Upload Fish-Agent Demo

app.py

@@ -1,64 +1,230 @@
+import re
 import gradio as gr
-from huggingface_hub import InferenceClient
+import numpy as np
+import os
+import threading
+import subprocess
+import sys
+import time
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+from huggingface_hub import snapshot_download
+from tools.fish_e2e import FishE2EAgent, FishE2EEventType
+from tools.schema import ServeMessage, ServeTextPart, ServeVQPart
 
+# Download Weights
+os.makedirs("checkpoints", exist_ok=True)
+snapshot_download(repo_id="fishaudio/fish-speech-1.4", local_dir="./checkpoints/fish-speech-1.4")
+snapshot_download(repo_id="fishaudio/fish-agent-v0.1-3b", local_dir="./checkpoints/fish-agent-v0.1-3b")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
+class ChatState:
+    def __init__(self):
+        self.conversation = []
+        self.added_systext = False
+        self.added_sysaudio = False
+
+    def get_history(self):
+        results = []
+        for msg in self.conversation:
+            results.append({"role": msg.role, "content": self.repr_message(msg)})
+
+        # Process assistant messages to extract questions and update user messages
+        for i, msg in enumerate(results):
+            if msg["role"] == "assistant":
+                match = re.search(r"Question: (.*?)\n\nResponse:", msg["content"])
+                if match and i > 0 and results[i - 1]["role"] == "user":
+                    # Update previous user message with extracted question
+                    results[i - 1]["content"] += "\n" + match.group(1)
+                    # Remove the Question/Answer format from assistant message
+                    msg["content"] = msg["content"].split("\n\nResponse: ", 1)[1]
+        return results
+
+    def repr_message(self, msg: ServeMessage):
+        response = ""
+        for part in msg.parts:
+            if isinstance(part, ServeTextPart):
+                response += part.text
+            elif isinstance(part, ServeVQPart):
+                response += f"<audio {len(part.codes[0]) / 21:.2f}s>"
+        return response
+
+
+def clear_fn():
+    return [], ChatState(), None, None, None
+
+
+async def process_audio_input(
+    sys_audio_input, sys_text_input, audio_input, state: ChatState, text_input: str
 ):
-    messages = [{"role": "system", "content": system_message}]
+    if audio_input is None and not text_input:
+        raise gr.Error("No input provided")
+
+    agent = FishE2EAgent()  # Create new agent instance for each request
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    # Convert audio input to numpy array
+    if isinstance(audio_input, tuple):
+        sr, audio_data = audio_input
+    elif text_input:
+        sr = 44100
+        audio_data = None
+    else:
+        raise gr.Error("Invalid audio format")
 
-    messages.append({"role": "user", "content": message})
+    if isinstance(sys_audio_input, tuple):
+        sr, sys_audio_data = sys_audio_input
+    elif text_input:
+        sr = 44100
+        sys_audio_data = None
+    else:
+        raise gr.Error("Invalid audio format")
 
-    response = ""
+    def append_to_chat_ctx(
+        part: ServeTextPart | ServeVQPart, role: str = "assistant"
+    ) -> None:
+        if not state.conversation or state.conversation[-1].role != role:
+            state.conversation.append(ServeMessage(role=role, parts=[part]))
+        else:
+            state.conversation[-1].parts.append(part)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
+    if state.added_systext is False and sys_text_input:
+        state.added_systext = True
+        append_to_chat_ctx(ServeTextPart(text=sys_text_input), role="system")
+    if text_input:
+        append_to_chat_ctx(ServeTextPart(text=text_input), role="user")
+        audio_data = None
+
+    result_audio = b""
+    async for event in agent.stream(
+        sys_audio_data,
+        audio_data,
+        sr,
+        1,
+        chat_ctx={
+            "messages": state.conversation,
+            "added_sysaudio": state.added_sysaudio,
+        },
+    ):
+        if event.type == FishE2EEventType.USER_CODES:
+            append_to_chat_ctx(ServeVQPart(codes=event.vq_codes), role="user")
+        elif event.type == FishE2EEventType.SPEECH_SEGMENT:
+            result_audio += event.frame.data
+            np_audio = np.frombuffer(result_audio, dtype=np.int16)
+            append_to_chat_ctx(ServeVQPart(codes=event.vq_codes))
+
+            yield state.get_history(), (44100, np_audio), None, None
+        elif event.type == FishE2EEventType.TEXT_SEGMENT:
+            append_to_chat_ctx(ServeTextPart(text=event.text))
+            if result_audio:
+                np_audio = np.frombuffer(result_audio, dtype=np.int16)
+                yield state.get_history(), (44100, np_audio), None, None
+            else:
+                yield state.get_history(), None, None, None
+
+    np_audio = np.frombuffer(result_audio, dtype=np.int16)
+    yield state.get_history(), (44100, np_audio), None, None
+
+
+async def process_text_input(
+    sys_audio_input, sys_text_input, state: ChatState, text_input: str
+):
+    async for event in process_audio_input(
+        sys_audio_input, sys_text_input, None, state, text_input
     ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        yield event
 
 
+def create_demo():
+    with gr.Blocks() as demo:
+        state = gr.State(ChatState())
+
+        with gr.Row():
+            # Left column (70%) for chatbot and notes
+            with gr.Column(scale=7):
+                chatbot = gr.Chatbot(
+                    [],
+                    elem_id="chatbot",
+                    bubble_full_width=False,
+                    height=600,
+                    type="messages",
+                )
+
+                notes = gr.Markdown(
+                    """
+                # Fish Agent
+                1. 此Demo为Fish Audio自研端到端语言模型Fish Agent 3B版本.
+                2. 你可以在我们的官方仓库找到代码以及权重，但是相关内容全部基于 CC BY-NC-SA 4.0 许可证发布.
+                3. Demo为早期灰度测试版本，推理速度尚待优化.
+                # 特色
+                1. 该模型自动集成ASR与TTS部分，不需要外挂其它模型，即真正的端到端，而非三段式(ASR+LLM+TTS).
+                2. 模型可以使用reference audio控制说话音色.
+                3. 可以生成具有较强情感与韵律的音频.
+                """
+                )
+
+            # Right column (30%) for controls
+            with gr.Column(scale=3):
+                sys_audio_input = gr.Audio(
+                    sources=["upload"],
+                    type="numpy",
+                    label="Give a timbre for your assistant",
+                )
+                sys_text_input = gr.Textbox(
+                    label="What is your assistant's role?",
+                    value='您是由 Fish Audio 设计的语音助手，提供端到端的语音交互，实现无缝用户体验。首先转录用户的语音，然后使用以下格式回答："Question: [用户语音]\n\nResponse: [你的回答]\n"。',
+                    type="text",
+                )
+                audio_input = gr.Audio(
+                    sources=["microphone"], type="numpy", label="Speak your message"
+                )
+
+                text_input = gr.Textbox(label="Or type your message", type="text")
+
+                output_audio = gr.Audio(label="Assistant's Voice", type="numpy")
+
+                send_button = gr.Button("Send", variant="primary")
+                clear_button = gr.Button("Clear")
+
+        # Event handlers
+        audio_input.stop_recording(
+            process_audio_input,
+            inputs=[sys_audio_input, sys_text_input, audio_input, state, text_input],
+            outputs=[chatbot, output_audio, audio_input, text_input],
+            show_progress=True,
+        )
+
+        send_button.click(
+            process_text_input,
+            inputs=[sys_audio_input, sys_text_input, state, text_input],
+            outputs=[chatbot, output_audio, audio_input, text_input],
+            show_progress=True,
+        )
+
+        text_input.submit(
+            process_text_input,
+            inputs=[sys_audio_input, sys_text_input, state, text_input],
+            outputs=[chatbot, output_audio, audio_input, text_input],
+            show_progress=True,
+        )
+
+        clear_button.click(
+            clear_fn,
+            inputs=[],
+            outputs=[chatbot, state, audio_input, output_audio, text_input],
+        )
+
+    return demo
+
+def run_api():
+    subprocess.run([sys.executable, "-m", "tools.api"])
+
 if __name__ == "__main__":
-    demo.launch()
+    
+    # 创建并启动 API 线程
+    api_thread = threading.Thread(target=run_api, daemon=True)
+    api_thread.start()
+
+    # 给 API 一些时间启动
+    time.sleep(60)
+
+    # 创建并启动 Gradio demo
+    demo = create_demo()
+    demo.launch(server_name="127.0.0.1", server_port=7860, share=True)

fish_speech/callbacks/__init__.py

@@ -0,0 +1,3 @@
+from .grad_norm import GradNormMonitor
+
+__all__ = ["GradNormMonitor"]

fish_speech/callbacks/grad_norm.py

@@ -0,0 +1,113 @@
+from typing import Optional, Union
+
+import lightning.pytorch as pl
+import torch
+from lightning import LightningModule, Trainer
+from lightning.pytorch.callbacks import Callback
+from torch import Tensor, nn
+from torch.utils._foreach_utils import (
+    _group_tensors_by_device_and_dtype,
+    _has_foreach_support,
+)
+
+
+@torch.no_grad()
+def grad_norm(
+    parameters: Union[Tensor, list[Tensor]],
+    norm_type: float = 2.0,
+) -> float:
+    """
+    Returns the norm of the gradients of the given parameters.
+
+    Args:
+        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+            single Tensor that will have gradients normalized
+        norm_type (float): type of the used p-norm.
+
+    Returns:
+        Total norm of the parameter gradients (viewed as a single vector).
+    """  # noqa: E501
+
+    if isinstance(parameters, Tensor):
+        parameters = [parameters]
+
+    grads = [p.grad for p in parameters if p.grad is not None]
+    if len(grads) == 0:
+        return None
+
+    first_device = grads[0].device
+    grouped_grads: dict[
+        tuple[torch.device, torch.dtype], list[list[Tensor]]
+    ] = _group_tensors_by_device_and_dtype(
+        [[g.detach() for g in grads]]
+    )  # type: ignore[assignment]
+
+    norms = []
+    for (device, _), ([grads], _) in grouped_grads.items():
+        if _has_foreach_support(grads, device=device):
+            norms.extend(torch._foreach_norm(grads, norm_type))
+        else:
+            norms.extend([torch.norm(g, norm_type) for g in grads])
+
+    return torch.norm(torch.stack([norm.to(first_device) for norm in norms]), norm_type)
+
+
+class GradNormMonitor(Callback):
+    """
+    Callback that computes the gradient norm of the model parameters.
+    """
+
+    def __init__(
+        self,
+        norm_type: float = 2.0,
+        logging_interval: str = "step",
+        sub_module: Optional[Union[str, list[str]]] = None,
+    ) -> None:
+        """
+        Args:
+            norm_type (float): type of the used p-norm.
+            logging_interval (str): "step" or "epoch".
+        """
+        super().__init__()
+
+        self.norm_type = norm_type
+        self.logging_interval = logging_interval
+        self.sub_module = sub_module
+
+    def on_after_backward(self, trainer: Trainer, model: LightningModule) -> None:
+        """
+        Computes the gradient norm of the model parameters and logs it to the logger.
+
+        Args:
+            trainer (Trainer): The trainer object
+            model (LightningModule): The current lightningModule
+        """
+
+        lightning_model = model
+
+        if self.sub_module is None:
+            return self.log_sub_module_grad_norm(lightning_model, model, "")
+
+        sub_modules = self.sub_module
+        if isinstance(sub_modules, str):
+            sub_modules = [sub_modules]
+
+        for sub_module in sub_modules:
+            self.log_sub_module_grad_norm(
+                lightning_model, getattr(model, sub_module), f"/{sub_module}"
+            )
+
+    def log_sub_module_grad_norm(
+        self, lightning_model: LightningModule, model: nn.Module, path: str
+    ) -> None:
+        grad_norm_val = grad_norm(model.parameters(), self.norm_type)
+        if grad_norm_val is None:
+            return
+
+        on_step = self.logging_interval == "step"
+        lightning_model.log(
+            f"train{path}/grad_norm",
+            grad_norm_val,
+            on_step=on_step,
+            on_epoch=not on_step,
+        )

fish_speech/configs/base.yaml

@@ -0,0 +1,87 @@
+# Base configuration for training a model
+paths:
+  run_dir: results/${project}
+  ckpt_dir: ${paths.run_dir}/checkpoints
+
+hydra:
+  run:
+    dir: ${paths.run_dir}
+
+# Lightning Trainer
+trainer:
+  _target_: lightning.pytorch.trainer.Trainer
+
+  default_root_dir: ${paths.run_dir}
+  accelerator: gpu
+  num_nodes: 1
+  devices: auto
+  strategy:
+    _target_: lightning.pytorch.strategies.DDPStrategy
+    process_group_backend: nccl  # This should be override when training on windows
+
+  precision: bf16-mixed
+
+  # disable validation by epoch end
+  check_val_every_n_epoch: null
+  val_check_interval: 5000
+  max_steps: 100_000
+
+  # Use torch.backends.cudnn.benchmark to speed up training
+  benchmark: true
+
+# Callbacks
+callbacks:
+  model_checkpoint:
+    _target_: lightning.pytorch.callbacks.ModelCheckpoint
+    dirpath: ${paths.ckpt_dir}
+    filename: "step_{step:09d}"
+    save_last: false # additionally always save an exact copy of the last checkpoint to a file last.ckpt
+    save_top_k: 5 # save 5 latest checkpoints
+    monitor: step # use step to monitor checkpoints
+    mode: max # save the latest checkpoint with the highest global_step
+    every_n_epochs: null # don't save checkpoints by epoch end
+    every_n_train_steps: 5000 # save checkpoints every 5000 steps
+    auto_insert_metric_name: false
+
+  model_summary:
+    _target_: lightning.pytorch.callbacks.ModelSummary
+    max_depth: 2 # the maximum depth of layer nesting that the summary will include
+
+  learning_rate_monitor:
+    _target_: lightning.pytorch.callbacks.LearningRateMonitor
+    logging_interval: step
+    log_momentum: false
+
+  grad_norm_monitor:
+    _target_: fish_speech.callbacks.GradNormMonitor
+    norm_type: 2
+    logging_interval: step
+
+# Logger
+logger:
+  tensorboard:
+    _target_: lightning.pytorch.loggers.tensorboard.TensorBoardLogger
+    save_dir: "${paths.run_dir}/tensorboard/"
+    name: null
+    log_graph: false
+    default_hp_metric: true
+    prefix: ""
+
+  # wandb:
+  #   _target_: lightning.pytorch.loggers.wandb.WandbLogger
+  #   # name: "" # name of the run (normally generated by wandb)
+  #   save_dir: "${paths.run_dir}"
+  #   offline: False
+  #   id: null # pass correct id to resume experiment!
+  #   anonymous: null # enable anonymous logging
+  #   project: "fish-speech"
+  #   log_model: False # upload lightning ckpts
+  #   prefix: "" # a string to put at the beginning of metric keys
+  #   # entity: "" # set to name of your wandb team
+  #   group: ""
+  #   tags: ["vq", "hq", "finetune"]
+  #   job_type: ""
+    
+# Loop
+train: true
+test: false

fish_speech/configs/firefly_gan_vq.yaml

@@ -0,0 +1,33 @@
+_target_: fish_speech.models.vqgan.modules.firefly.FireflyArchitecture
+spec_transform:
+  _target_: fish_speech.utils.spectrogram.LogMelSpectrogram
+  sample_rate: 44100
+  n_mels: 160
+  n_fft: 2048
+  hop_length: 512
+  win_length: 2048
+backbone:
+  _target_: fish_speech.models.vqgan.modules.firefly.ConvNeXtEncoder
+  input_channels: 160
+  depths: [3, 3, 9, 3]
+  dims: [128, 256, 384, 512]
+  drop_path_rate: 0.2
+  kernel_size: 7
+head:
+  _target_: fish_speech.models.vqgan.modules.firefly.HiFiGANGenerator
+  hop_length: 512
+  upsample_rates: [8, 8, 2, 2, 2]  # aka. strides
+  upsample_kernel_sizes: [16, 16, 4, 4, 4]
+  resblock_kernel_sizes: [3, 7, 11]
+  resblock_dilation_sizes: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+  num_mels: 512
+  upsample_initial_channel: 512
+  pre_conv_kernel_size: 13
+  post_conv_kernel_size: 13
+quantizer:
+  _target_: fish_speech.models.vqgan.modules.fsq.DownsampleFiniteScalarQuantize
+  input_dim: 512
+  n_groups: 8
+  n_codebooks: 1
+  levels: [8, 5, 5, 5]
+  downsample_factor: [2, 2]

fish_speech/configs/lora/r_8_alpha_16.yaml

@@ -0,0 +1,4 @@
+_target_: fish_speech.models.text2semantic.lora.LoraConfig
+r: 8
+lora_alpha: 16
+lora_dropout: 0.01

fish_speech/configs/text2semantic_finetune.yaml

@@ -0,0 +1,83 @@
+defaults:
+  - base
+  - _self_
+
+project: text2semantic_finetune_dual_ar
+max_length: 4096
+pretrained_ckpt_path: checkpoints/fish-speech-1.4
+
+# Lightning Trainer
+trainer:
+  accumulate_grad_batches: 1
+  gradient_clip_val: 1.0
+  gradient_clip_algorithm: "norm"
+  max_steps: 1000
+  precision: bf16-true
+  limit_val_batches: 10
+  val_check_interval: 100
+
+# Dataset Configuration
+tokenizer:
+  _target_: transformers.AutoTokenizer.from_pretrained
+  pretrained_model_name_or_path: ${pretrained_ckpt_path}
+
+# Dataset Configuration
+train_dataset:
+  _target_: fish_speech.datasets.semantic.AutoTextSemanticInstructionDataset
+  proto_files:
+    - data/protos
+  tokenizer: ${tokenizer}
+  causal: true
+  max_length: ${max_length}
+  use_speaker: false
+  interactive_prob: 0.7
+
+val_dataset:
+  _target_: fish_speech.datasets.semantic.AutoTextSemanticInstructionDataset
+  proto_files:
+    - data/protos
+  tokenizer: ${tokenizer}
+  causal: true
+  max_length: ${max_length}
+  use_speaker: false
+  interactive_prob: 0.7
+
+data:
+  _target_: fish_speech.datasets.semantic.SemanticDataModule
+  train_dataset: ${train_dataset}
+  val_dataset: ${val_dataset}
+  num_workers: 4
+  batch_size: 8
+  tokenizer: ${tokenizer}
+  max_length: ${max_length}
+
+# Model Configuration
+model:
+  _target_: fish_speech.models.text2semantic.lit_module.TextToSemantic
+  model: 
+    _target_: fish_speech.models.text2semantic.llama.BaseTransformer.from_pretrained
+    path: ${pretrained_ckpt_path}
+    load_weights: true
+    max_length: ${max_length}
+    lora_config: null
+
+  optimizer:
+    _target_: torch.optim.AdamW
+    _partial_: true
+    lr: 1e-4
+    weight_decay: 0
+    betas: [0.9, 0.95]
+    eps: 1e-5
+
+  lr_scheduler:
+    _target_: torch.optim.lr_scheduler.LambdaLR
+    _partial_: true
+    lr_lambda:
+      _target_: fish_speech.scheduler.get_constant_schedule_with_warmup_lr_lambda
+      _partial_: true
+      num_warmup_steps: 10
+
+# Callbacks
+callbacks:
+  model_checkpoint:
+    every_n_train_steps: ${trainer.val_check_interval}

