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| import gc | |
| import threading | |
| import traceback | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from transformers import pipeline | |
| MODEL_ID = "fishaudio/s2-pro" | |
| _pipe = None | |
| _pipe_error = None | |
| _pipe_lock = threading.Lock() | |
| _gen_lock = threading.Lock() | |
| def load_pipeline(): | |
| global _pipe, _pipe_error | |
| if _pipe is not None: | |
| return _pipe | |
| if _pipe_error is not None: | |
| raise RuntimeError(_pipe_error) | |
| with _pipe_lock: | |
| if _pipe is not None: | |
| return _pipe | |
| if _pipe_error is not None: | |
| raise RuntimeError(_pipe_error) | |
| try: | |
| _pipe = pipeline( | |
| task="text-to-audio", | |
| model=MODEL_ID, | |
| device=-1, | |
| trust_remote_code=True, | |
| ) | |
| return _pipe | |
| except Exception as e: | |
| _pipe_error = f"Failed to load {MODEL_ID}: {e}" | |
| raise RuntimeError(_pipe_error) from e | |
| def synthesize(text: str): | |
| text = (text or "").strip() | |
| if not text: | |
| raise gr.Error("Please enter some text.") | |
| pipe = load_pipeline() | |
| with _gen_lock: | |
| try: | |
| result = pipe(text) | |
| except Exception as e: | |
| tb = traceback.format_exc(limit=2) | |
| raise gr.Error(f"Generation failed: {e}\n\n{tb}") from e | |
| finally: | |
| gc.collect() | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| if isinstance(result, dict): | |
| audio = result.get("audio") | |
| sr = result.get("sampling_rate") or result.get("sample_rate") or 24000 | |
| elif isinstance(result, tuple) and len(result) == 2: | |
| sr, audio = result | |
| else: | |
| raise gr.Error(f"Unexpected model output type: {type(result)}") | |
| if audio is None: | |
| raise gr.Error("Model returned no audio.") | |
| audio = np.asarray(audio) | |
| if audio.ndim > 1: | |
| audio = audio.squeeze() | |
| return (int(sr), audio), f"Done. Model: {MODEL_ID}" | |
| def app_info(): | |
| return ( | |
| "This app tries to run fishaudio/s2-pro directly from Hugging Face using Transformers and trust_remote_code=True. " | |
| "If the upstream repo requires a different runtime than standard Transformers, you may need a Docker Space or GPU." | |
| ) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Fish Audio S2 Pro Text to Speech") | |
| gr.Markdown(app_info()) | |
| with gr.Row(): | |
| text = gr.Textbox( | |
| label="Text", | |
| lines=6, | |
| placeholder="Type text to convert to speech...", | |
| ) | |
| btn = gr.Button("Generate Speech") | |
| audio = gr.Audio(label="Audio", type="numpy") | |
| status = gr.Textbox(label="Status", interactive=False) | |
| btn.click(synthesize, inputs=text, outputs=[audio, status], api_name="tts") | |
| if __name__ == "__main__": | |
| demo.launch() | |