import os import queue from huggingface_hub import snapshot_download import hydra # Download if not exists os.makedirs("checkpoints", exist_ok=True) snapshot_download(repo_id="fishaudio/fish-speech-1", local_dir="./checkpoints/fish-speech-1") print("All checkpoints downloaded") import html import os import threading from argparse import ArgumentParser from pathlib import Path import gradio as gr import librosa import torch from loguru import logger from transformers import AutoTokenizer from tools.llama.generate import launch_thread_safe_queue from tools.vqgan.inference import load_model as load_vqgan_model # Make einx happy os.environ["EINX_FILTER_TRACEBACK"] = "false" HEADER_MD = """# Fish Speech A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio). 由 [Fish Audio](https://fish.audio) 研发的基于 VQ-GAN 和 Llama 的多语种语音合成. You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1). 你可以在 [这里](https://github.com/fishaudio/fish-speech) 找到源代码和 [这里](https://huggingface.co/fishaudio/fish-speech-1) 找到模型. Related code are released under BSD-3-Clause License, and weights are released under CC BY-NC-SA 4.0 License. 相关代码使用 BSD-3-Clause 许可证发布,权重使用 CC BY-NC-SA 4.0 许可证发布. We are not responsible for any misuse of the model, please consider your local laws and regulations before using it. 我们不对模型的任何滥用负责,请在使用之前考虑您当地的法律法规. """ TEXTBOX_PLACEHOLDER = """Put your text here. 在此处输入文本.""" try: import spaces GPU_DECORATOR = spaces.GPU except ImportError: def GPU_DECORATOR(func): def wrapper(*args, **kwargs): return func(*args, **kwargs) return wrapper def build_html_error_message(error): return f"""
{html.escape(error)}
""" @GPU_DECORATOR @torch.inference_mode() def inference( text, enable_reference_audio, reference_audio, reference_text, max_new_tokens, chunk_length, top_k, top_p, repetition_penalty, temperature, speaker, ): if args.max_gradio_length > 0 and len(text) > args.max_gradio_length: return None, f"Text is too long, please keep it under {args.max_gradio_length} characters." # Parse reference audio aka prompt prompt_tokens = None if enable_reference_audio and reference_audio is not None: # reference_audio_sr, reference_audio_content = reference_audio reference_audio_content, _ = librosa.load( reference_audio, sr=vqgan_model.sampling_rate, mono=True ) audios = torch.from_numpy(reference_audio_content).to(vqgan_model.device)[ None, None, : ] logger.info( f"Loaded audio with {audios.shape[2] / vqgan_model.sampling_rate:.2f} seconds" ) # VQ Encoder audio_lengths = torch.tensor( [audios.shape[2]], device=vqgan_model.device, dtype=torch.long ) prompt_tokens = vqgan_model.encode(audios, audio_lengths)[0][0] # LLAMA Inference request = dict( tokenizer=llama_tokenizer, device=vqgan_model.device, max_new_tokens=max_new_tokens, text=text, top_k=int(top_k) if top_k > 0 else None, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature, compile=args.compile, iterative_prompt=chunk_length > 0, chunk_length=chunk_length, max_length=args.max_length, speaker=speaker if speaker else None, prompt_tokens=prompt_tokens if enable_reference_audio else None, prompt_text=reference_text if enable_reference_audio else None, ) payload = dict( response_queue=queue.Queue(), request=request, ) llama_queue.put(payload) codes = [] while True: result = payload["response_queue"].get() if result == "next": # TODO: handle next sentence continue if result == "done": if payload["success"] is False: raise payload["response"] break codes.append(result) codes = torch.cat(codes, dim=1) # VQGAN Inference feature_lengths = torch.tensor([codes.shape[1]], device=vqgan_model.device) fake_audios = vqgan_model.decode( indices=codes[None], feature_lengths=feature_lengths, return_audios=True )[0, 0] fake_audios = fake_audios.float().cpu().numpy() return (vqgan_model.sampling_rate, fake_audios), None def build_app(): with gr.Blocks(theme=gr.themes.Base()) as app: gr.Markdown(HEADER_MD) # Use light theme by default app.load( None, None, js="() => {const params = new URLSearchParams(window.location.search);if (!params.has('__theme')) {params.set('__theme', 'light');window.location.search = params.toString();}}", ) # Inference with