fish_speech/conversation.py

@@ -0,0 +1,256 @@
+from dataclasses import dataclass, field
+from typing import Literal
+
+import torch
+from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerFast
+
+IM_START_TOKEN = "<|im_start|>"
+IM_END_TOKEN = "<|im_end|>"
+SEMANTIC_TOKEN = "<|semantic|>"
+MEL_TOKEN = "<|mel|>"
+PHONEME_START_TOKEN = "<|phoneme_start|>"
+PHONEME_END_TOKEN = "<|phoneme_end|>"
+ALL_SPECIAL_TOKENS = [
+    IM_START_TOKEN,
+    IM_END_TOKEN,
+    SEMANTIC_TOKEN,
+    MEL_TOKEN,
+    PHONEME_START_TOKEN,
+    PHONEME_END_TOKEN,
+]
+
+CODEBOOK_PAD_TOKEN_ID = 0
+
+
+class FishTokenizerConfig(PretrainedConfig):
+    share_codebook_embeddings: bool = True
+    codebook_size: int = 1024
+    num_codebooks: int = 8
+
+
+class FishTokenizerFast(PreTrainedTokenizerFast):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.share_codebook_embeddings = kwargs.pop("share_codebook_embeddings", True)
+        self.codebook_size = kwargs.pop("codebook_size", 1024)
+        self.num_codebooks = kwargs.pop("num_codebooks", 8)
+
+
+AutoTokenizer.register(FishTokenizerConfig, fast_tokenizer_class=FishTokenizerFast)
+
+
+@dataclass(kw_only=True)
+class BasePart:
+    pass
+
+
+@dataclass(kw_only=True)
+class VQPart(BasePart):
+    codes: torch.Tensor
+
+
+@dataclass(kw_only=True)
+class TextPart(BasePart):
+    text: str
+
+
+@dataclass(kw_only=True)
+class MelPart(BasePart):
+    mels: torch.Tensor
+
+
+@dataclass(kw_only=True)
+class EncodedMessage:
+    tokens: torch.Tensor
+    labels: torch.Tensor
+    vq_parts: list[torch.Tensor]
+    mel_parts: list[torch.Tensor]
+    vq_require_losses: torch.Tensor | None = None
+
+
+@dataclass(kw_only=True)
+class Message:
+    role: Literal["system", "user", "assistant"]
+    parts: list[VQPart | TextPart | MelPart] = field(default_factory=list)
+    add_im_start: bool = True
+    add_im_end: bool = True
+    cal_loss: bool = False
+
+    # By default, ignore the loss of the auto-generated im_start token
+    ignore_im_start_loss: bool = True
+
+    def encode(
+        self: "Message",
+        tokenizer: AutoTokenizer,
+    ) -> EncodedMessage:
+        all_tokens = []
+        all_labels = []
+
+        # Multi-modal tokens
+        vq_parts = []
+        mel_parts = []
+
+        semantic_id, mel_id = tokenizer.convert_tokens_to_ids(
+            [SEMANTIC_TOKEN, MEL_TOKEN]
+        )
+
+        parts = self.parts.copy()
+        if self.add_im_start:
+            parts.insert(0, TextPart(text=f"<|im_start|>{self.role}\n"))
+
+        if self.add_im_end:
+            parts.append(TextPart(text="<|im_end|>"))
+
+        for part in parts:
+            if isinstance(part, TextPart):
+                tokens = tokenizer.encode(
+                    part.text,
+                    add_special_tokens=False,
+                    truncation=False,
+                    return_tensors="pt",
+                ).int()[0]
+            elif isinstance(part, VQPart):
+                tokens = torch.zeros(part.codes.shape[1], dtype=torch.int) + semantic_id
+                codes = part.codes.clone() + 1
+
+                if getattr(tokenizer, "share_codebook_embeddings", True) is False:
+                    for i in range(len(codes)):
+                        codes[i] += tokenizer.codebook_size * i
+
+                vq_parts.append(codes)
+            elif isinstance(part, MelPart):
+                tokens = torch.zeros(part.mels.shape[1], dtype=torch.int) + mel_id
+                mel_parts.append(part.mels)
+            else:
+                raise ValueError(f"Unsupported part type: {type(part)}")
+
+            all_tokens.append(tokens)
+            if self.cal_loss:
+                all_labels.append(tokens.clone())
+            else:
+                all_labels.append(torch.full_like(tokens, -100))
+
+        tokens = torch.cat(all_tokens, dim=0)
+        labels = torch.cat(all_labels, dim=0)
+        assert tokens.shape == labels.shape
+
+        if self.ignore_im_start_loss and self.add_im_start:
+            labels[: len(all_tokens[0])] = -100
+
+        return EncodedMessage(
+            tokens=tokens,
+            labels=labels,
+            vq_parts=vq_parts,
+            mel_parts=mel_parts,
+        )
+
+
+@dataclass
+class Conversation:
+    messages: list[Message]
+
+    def encode(
+        self: "Conversation",
+        tokenizer: AutoTokenizer,
+        add_shift: bool = True,
+    ) -> EncodedMessage:
+        # Build the input_ids and labels
+        tokens = []
+        labels = []
+        vq_parts = []
+        mel_parts = []
+        vq_require_losses = []
+
+        for message in self.messages:
+            encoded = message.encode(
+                tokenizer,
+            )
+            tokens.append(encoded.tokens)
+            labels.append(encoded.labels)
+            vq_parts.extend(encoded.vq_parts)
+            mel_parts.extend(encoded.mel_parts)
+            vq_require_losses.extend([message.cal_loss] * len(encoded.vq_parts))
+
+        tokens = torch.cat(tokens, dim=0)
+        labels = torch.cat(labels, dim=0)
+        vq_require_losses = torch.tensor(vq_require_losses, dtype=torch.bool)
+
+        if add_shift:
+            tokens = tokens[:-1]
+            labels = labels[1:]
+
+        assert tokens.dtype in [
+            torch.int,
+            torch.long,
+        ], f"Invalid dtype: {tokens.dtype}, conv: {conversation}"
+
+        return EncodedMessage(
+            tokens=tokens,
+            labels=labels,
+            vq_parts=vq_parts,
+            mel_parts=mel_parts,
+            vq_require_losses=vq_require_losses,
+        )
+
+    def encode_for_inference(
+        self: "Conversation",
+        tokenizer: AutoTokenizer,
+        num_codebooks: int,
+    ) -> EncodedMessage:
+        encoded = self.encode(tokenizer, add_shift=False)
+        tokens = encoded.tokens
+        values = torch.zeros((num_codebooks + 1, len(tokens)), dtype=torch.int)
+        values[0] = tokens
+
+        if encoded.vq_parts is None or len(encoded.vq_parts) == 0:
+            return values
+
+        semantic_id, mel_id = tokenizer.convert_tokens_to_ids(
+            [SEMANTIC_TOKEN, MEL_TOKEN]
+        )
+        vq_parts = encoded.vq_parts
+        vq_parts = torch.cat(vq_parts, dim=1)
+        values[1:, tokens == semantic_id] = vq_parts
+        return values
+
+    def visualize(self: "Conversation", tokenizer: AutoTokenizer):
+        encoded = self.encode(tokenizer, add_shift=False)
+
+        print_in_blue = lambda x: print("\033[94m" + x + "\033[0m", end="")
+        print_in_green = lambda x: print("\033[92m" + x + "\033[0m", end="")
+
+        for tok, lab in zip(encoded.tokens, encoded.labels):
+            val = tokenizer.decode(tok, skip_special_tokens=False)
+            if val == "\n":
+                val = "\\n\n"
+
+            if lab == -100:
+                print_in_green(val)
+            else:
+                print_in_blue(val)
+
+        print()
+
+
+if __name__ == "__main__":
+    message0 = Message(
+        role="user",
+        parts=[
+            TextPart(text="Hello, how are you?"),
+            VQPart(codes=torch.zeros((4, 10))),
+        ],
+        cal_loss=False,
+    )
+
+    message1 = Message(
+        role="assistant",
+        parts=[TextPart(text="I'm fine, thank you.")],
+        cal_loss=True,
+    )
+    conversation = Conversation([message0, message1])
+    tokenizer = AutoTokenizer.from_pretrained("checkpoints/Qwen2-1.5B-Instruct")
+    conversation.visualize(tokenizer)
+
+    encoded = conversation.encode(tokenizer)
+    print(encoded)
+    print(tokenizer.batch_decode(encoded.tokens))

fish_speech/datasets/concat_repeat.py

@@ -0,0 +1,53 @@
+import bisect
+import random
+from typing import Iterable
+
+from torch.utils.data import Dataset, IterableDataset
+
+
+class ConcatRepeatDataset(Dataset):
+    datasets: list[Dataset]
+    cumulative_sizes: list[int]
+    repeats: list[int]
+
+    @staticmethod
+    def cumsum(sequence, repeats):
+        r, s = [], 0
+        for dataset, repeat in zip(sequence, repeats):
+            l = len(dataset) * repeat
+            r.append(l + s)
+            s += l
+        return r
+
+    def __init__(self, datasets: Iterable[Dataset], repeats: list[int]):
+        super().__init__()
+
+        self.datasets = list(datasets)
+        self.repeats = repeats
+
+        assert len(self.datasets) > 0, "datasets should not be an empty iterable"
+        assert len(self.datasets) == len(
+            repeats
+        ), "datasets and repeats should have the same length"
+
+        for d in self.datasets:
+            assert not isinstance(
+                d, IterableDataset
+            ), "ConcatRepeatDataset does not support IterableDataset"
+
+        self.cumulative_sizes = self.cumsum(self.datasets, self.repeats)
+
+    def __len__(self):
+        return self.cumulative_sizes[-1]
+
+    def __getitem__(self, idx):
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+
+        dataset = self.datasets[dataset_idx]
+
+        return dataset[sample_idx % len(dataset)]

fish_speech/datasets/protos/text-data.proto

@@ -0,0 +1,24 @@
+syntax = "proto3";
+
+package text_data;
+
+message Semantics {
+    repeated uint32 values = 1;
+}
+
+message Sentence {
+    repeated string texts = 1;
+    repeated Semantics semantics = 3;
+}
+
+message TextData {
+    string source = 1;
+    string name = 2;
+    repeated Sentence sentences = 4;
+}
+
+message SampledData {
+    string source = 1;
+    string name = 2;
+    repeated Sentence samples = 3;
+}

fish_speech/datasets/protos/text_data_pb2.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: text-data.proto
+# Protobuf Python Version: 4.25.1
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(
+    b'\n\x0ftext-data.proto\x12\ttext_data"\x1b\n\tSemantics\x12\x0e\n\x06values\x18\x01 \x03(\r"B\n\x08Sentence\x12\r\n\x05texts\x18\x01 \x03(\t\x12\'\n\tsemantics\x18\x03 \x03(\x0b\x32\x14.text_data.Semantics"P\n\x08TextData\x12\x0e\n\x06source\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12&\n\tsentences\x18\x04 \x03(\x0b\x32\x13.text_data.Sentence"Q\n\x0bSampledData\x12\x0e\n\x06source\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12$\n\x07samples\x18\x03 \x03(\x0b\x32\x13.text_data.Sentenceb\x06proto3'
+)
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "text_data_pb2", _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+    DESCRIPTOR._options = None
+    _globals["_SEMANTICS"]._serialized_start = 30
+    _globals["_SEMANTICS"]._serialized_end = 57
+    _globals["_SENTENCE"]._serialized_start = 59
+    _globals["_SENTENCE"]._serialized_end = 125
+    _globals["_TEXTDATA"]._serialized_start = 127
+    _globals["_TEXTDATA"]._serialized_end = 207
+    _globals["_SAMPLEDDATA"]._serialized_start = 209
+    _globals["_SAMPLEDDATA"]._serialized_end = 290
+# @@protoc_insertion_point(module_scope)

fish_speech/datasets/protos/text_data_stream.py

@@ -0,0 +1,36 @@
+import struct
+
+from .text_data_pb2 import TextData
+
+
+def read_pb_stream(f):
+    while True:
+        buf = f.read(4)
+        if len(buf) == 0:
+            break
+        size = struct.unpack("I", buf)[0]
+        buf = f.read(size)
+        text_data = TextData()
+        text_data.ParseFromString(buf)
+        yield text_data
+
+
+def write_pb_stream(f, text_data):
+    buf = text_data.SerializeToString()
+    f.write(struct.pack("I", len(buf)))
+    f.write(buf)
+
+
+def pack_pb_stream(text_data):
+    buf = text_data.SerializeToString()
+    return struct.pack("I", len(buf)) + buf
+
+
+def split_pb_stream(f):
+    while True:
+        head = f.read(4)
+        if len(head) == 0:
+            break
+        size = struct.unpack("I", head)[0]
+        buf = f.read(size)
+        yield head + buf

