-            torch.zeros(1 + ref_len + 1),   # ref part: no loss
+            torch.cat(
-            torch.zeros(txt_len),            # text: no loss
+                [
-            torch.zeros(1),                  # 101: no loss
+                    torch.zeros(1 + ref_len + 1),  # ref part: no loss
-            torch.ones(tgt_len),             # target audio: LOSS
+                    torch.zeros(txt_len),  # text: no loss
-            torch.zeros(1),                  # 102: no loss
+                    torch.zeros(1),  # 101: no loss
-        ]).to(torch.int32).to(device)
+                    torch.ones(tgt_len),  # target audio: LOSS
+                    torch.zeros(1),  # 102: no loss
+                ]
+            )
+            .to(torch.int32)
+            .to(device)
+        )
 
         total_len = 1 + ref_len + 1 + txt_len + 1 + tgt_len + 1
         labels = torch.zeros(total_len, dtype=torch.int32, device=device)

src/voxcpm/training/validate.py

@@ -44,10 +44,7 @@ def _check_audio_file(audio_path: str, sample_rate: int) -> Optional[str]:
         if info.frames == 0:
             return f"Audio file is empty: {audio_path}"
         if info.samplerate != sample_rate:
-            return (
+            return f"Sample rate mismatch in {audio_path}: " f"expected {sample_rate} Hz, got {info.samplerate} Hz"
-                f"Sample rate mismatch in {audio_path}: "
-                f"expected {sample_rate} Hz, got {info.samplerate} Hz"
-            )
         return None
     except ImportError:
         # soundfile not available; just check existence
@@ -187,13 +184,9 @@ def validate_manifest(
                 if duration is not None:
                     result.audio_durations.append(duration)
                     if duration < 0.3:
-                        result.warnings.append(
+                        result.warnings.append(f"Line {i + 1}: Very short audio ({duration:.2f}s)")
-                            f"Line {i + 1}: Very short audio ({duration:.2f}s)"
-                        )
                     elif duration > 30.0:
-                        result.warnings.append(
+                        result.warnings.append(f"Line {i + 1}: Very long audio ({duration:.1f}s), may cause OOM")
-                            f"Line {i + 1}: Very long audio ({duration:.1f}s), may cause OOM"
-                        )
         else:
             result.errors.append(f"Line {i + 1}: Invalid audio path")
             has_error = True
@@ -209,9 +202,7 @@ def validate_manifest(
                 if os.path.isfile(ref_path):
                     result.has_ref_audio += 1
                 else:
-                    result.warnings.append(
+                    result.warnings.append(f"Line {i + 1}: ref_audio file not found: {ref_path}")
-                        f"Line {i + 1}: ref_audio file not found: {ref_path}"
-                    )
 
         if not has_error:
             result.valid_samples += 1
@@ -222,14 +213,10 @@ def validate_manifest(
     # Summarize truncated errors
     if missing_audio_count > 5:
         result.errors.append(
-            f"... and {missing_audio_count - 5} more missing audio files "
+            f"... and {missing_audio_count - 5} more missing audio files " f"({missing_audio_count} total)"
-            f"({missing_audio_count} total)"
         )
     if empty_text_count > 5:
-        result.warnings.append(
+        result.warnings.append(f"... and {empty_text_count - 5} more empty text entries " f"({empty_text_count} total)")
-            f"... and {empty_text_count - 5} more empty text entries "
-            f"({empty_text_count} total)"
-        )
 
     return result
 

tests/test_cli.py

@@ -455,10 +455,7 @@ def test_clone_rejects_prompt_audio_without_transcript(monkeypatch, tmp_path, ca
     with pytest.raises(SystemExit):
         cli.main()
 
-    assert (
+    assert "--prompt-audio requires --prompt-text or --prompt-file" in capsys.readouterr().err
-        "--prompt-audio requires --prompt-text or --prompt-file"
-        in capsys.readouterr().err
-    )
 
 
 def test_clone_rejects_transcript_without_prompt_audio(monkeypatch, tmp_path, capsys):
@@ -480,9 +477,7 @@ def test_clone_rejects_transcript_without_prompt_audio(monkeypatch, tmp_path, ca
     with pytest.raises(SystemExit):
         cli.main()
 
-    assert (
+    assert "--prompt-text/--prompt-file requires --prompt-audio" in capsys.readouterr().err
-        "--prompt-text/--prompt-file requires --prompt-audio" in capsys.readouterr().err
-    )
 
 
 def test_batch_rejects_control_with_prompt_transcript(monkeypatch, tmp_path, capsys):
@@ -603,4 +598,4 @@ def test_batch_subcommand_passes_seed(monkeypatch, tmp_path):
 
     assert len(dummy_model.calls) == 2
     assert dummy_model.calls[0]["seed"] == 999
-    assert dummy_model.calls[1]["seed"] == 999
+    assert dummy_model.calls[1]["seed"] == 999

tests/test_validate.py

@@ -207,6 +207,7 @@ class TestValidateManifest:
 
         audio = tmp_dir / "audio_8k.wav"
         import numpy as np
+
         samples = np.zeros(8000, dtype=np.float32)
         sf.write(str(audio), samples, 8000)
 
@@ -240,6 +241,7 @@ class TestValidateManifest:
     def test_cli_validate_exit_code(self, tmp_dir):
         """validate subcommand must exit 1 on validation error (missing audio)."""
         import subprocess
+
         manifest = tmp_dir / "bad.jsonl"
         _write_manifest(manifest, [{"text": "hi", "audio": "/nonexistent/x.wav"}])