gr.Row(): with gr.Column(scale=3): text = gr.Textbox( label="Input Text / 输入文本", placeholder=TEXTBOX_PLACEHOLDER, lines=15 ) with gr.Row(): with gr.Tab(label="Advanced Config / 高级参数"): chunk_length = gr.Slider( label="Iterative Prompt Length, 0 means off / 迭代提示长度,0 表示关闭", minimum=0, maximum=100, value=30, step=8, ) max_new_tokens = gr.Slider( label="Maximum tokens per batch, 0 means no limit / 每批最大令牌数,0 表示无限制", minimum=128, maximum=512, value=512, # 0 means no limit step=8, ) top_k = gr.Slider( label="Top-K", minimum=0, maximum=5, value=0, step=1 ) top_p = gr.Slider( label="Top-P", minimum=0, maximum=1, value=0.7, step=0.01 ) repetition_penalty = gr.Slider( label="Repetition Penalty", minimum=0, maximum=2, value=1.5, step=0.01, ) temperature = gr.Slider( label="Temperature", minimum=0, maximum=2, value=0.7, step=0.01, ) speaker = gr.Textbox( label="Speaker / 说话人", placeholder="Type name of the speaker / 输入说话人的名称", lines=1, ) with gr.Tab(label="Reference Audio / 参考音频"): gr.Markdown( "5 to 10 seconds of reference audio, useful for specifying speaker. \n5 到 10 秒的参考音频,适用于指定音色。" ) enable_reference_audio = gr.Checkbox( label="Enable Reference Audio / 启用参考音频", ) reference_audio = gr.Audio( label="Reference Audio / 参考音频", type="filepath", ) reference_text = gr.Textbox( label="Reference Text / 参考文本", placeholder="参考文本", lines=1, ) with gr.Column(scale=3): with gr.Row(): error = gr.HTML(label="Error Message / 错误信息") with gr.Row(): audio = gr.Audio(label="Generated Audio / 音频", type="numpy") with gr.Row(): with gr.Column(scale=3): generate = gr.Button( value="\U0001F3A7 Generate / 合成", variant="primary" ) # # Submit generate.click( inference, [ text, enable_reference_audio, reference_audio, reference_text, max_new_tokens, chunk_length, top_k, top_p, repetition_penalty, temperature, speaker, ], [audio, error], concurrency_limit=1, ) return app def parse_args(): parser = ArgumentParser() parser.add_argument( "--llama-checkpoint-path", type=Path, default="checkpoints/text2semantic-sft-large-v1-4k.pth", ) parser.add_argument( "--llama-config-name", type=str, default="dual_ar_2_codebook_large" ) parser.add_argument( "--vqgan-checkpoint-path", type=Path, default="checkpoints/vq-gan-group-fsq-2x1024.pth", ) parser.add_argument("--vqgan-config-name", type=str, default="vqgan_pretrain") parser.add_argument("--tokenizer", type=str, default="fishaudio/fish-speech-1") parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--half", action="store_true") parser.add_argument("--max-length", type=int, default=2048) parser.add_argument("--compile", action="store_true") parser.add_argument("--max-gradio-length", type=int, default=0) return parser.parse_args() if __name__ == "__main__": args = parse_args() args.precision = torch.half if args.half else torch.bfloat16 args.compile = True args.max_gradio_length = 1024 args.tokenizer = "./checkpoints/fish-speech-1" args.llama_checkpoint_path = "./checkpoints/fish-speech-1/text2semantic-sft-large-v1-4k.pth" args.llama_config_name = "dual_ar_2_codebook_large" args.vqgan_checkpoint_path = "./checkpoints/fish-speech-1/vq-gan-group-fsq-2x1024.pth" args.vqgan_config_name = "vqgan_pretrain" logger.info("Loading Llama model...") llama_queue = launch_thread_safe_queue( config_name=args.llama_config_name, checkpoint_path=args.llama_checkpoint_path, device=args.device, precision=args.precision, max_length=args.max_length, compile=args.compile, ) llama_tokenizer = AutoTokenizer.from_pretrained(args.tokenizer) logger.info("Llama model loaded, loading VQ-GAN model...") vqgan_model = load_vqgan_model( config_name=args.vqgan_config_name, checkpoint_path=args.vqgan_checkpoint_path, device=args.device, ) logger.info("VQ-GAN model loaded, warming up...") # Dry run to check if the model is loaded correctly and avoid the first-time latency inference( text="Hello, world!", enable_reference_audio=False, reference_audio=None, reference_text="", max_new_tokens=0, chunk_length=0, top_k=0, # 0 means no limit top_p=0.7, repetition_penalty=1.5, temperature=0.7, speaker=None, ) logger.info("Warming up done, launching the web UI...") app = build_app() app.launch(show_api=False)