fish_speech/datasets/semantic.py

@@ -0,0 +1,496 @@
+import random
+from dataclasses import dataclass
+from itertools import chain
+from pathlib import Path
+from random import Random
+from typing import Optional, Union
+
+import numpy as np
+import pyarrow.parquet as pq
+import torch
+import torch.nn.functional as F
+from datasets.download.streaming_download_manager import xopen
+from huggingface_hub import HfApi
+from lightning import LightningDataModule
+from torch.distributed import get_rank, get_world_size, is_initialized
+from torch.utils.data import DataLoader, IterableDataset, get_worker_info
+from transformers import AutoTokenizer
+
+from fish_speech.conversation import CODEBOOK_PAD_TOKEN_ID
+from fish_speech.datasets.protos.text_data_pb2 import SampledData
+from fish_speech.datasets.protos.text_data_stream import read_pb_stream
+from fish_speech.text.clean import clean_text
+from fish_speech.utils import RankedLogger
+from fish_speech.utils.braceexpand import braceexpand
+
+log = RankedLogger(__name__, rank_zero_only=True)
+
+
+def split_by_rank_worker(files):
+    # We need to know the total number of devices
+    # to split the data properly
+
+    total_devices = 1
+    if is_initialized():
+        total_devices = get_world_size()
+
+    worker_info = get_worker_info()
+    if worker_info is not None:
+        total_devices *= worker_info.num_workers
+
+    if len(files) < total_devices:
+        # Repeat the files N times to match the number of devices
+        files = files * (total_devices // len(files) + 1)
+
+    # DDP
+    if is_initialized():
+        files = files[get_rank() :: get_world_size()]
+
+    # Split by worker
+    if worker_info is not None:
+        files = files[worker_info.id :: worker_info.num_workers]
+
+    return files
+
+
+class AutoTextSemanticInstructionDataset(IterableDataset):
+    """
+    Auto Augment Dataset by Speaker
+
+    1. Random concatenate multiple sentences from the same speaker to form a longer sentence
+    2. Automatically normalize the text
+
+    For interactive mode, we use the following format (multiple sequences):
+    <s> [INST] [SPK: speaker] text [/INST] ... [INST] text [/INST] </s>
+
+    For non-interactive mode, we use the following format (one long sequence):
+    <s> [INST] text [/INST] ... </s>
+    """
+
+    def __init__(
+        self,
+        proto_files: list[str],
+        seed: int = 42,
+        interactive_prob: float = 0.5,
+        max_length: int = 1024,
+        tokenizer: AutoTokenizer = None,
+        use_speaker: bool | float = True,
+        causal: bool = True,
+        num_codebooks: Optional[int] = None,
+        skip_text_prob: float = 0.0,
+    ):
+        """
+        Args:
+            proto_files: proto buf files if using local data
+            seed: random seed
+            interactive_prob: probability to use interactive mode
+            max_length: max length of the text
+            tokenizer: tokenizer
+            use_speaker: include speaker information in the prompt
+            causal: use causal sampling when using local data, disable will lead to random sampling
+            num_codebooks: number of codebooks, if None, it will be automatically detected
+            skip_text_prob: probability to skip the text (audio only), this only applies to interactive mode
+        """
+
+        super().__init__()
+
+        assert 0 <= interactive_prob <= 1, "interactive_prob must be in [0, 1]"
+
+        self.seed = seed
+        self.max_length = max_length
+        self.tokenizer = tokenizer
+        self.interactive_prob = interactive_prob
+        self.use_speaker = use_speaker
+        self.proto_files = proto_files
+        self.causal = causal
+        self.num_codebooks = num_codebooks
+        self.skip_text_prob = skip_text_prob
+
+        self.semantic_token_id = self.tokenizer.convert_tokens_to_ids("<|semantic|>")
+        self.groups = None
+
+    def init_mock_data_server(self):
+        if self.groups is not None:
+            return
+
+        # Expand the proto files
+        expanded_proto_files = []
+        for filename in self.proto_files:
+            for i in braceexpand(filename):
+                i = Path(i)
+                if i.is_file():
+                    expanded_proto_files.append(i)
+                elif i.is_dir():
+                    expanded_proto_files.extend(i.rglob("*.proto"))
+                    expanded_proto_files.extend(i.rglob("*.protos"))
+                else:
+                    raise ValueError(f"{i} is not a file or directory")
+
+        expanded_proto_files = sorted(expanded_proto_files)
+        Random(self.seed).shuffle(expanded_proto_files)
+
+        self.groups = []
+        shard_proto_files = split_by_rank_worker(expanded_proto_files)
+        log.info(
+            f"Reading {len(shard_proto_files)} / {len(expanded_proto_files)} files"
+        )
+
+        count = 0
+        for filename in shard_proto_files:
+            with open(filename, "rb") as f:
+                for text_data in read_pb_stream(f):
+                    self.groups.append(text_data)
+                    count += 1
+
+        log.info(f"Read total {count} groups of data")
+
+        # Shuffle the lines
+        Random(self.seed).shuffle(self.groups)
+        self.group_weights = [len(i.sentences) for i in self.groups]
+
+    def __iter__(self):
+        while True:
+            yield self.augment()
+
+    def tokenize_sentence(self, sentence: str):
+        sentence = clean_text(sentence)
+        tokens = self.tokenizer.encode(
+            f"{sentence}",
+            max_length=10**6,
+            add_special_tokens=False,
+            truncation=False,
+        )
+        return sentence, len(tokens)
+
+    def sample_data(self):
+        if self.groups is None:
+            self.init_mock_data_server()
+
+        # Shuffle unique lines, estimate that each sample is at least 20 tokens
+        num_samples = self.max_length // 20
+
+        # choice group based on their number of samples
+        group = random.choices(self.groups, weights=self.group_weights, k=1)[0]
+
+        if self.causal:
+            # Sample in order
+            if num_samples >= len(group.sentences):
+                samples = group.sentences
+            else:
+                begin = random.randint(0, len(group.sentences) - num_samples)
+                samples = group.sentences[begin : begin + num_samples]
+        else:
+            samples = random.choices(
+                group.sentences, k=min(num_samples, len(group.sentences))
+            )
+
+        return SampledData(
+            source=group.source,
+            name=group.name,
+            samples=samples,
+        )
+
+    def augment(self):
+        final_text, final_semantic = [], []
+        response = self.sample_data()
+        if len(response.samples) == 0:
+            # Invalid group
+            return None
+
+        samples = list(response.samples)
+        idx = 0
+        use_interactive = random.random() < self.interactive_prob
+
+        if use_interactive is False:
+            # Random sample based on speaker using a truncated normal distribution
+            a = torch.tensor([0], dtype=torch.float32)
+            torch.nn.init.trunc_normal_(
+                a,
+                mean=self.max_length // 2,
+                std=self.max_length // 4,
+                a=10,
+                b=self.max_length,
+            )
+            remaining_tokens = a.long().item() - 4
+        else:
+            remaining_tokens = self.max_length
+
+        # Use speaker
+        if isinstance(self.use_speaker, float):
+            use_speaker = random.random() < self.use_speaker
+        else:
+            use_speaker = self.use_speaker
+
+        all_tokens, all_labels = [], []
+        while remaining_tokens > 0 and len(samples) > 0:
+            sentence = samples.pop(0)
+
+            text = random.choice(sentence.texts)
+            text, length = self.tokenize_sentence(text)
+            remaining_tokens -= length + len(sentence.semantics[0].values)
+
+            if use_interactive is False:
+                final_text.append(text)
+                final_semantic.append(sentence.semantics)
+            else:
+                # For interactive mode, we only apply speaker for the first sentence
+                # [INST] [SPK: speaker] text [/INST] ... [INST] text [/INST]
+                tokens, labels = self.pack_sentences(
+                    sentences=[text],
+                    semantics=[sentence.semantics],
+                    speaker=response.name if use_speaker else None,
+                    skip_text=random.random() < self.skip_text_prob,
+                )
+
+                all_tokens.append(tokens)
+                all_labels.append(labels)
+
+            idx += 1
+
+        if use_interactive is False:
+            tokens, labels = self.pack_sentences(
+                final_text,
+                semantics=final_semantic,
+                speaker=response.name if use_speaker else None,
+            )
+            all_tokens.append(tokens)
+            all_labels.append(labels)
+
+        tokens = torch.cat(all_tokens, dim=1)
+        labels = torch.cat(all_labels, dim=1)
+
+        # Verify that the length is correct
+        assert tokens.size(1) == labels.size(1), f"{tokens.size(1)} != {labels.size(1)}"
+
+        data = {"tokens": tokens, "labels": labels}
+
+        return data
+
+    def pack_sentences(
+        self,
+        sentences: list[str],
+        semantics: list,
+        speaker: Optional[str] = None,
+        skip_text: bool = False,
+    ):
+        if speaker is None:
+            speaker = "assistant"
+
+        cated_sentences = " ".join(sentences)
+        if skip_text:
+            cated_sentences = "<|skip_text|>"
+
+        final_text = "<|im_start|>user\n" + cated_sentences + "<|im_end|>"
+        final_text = final_text + f"<|im_start|>{speaker}\n"
+
+        encoded = self.tokenizer.encode(
+            final_text,
+            add_special_tokens=False,
+            truncation=False,
+            max_length=10**6,
+        )
+        semantic_length = sum([len(i[0].values) for i in semantics])
+        prompt_length = len(encoded)
+        num_codebooks = (
+            len(semantics[0]) if self.num_codebooks is None else self.num_codebooks
+        )
+
+        # Pack the tokens and semantics (add <s> and </s> to semantic tokens)
+        tokens = (
+            encoded
+            + [self.semantic_token_id] * semantic_length
+            + self.tokenizer.convert_tokens_to_ids(["<|im_end|>"])
+        )
+
+        # Codebook bos/padding: 0, eos: 1
+        codes = [[CODEBOOK_PAD_TOKEN_ID] * prompt_length for _ in range(num_codebooks)]
+        for segment in semantics:
+            for book_idx, book in zip(range(num_codebooks), segment):
+                for j in book.values:
+                    codes[book_idx].append(int(j) + 1)
+
+        for book in codes:
+            book.extend([CODEBOOK_PAD_TOKEN_ID] * 1)
+
+        tokens = [tokens] + codes
+
+        tokens = torch.tensor(tokens, dtype=torch.long)
+        labels = tokens.clone()
+
+        if skip_text:
+            # If text is not provided, the sentence is used for condition only, all labels are -100
+            torch.fill_(labels, -100)
+            return tokens, labels
+
+        # Mask out the <s> tokens for semantic, predict semantic tokens only
+        # Since we don't mask out the input tokens, the language modeling still works
+        labels[1:, :prompt_length] = -100
+
+        tokens = tokens[:, :-1]
+        labels = labels[:, 1:]
+
+        # Verify the padding is correct, and the last token is eos
+        assert (tokens[1:, :prompt_length] == CODEBOOK_PAD_TOKEN_ID).all()
+        assert (labels[1:, -1:] == CODEBOOK_PAD_TOKEN_ID).all()
+
+        return tokens, labels
+
+
+@dataclass
+class TextDataCollator:
+    tokenizer: AutoTokenizer
+    max_length: int = 1024
+
+    def __call__(self, examples):
+        if "negative_tokens" in examples:
+            positive_examples = []
+            negative_examples = []
+
+            for i in examples:
+                positive_examples.append(
+                    {
+                        "tokens": i["tokens"],
+                        "labels": i["labels"],
+                    }
+                )
+                negative_examples.append(
+                    {
+                        "tokens": i["negative_tokens"],
+                        "labels": i["negative_labels"],
+                    }
+                )
+
+            examples = positive_examples + negative_examples
+
+        return self.batchify(examples)
+
+    def batchify(self, examples, tokens_key="tokens", labels_key="labels"):
+        tokens, attention_masks, labels = [], [], []
+
+        # Calculate the max length
+        max_tokens_length = 0
+        for example in examples:
+            max_tokens_length = max(max_tokens_length, example[tokens_key].size(1))
+        max_tokens_length = min(max_tokens_length, self.max_length)
+
+        for example in examples:
+            _tokens = example[tokens_key][:, :max_tokens_length]
+            _labels = example[labels_key][:, :max_tokens_length]
+            _attention_mask = torch.ones((max_tokens_length,), dtype=torch.bool)
+            tokens_length = _tokens.size(1)
+            _attention_mask[:tokens_length] = False
+
+            assert tokens_length == _labels.size(
+                1
+            ), f"{tokens_length} != {_labels.size(1)}"
+
+            if tokens_length < max_tokens_length:
+                _tokens = F.pad(
+                    _tokens,
+                    (0, max_tokens_length - tokens_length),
+                    value=self.tokenizer.eos_token_id,
+                )
+                _tokens[1:, tokens_length:] = CODEBOOK_PAD_TOKEN_ID
+                _labels = F.pad(
+                    _labels, (0, max_tokens_length - _labels.size(1)), value=-100
+                )
+
+            tokens.append(_tokens)
+            attention_masks.append(_attention_mask)
+            labels.append(_labels)
+
+        tokens = torch.stack(tokens, dim=0)
+        attention_masks = torch.stack(attention_masks, dim=0)
+        labels = torch.stack(labels, dim=0)
+
+        return {
+            "inputs": tokens,
+            "attention_masks": attention_masks,
+            "labels": labels,
+        }
+
+
+class InterleaveDataset(IterableDataset):
+    def __init__(
+        self,
+        datasets: list[IterableDataset],
+        probabilities: list[float],
+        seed: int = 42,
+    ):
+        super().__init__()
+
+        self.datasets = datasets
+        self.probabilities = probabilities
+        self.seed = seed
+
+    def __iter__(self):
+        rng = np.random.default_rng(self.seed)
+        dataset_iterators = [iter(dataset) for dataset in self.datasets]
+
+        while True:
+            # Random choice one
+            dataset_idx = rng.choice(len(self.datasets), p=self.probabilities)
+            dataset_iterator = dataset_iterators[dataset_idx]
+
+            try:
+                yield next(dataset_iterator)
+            except StopIteration:
+                # Exhausted, create a new iterator
+                dataset_iterators[dataset_idx] = iter(self.datasets[dataset_idx])
+                yield next(dataset_iterators[dataset_idx])
+
+
+class SemanticDataModule(LightningDataModule):
+    def __init__(
+        self,
+        train_dataset: Union[AutoTextSemanticInstructionDataset, InterleaveDataset],
+        val_dataset: Union[AutoTextSemanticInstructionDataset, InterleaveDataset],
+        batch_size: int = 32,
+        tokenizer: AutoTokenizer = None,
+        max_length: int = 1024,
+        num_workers: int = 4,
+    ):
+        super().__init__()
+
+        self.train_dataset = train_dataset
+        self.val_dataset = val_dataset
+        self.batch_size = batch_size
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+        self.num_workers = num_workers
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            collate_fn=TextDataCollator(self.tokenizer, self.max_length),
+            num_workers=self.num_workers,
+            persistent_workers=True,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.batch_size,
+            collate_fn=TextDataCollator(self.tokenizer, self.max_length),
+            num_workers=self.num_workers,
+            persistent_workers=True,
+        )
+
+
+if __name__ == "__main__":
+    from tqdm import tqdm
+
+    ds = AutoTextSemanticInstructionDataset(
+        ["data/protos"],
+        tokenizer=AutoTokenizer.from_pretrained("fishaudio/fish-speech-1"),
+        use_speaker=False,
+        interactive_prob=1.0,
+        skip_text_prob=0.5,
+    )
+
+    for i in ds:
+        print(ds.tokenizer.decode(i["tokens"][0], skip_special_tokens=False))
+        # i["labels"][0][i["labels"][0] == -100] = 0
+        # print(ds.tokenizer.decode(i["labels"][0], skip_special_tokens=False))
+        break

fish_speech/datasets/vqgan.py

@@ -0,0 +1,147 @@
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import librosa
+import numpy as np
+import torch
+from lightning import LightningDataModule
+from torch.utils.data import DataLoader, Dataset
+
+from fish_speech.utils import RankedLogger
+
+logger = RankedLogger(__name__, rank_zero_only=False)
+
+
+class VQGANDataset(Dataset):
+    def __init__(
+        self,
+        filelist: str,
+        sample_rate: int = 32000,
+        hop_length: int = 640,
+        slice_frames: Optional[int] = None,
+    ):
+        super().__init__()
+
+        filelist = Path(filelist)
+        root = filelist.parent
+
+        self.files = [
+            root / line.strip()
+            for line in filelist.read_text(encoding="utf-8").splitlines()
+            if line.strip()
+        ]
+        self.sample_rate = sample_rate
+        self.hop_length = hop_length
+        self.slice_frames = slice_frames
+
+    def __len__(self):
+        return len(self.files)
+
+    def get_item(self, idx):
+        file = self.files[idx]
+
+        audio, _ = librosa.load(file, sr=self.sample_rate, mono=True)
+
+        # Slice audio and features
+        if (
+            self.slice_frames is not None
+            and audio.shape[0] > self.slice_frames * self.hop_length
+        ):
+            start = np.random.randint(
+                0, audio.shape[0] - self.slice_frames * self.hop_length
+            )
+            audio = audio[start : start + self.slice_frames * self.hop_length]
+
+        if len(audio) == 0:
+            return None
+
+        max_value = np.abs(audio).max()
+        if max_value > 1.0:
+            audio = audio / max_value
+
+        return {
+            "audio": torch.from_numpy(audio),
+        }
+
+    def __getitem__(self, idx):
+        try:
+            return self.get_item(idx)
+        except Exception as e:
+            import traceback
+
+            traceback.print_exc()
+            logger.error(f"Error loading {self.files[idx]}: {e}")
+            return None
+
+
+@dataclass
+class VQGANCollator:
+    def __call__(self, batch):
+        batch = [x for x in batch if x is not None]
+
+        audio_lengths = torch.tensor([len(x["audio"]) for x in batch])
+        audio_maxlen = audio_lengths.max()
+
+        # Rounds up to nearest multiple of 2 (audio_lengths)
+        audios = []
+        for x in batch:
+            audios.append(
+                torch.nn.functional.pad(x["audio"], (0, audio_maxlen - len(x["audio"])))
+            )
+
+        return {
+            "audios": torch.stack(audios),
+            "audio_lengths": audio_lengths,
+        }
+
+
+class VQGANDataModule(LightningDataModule):
+    def __init__(
+        self,
+        train_dataset: VQGANDataset,
+        val_dataset: VQGANDataset,
+        batch_size: int = 32,
+        num_workers: int = 4,
+        val_batch_size: Optional[int] = None,
+    ):
+        super().__init__()
+
+        self.train_dataset = train_dataset
+        self.val_dataset = val_dataset
+        self.batch_size = batch_size
+        self.val_batch_size = val_batch_size or batch_size
+        self.num_workers = num_workers
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            collate_fn=VQGANCollator(),
+            num_workers=self.num_workers,
+            shuffle=True,
+            persistent_workers=True,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.val_dataset,
+            batch_size=self.val_batch_size,
+            collate_fn=VQGANCollator(),
+            num_workers=self.num_workers,
+            persistent_workers=True,
+        )
+
+
+if __name__ == "__main__":
+    dataset = VQGANDataset("data/LibriTTS_R/vq_train_filelist.txt")
+    dataloader = DataLoader(
+        dataset, batch_size=4, shuffle=False, collate_fn=VQGANCollator()
+    )
+
+    for batch in dataloader:
+        print(batch["audios"].shape)
+        print(batch["features"].shape)
+        print(batch["audio_lengths"])
+        print(batch["feature_lengths"])
+        break

fish_speech/i18n/README.md

@@ -0,0 +1,27 @@
+## i18n Folder Attribution
+
+The `i18n` folder within the `fish_speech` directory contains files initially sourced from the RVC project. In compliance with the MIT license under which these files were released, we acknowledge the original authors and sources below:
+
+### fish_speech/i18n/core.py
+
+**Related code from RVC:**
+[https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/83d6a64e675d9bbd6e92ee450c5f807ed2bb54d8/i18n/i18n.py](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/83d6a64e675d9bbd6e92ee450c5f807ed2bb54d8/i18n/i18n.py)
+
+**Initial commit:**
+add localization(添加本地化) [RVC-Project/Retrieval-based-Voice-Conversion-WebUI#35](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/pull/35)
+
+**Initial author:**
+[@L4Ph](https://github.com/L4Ph)
+
+### fish_speech/i18n/scan.py
+
+**Related code from RVC:**
+[https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/83d6a64e675d9bbd6e92ee450c5f807ed2bb54d8/i18n/scan_i18n.py](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/blob/83d6a64e675d9bbd6e92ee450c5f807ed2bb54d8/i18n/scan_i18n.py)
+
+**Initial commit:**
+File for detecting i18n missing keys [RVC-Project/Retrieval-based-Voice-Conversion-WebUI#1058](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/pull/1058)
+
+**Initial author:**
+[@towzeur](https://github.com/towzeur)
+
+We appreciate the contributions of the RVC project and its authors.

