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Running on Zero
Running on Zero
| import os | |
| from pathlib import Path | |
| os.environ.setdefault("TOKENIZERS_PARALLELISM", "false") | |
| os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True") | |
| import spaces | |
| import gradio as gr | |
| import torch | |
| from huggingface_hub import snapshot_download | |
| from m5_infer import GenerationOptions, M5TTS | |
| from tn_frontend import get_normalizer | |
| ROOT = Path(__file__).resolve().parent | |
| DEVICE = "cuda" if torch.cuda.is_available() else "cpu" | |
| DEFAULT_VOICE = ROOT / "assets" / "aslp_default_voice.wav" | |
| DEFAULT_PROMPT = "猪携带该疾病，然后通过蚊子传染给人类。" | |
| DEFAULT_TARGET = ( | |
| "欢迎来到ASLP实验室。我们专注于语音、语言与智能交互技术研究，" | |
| "期待用声音连接更广阔的世界。" | |
| ) | |
| QWEN_ROOT = Path(snapshot_download( | |
| repo_id="Qwen/Qwen3-TTS-12Hz-1.7B-Base", | |
| revision="fd4b254389122332181a7c3db7f27e918eec64e3", | |
| allow_patterns=["speech_tokenizer/*"], | |
| )) | |
| get_normalizer("zh") | |
| get_normalizer("en") | |
| tts = M5TTS( | |
| ROOT / "model" / "model.safetensors", | |
| QWEN_ROOT / "speech_tokenizer", | |
| device=DEVICE, | |
| runtime_root=ROOT, | |
| expected_sha256="6c1ac8578aafb07d444e284c76d16d6529e87e1971189c76ff1795bf1252d623", | |
| ) | |
| def estimate_duration(_audio, _prompt, target, *_args, **_kwargs): | |
| return min(120, max(30, 20 + len(target or "") // 2)) | |
| def synthesize( | |
| reference_audio, | |
| prompt_text, | |
| target_text, | |
| language, | |
| accent, | |
| temperature, | |
| top_k, | |
| seed, | |
| ): | |
| if not reference_audio: | |
| raise gr.Error("Please upload or record reference audio.") | |
| prompt_text = (prompt_text or "").strip() | |
| target_text = (target_text or "").strip() | |
| if not prompt_text: | |
| raise gr.Error("Reference transcript is required.") | |
| if not target_text: | |
| raise gr.Error("Target text is required.") | |
| if len(target_text) > 250: | |
| raise gr.Error("Target text is limited to 250 characters per request.") | |
| accent = None if accent == "None" else accent | |
| options = GenerationOptions( | |
| seed=int(seed), | |
| temperature=float(temperature), | |
| top_k=int(top_k), | |
| text_token_limit=96, | |
| max_audio_ms=30_000, | |
| max_seq_len=2048, | |
| allow_truncated=False, | |
| ) | |
| result = tts.synthesize( | |
| target_text, | |
| reference_audio, | |
| prompt_text, | |
| language=language, | |
| reference_language=language, | |
| accent=accent, | |
| speaker=None, | |
| caption=None, | |
| utt="space", | |
| tn="wetext", | |
| options=options, | |
| ) | |
| info = result["metadata"] | |
| return (result["sample_rate"], result["audio"]), { | |
| "sample_rate": result["sample_rate"], | |
| "duration_seconds": round(info["duration"], 3), | |
| "chunks": info["chunks"], | |
| "target_tn": info["prepared"]["target"], | |
| "reference_tn": info["prepared"]["reference"], | |
| "conditioning": { | |
| "language": info["language"], | |
| "accent": info["accent"], | |
| "speaker": info["speaker"], | |
| }, | |
| } | |
| with gr.Blocks(title="M5-TTS") as demo: | |
| gr.Markdown( | |
| "# M5-TTS\n" | |
| "Upload a reference recording and its exact transcript, then synthesize " | |
| "new speech with the same voice." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| reference_audio = gr.Audio( | |
| sources=["upload", "microphone"], type="filepath", | |
| value=str(DEFAULT_VOICE), label="Reference audio", | |
| ) | |
| prompt_text = gr.Textbox( | |
| value=DEFAULT_PROMPT, label="Reference transcript", lines=3, | |
| info="Update this transcript when uploading another voice.", | |
| ) | |
| target_text = gr.Textbox( | |
| value=DEFAULT_TARGET, label="Target text", lines=5, max_length=250, | |
| placeholder="Text to synthesize (maximum 250 characters)", | |
| ) | |
| with gr.Column(): | |
| language = gr.Dropdown( | |
| [ | |
| ("Chinese", "zh"), ("English", "en"), ("German", "de"), | |
| ("French", "fr"), ("Japanese", "ja"), ("Korean", "ko"), | |
| ], | |
| value="zh", label="Language", | |
| ) | |
| accent = gr.Dropdown( | |
| ["None", "yue", "chuan"], value="None", label="Accent", | |
| ) | |
| temperature = gr.Slider( | |
| 0.1, 1.5, value=0.9, step=0.05, label="Temperature", | |
| ) | |
| top_k = gr.Slider(1, 50, value=10, step=1, label="Top-k") | |
| seed = gr.Number(value=42, precision=0, label="Seed") | |
| generate_button = gr.Button("Generate", variant="primary") | |
| output_audio = gr.Audio(label="Generated audio") | |
| metadata = gr.JSON(label="Generation metadata") | |
| generate_button.click( | |
| fn=synthesize, | |
| inputs=[ | |
| reference_audio, prompt_text, target_text, language, accent, | |
| temperature, top_k, seed, | |
| ], | |
| outputs=[output_audio, metadata], | |
| api_name="synthesize", | |
| concurrency_limit=1, | |
| ) | |
| demo.queue(default_concurrency_limit=1).launch() | |