fish_speech/i18n/__init__.py

@@ -0,0 +1,3 @@
+from .core import i18n
+
+__all__ = ["i18n"]

fish_speech/i18n/core.py

@@ -0,0 +1,40 @@
+import json
+import locale
+from pathlib import Path
+
+I18N_FILE_PATH = Path(__file__).parent / "locale"
+DEFAULT_LANGUAGE = "en_US"
+
+
+def load_language_list(language):
+    with open(I18N_FILE_PATH / f"{language}.json", "r", encoding="utf-8") as f:
+        language_list = json.load(f)
+
+    return language_list
+
+
+class I18nAuto:
+    def __init__(self):
+        i18n_file = Path(".locale")
+
+        if i18n_file.exists():
+            with open(i18n_file, "r", encoding="utf-8") as f:
+                language = f.read().strip()
+        else:
+            # getlocale can't identify the system's language ((None, None))
+            language = locale.getdefaultlocale()[0]
+
+        if (I18N_FILE_PATH / f"{language}.json").exists() is False:
+            language = DEFAULT_LANGUAGE
+
+        self.language = language
+        self.language_map = load_language_list(language)
+
+    def __call__(self, key):
+        return self.language_map.get(key, key)
+
+    def __repr__(self):
+        return "Use Language: " + self.language
+
+
+i18n = I18nAuto()

fish_speech/i18n/locale/en_US.json

@@ -0,0 +1,123 @@
+{
+  "16-mixed is recommended for 10+ series GPU": "16-mixed is recommended for 10+ series GPU",
+  "5 to 10 seconds of reference audio, useful for specifying speaker.": "5 to 10 seconds of reference audio, useful for specifying speaker.",
+  "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).": "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).",
+  "Accumulate Gradient Batches": "Accumulate Gradient Batches",
+  "Add to Processing Area": "Add to Processing Area",
+  "Added path successfully!": "Added path successfully!",
+  "Advanced Config": "Advanced Config",
+  "Base LLAMA Model": "Base LLAMA Model",
+  "Batch Inference": "Batch Inference",
+  "Batch Size": "Batch Size",
+  "Changing with the Model Path": "Changing with the Model Path",
+  "Chinese": "Chinese",
+  "Compile Model": "Compile Model",
+  "Compile the model can significantly reduce the inference time, but will increase cold start time": "Compile the model can significantly reduce the inference time, but will increase cold start time",
+  "Copy": "Copy",
+  "Data Preprocessing": "Data Preprocessing",
+  "Data Preprocessing Path": "Data Preprocessing Path",
+  "Data Source": "Data Source",
+  "Decoder Model Config": "Decoder Model Config",
+  "Decoder Model Path": "Decoder Model Path",
+  "Disabled": "Disabled",
+  "Enable Reference Audio": "Enable Reference Audio",
+  "English": "English",
+  "Error Message": "Error Message",
+  "File Preprocessing": "File Preprocessing",
+  "Generate": "Generate",
+  "Generated Audio": "Generated Audio",
+  "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format",
+  "Infer interface is closed": "Infer interface is closed",
+  "Inference Configuration": "Inference Configuration",
+  "Inference Server Configuration": "Inference Server Configuration",
+  "Inference Server Error": "Inference Server Error",
+  "Inferring interface is launched at {}": "Inferring interface is launched at {}",
+  "Initial Learning Rate": "Initial Learning Rate",
+  "Input Audio & Source Path for Transcription": "Input Audio & Source Path for Transcription",
+  "Input Text": "Input Text",
+  "Invalid path: {}": "Invalid path: {}",
+  "It is recommended to use CUDA, if you have low configuration, use CPU": "It is recommended to use CUDA, if you have low configuration, use CPU",
+  "Iterative Prompt Length, 0 means off": "Iterative Prompt Length, 0 means off",
+  "Japanese": "Japanese",
+  "LLAMA Configuration": "LLAMA Configuration",
+  "LLAMA Model Config": "LLAMA Model Config",
+  "LLAMA Model Path": "LLAMA Model Path",
+  "Labeling Device": "Labeling Device",
+  "LoRA Model to be merged": "LoRA Model to be merged",
+  "Maximum Audio Duration": "Maximum Audio Duration",
+  "Maximum Length per Sample": "Maximum Length per Sample",
+  "Maximum Training Steps": "Maximum Training Steps",
+  "Maximum tokens per batch, 0 means no limit": "Maximum tokens per batch, 0 means no limit",
+  "Merge": "Merge",
+  "Merge LoRA": "Merge LoRA",
+  "Merge successfully": "Merge successfully",
+  "Minimum Audio Duration": "Minimum Audio Duration",
+  "Model Output Path": "Model Output Path",
+  "Model Size": "Model Size",
+  "Move": "Move",
+  "Move files successfully": "Move files successfully",
+  "No audio generated, please check the input text.": "No audio generated, please check the input text.",
+  "No selected options": "No selected options",
+  "Number of Workers": "Number of Workers",
+  "Open Inference Server": "Open Inference Server",
+  "Open Labeler WebUI": "Open Labeler WebUI",
+  "Open Tensorboard": "Open Tensorboard",
+  "Opened labeler in browser": "Opened labeler in browser",
+  "Optional Label Language": "Optional Label Language",
+  "Optional online ver": "Optional online ver",
+  "Output Path": "Output Path",
+  "Path error, please check the model file exists in the corresponding path": "Path error, please check the model file exists in the corresponding path",
+  "Precision": "Precision",
+  "Probability of applying Speaker Condition": "Probability of applying Speaker Condition",
+  "Put your text here.": "Put your text here.",
+  "Reference Audio": "Reference Audio",
+  "Reference Text": "Reference Text",
+  "Related code and weights are released under CC BY-NC-SA 4.0 License.": "Related code and weights are released under CC BY-NC-SA 4.0 License.",
+  "Remove Selected Data": "Remove Selected Data",
+  "Removed path successfully!": "Removed path successfully!",
+  "Repetition Penalty": "Repetition Penalty",
+  "Save model every n steps": "Save model every n steps",
+  "Select LLAMA ckpt": "Select LLAMA ckpt",
+  "Select VITS ckpt": "Select VITS ckpt",
+  "Select VQGAN ckpt": "Select VQGAN ckpt",
+  "Select source file processing method": "Select source file processing method",
+  "Select the model to be trained (Depending on the Tab page you are on)": "Select the model to be trained (Depending on the Tab page you are on)",
+  "Selected: {}": "Selected: {}",
+  "Speaker": "Speaker",
+  "Speaker is identified by the folder name": "Speaker is identified by the folder name",
+  "Start Training": "Start Training",
+  "Streaming Audio": "Streaming Audio",
+  "Streaming Generate": "Streaming Generate",
+  "Tensorboard Host": "Tensorboard Host",
+  "Tensorboard Log Path": "Tensorboard Log Path",
+  "Tensorboard Port": "Tensorboard Port",
+  "Tensorboard interface is closed": "Tensorboard interface is closed",
+  "Tensorboard interface is launched at {}": "Tensorboard interface is launched at {}",
+  "Text is too long, please keep it under {} characters.": "Text is too long, please keep it under {} characters.",
+  "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.": "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.",
+  "Training Configuration": "Training Configuration",
+  "Training Error": "Training Error",
+  "Training stopped": "Training stopped",
+  "Type name of the speaker": "Type name of the speaker",
+  "Type the path or select from the dropdown": "Type the path or select from the dropdown",
+  "Use LoRA": "Use LoRA",
+  "Use LoRA can save GPU memory, but may reduce the quality of the model": "Use LoRA can save GPU memory, but may reduce the quality of the model",
+  "Use filelist": "Use filelist",
+  "Use large for 10G+ GPU, medium for 5G, small for 2G": "Use large for 10G+ GPU, medium for 5G, small for 2G",
+  "VITS Configuration": "VITS Configuration",
+  "VQGAN Configuration": "VQGAN Configuration",
+  "Validation Batch Size": "Validation Batch Size",
+  "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "View the status of the preprocessing folder (use the slider to control the depth of the tree)",
+  "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.": "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.",
+  "WebUI Host": "WebUI Host",
+  "WebUI Port": "WebUI Port",
+  "Whisper Model": "Whisper Model",
+  "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).": "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).",
+  "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU",
+  "latest": "latest",
+  "new": "new",
+  "Realtime Transform Text": "Realtime Transform Text",
+  "Normalization Result Preview (Currently Only Chinese)": "Normalization Result Preview (Currently Only Chinese)",
+  "Text Normalization": "Text Normalization",
+  "Select Example Audio": "Select Example Audio"
+}

fish_speech/i18n/locale/es_ES.json

@@ -0,0 +1,123 @@
+{
+  "16-mixed is recommended for 10+ series GPU": "se recomienda 16-mixed para GPU de la serie 10+",
+  "5 to 10 seconds of reference audio, useful for specifying speaker.": "5 a 10 segundos de audio de referencia, útil para especificar el hablante.",
+  "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).": "Un modelo de texto a voz basado en VQ-GAN y Llama desarrollado por [Fish Audio](https://fish.audio).",
+  "Accumulate Gradient Batches": "Acumular lotes de gradientes",
+  "Add to Processing Area": "Agregar al Área de Procesamiento",
+  "Added path successfully!": "¡Ruta agregada exitosamente!",
+  "Advanced Config": "Configuración Avanzada",
+  "Base LLAMA Model": "Modelo Base LLAMA",
+  "Batch Inference": "Inferencia por Lote",
+  "Batch Size": "Tamaño del Lote",
+  "Changing with the Model Path": "Cambiando con la Ruta del Modelo",
+  "Chinese": "Chino",
+  "Compile Model": "Compilar Modelo",
+  "Compile the model can significantly reduce the inference time, but will increase cold start time": "Compilar el modelo puede reducir significativamente el tiempo de inferencia, pero aumentará el tiempo de inicio en frío",
+  "Copy": "Copiar",
+  "Data Preprocessing": "Preprocesamiento de Datos",
+  "Data Preprocessing Path": "Ruta de Preprocesamiento de Datos",
+  "Data Source": "Fuente de Datos",
+  "Decoder Model Config": "Configuración del modelo decodificador",
+  "Decoder Model Path": "Ruta del modelo decodificador",
+  "Disabled": "Desactivado",
+  "Enable Reference Audio": "Habilitar Audio de Referencia",
+  "English": "Inglés",
+  "Error Message": "Mensaje de Error",
+  "File Preprocessing": "Preprocesamiento de Archivos",
+  "Generate": "Generar",
+  "Generated Audio": "Audio Generado",
+  "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "Si no hay texto correspondiente para el audio, aplique ASR para asistencia, soporte para formato .txt o .lab",
+  "Infer interface is closed": "La interfaz de inferencia está cerrada",
+  "Inference Configuration": "Configuración de Inferencia",
+  "Inference Server Configuration": "Configuración del Servidor de Inferencia",
+  "Inference Server Error": "Error del Servidor de Inferencia",
+  "Inferring interface is launched at {}": "La interfaz de inferencia se ha lanzado en {}",
+  "Initial Learning Rate": "Tasa de Aprendizaje Inicial",
+  "Input Audio & Source Path for Transcription": "Audio de Entrada y Ruta de Origen para Transcripción",
+  "Input Text": "Texto de Entrada",
+  "Invalid path: {}": "Ruta inválida: {}",
+  "It is recommended to use CUDA, if you have low configuration, use CPU": "Se recomienda usar CUDA, si tiene una configuración baja, use CPU",
+  "Iterative Prompt Length, 0 means off": "Longitud de la Indicación Iterativa, 0 significa apagado",
+  "Japanese": "Japonés",
+  "LLAMA Configuration": "Configuración de LLAMA",
+  "LLAMA Model Config": "Configuración del Modelo LLAMA",
+  "LLAMA Model Path": "Ruta del Modelo LLAMA",
+  "Labeling Device": "Dispositivo de Etiquetado",
+  "LoRA Model to be merged": "Modelo LoRA a fusionar",
+  "Maximum Audio Duration": "Duración máxima de audio",
+  "Maximum Length per Sample": "Longitud Máxima por Muestra",
+  "Maximum Training Steps": "Pasos Máximos de Entrenamiento",
+  "Maximum tokens per batch, 0 means no limit": "Máximo de tokens por lote, 0 significa sin límite",
+  "Merge": "Fusionar",
+  "Merge LoRA": "Fusionar LoRA",
+  "Merge successfully": "Fusionado exitosamente",
+  "Minimum Audio Duration": "Duración mínima de audio",
+  "Model Output Path": "Ruta de Salida del Modelo",
+  "Model Size": "Tamaño del Modelo",
+  "Move": "Mover",
+  "Move files successfully": "Archivos movidos exitosamente",
+  "No audio generated, please check the input text.": "No se generó audio, por favor verifique el texto de entrada.",
+  "No selected options": "No hay opciones seleccionadas",
+  "Number of Workers": "Número de Trabajadores",
+  "Open Inference Server": "Abrir Servidor de Inferencia",
+  "Open Labeler WebUI": "Abrir Interfaz Web del Etiquetador",
+  "Open Tensorboard": "Abrir Tensorboard",
+  "Opened labeler in browser": "Se abrió el etiquetador en el navegador",
+  "Optional Label Language": "Idioma de Etiquetado Opcional",
+  "Optional online ver": "Ver en línea opcional",
+  "Output Path": "Ruta de Salida",
+  "Path error, please check the model file exists in the corresponding path": "Error de ruta, por favor verifique que el archivo del modelo exista en la ruta correspondiente",
+  "Precision": "Precisión",
+  "Probability of applying Speaker Condition": "Probabilidad de aplicar Condición de Hablante",
+  "Put your text here.": "Ponga su texto aquí.",
+  "Reference Audio": "Audio de Referencia",
+  "Reference Text": "Texto de Referencia",
+  "Related code and weights are released under CC BY-NC-SA 4.0 License.": "El código relacionado y los pesos se publican bajo la Licencia CC BY-NC-SA 4.0.",
+  "Remove Selected Data": "Eliminar Datos Seleccionados",
+  "Removed path successfully!": "¡Ruta eliminada exitosamente!",
+  "Repetition Penalty": "Penalización por Repetición",
+  "Save model every n steps": "Guardar modelo cada n pasos",
+  "Select LLAMA ckpt": "Seleccionar punto de control LLAMA",
+  "Select VITS ckpt": "Seleccionar punto de control VITS",
+  "Select VQGAN ckpt": "Seleccionar punto de control VQGAN",
+  "Select source file processing method": "Seleccione el método de procesamiento de archivos fuente",
+  "Select the model to be trained (Depending on the Tab page you are on)": "Seleccione el modelo a entrenar (Dependiendo de la pestaña en la que se encuentre)",
+  "Selected: {}": "Seleccionado: {}",
+  "Speaker": "Hablante",
+  "Speaker is identified by the folder name": "El hablante se identifica por el nombre de la carpeta",
+  "Start Training": "Iniciar Entrenamiento",
+  "Streaming Audio": "transmisión de audio",
+  "Streaming Generate": "síntesis en flujo",
+  "Tensorboard Host": "Host de Tensorboard",
+  "Tensorboard Log Path": "Ruta de Registro de Tensorboard",
+  "Tensorboard Port": "Puerto de Tensorboard",
+  "Tensorboard interface is closed": "La interfaz de Tensorboard está cerrada",
+  "Tensorboard interface is launched at {}": "La interfaz de Tensorboard se ha lanzado en {}",
+  "Text is too long, please keep it under {} characters.": "El texto es demasiado largo, por favor manténgalo por debajo de {} caracteres.",
+  "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.": "La ruta de la carpeta de entrada a la izquierda o la lista de archivos. Ya sea que esté marcado o no, se utilizará para el entrenamiento posterior en esta lista.",
+  "Training Configuration": "Configuración de Entrenamiento",
+  "Training Error": "Error de Entrenamiento",
+  "Training stopped": "Entrenamiento detenido",
+  "Type name of the speaker": "Escriba el nombre del hablante",
+  "Type the path or select from the dropdown": "Escriba la ruta o seleccione de la lista desplegable",
+  "Use LoRA": "Usar LoRA",
+  "Use LoRA can save GPU memory, but may reduce the quality of the model": "Usar LoRA puede ahorrar memoria GPU, pero puede reducir la calidad del modelo",
+  "Use filelist": "Usar lista de archivos",
+  "Use large for 10G+ GPU, medium for 5G, small for 2G": "Use grande para GPU de 10G+, mediano para 5G, pequeño para 2G",
+  "VITS Configuration": "Configuración de VITS",
+  "VQGAN Configuration": "Configuración de VQGAN",
+  "Validation Batch Size": "Tamaño del Lote de Validación",
+  "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "Vea el estado de la carpeta de preprocesamiento (use el control deslizante para controlar la profundidad del árbol)",
+  "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.": "No somos responsables de ningún mal uso del modelo, por favor considere sus leyes y regulaciones locales antes de usarlo.",
+  "WebUI Host": "Host de WebUI",
+  "WebUI Port": "Puerto de WebUI",
+  "Whisper Model": "Modelo Whisper",
+  "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).": "Puede encontrar el código fuente [aquí](https://github.com/fishaudio/fish-speech) y los modelos [aquí](https://huggingface.co/fishaudio/fish-speech-1).",
+  "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "Se recomienda bf16-true para GPU de la serie 30+, se recomienda 16-mixed para GPU de la serie 10+",
+  "latest": "más reciente",
+  "new": "nuevo",
+  "Realtime Transform Text": "Transformación de Texto en Tiempo Real",
+  "Normalization Result Preview (Currently Only Chinese)": "Vista Previa del Resultado de Normalización (Actualmente Solo Chino)",
+  "Text Normalization": "Normalización de Texto",
+  "Select Example Audio": "Selecionar áudio de exemplo"
+}

fish_speech/i18n/locale/ja_JP.json

@@ -0,0 +1,123 @@
+{
+  "16-mixed is recommended for 10+ series GPU": "10シリーズ以降のGPUには16-mixedをお勧めします",
+  "5 to 10 seconds of reference audio, useful for specifying speaker.": "話者を指定するのに役立つ、5～10秒のリファレンスオーディオ。",
+  "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).": "[Fish Audio](https://fish.audio)が開発したVQ-GANとLlamaに基づくテキスト音声合成モデル。",
+  "Accumulate Gradient Batches": "勾配バッチの累積",
+  "Add to Processing Area": "処理エリアに追加",
+  "Added path successfully!": "パスの追加に成功しました！",
+  "Advanced Config": "詳細設定",
+  "Base LLAMA Model": "基本LLAMAモデル",
+  "Batch Inference": "バッチ推論",
+  "Batch Size": "バッチサイズ",
+  "Changing with the Model Path": "モデルのパスに伴って変化する",
+  "Chinese": "中国語",
+  "Compile Model": "モデルのコンパイル",
+  "Compile the model can significantly reduce the inference time, but will increase cold start time": "モデルをコンパイルすると推論時間を大幅に短縮できますが、コールドスタート時間が長くなります",
+  "Copy": "コピー",
+  "Data Preprocessing": "データ前処理",
+  "Data Preprocessing Path": "データ前処理パス",
+  "Data Source": "データソース",
+  "Decoder Model Config": "デコーダーモデルの構成",
+  "Decoder Model Path": "デコーダーモデルのパス",
+  "Disabled": "無効",
+  "Enable Reference Audio": "リファレンスオーディオを有効にする",
+  "English": "英語",
+  "Error Message": "エラーメッセージ",
+  "File Preprocessing": "文書前处理",
+  "Generate": "生成",
+  "Generated Audio": "生成されたオーディオ",
+  "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "音声に対応するテキストがない場合は、ASRを適用してサポートします。.txtまたは.lab形式をサポートしています",
+  "Infer interface is closed": "推論インターフェースが閉じられています",
+  "Inference Configuration": "推論設定",
+  "Inference Server Configuration": "推論サーバー設定",
+  "Inference Server Error": "推論サーバーエラー",
+  "Inferring interface is launched at {}": "推論インターフェースが{}で起動しました",
+  "Initial Learning Rate": "初期学習率",
+  "Input Audio & Source Path for Transcription": "入力オーディオと文字起こしのソースパス",
+  "Input Text": "入力テキスト",
+  "Invalid path: {}": "無効なパス: {}",
+  "It is recommended to use CUDA, if you have low configuration, use CPU": "CUDAの使用をお勧めします。低い構成の場合はCPUを使用してください",
+  "Iterative Prompt Length, 0 means off": "反復プロンプト長。0はオフを意味します",
+  "Japanese": "日本語",
+  "LLAMA Configuration": "LLAMA設定",
+  "LLAMA Model Config": "LLAMAモデル設定",
+  "LLAMA Model Path": "LLAMAモデルパス",
+  "Labeling Device": "ラベリングデバイス",
+  "LoRA Model to be merged": "マージするLoRAモデル",
+  "Maximum Audio Duration": "最大オーディオの長さ",
+  "Maximum Length per Sample": "サンプルあたりの最大長",
+  "Maximum Training Steps": "最大トレーニングステップ数",
+  "Maximum tokens per batch, 0 means no limit": "バッチあたりの最大トークン数。0は制限なしを意味します",
+  "Merge": "マージ",
+  "Merge LoRA": "LoRAのマージ",
+  "Merge successfully": "マージに成功しました",
+  "Minimum Audio Duration": "最小オーディオの長さ",
+  "Model Output Path": "モデル出力パス",
+  "Model Size": "モデルサイズ",
+  "Move": "移動",
+  "Move files successfully": "ファイルの移動に成功しました",
+  "No audio generated, please check the input text.": "オーディオが生成されていません。入力テキストを確認してください。",
+  "No selected options": "選択されたオプションはありません",
+  "Number of Workers": "ワーカー数",
+  "Open Inference Server": "推論サーバーを開く",
+  "Open Labeler WebUI": "ラベラーWebUIを開く",
+  "Open Tensorboard": "Tensorboardを開く",
+  "Opened labeler in browser": "ブラウザでラベラーを開きました",
+  "Optional Label Language": "オプションのラベル言語",
+  "Optional online ver": "オプションのオンラインバージョン",
+  "Output Path": "出力パス",
+  "Path error, please check the model file exists in the corresponding path": "パスエラー。対応するパスにモデルファイルが存在するか確認してください",
+  "Precision": "精度",
+  "Probability of applying Speaker Condition": "話者条件を適用する確率",
+  "Put your text here.": "ここにテキストを入力してください。",
+  "Reference Audio": "リファレンスオーディオ",
+  "Reference Text": "リファレンステキスト",
+  "Related code and weights are released under CC BY-NC-SA 4.0 License.": "関連コードと重みはCC BY-NC-SA 4.0ライセンスの下でリリースされます。",
+  "Remove Selected Data": "選択したデータを削除",
+  "Removed path successfully!": "パスの削除に成功しました！",
+  "Repetition Penalty": "反復ペナルティ",
+  "Save model every n steps": "nステップごとにモデルを保存",
+  "Select LLAMA ckpt": " LLAMA チェックポイントを選択",
+  "Select VITS ckpt": "VITS チェックポイントを選択",
+  "Select VQGAN ckpt": "VQGAN チェックポイントを選択",
+  "Select source file processing method": "ソースファイルの処理方法を選択",
+  "Select the model to be trained (Depending on the Tab page you are on)": "タブページに応じてトレーニングするモデルを選択してください",
+  "Selected: {}": "選択済み: {}",
+  "Speaker": "話者",
+  "Speaker is identified by the folder name": "話者はフォルダ名で識別されます",
+  "Start Training": "トレーニング開始",
+  "Streaming Audio": "ストリーミングオーディオ",
+  "Streaming Generate": "ストリーミング合成",
+  "Tensorboard Host": "Tensorboardホスト",
+  "Tensorboard Log Path": "Tensorboardログパス",
+  "Tensorboard Port": "Tensorboardポート",
+  "Tensorboard interface is closed": "Tensorboardインターフェースが閉じられています",
+  "Tensorboard interface is launched at {}": "Tensorboardインターフェースが{}で起動されました",
+  "Text is too long, please keep it under {} characters.": "テキストが長すぎます。{}文字以内に抑えてください。",
+  "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.": "左側の入力フォルダまたはファイルリストのパス。チェックの有無にかかわらず、このリストの後続のトレーニングに使用されます。",
+  "Training Configuration": "トレーニング設定",
+  "Training Error": "トレーニングエラー",
+  "Training stopped": "トレーニングが停止しました",
+  "Type name of the speaker": "話者の名前を入力",
+  "Type the path or select from the dropdown": "パスを入力するか、ドロップダウンから選択してください",
+  "Use LoRA": "LoRAを使用",
+  "Use LoRA can save GPU memory, but may reduce the quality of the model": "LoRAを使用するとGPUメモリを節約できますが、モデルの品質が低下する可能性があります",
+  "Use filelist": "ファイルリストを使用",
+  "Use large for 10G+ GPU, medium for 5G, small for 2G": "10G以上のGPUには大、5Gには中、2Gには小を使用してください",
+  "VITS Configuration": "VITS の構成",
+  "VQGAN Configuration": "VQGAN の構成",
+  "Validation Batch Size": "検証バッチサイズ",
+  "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "前処理フォルダの状態を表示（スライダーを使用してツリーの深さを制御）",
+  "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.": "モデルの誤用については一切責任を負いません。使用する前に、現地の法律と規制を考慮してください。",
+  "WebUI Host": "WebUIホスト",
+  "WebUI Port": "WebUIポート",
+  "Whisper Model": "Whisperモデル",
+  "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).": "ソースコードは[こちら](https://github.com/fishaudio/fish-speech)、モデルは[こちら](https://huggingface.co/fishaudio/fish-speech-1)にあります。",
+  "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "30シリーズ以降のGPUにはbf16-trueを、10シリーズ以降のGPUには16-mixedをお勧めします",
+  "latest": "最新",
+  "new": "新規",
+  "Realtime Transform Text": "リアルタイム変換テキスト",
+  "Normalization Result Preview (Currently Only Chinese)": "正規化結果プレビュー（現在は中国語のみ）",
+  "Text Normalization": "テキスト正規化",
+  "Select Example Audio": "サンプル音声を選択"
+}

fish_speech/i18n/locale/ko_KR.json

@@ -0,0 +1,123 @@
+{
+  "16-mixed is recommended for 10+ series GPU": "10+ 시리즈 GPU에는 16-mixed를 권장합니다.",
+  "5 to 10 seconds of reference audio, useful for specifying speaker.": "화자를 특정하는 데 유의미한 5~10초의 길이의 참조 오디오 데이터.",
+  "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).": "[Fish Audio](https://fish.audio)에서 개발한 VQ-GAN 및 Llama 기반의 텍스트 음성 변환 모델.",
+  "Accumulate Gradient Batches": "그라디언트 배치 누적",
+  "Add to Processing Area": "처리 영역에 추가",
+  "Added path successfully!": "경로가 성공적으로 추가되었습니다!",
+  "Advanced Config": "고급 설정",
+  "Base LLAMA Model": "기본 LLAMA 모델",
+  "Batch Inference": "배치 추론",
+  "Batch Size": "배치 크기",
+  "Changing with the Model Path": "모델 경로에 따라 변경 중",
+  "Chinese": "중국어",
+  "Compile Model": "모델 컴파일",
+  "Compile the model can significantly reduce the inference time, but will increase cold start time": "모델을 컴파일하면 추론 시간이 크게 줄어들지만, 초기 시작 시간이 길어집니다.",
+  "Copy": "복사",
+  "Data Preprocessing": "데이터 전처리",
+  "Data Preprocessing Path": "데이터 전처리 경로",
+  "Data Source": "데이터 소스",
+  "Decoder Model Config": "디코더 모델 설정",
+  "Decoder Model Path": "디코더 모델 경로",
+  "Disabled": "비활성화 됨",
+  "Enable Reference Audio": "참고 음성 활성화",
+  "English": "영어",
+  "Error Message": "오류 메시지",
+  "File Preprocessing": "파일 전처리",
+  "Generate": "생성",
+  "Generated Audio": "생성된 오디오",
+  "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "오디오애 대응하는 텍스트가 없을 경우, ASR을 적용해 지원하며, .txt 또는 .lab 형식을 지원합니다.",
+  "Infer interface is closed": "추론 인터페이스가 닫혔습니다.",
+  "Inference Configuration": "추론 설정",
+  "Inference Server Configuration": "추론 서버 설정",
+  "Inference Server Error": "추론 서버 오류",
+  "Inferring interface is launched at {}": "추론 인터페이스가 {}에서 시작되었습니다.",
+  "Initial Learning Rate": "초기 학습률",
+  "Input Audio & Source Path for Transcription": "전사할 입력 오디오 및 소스 경로",
+  "Input Text": "입력 텍스트",
+  "Invalid path: {}": "유효하지 않은 경로: {}",
+  "It is recommended to use CUDA, if you have low configuration, use CPU": "CUDA 사용을 권장하며, 낮은 사양일 경우 CPU를 사용하는 것을 권장합니다.",
+  "Iterative Prompt Length, 0 means off": "반복 프롬프트 길이. (0:비활성화)",
+  "Japanese": "일본어",
+  "LLAMA Configuration": "LLAMA 설정",
+  "LLAMA Model Config": "LLAMA 모델 설정",
+  "LLAMA Model Path": "LLAMA 모델 경로",
+  "Labeling Device": "라벨링 장치",
+  "LoRA Model to be merged": "병합할 LoRA 모델",
+  "Maximum Audio Duration": "최대 오디오 길이",
+  "Maximum Length per Sample": "샘플당 최대 길이",
+  "Maximum Training Steps": "최대 학습 단계",
+  "Maximum tokens per batch, 0 means no limit": "배치당 최대 토큰 수(0:제한 없음)",
+  "Merge": "병합",
+  "Merge LoRA": "LoRA 병합",
+  "Merge successfully": "성공적으로 병합 되었습니다.",
+  "Minimum Audio Duration": "최소 오디오 길이",
+  "Model Output Path": "모델 출력 경로",
+  "Model Size": "모델 크기",
+  "Move": "이동",
+  "Move files successfully": "파일이 성공적으로 이동되었습니다.",
+  "No audio generated, please check the input text.": "생성된 오디오가 없습니다. 입력된 텍스트를 확인하세요.",
+  "No selected options": "옵션이 선택되지 않았습니다.",
+  "Number of Workers": "작업자 수",
+  "Open Inference Server": "추론 서버 열기",
+  "Open Labeler WebUI": "라벨러 WebUI 열기",
+  "Open Tensorboard": "Tensorboard 열기",
+  "Opened labeler in browser": "브라우저에서 라벨러가 열렸습니다.",
+  "Optional Label Language": "선택적 라벨 언어",
+  "Optional online ver": "온라인 버전 선택",
+  "Output Path": "출력 경로",
+  "Path error, please check the model file exists in the corresponding path": "경로 오류, 해당 경로에 모델 파일이 있는지 확인하십시오.",
+  "Precision": "정밀도",
+  "Probability of applying Speaker Condition": "화자 조건 적용 확률",
+  "Put your text here.": "여기에 텍스트를 입력하세요.",
+  "Reference Audio": "참고 오디오",
+  "Reference Text": "참고 텍스트",
+  "Related code and weights are released under CC BY-NC-SA 4.0 License.": "관련 코드 및 가중치는 CC BY-NC-SA 4.0 라이선스 하에 배포됩니다.",
+  "Remove Selected Data": "선택한 데이터 제거",
+  "Removed path successfully!": "경로가 성공적으로 제거되었습니다!",
+  "Repetition Penalty": "���복 패널티",
+  "Save model every n steps": "n 단계마다 모델 저장",
+  "Select LLAMA ckpt": "LLAMA ckpt 선택",
+  "Select VITS ckpt": "VITS ckpt 선택",
+  "Select VQGAN ckpt": "VQGAN ckpt 선택",
+  "Select source file processing method": "소스 파일 처리 방법 선택",
+  "Select the model to be trained (Depending on the Tab page you are on)": "학습할 모델 선택(탭 페이지에 따라 다름)",
+  "Selected: {}": "선택됨: {}",
+  "Speaker": "화자",
+  "Speaker is identified by the folder name": "화자는 폴더 이름으로 식별됩니다",
+  "Start Training": "학습 시작",
+  "Streaming Audio": "스트리밍 오디오",
+  "Streaming Generate": "스트리밍 생성",
+  "Tensorboard Host": "Tensorboard 호스트",
+  "Tensorboard Log Path": "Tensorboard 로그 경로",
+  "Tensorboard Port": "Tensorboard 포트",
+  "Tensorboard interface is closed": "Tensorboard 인터페이스가 닫혔습니다",
+  "Tensorboard interface is launched at {}": "Tensorboard 인터페이스가 {}에서 시작되었습니다.",
+  "Text is too long, please keep it under {} characters.": "텍스트가 너무 깁니다. {}자 이하로 입력해주세요.",
+  "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.": "왼쪽의 입력 폴더 경로 또는 파일 목록의 경로. 체크 여부에 관계없이 이 목록에서 후속 학습에 사용됩니다.",
+  "Training Configuration": "학습 설정",
+  "Training Error": "학습 오류",
+  "Training stopped": "학습이 중지되었습니다.",
+  "Type name of the speaker": "화자의 이름을 입력하세요.",
+  "Type the path or select from the dropdown": "경로를 입력하거나 드롭다운에서 선택하세요.",
+  "Use LoRA": "LoRA 사용",
+  "Use LoRA can save GPU memory, but may reduce the quality of the model": "LoRA를 사용하면 GPU 메모리를 절약할 수 있지만, 모델의 품질이 저하될 수 있습니다.",
+  "Use filelist": "파일 목록 사용",
+  "Use large for 10G+ GPU, medium for 5G, small for 2G": "10G+ GPU 환경에선 large, 5G에선 medium, 2G에선 small을 사용할 것을 권장합니다.",
+  "VITS Configuration": "VITS 설정",
+  "VQGAN Configuration": "VQGAN 설정",
+  "Validation Batch Size": "검증 배치 크기",
+  "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "전처리 폴더의 상태를 확인합니다(슬라이더를 사용하여 트리의 깊이를 조절합니다)",
+  "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.": "모델의 오용에 대해 책임지지 않습니다. 사용하기 전에 현지 법률과 규정을 고려하시길 바랍니다.",
+  "WebUI Host": "WebUI 호스트",
+  "WebUI Port": "WebUI 포트",
+  "Whisper Model": "Whisper 모델",
+  "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).": "소스 코드는 [이곳](https://github.com/fishaudio/fish-speech)에서, 모델은 [이곳](https://huggingface.co/fishaudio/fish-speech-1)에서 확인하실 수 있습니다.",
+  "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "30+ 시리즈 GPU에는 bf16-true를, 10+ 시리즈 GPU에는 16-mixed를 권장합니다",
+  "latest": "최신",
+  "new": "새로운",
+  "Realtime Transform Text": "실시간 텍스트 변환",
+  "Normalization Result Preview (Currently Only Chinese)": "정규화 결과 미리보기(현재 중국어만 지원)",
+  "Text Normalization": "텍스트 정규화",
+  "Select Example Audio": "예시 오디오 선택"
+}

fish_speech/i18n/locale/pt_BR.json

@@ -0,0 +1,133 @@
+{
+  "5 to 10 seconds of reference audio, useful for specifying speaker.": "5 a 10 segundos de áudio de referência, útil para especificar o orador.",
+  "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).": "Um modelo de texto para fala baseado em VQ-GAN e Llama desenvolvido por [Fish Audio](https://fish.audio).",
+  "Accumulate Gradient Batches": "Acumular Lotes de Gradiente",
+  "Add to Processing Area": "Adicionar à Área de Processamento",
+  "Added path successfully!": "Caminho adicionado com sucesso!",
+  "Advanced Config": "Configuração Avançada",
+  "Base LLAMA Model": "Modelo LLAMA Base",
+  "Batch Inference": "Inferência em Lote",
+  "Batch Size": "Tamanho do Lote",
+  "Changing with the Model Path": "Alterando com o Caminho do Modelo",
+
+  "Compile Model": "Compilar Modelo",
+  "Compile the model can significantly reduce the inference time, but will increase cold start time": "Compilar o modelo pode reduzir significativamente o tempo de inferência, mas aumentará a latência inicial",
+  "Copy": "Copiar",
+  "Data Preprocessing": "Pré-processamento de Dados",
+  "Data Preprocessing Path": "Caminho de Pré-processamento de Dados",
+  "Data Source": "Fonte de Dados",
+  "Decoder Model Config": "Configuração do Modelo Decodificador",
+  "Decoder Model Path": "Caminho do Modelo Decodificador",
+  "Disabled": "Desativado",
+  "Enable Initial Prompt": "Habilitar Prompt Inicial",
+  "Enable Reference Audio": "Habilitar Áudio de Referência",
+  "English": "Inglês",
+  "Japanese": "Japonês",
+  "Chinese": "Chinês",
+  "Portuguese": "Português",
+  "Spanish": "Espanhol",
+  "Error Message": "Mensagem de Erro",
+  "Faster Whisper, Up to 5g GPU memory usage": "Faster Whisper (Usa até 5 GB de vRAM)",
+  "File Preprocessing": "Pré-processamento de Arquivos",
+  "Generate": "Gerar",
+  "Generated Audio": "Áudio Gerado",
+  "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "Se não houver texto correspondente ao áudio, utilize o ASR para assistência (formatos .txt ou .lab)",
+  "Infer interface is closed": "A interface de inferência foi fechada",
+  "Inference Configuration": "Configuração de Inferência",
+  "Inference Server Configuration": "Configuração do Servidor de Inferência",
+  "Inference Server Error": "Erro do Servidor de Inferência",
+  "Inferring interface is launched at {}": "A interface de inferência foi iniciada em {}",
+  "Initial Learning Rate": "Taxa de Aprendizagem Inicial",
+  "Initial Prompt": "Prompt Inicial",
+  "Initial prompt can provide contextual or vocabulary-specific guidance to the model.": "O prompt inicial pode fornecer orientação contextual ou específica de vocabulário para o modelo.",
+  "Input Audio & Source Path for Transcription": "Entrada de Áudio/Caminho de Origem para Transcrição",
+  "Input Text": "Texto de Entrada",
+  "Invalid path: {}": "Caminho inválido: {}",
+  "It is recommended to use CUDA, if you have low configuration, use CPU": "Para GPUs Nvidia é recomendado usar CUDA. Se não tiver uma GPU Nvidia, use CPU",
+  "Iterative Prompt Length, 0 means off": "Comprimento do Prompt Iterativo (0 = desativado)",
+  "LLAMA Configuration": "Configuração do LLAMA",
+  "LLAMA Model Config": "Configuração do Modelo LLAMA",
+  "LLAMA Model Path": "Caminho do Modelo LLAMA",
+  "Labeling Device": "Dispositivo de Rotulagem",
+  "LoRA Model to be merged": "Modelo LoRA para mesclagem",
+  "Maximum Length per Sample": "Comprimento Máximo por Amostra",
+  "Maximum Training Steps": "Etapas Máximas de Treinamento",
+  "Maximum tokens per batch, 0 means no limit": "Número máximo de tokens por lote, 0 significa sem limite",
+  "Merge": "Mesclar",
+  "Merge LoRA": "Mesclar LoRA",
+  "Merge successfully": "Mesclado com sucesso",
+  "Model Output Path": "Caminho de Saída do Modelo",
+  "Model Quantization": "Quantização do Modelo",
+  "Model Size": "Tamanho do Modelo",
+  "Move": "Mover",
+  "Move files successfully": "Arquivos movidos com sucesso",
+  "No audio generated, please check the input text.": "Nenhum áudio gerado, verifique o texto de entrada.",
+  "No selected options": "Nenhuma opção selecionada",
+  "Normalization Result Preview (Currently Only Chinese)": "Pré-visualização do Resultado da Normalização (Atualmente Apenas Chinês)",
+  "Number of Workers": "Número de Processos",
+  "Open Inference Server": "Abrir Servidor de Inferência",
+  "Open Labeler WebUI": "Abrir WebUI de Rotulagem",
+  "Open Tensorboard": "Abrir Tensorboard",
+  "Opened labeler in browser": "WebUI de rotulagem aberta no navegador",
+  "Optional Label Language": "Idioma do Rótulo (Opcional)",
+  "Optional online ver": "Versão online (opcional)",
+  "Output Path": "Caminho de Saída",
+  "Path error, please check the model file exists in the corresponding path": "Erro de caminho, verifique se o arquivo do modelo existe no caminho correspondente",
+  "Post-quantification Precision": "Precisão Pós-quantização",
+  "Precision": "Precisão",
+  "Probability of applying Speaker Condition": "Probabilidade de Aplicar Condição de Orador",
+  "Put your text here.": "Insira seu texto aqui.",
+  "Quantify": "Quantizar",
+  "Quantify successfully": "Quantizado com sucesso",
+  "Realtime Transform Text": "Transformar Texto em Tempo Real",
+  "Reference Audio": "Áudio de Referência",
+  "Reference Text": "Texto de Referência",
+  "warning": "Aviso",
+  "Pre-processing begins...": "O pré-processamento começou!",
+  "Related code and weights are released under CC BY-NC-SA 4.0 License.": "O código relacionado e os pesos são licenciados sob a Licença CC BY-NC-SA 4.0.",
+  "Remove Selected Data": "Remover Dados Selecionados",
+  "Removed path successfully!": "Caminho removido com sucesso!",
+  "Repetition Penalty": "Penalidade de Repetição",
+  "Save model every n steps": "Salvar modelo a cada n etapas",
+  "Select LLAMA ckpt": "Selecionar .ckpt do LLAMA",
+  "Select source file processing method": "Escolha como processar o arquivo de origem",
+  "Select the model to be trained (Depending on the Tab page you are on)": "Selecione o modelo para o treinamento (dependendo da aba em que você está)",
+  "Selected: {}": "Selecionado: {}",
+  "Speaker is identified by the folder name": "O orador é identificado pelo nome da pasta",
+  "Start Training": "Iniciar Treinamento",
+  "Streaming Audio": "Áudio em Streaming",
+  "Streaming Generate": "Geração em Streaming",
+  "Tensorboard Host": "Host do Tensorboard",
+  "Tensorboard Log Path": "Caminho de Log do Tensorboard",
+  "Tensorboard Port": "Porta do Tensorboard",
+  "Tensorboard interface is closed": "A interface do Tensorboard está fechada",
+  "Tensorboard interface is launched at {}": "A interface do Tensorboard foi iniciada em {}",
+  "Text Normalization": "Normalização de Texto",
+  "Text is too long, please keep it under {} characters.": "O texto é muito longo. Mantenha-o com menos de {} caracteres.",
+  "The lower the quantitative precision, the more the effectiveness may decrease, but the greater the efficiency will increase": "Quanto menor a precisão quantitativa, mais a eficácia pode diminuir, mas maior será o aumento da eficiência",
+  "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.": "O caminho da pasta de entrada à esquerda ou a lista de arquivos. Independentemente de estar marcada ou não, ela será utilizada para o treinamento subsequente nesta lista.",
+  "Training Configuration": "Configuração de Treinamento",
+  "Training Error": "Erro de Treinamento",
+  "Training stopped": "Treinamento interrompido!",
+  "Type the path or select from the dropdown": "Digite o caminho ou selecione no menu suspenso",
+  "Use LoRA": "Usar LoRA",
+  "Use LoRA can save GPU memory, but may reduce the quality of the model": "O uso de LoRAs pode economizar memória da GPU, mas também pode reduzir a qualidade",
+  "Use filelist": "Usar lista de arquivos",
+  "VQGAN Configuration": "Configuração do VQGAN",
+  "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "Visualizar o status da pasta de pré-processamento (use o controle deslizante para controlar a profundidade da árvore)",
+  "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.": "Não nos responsabilizamos por qualquer uso indevido do modelo. Por favor, considere as leis e regulamentações locais antes de usá-lo.",
+  "WebUI Host": "Host da WebUI",
+  "WebUI Port": "Porta da WebUI",
+  "Whisper Model": "Modelo Whisper",
+  "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1).": "Você pode encontrar o código fonte [aqui](https://github.com/fishaudio/fish-speech) e os modelos [aqui](https://huggingface.co/fishaudio/fish-speech-1).",
+  "auto": "automático",
+  "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "bf16-true é recomendado para GPUs da série 30+, 16-mixed é recomendado para GPUs da série 10+",
+  "latest": "mais recente",
+  "new": "novo",
+  "This audio introduces the basic concepts and applications of artificial intelligence and machine learning.": "Este áudio introduz os conceitos básicos e aplicações de inteligência artificial e aprendizado de máquina.",
+  "You don't need to train this model!": "Não é necessário treinar este modelo!",
+  "Yes": "Sim",
+  "No": "Não",
+  "version:": "versão:",
+  "author:": "autor:"
+}

fish_speech/i18n/locale/zh_CN.json

@@ -0,0 +1,123 @@
+{
+  "16-mixed is recommended for 10+ series GPU": "10+ 系列 GPU 建议使用 16-mixed",
+  "5 to 10 seconds of reference audio, useful for specifying speaker.": "5 到 10 秒的参考音频，适用于指定音色。",
+  "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.",
+  "Accumulate Gradient Batches": "梯度累积批次",
+  "Add to Processing Area": "加入处理区",
+  "Added path successfully!": "添加路径成功!",
+  "Advanced Config": "高级参数",
+  "Base LLAMA Model": "基础 LLAMA 模型",
+  "Batch Inference": "批量推理",
+  "Batch Size": "批次大小",
+  "Changing with the Model Path": "随模型路径变化",
+  "Chinese": "中文",
+  "Compile Model": "编译模型",
+  "Compile the model can significantly reduce the inference time, but will increase cold start time": "编译模型可以显著减少推理时间，但会增加冷启动时间",
+  "Copy": "复制",
+  "Data Preprocessing": "数据预处理",
+  "Data Preprocessing Path": "数据预处理路径",
+  "Data Source": "数据源",
+  "Decoder Model Config": "解码器模型配置",
+  "Decoder Model Path": "解码器模型路径",
+  "Disabled": "禁用",
+  "Enable Reference Audio": "启用参考音频",
+  "English": "英文",
+  "Error Message": "错误信息",
+  "File Preprocessing": "文件预处理",
+  "Generate": "生成",
+  "Generated Audio": "音频",
+  "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "如果音频没有对应的文本，可以应用 ASR 辅助，支持 .txt 或 .lab 格式",
+  "Infer interface is closed": "推理界面已关闭",
+  "Inference Configuration": "推理配置",
+  "Inference Server Configuration": "推理服务器配置",
+  "Inference Server Error": "推理服务器错误",
+  "Inferring interface is launched at {}": "推理界面已在 {} 上启动",
+  "Initial Learning Rate": "初始学习率",
+  "Input Audio & Source Path for Transcription": "输入音频和转录源路径",
+  "Input Text": "输入文本",
+  "Invalid path: {}": "无效路径: {}",
+  "It is recommended to use CUDA, if you have low configuration, use CPU": "建议使用 CUDA，如果配置较低，使用 CPU",
+  "Iterative Prompt Length, 0 means off": "迭代提示长度，0 表示关闭",
+  "Japanese": "日文",
+  "LLAMA Configuration": "LLAMA 配置",
+  "LLAMA Model Config": "LLAMA 模型配置",
+  "LLAMA Model Path": "LLAMA 模型路径",
+  "Labeling Device": "标注加速设备",
+  "LoRA Model to be merged": "要合并的 LoRA 模型",
+  "Maximum Audio Duration": "最大音频时长",
+  "Maximum Length per Sample": "每个样本的最大长度",
+  "Maximum Training Steps": "最大训练步数",
+  "Maximum tokens per batch, 0 means no limit": "每批最大令牌数，0 表示无限制",
+  "Merge": "合并",
+  "Merge LoRA": "合并 LoRA",
+  "Merge successfully": "合并成功",
+  "Minimum Audio Duration": "最小音频时长",
+  "Model Output Path": "模型输出路径",
+  "Model Size": "模型规模",
+  "Move": "移动",
+  "Move files successfully": "移动文件成功",
+  "No audio generated, please check the input text.": "没有生成音频，请检查输入文本.",
+  "No selected options": "没有选择的选项",
+  "Number of Workers": "数据加载进程数",
+  "Open Inference Server": "打开推理服务器",
+  "Open Labeler WebUI": "打开标注工具",
+  "Open Tensorboard": "打开 Tensorboard",
+  "Opened labeler in browser": "在浏览器中打开标注工具",
+  "Optional Label Language": "[可选] 标注语言",
+  "Optional online ver": "[可选] 使用在线版",
+  "Output Path": "输出路径",
+  "Path error, please check the model file exists in the corresponding path": "路径错误，请检查模型文件是否存在于相应路径",
+  "Precision": "精度",
+  "Probability of applying Speaker Condition": "应用说话人条件的概率",
+  "Put your text here.": "在此处输入文本.",
+  "Reference Audio": "参考音频",
+  "Reference Text": "参考文本",
+  "Related code and weights are released under CC BY-NC-SA 4.0 License.": "相关代码和权重使用 CC BY-NC-SA 4.0 许可证发布.",
+  "Remove Selected Data": "移除选中数据",
+  "Removed path successfully!": "移除路径成功!",
+  "Repetition Penalty": "重复惩罚",
+  "Save model every n steps": "每 n 步保存模型",
+  "Select LLAMA ckpt": "选择 LLAMA 检查点",
+  "Select VITS ckpt": "选择 VITS 检查点",
+  "Select VQGAN ckpt": "选择 VQGAN 检查点",
+  "Select source file processing method": "选择源文件处理方法",
+  "Select the model to be trained (Depending on the Tab page you are on)": "根据您所在的选项卡页面选择要训练的模型",
+  "Selected: {}": "已选择: {}",
+  "Speaker": "说话人",
+  "Speaker is identified by the folder name": "自动根据父目录名称识别说话人",
+  "Start Training": "开始训练",
+  "Streaming Audio": "流式音频",
+  "Streaming Generate": "流式合成",
+  "Tensorboard Host": "Tensorboard 监听地址",
+  "Tensorboard Log Path": "Tensorboard 日志路径",
+  "Tensorboard Port": "Tensorboard 端口",
+  "Tensorboard interface is closed": "Tensorboard 界面已关闭",
+  "Tensorboard interface is launched at {}": "Tensorboard 界面已在 {} 上启动",
+  "Text is too long, please keep it under {} characters.": "文本太长，请保持在 {} 个字符以内.",
+  "The path of the input folder on the left or the filelist. Whether checked or not, it will be used for subsequent training in this list.": "左侧输入文件夹的路径或文件列表。无论是否选中，都将在此列表中用于后续训练.",
+  "Training Configuration": "训练配置",
+  "Training Error": "训练错误",
+  "Training stopped": "训练已停止",
+  "Type name of the speaker": "输入说话人的名称",
+  "Type the path or select from the dropdown": "输入路径或从下拉菜单中选择",
+  "Use LoRA": "使用 LoRA",
+  "Use LoRA can save GPU memory, but may reduce the quality of the model": "使用 LoRA 可以节省 GPU 内存，但可能会降低模型质量",
+  "Use filelist": "使用文件列表",
+  "Use large for 10G+ GPU, medium for 5G, small for 2G": "10G+ GPU 使用 large, 5G 使用 medium, 2G 使用 small",
+  "VITS Configuration": "VITS 配置",
+  "VQGAN Configuration": "VQGAN 配置",
+  "Validation Batch Size": "验证批次大小",
+  "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "查看预处理文件夹的状态 (使用滑块控制树的深度)",
+  "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.",
+  "WebUI Host": "WebUI 监听地址",
+  "WebUI Port": "WebUI 端口",
+  "Whisper Model": "Whisper 模型",
+  "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.",
+  "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "30+ 系列 GPU 建议使用 bf16-true, 10+ 系列 GPU 建议使用 16-mixed",
+  "latest": "最近的检查点",
+  "new": "创建新的检查点",
+  "Realtime Transform Text": "实时规范化文本",
+  "Normalization Result Preview (Currently Only Chinese)": "规范化结果预览",
+  "Text Normalization": "文本规范化",
+  "Select Example Audio": "选择参考音频"
+}

fish_speech/i18n/scan.py

@@ -0,0 +1,122 @@
+import ast
+import glob
+import json
+from collections import OrderedDict
+from pathlib import Path
+
+from loguru import logger
+
+from .core import DEFAULT_LANGUAGE, I18N_FILE_PATH
+
+
+def extract_i18n_strings(node):
+    i18n_strings = []
+
+    if (
+        isinstance(node, ast.Call)
+        and isinstance(node.func, ast.Name)
+        and node.func.id == "i18n"
+    ):
+        for arg in node.args:
+            if isinstance(arg, ast.Str):
+                i18n_strings.append(arg.s)
+
+    for child_node in ast.iter_child_nodes(node):
+        i18n_strings.extend(extract_i18n_strings(child_node))
+
+    return i18n_strings
+
+
+# scan the directory for all .py files (recursively)
+# for each file, parse the code into an AST
+# for each AST, extract the i18n strings
+
+strings = []
+folders = ["fish_speech", "tools"]
+# for filename in glob.iglob("**/*.py", recursive=True):
+for folder in folders:
+    for f in Path(folder).rglob("*.py"):
+        code = f.read_text(encoding="utf-8")
+        if "i18n(" in code:
+            tree = ast.parse(code)
+            i18n_strings = extract_i18n_strings(tree)
+            logger.info(f"Found {len(i18n_strings)} i18n strings in {f}")
+            strings.extend(i18n_strings)
+
+code_keys = set(strings)
+logger.info(f"Total unique: {len(code_keys)}")
+
+
+standard_file = I18N_FILE_PATH / f"{DEFAULT_LANGUAGE}.json"
+with open(standard_file, "r", encoding="utf-8") as f:
+    standard_data = json.load(f, object_pairs_hook=OrderedDict)
+standard_keys = set(standard_data.keys())
+
+# Define the standard file name
+unused_keys = standard_keys - code_keys
+logger.info(f"Found {len(unused_keys)} unused keys in {standard_file}")
+for unused_key in unused_keys:
+    logger.info(f"\t{unused_key}")
+
+missing_keys = code_keys - standard_keys
+logger.info(f"Found {len(missing_keys)} missing keys in {standard_file}")
+for missing_key in missing_keys:
+    logger.info(f"\t{missing_key}")
+
+code_keys_dict = OrderedDict()
+for s in strings:
+    code_keys_dict[s] = s
+
+# write back
+with open(standard_file, "w", encoding="utf-8") as f:
+    json.dump(code_keys_dict, f, ensure_ascii=False, indent=4, sort_keys=True)
+    f.write("\n")
+
+logger.info(f"Updated {standard_file}")
+
+
+# Define the standard file name
+standard_file = I18N_FILE_PATH / f"{DEFAULT_LANGUAGE}.json"
+
+# Find all JSON files in the directory
+dir_path = I18N_FILE_PATH
+languages = [f for f in dir_path.glob("*.json") if f.stem != DEFAULT_LANGUAGE]
+
+# Load the standard file
+with open(standard_file, "r", encoding="utf-8") as f:
+    standard_data = json.load(f, object_pairs_hook=OrderedDict)
+
+# Loop through each language file
+for lang_file in languages:
+    # Load the language file
+    with open(lang_file, "r", encoding="utf-8") as f:
+        lang_data = json.load(f, object_pairs_hook=OrderedDict)
+
+    # Find the difference between the language file and the standard file
+    diff = set(standard_data.keys()) - set(lang_data.keys())
+
+    miss = set(lang_data.keys()) - set(standard_data.keys())
+
+    # Add any missing keys to the language file
+    for key in diff:
+        lang_data[key] = "#!" + key
+        logger.info(f"Added missing key: {key} to {lang_file}")
+
+    # Del any extra keys to the language file
+    for key in miss:
+        del lang_data[key]
+        logger.info(f"Del extra key: {key} from {lang_file}")
+
+    # Sort the keys of the language file to match the order of the standard file
+    lang_data = OrderedDict(
+        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
+    )
+
+    # Save the updated language file
+    with open(lang_file, "w", encoding="utf-8") as f:
+        json.dump(lang_data, f, ensure_ascii=False, indent=4, sort_keys=True)
+        f.write("\n")
+
+    logger.info(f"Updated {lang_file}")
+
+logger.info("Done")

fish_speech/models/text2semantic/lit_module.py

@@ -0,0 +1,202 @@
+from typing import Any, Optional
+
+import lightning as L
+import torch
+import torch.nn.functional as F
+from lightning.pytorch.utilities.types import OptimizerLRScheduler
+
+import fish_speech.utils as utils
+from fish_speech.conversation import CODEBOOK_PAD_TOKEN_ID
+from fish_speech.models.text2semantic.llama import NaiveTransformer
+
+log = utils.RankedLogger(__name__, rank_zero_only=True)
+
+
+class TextToSemantic(L.LightningModule):
+    def __init__(
+        self,
+        model: NaiveTransformer,
+        optimizer: Any,
+        lr_scheduler: Any,
+    ):
+        super().__init__()
+
+        self.model = model
+        self.optimizer_builder = optimizer
+        self.lr_scheduler_builder = lr_scheduler
+
+    def forward(self, x):
+        return self.model(x)
+
+    def on_save_checkpoint(self, checkpoint):
+        # Save only LoRA parameters
+        state_dict = checkpoint["state_dict"]
+        use_lora = any("lora" in name for name in state_dict.keys())
+        if not use_lora:
+            return
+
+        for name in list(state_dict.keys()):
+            if "lora" not in name:
+                state_dict.pop(name)
+
+    def configure_optimizers(self) -> OptimizerLRScheduler:
+        # Get weight decay parameters
+        weight_decay_parameters, other_parameters = [], []
+        for name, param in self.named_parameters():
+            if ".bias" in name or "norm.weight" in name or ".embeddings." in name:
+                other_parameters.append(param)
+            else:
+                weight_decay_parameters.append(param)
+
+        optimizer = self.optimizer_builder(
+            [
+                {"params": weight_decay_parameters},
+                {"params": other_parameters, "weight_decay": 0.0},
+            ]
+        )
+
+        # Print the parameters and their weight decay
+        for i in optimizer.param_groups:
+            log.info(
+                f"Set weight decay: {i['weight_decay']} for {len(i['params'])} parameters"
+            )
+
+        lr_scheduler = self.lr_scheduler_builder(optimizer)
+
+        return {
+            "optimizer": optimizer,
+            "lr_scheduler": {
+                "scheduler": lr_scheduler,
+                "interval": "step",
+            },
+        }
+
+    # Copied from https://github.com/eric-mitchell/direct-preference-optimization/blob/main/trainers.py#L90
+    def get_batch_logps(
+        self,
+        logits: torch.FloatTensor,
+        labels: torch.LongTensor,
+        average_log_prob: bool = False,
+    ) -> torch.FloatTensor:
+        """Compute the log probabilities of the given labels under the given logits.
+
+        Args:
+            logits: Logits of the model (unnormalized). Shape: (batch_size, sequence_length, codebook_size, vocab_size)
+            labels: Labels for which to compute the log probabilities. Label tokens with a value of -100 are ignored. Shape: (batch_size, sequence_length, codebook_size)
+            average_log_prob: If True, return the average log probability per (non-masked) token. Otherwise, return the sum of the log probabilities of the (non-masked) tokens.
+
+        Returns:
+            A tensor of shape (batch_size,) containing the average/sum log probabilities of the given labels under the given logits.
+        """
+        assert logits.shape[:-1] == labels.shape
+
+        labels = labels.clone()
+        loss_mask = labels != -100
+
+        # dummy token; we'll ignore the losses on these tokens later
+        labels[labels == -100] = 0
+
+        per_token_logps = torch.gather(
+            logits.log_softmax(-1), dim=-1, index=labels.unsqueeze(-1)
+        ).squeeze(-1)
+
+        if average_log_prob:
+            return (per_token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
+        else:
+            return (per_token_logps * loss_mask).sum(-1)
+
+    def _step(self, batch, batch_idx, stage: str):
+        is_train = stage == "train"
+
+        if is_train:
+            # Key part to make lora work
+            # Otherwise the parameters are merged, which lead to incorrect gradients
+            self.model.train()
+
+        # Do positive and negative samples in the same batch to speed up training
+        labels = batch["labels"]
+        outputs = self.model(
+            inp=batch["inputs"],
+            key_padding_mask=batch["attention_masks"],
+        )
+        token_logits = outputs.token_logits
+        codebook_logits = outputs.codebook_logits
+
+        # Generate labels
+        base_loss = F.cross_entropy(
+            token_logits.view(-1, token_logits.size(-1)),
+            labels[:, 0].reshape(-1),
+            ignore_index=-100,
+        )
+
+        codebook_labels = labels[:, 1 : 1 + self.model.config.num_codebooks].mT
+        semantic_loss = F.cross_entropy(
+            codebook_logits.view(-1, codebook_logits.size(-1)),
+            codebook_labels.reshape(-1),
+            ignore_index=-100,
+        )
+
+        loss = base_loss + semantic_loss
+
+        self.log(
+            f"{stage}/loss",
+            loss,
+            on_step=is_train,
+            on_epoch=not is_train,
+            prog_bar=True,
+            logger=True,
+            sync_dist=not is_train,
+        )
+
+        self.log(
+            f"{stage}/base_loss",
+            base_loss,
+            on_step=is_train,
+            on_epoch=not is_train,
+            prog_bar=False,
+            logger=True,
+            sync_dist=not is_train,
+        )
+
+        self.log(
+            f"{stage}/semantic_loss",
+            semantic_loss,
+            on_step=is_train,
+            on_epoch=not is_train,
+            prog_bar=False,
+            logger=True,
+            sync_dist=not is_train,
+        )
+
+        # Top-5 accuracy
+        accuracy = self.get_accuracy(codebook_logits, codebook_labels)
+        self.log(
+            f"{stage}/top_5_accuracy",
+            accuracy,
+            on_step=is_train,
+            on_epoch=not is_train,
+            prog_bar=True,
+            logger=True,
+            sync_dist=not is_train,
+        )
+
+        return loss
+
+    def get_accuracy(self, logits, labels):
+        mask = (labels != -100) & (labels != CODEBOOK_PAD_TOKEN_ID)
+        if mask.sum() == 0:
+            return torch.tensor(0.0, device=logits.device)
+
+        _, indices = logits.topk(5, dim=-1)
+        correct = indices.eq(labels.unsqueeze(-1))
+        correct[~mask] = 0
+        correct = correct.sum()
+        accuracy = correct / mask.sum()
+
+        return accuracy
+
+    def training_step(self, batch, batch_idx):
+        return self._step(batch, batch_idx, "train")
+
+    def validation_step(self, batch, batch_idx):
+        return self._step(batch, batch_idx, "val")

fish_speech/models/text2semantic/llama.py

@@ -0,0 +1,844 @@
+import dataclasses
+import json
+import math
+from collections import OrderedDict
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from loguru import logger
+from torch import Tensor
+from torch.nn import functional as F
+from torch.nn.attention import SDPBackend, sdpa_kernel
+from torch.utils.checkpoint import checkpoint
+from transformers import AutoTokenizer
+
+from fish_speech.conversation import SEMANTIC_TOKEN
+from fish_speech.utils import RankedLogger
+
+from .lora import LoraConfig, setup_lora
+
+log = RankedLogger(__name__, rank_zero_only=True)
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+@dataclass
+class BaseModelArgs:
+    model_type: str = "base"
+
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    max_seq_len: int = 2048
+    dropout: float = 0.0
+    tie_word_embeddings: bool = True
+    attention_qkv_bias: bool = False
+
+    # Codebook configs
+    codebook_size: int = 160
+    num_codebooks: int = 4
+
+    # Gradient checkpointing
+    use_gradient_checkpointing: bool = True
+
+    # Initialize the model
+    initializer_range: float = 0.02
+
+    # Dummy vars
+    is_reward_model: bool = False
+    share_codebook_embeddings: bool = True
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @staticmethod
+    def from_pretrained(path: str):
+        path = Path(path)
+
+        if path.is_dir():
+            path = path / "config.json"
+
+        with open(path, "r", encoding="utf-8") as f:
+            data = json.load(f)
+
+        match data["model_type"]:
+            case "naive":
+                cls = NaiveModelArgs
+            case "dual_ar":
+                cls = DualARModelArgs
+            case _:
+                raise ValueError(f"Unknown model type: {data['model_type']}")
+
+        return cls(**data)
+
+    def save(self, path: str):
+        with open(path, "w") as f:
+            json.dump(self.__dict__, f, indent=4, sort_keys=True, ensure_ascii=False)
+
+
+@dataclass
+class NaiveModelArgs(BaseModelArgs):
+    model_type: str = "naive"
+
+
+@dataclass
+class DualARModelArgs(BaseModelArgs):
+    model_type: str = "dual_ar"
+    n_fast_layer: int = 4
+    fast_dim: int | None = None
+    fast_n_head: int | None = None
+    fast_n_local_heads: int | None = None
+    fast_head_dim: int | None = None
+    fast_intermediate_size: int | None = None
+    fast_attention_qkv_bias: bool | None = None
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        self.fast_dim = self.fast_dim or self.dim
+        self.fast_n_head = self.fast_n_head or self.n_head
+        self.fast_n_local_heads = self.fast_n_local_heads or self.n_local_heads
+        self.fast_head_dim = self.fast_head_dim or self.head_dim
+        self.fast_intermediate_size = (
+            self.fast_intermediate_size or self.intermediate_size
+        )
+        self.fast_attention_qkv_bias = (
+            self.fast_attention_qkv_bias
+            if self.fast_attention_qkv_bias is not None
+            else self.attention_qkv_bias
+        )
+
+
+class KVCache(nn.Module):
+    def __init__(
+        self, max_batch_size, max_seq_len, n_heads, head_dim, dtype=torch.bfloat16
+    ):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_len, head_dim)
+        self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+
+@dataclass
+class TransformerForwardResult:
+    token_logits: Tensor
+    codebook_logits: Tensor
+
+
+@dataclass
+class BaseTransformerForwardResult:
+    logits: Tensor
+    hidden_states: Tensor
+
+
+class BaseTransformer(nn.Module):
+    def __init__(
+        self, config: BaseModelArgs, tokenizer: AutoTokenizer, init_weights: bool = True
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.tokenizer = tokenizer
+
+        self.semantic_token_id = tokenizer.convert_tokens_to_ids(SEMANTIC_TOKEN)
+
+        # Slow transformer
+        self.embeddings = nn.Embedding(
+            config.vocab_size,
+            config.dim,
+        )
+        self.codebook_embeddings = nn.Embedding(
+            config.codebook_size * config.num_codebooks,
+            config.dim,
+        )
+        self.layers = nn.ModuleList(
+            TransformerBlock(config, use_sdpa=True) for _ in range(config.n_layer)
+        )
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+
+        if self.config.tie_word_embeddings is False:
+            self.output = nn.Linear(
+                config.dim,
+                config.vocab_size,
+                bias=False,
+            )
+
+        self.register_buffer(
+            "freqs_cis",
+            precompute_freqs_cis(
+                config.max_seq_len,
+                config.dim // config.n_head,
+                config.rope_base,
+            ),
+            persistent=False,
+        )
+        self.register_buffer(
+            "causal_mask",
+            torch.tril(
+                torch.ones(
+                    config.max_seq_len,
+                    config.max_seq_len,
+                    dtype=torch.bool,
+                )
+            ),
+            persistent=False,
+        )
+
+        # For kv cache
+        self.max_batch_size = -1
+        self.max_seq_len = -1
+
+        if init_weights:
+            self.apply(self._init_weights)
+
+    def setup_caches(
+        self, max_batch_size: int, max_seq_len: int, dtype: torch.dtype = torch.bfloat16
+    ):
+        if self.max_seq_len >= max_seq_len and self.max_batch_size >= max_batch_size:
+            return
+
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_len = find_multiple(max_seq_len, 8)
+        self.max_seq_len = max_seq_len
+        self.max_batch_size = max_batch_size
+
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(
+                max_batch_size,
+                max_seq_len,
+                self.config.n_local_heads,
+                head_dim,
+                dtype=dtype,
+            )
+
+    def embed(self, x: Tensor) -> Tensor:
+        vocab_embeds = [self.embeddings(x[:, 0])]
+        for i in range(self.config.num_codebooks):
+            emb = self.codebook_embeddings(x[:, i + 1] + i * self.config.codebook_size)
+            emb[x[:, 0] != self.semantic_token_id] = 0
+            vocab_embeds.append(emb)
+
+        x = torch.stack(vocab_embeds, dim=3)
+        x = x.sum(dim=3)
+
+        return x
+
+    def forward(
+        self,
+        inp: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+    ) -> BaseTransformerForwardResult:
+        seq_len = inp.size(2)
+
+        # Here we want to merge the embeddings of the codebooks
+        x = self.embed(inp)
+
+        freqs_cis = self.freqs_cis[:seq_len]
+
+        # Not that the causal mask here follows the definition of scaled_dot_product_attention
+        # That is, FALSE means masked out
+        # To maintain consistency, key_padding_mask use TRUE to mask out
+        mask = None
+        if key_padding_mask is not None:
+            mask = self.causal_mask[None, None, :seq_len, :seq_len]  # (B, N, Q, K)
+            mask = mask & key_padding_mask[:, None, None, :].logical_not()
+
+        for layer in self.layers:
+            if self.config.use_gradient_checkpointing and self.training:
+                x = checkpoint(layer, x, freqs_cis, mask, use_reentrant=True)
+            else:
+                x = layer(x, freqs_cis, mask)
+
+        # We got slow_out here
+        slow_out = self.norm(x)
+
+        if self.config.tie_word_embeddings:
+            token_logits = F.linear(slow_out, self.embeddings.weight)
+        else:
+            token_logits = self.output(slow_out)
+
+        return BaseTransformerForwardResult(
+            logits=token_logits,
+            hidden_states=x,
+        )
+
+    def forward_generate(
+        self,
+        x: Tensor,
+        input_pos: Optional[Tensor] = None,
+        return_all: bool = False,
+    ) -> BaseTransformerForwardResult:
+        # This is used for generation, optimized for torch compile
+        assert (
+            self.max_seq_len != -1 and self.max_batch_size != -1
+        ), "Please call setup_caches before forward_generate"
+
+        x = self.embed(x)
+
+        mask = self.causal_mask[
+            None, None, input_pos, : self.max_seq_len
+        ]  # (B, N, Q, K)
+        freqs_cis = self.freqs_cis[input_pos]
+
+        for layer in self.layers:
+            x = layer(x, freqs_cis, mask, input_pos=input_pos)
+
+        # If prefill, we only calculate the logits of last token
+        if x.size(1) > 1 and not return_all:
+            x = x[:, -1:]
+
+        # We got slow_out here
+        slow_out = self.norm(x)
+
+        if self.config.tie_word_embeddings:
+            token_logits = F.linear(slow_out, self.embeddings.weight)
+        else:
+            token_logits = self.output(slow_out)
+
+        return BaseTransformerForwardResult(
+            logits=token_logits,
+            hidden_states=x,
+        )
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    @staticmethod
+    def from_pretrained(
+        path: str,
+        load_weights: bool = False,
+        max_length: int | None = None,
+        lora_config: LoraConfig | None = None,
+        rope_base: int | None = None,
+    ) -> "BaseTransformer":
+        config = BaseModelArgs.from_pretrained(str(path))
+        if max_length is not None:
+            config.max_seq_len = max_length
+            log.info(f"Override max_seq_len to {max_length}")
+
+        if rope_base is not None:
+            config.rope_base = rope_base
+            log.info(f"Override rope_base to {rope_base}")
+
+        match config.model_type:
+            case "naive":
+                model_cls = NaiveTransformer
+            case "dual_ar":
+                model_cls = DualARTransformer
+            case _:
+                raise ValueError(f"Unknown model type: {config.model_type}")
+
+        tokenizer = AutoTokenizer.from_pretrained(str(path))
+        log.info(f"Loading model from {path}, config: {config}")
+        model = model_cls(config, tokenizer=tokenizer)
+
+        if lora_config is not None:
+            setup_lora(model, lora_config)
+            log.info(f"LoRA setup: {lora_config}")
+
+        if load_weights is False:
+            log.info("Randomly initialized model")
+        else:
+
+            if "int8" in str(Path(path)):
+                logger.info("Using int8 weight-only quantization!")
+                from tools.llama.quantize import WeightOnlyInt8QuantHandler
+
+                simple_quantizer = WeightOnlyInt8QuantHandler(model)
+                model = simple_quantizer.convert_for_runtime()
+
+            if "int4" in str(Path(path)):
+                logger.info("Using int4 quantization!")
+                path_comps = path.name.split("-")
+                assert path_comps[-2].startswith("g")
+                groupsize = int(path_comps[-2][1:])
+                from tools.llama.quantize import WeightOnlyInt4QuantHandler
+
+                simple_quantizer = WeightOnlyInt4QuantHandler(model, groupsize)
+                model = simple_quantizer.convert_for_runtime()
+
+            weights = torch.load(
+                Path(path) / "model.pth",
+                map_location="cpu",
+                mmap=True,
+                weights_only=True,
+            )
+
+            if "state_dict" in weights:
+                logger.warning(
+                    "Using a TextToSemantic LightningModule checkpoint, "
+                    "please make sure it is a full model, not a LoRA model."
+                )
+                weights = weights["state_dict"]
+
+            if next(iter(weights.keys())).startswith("model."):
+                logger.info(
+                    f"Remove prefix 'model.' created by TextToSemantic LightningModule from keys"
+                )
+                new_weights = OrderedDict()
+                for k, v in weights.items():
+                    new_weights[k.replace("model.", "")] = v
+                weights = new_weights
+
+            # Verify the name and shape of parameters since strict=False in load_state_dict.
+            for k, v in model.named_parameters():
+                if k not in weights:
+                    logger.warning(f"No weight for {k}")
+                elif v.shape != weights[k].shape:
+                    logger.warning(
+                        f"Shape mismatch for {k}: {v.shape} vs {weights[k].shape}"
+                    )
+
+            err = model.load_state_dict(weights, strict=False, assign=True)
+            log.info(f"Loaded weights with error: {err}")
+
+        return model
+
+    def save_pretrained(self, path: str, drop_lora: bool = False):
+        path = Path(path)
+        path.mkdir(parents=True, exist_ok=True)
+
+        self.config.save(path / "config.json")
+        state_dict = self.state_dict()
+
+        if drop_lora:
+            for key in list(state_dict.keys()):
+                if "lora" not in key:
+                    continue
+
+                state_dict.pop(key)
+                log.info(f"Drop LoRA parameter: {key}")
+
+        torch.save(state_dict, path / "model.pth")
+        self.tokenizer.save_pretrained(path)
+
+
+class NaiveTransformer(BaseTransformer):
+    def __init__(self, config: NaiveModelArgs, tokenizer: AutoTokenizer) -> None:
+        super().__init__(config, init_weights=False, tokenizer=tokenizer)
+
+        self.codebook_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.codebook_output = nn.Linear(
+            config.dim,
+            config.codebook_size * config.num_codebooks,
+            bias=False,
+        )
+
+        self.apply(self._init_weights)
+
+    def decode(self, result: BaseTransformerForwardResult) -> TransformerForwardResult:
+        token_logits = result.logits
+        x = result.hidden_states
+
+        # Codebook
+        codebook_logits = self.codebook_output(self.codebook_norm(x))
+        codebook_logits = rearrange(
+            codebook_logits, "b n (c d) -> b n c d", c=self.config.num_codebooks
+        )
+
+        return TransformerForwardResult(
+            token_logits=token_logits,
+            codebook_logits=codebook_logits,
+        )
+
+    def forward(
+        self,
+        inp: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+    ) -> TransformerForwardResult:
+        result = super().forward(
+            inp=inp,
+            key_padding_mask=key_padding_mask,
+        )
+        return self.decode(result)
+
+    def forward_generate(
+        self, x: Tensor, input_pos: Optional[Tensor] = None
+    ) -> TransformerForwardResult:
+        result = super().forward_generate(x, input_pos)
+        return self.decode(result)
+
+
+class DualARTransformer(BaseTransformer):
+    def __init__(self, config: NaiveModelArgs, tokenizer: AutoTokenizer) -> None:
+        super().__init__(config, init_weights=False, tokenizer=tokenizer)
+
+        # Project to fast dim if needed
+        if config.fast_dim is not None and config.fast_dim != config.dim:
+            self.fast_project_in = nn.Linear(config.dim, config.fast_dim)
+        else:
+            self.fast_project_in = nn.Identity()
+
+        # Fast transformer
+        self.fast_embeddings = nn.Embedding(config.codebook_size, config.fast_dim)
+
+        # The equivalent bs is so large that sdpa doesn't work
+        override_config = dataclasses.replace(
+            config,
+            dim=config.fast_dim,
+            n_head=config.fast_n_head,
+            n_local_heads=config.fast_n_local_heads,
+            head_dim=config.fast_head_dim,
+            intermediate_size=config.fast_intermediate_size,
+            attention_qkv_bias=config.fast_attention_qkv_bias,
+        )
+
+        self.fast_layers = nn.ModuleList(
+            TransformerBlock(override_config, use_sdpa=False)
+            for _ in range(config.n_fast_layer)
+        )
+        self.fast_norm = RMSNorm(config.fast_dim, eps=config.norm_eps)
+        self.fast_output = nn.Linear(
+            config.fast_dim,
+            config.codebook_size,
+            bias=False,
+        )
+
+        self.register_buffer(
+            "fast_freqs_cis",
+            precompute_freqs_cis(
+                config.num_codebooks,
+                config.fast_dim // config.fast_n_head,
+                config.rope_base,
+            ),
+            persistent=False,
+        )
+        self.apply(self._init_weights)
+
+    def setup_caches(
+        self, max_batch_size: int, max_seq_len: int, dtype: torch.dtype = torch.bfloat16
+    ):
+        super().setup_caches(max_batch_size, max_seq_len, dtype)
+
+        head_dim = self.config.fast_dim // self.config.fast_n_head
+
+        # Fast transformer
+        # The max seq len here is the number of codebooks
+        for b in self.fast_layers:
+            b.attention.kv_cache = KVCache(
+                max_batch_size,
+                self.config.num_codebooks,
+                self.config.fast_n_local_heads,
+                head_dim,
+                dtype=dtype,
+            )
+
+    def forward(
+        self,
+        inp: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+    ) -> TransformerForwardResult:
+        parent_result = super().forward(inp, key_padding_mask)
+        token_logits = parent_result.logits
+        x = parent_result.hidden_states
+        x = self.fast_project_in(x)
+
+        # Fast transformer
+        fast_seq_len = self.config.num_codebooks
+        fast_mask = self.causal_mask[
+            None, None, :fast_seq_len, :fast_seq_len
+        ]  # (B, N, Q, K)
+
+        # Drop the last token and rotate left
+        codebooks = inp[:, 1:-1, 1:]
+        codebooks = F.pad(codebooks, (0, 1), value=0)
+        codebook_embeddings = self.fast_embeddings(codebooks)
+        x = torch.cat([x[:, None], codebook_embeddings], dim=1)
+        b, s = x.size(0), x.size(2)
+        x = rearrange(x, "b n s d -> (b s) n d")  # flatten the batch and seq_len
+
+        # Remove padded part
+        codebooks = rearrange(codebooks, "b n s -> (b s) n")
+        codebook_mask = (codebooks == 0).all(dim=-1)
+
+        if torch.all(codebook_mask):
+            # If all codebooks are padded, we keep first 8 to make sure the model runs
+            codebook_mask[:8] = False
+
+        x_bs, x_len = x.size(0), x.size(1)
+        x = x[~codebook_mask]
+
+        for layer in self.fast_layers:
+            if self.config.use_gradient_checkpointing and self.training:
+                x = checkpoint(
+                    layer, x, self.fast_freqs_cis, fast_mask, use_reentrant=True
+                )
+            else:
+                x = layer(x, self.fast_freqs_cis, fast_mask)
+
+        # unflatten the batch and num_codebooks
+        fast_out = self.fast_norm(x)
+        codebook_logits = self.fast_output(fast_out)
+
+        # Re-pad the codebook_logits
+        buffer = torch.zeros(
+            x_bs,
+            x_len,
+            codebook_logits.size(-1),
+            device=codebook_logits.device,
+            dtype=codebook_logits.dtype,
+        )
+        buffer[~codebook_mask] = codebook_logits
+        codebook_logits = buffer
+
+        assert codebook_logits.shape[1] == self.config.num_codebooks
+        codebook_logits = rearrange(
+            codebook_logits,
+            "(b s) n d -> b s n d",
+            b=b,
+            s=s,
+            n=self.config.num_codebooks,
+        )
+
+        return TransformerForwardResult(
+            token_logits=token_logits,
+            codebook_logits=codebook_logits,
+        )
+
+    def forward_generate_fast(
+        self, x: Tensor, input_pos: Optional[Tensor] = None
+    ) -> Tensor:
+        # Fast transformer
+        x = x.view(1, 1, -1)
+
+        fast_mask = self.causal_mask[
+            None, None, input_pos, : self.config.num_codebooks
+        ]  # (B, N, Q, K)
+        fast_freqs_cis = self.fast_freqs_cis[input_pos]
+
+        for layer in self.fast_layers:
+            x = layer(x, fast_freqs_cis, fast_mask, input_pos=input_pos)
+
+        # unflatten the batch and num_codebooks
+        fast_out = self.fast_norm(x)  # only take the last token
+        codebook_logits = self.fast_output(fast_out)
+
+        return codebook_logits
+
+    def forward_generate(
+        self, x: Tensor, input_pos: Optional[Tensor] = None
+    ) -> TransformerForwardResult:
+        x = super().forward_generate(x, input_pos)
+        x.hidden_states = self.fast_project_in(x.hidden_states)
+        return x
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: BaseModelArgs, use_sdpa: bool = True) -> None:
+        super().__init__()
+        self.attention = Attention(config, use_sdpa=use_sdpa)
+        self.feed_forward = FeedForward(config)
+        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
+
+    def forward(
+        self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Tensor = None
+    ) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: BaseModelArgs, use_sdpa: bool = True):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        self.wqkv = nn.Linear(
+            config.dim, total_head_dim, bias=config.attention_qkv_bias
+        )
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        self.kv_cache = None
+
+        self.dropout = config.dropout
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self.use_sdpa = use_sdpa
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(
+        self,
+        x: Tensor,
+        freqs_cis: Tensor,
+        mask: Tensor,
+        input_pos: Optional[Tensor] = None,
+    ) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+
+        if self.use_sdpa:
+            if mask is None:
+                with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
+                    y = F.scaled_dot_product_attention(
+                        q,
+                        k,
+                        v,
+                        dropout_p=self.dropout if self.training else 0.0,
+                        is_causal=True,
+                        # No third party attn_mask here to use flash_attention
+                    )
+            else:
+                y = F.scaled_dot_product_attention(
+                    q,
+                    k,
+                    v,
+                    attn_mask=mask,
+                    dropout_p=self.dropout if self.training else 0.0,
+                )
+        else:
+            y = self.eq_scaled_dot_product_attention(
+                q,
+                k,
+                v,
+                attn_mask=mask,
+                dropout_p=self.dropout if self.training else 0.0,
+            )
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        return self.wo(y)
+
+    def eq_scaled_dot_product_attention(
+        self,
+        query,
+        key,
+        value,
+        attn_mask=None,
+        dropout_p=0.0,
+    ) -> torch.Tensor:
+        # This is a standard scaled dot product attention
+        # It's low efficient, but it doesn't raise cuda error
+
+        L, S = query.size(-2), key.size(-2)
+        scale_factor = 1 / math.sqrt(query.size(-1))
+        attn_bias = torch.zeros(1, 1, L, S, dtype=query.dtype, device=query.device)
+
+        if attn_mask is not None:
+            if attn_mask.dtype == torch.bool:
+                attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
+            else:
+                attn_bias += attn_mask
+
+        attn_weight = query @ key.transpose(-2, -1) * scale_factor
+        attn_weight += attn_bias
+        attn_weight = torch.softmax(attn_weight, dim=-1)
+        attn_weight = torch.dropout(attn_weight, dropout_p, train=True)
+
+        return attn_weight @ value
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config: BaseModelArgs) -> None:
+        super().__init__()
+        self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=False)
+        self.w3 = nn.Linear(config.dim, config.intermediate_size, bias=False)
+        self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.w2(F.silu(self.w1(x)) * self.w3(x))
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(seq_len: int, n_elem: int, base: int = 10000) -> Tensor:
+    freqs = 1.0 / (
+        base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem)
+    )
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)

fish_speech/models/text2semantic/lora.py

@@ -0,0 +1,92 @@
+from dataclasses import dataclass
+
+import loralib as lora
+
+
+@dataclass
+class LoraConfig:
+    r: int
+    lora_alpha: float
+    lora_dropout: float = 0.0
+
+
+def setup_lora(model, lora_config):
+    # Replace the embedding layer with a LoRA layer
+    model.embeddings = lora.Embedding(
+        num_embeddings=model.embeddings.num_embeddings,
+        embedding_dim=model.embeddings.embedding_dim,
+        padding_idx=model.embeddings.padding_idx,
+        r=lora_config.r,
+        lora_alpha=lora_config.lora_alpha,
+    )
+
+    model.codebook_embeddings = lora.Embedding(
+        num_embeddings=model.codebook_embeddings.num_embeddings,
+        embedding_dim=model.codebook_embeddings.embedding_dim,
+        padding_idx=model.codebook_embeddings.padding_idx,
+        r=lora_config.r,
+        lora_alpha=lora_config.lora_alpha,
+    )
+
+    # Replace output layer with a LoRA layer
+    linears = [(model, "output")]
+
+    # Replace all linear layers with LoRA layers
+    for layer in model.layers:
+        linears.extend([(layer.attention, "wqkv"), (layer.attention, "wo")])
+        linears.extend(
+            [
+                (layer.feed_forward, "w1"),
+                (layer.feed_forward, "w2"),
+                (layer.feed_forward, "w3"),
+            ]
+        )
+
+    if hasattr(model, "fast_layers"):
+        model.fast_embeddings = lora.Embedding(
+            num_embeddings=model.fast_embeddings.num_embeddings,
+            embedding_dim=model.fast_embeddings.embedding_dim,
+            padding_idx=model.fast_embeddings.padding_idx,
+            r=lora_config.r,
+            lora_alpha=lora_config.lora_alpha,
+        )
+
+        # Dual-AR model
+        linears.append((model, "fast_output"))
+
+        for layer in model.fast_layers:
+            linears.extend([(layer.attention, "wqkv"), (layer.attention, "wo")])
+            linears.extend(
+                [
+                    (layer.feed_forward, "w1"),
+                    (layer.feed_forward, "w2"),
+                    (layer.feed_forward, "w3"),
+                ]
+            )
+
+    for module, layer in linears:
+        updated_linear = lora.Linear(
+            in_features=getattr(module, layer).in_features,
+            out_features=getattr(module, layer).out_features,
+            bias=getattr(module, layer).bias,
+            r=lora_config.r,
+            lora_alpha=lora_config.lora_alpha,
+            lora_dropout=lora_config.lora_dropout,
+        )
+        setattr(module, layer, updated_linear)
+
+    # Mark only the LoRA layers as trainable
+    lora.mark_only_lora_as_trainable(model, bias="none")
+
+
+def get_merged_state_dict(model):
+    # This line will merge the state dict of the model and the LoRA parameters
+    model.eval()
+
+    # Then we need to remove the LoRA parameters from the state dict
+    state_dict = model.state_dict()
+    for name in list(state_dict.keys()):
+        if "lora" in name:
+            state_dict.pop(name)
+
+    return state_dict

fish_speech/models/vqgan/modules/firefly.py

@@ -0,0 +1,596 @@
+import math
+from functools import partial
+from math import prod
+from typing import Callable
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils.parametrize import remove_parametrizations
+from torch.utils.checkpoint import checkpoint
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv1D") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return (kernel_size * dilation - dilation) // 2
+
+
+def unpad1d(x: torch.Tensor, paddings: tuple[int, int]):
+    """Remove padding from x, handling properly zero padding. Only for 1d!"""
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    assert (padding_left + padding_right) <= x.shape[-1]
+    end = x.shape[-1] - padding_right
+    return x[..., padding_left:end]
+
+
+def get_extra_padding_for_conv1d(
+    x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0
+) -> int:
+    """See `pad_for_conv1d`."""
+    length = x.shape[-1]
+    n_frames = (length - kernel_size + padding_total) / stride + 1
+    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
+    return ideal_length - length
+
+
+def pad1d(
+    x: torch.Tensor,
+    paddings: tuple[int, int],
+    mode: str = "zeros",
+    value: float = 0.0,
+):
+    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
+    If this is the case, we insert extra 0 padding to the right
+    before the reflection happen.
+    """
+    length = x.shape[-1]
+    padding_left, padding_right = paddings
+    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
+    if mode == "reflect":
+        max_pad = max(padding_left, padding_right)
+        extra_pad = 0
+        if length <= max_pad:
+            extra_pad = max_pad - length + 1
+            x = F.pad(x, (0, extra_pad))
+        padded = F.pad(x, paddings, mode, value)
+        end = padded.shape[-1] - extra_pad
+        return padded[..., :end]
+    else:
+        return F.pad(x, paddings, mode, value)
+
+
+class FishConvNet(nn.Module):
+    def __init__(
+        self, in_channels, out_channels, kernel_size, dilation=1, stride=1, groups=1
+    ):
+        super(FishConvNet, self).__init__()
+        self.conv = nn.Conv1d(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=stride,
+            dilation=dilation,
+            groups=groups,
+        )
+        self.stride = stride
+        self.kernel_size = (kernel_size - 1) * dilation + 1
+        self.dilation = dilation
+
+    def forward(self, x):
+        pad = self.kernel_size - self.stride
+        extra_padding = get_extra_padding_for_conv1d(
+            x, self.kernel_size, self.stride, pad
+        )
+        x = pad1d(x, (pad, extra_padding), mode="constant", value=0)
+        return self.conv(x).contiguous()
+
+    def weight_norm(self, name="weight", dim=0):
+        self.conv = weight_norm(self.conv, name=name, dim=dim)
+        return self
+
+    def remove_parametrizations(self, name="weight"):
+        self.conv = remove_parametrizations(self.conv, name)
+        return self
+
+
+class FishTransConvNet(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, dilation=1, stride=1):
+        super(FishTransConvNet, self).__init__()
+        self.conv = nn.ConvTranspose1d(
+            in_channels, out_channels, kernel_size, stride=stride, dilation=dilation
+        )
+        self.stride = stride
+        self.kernel_size = kernel_size
+
+    def forward(self, x):
+        x = self.conv(x)
+        pad = self.kernel_size - self.stride
+        padding_right = math.ceil(pad)
+        padding_left = pad - padding_right
+        x = unpad1d(x, (padding_left, padding_right))
+        return x.contiguous()
+
+    def weight_norm(self, name="weight", dim=0):
+        self.conv = weight_norm(self.conv, name=name, dim=dim)
+        return self
+
+    def remove_parametrizations(self, name="weight"):
+        self.conv = remove_parametrizations(self.conv, name)
+        return self
+
+
+class ResBlock1(torch.nn.Module):
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super().__init__()
+
+        self.convs1 = nn.ModuleList(
+            [
+                FishConvNet(
+                    channels, channels, kernel_size, stride=1, dilation=dilation[0]
+                ).weight_norm(),
+                FishConvNet(
+                    channels, channels, kernel_size, stride=1, dilation=dilation[1]
+                ).weight_norm(),
+                FishConvNet(
+                    channels, channels, kernel_size, stride=1, dilation=dilation[2]
+                ).weight_norm(),
+            ]
+        )
+        self.convs1.apply(init_weights)
+
+        self.convs2 = nn.ModuleList(
+            [
+                FishConvNet(
+                    channels, channels, kernel_size, stride=1, dilation=dilation[0]
+                ).weight_norm(),
+                FishConvNet(
+                    channels, channels, kernel_size, stride=1, dilation=dilation[1]
+                ).weight_norm(),
+                FishConvNet(
+                    channels, channels, kernel_size, stride=1, dilation=dilation[2]
+                ).weight_norm(),
+            ]
+        )
+        self.convs2.apply(init_weights)
+
+    def forward(self, x):
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.silu(x)
+            xt = c1(xt)
+            xt = F.silu(xt)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_parametrizations(self):
+        for conv in self.convs1:
+            conv.remove_parametrizations()
+        for conv in self.convs2:
+            conv.remove_parametrizations()
+
+
+class ParallelBlock(nn.Module):
+    def __init__(
+        self,
+        channels: int,
+        kernel_sizes: tuple[int] = (3, 7, 11),
+        dilation_sizes: tuple[tuple[int]] = ((1, 3, 5), (1, 3, 5), (1, 3, 5)),
+    ):
+        super().__init__()
+
+        assert len(kernel_sizes) == len(dilation_sizes)
+
+        self.blocks = nn.ModuleList()
+        for k, d in zip(kernel_sizes, dilation_sizes):
+            self.blocks.append(ResBlock1(channels, k, d))
+
+    def forward(self, x):
+        return torch.stack([block(x) for block in self.blocks], dim=0).mean(dim=0)
+
+    def remove_parametrizations(self):
+        for block in self.blocks:
+            block.remove_parametrizations()
+
+
+class HiFiGANGenerator(nn.Module):
+    def __init__(
+        self,
+        *,
+        hop_length: int = 512,
+        upsample_rates: tuple[int] = (8, 8, 2, 2, 2),
+        upsample_kernel_sizes: tuple[int] = (16, 16, 8, 2, 2),
+        resblock_kernel_sizes: tuple[int] = (3, 7, 11),
+        resblock_dilation_sizes: tuple[tuple[int]] = ((1, 3, 5), (1, 3, 5), (1, 3, 5)),
+        num_mels: int = 128,
+        upsample_initial_channel: int = 512,
+        pre_conv_kernel_size: int = 7,
+        post_conv_kernel_size: int = 7,
+        post_activation: Callable = partial(nn.SiLU, inplace=True),
+    ):
+        super().__init__()
+
+        assert (
+            prod(upsample_rates) == hop_length
+        ), f"hop_length must be {prod(upsample_rates)}"
+
+        self.conv_pre = FishConvNet(
+            num_mels,
+            upsample_initial_channel,
+            pre_conv_kernel_size,
+            stride=1,
+        ).weight_norm()
+
+        self.num_upsamples = len(upsample_rates)
+        self.num_kernels = len(resblock_kernel_sizes)
+
+        self.noise_convs = nn.ModuleList()
+        self.ups = nn.ModuleList()
+
+        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
+            self.ups.append(
+                FishTransConvNet(
+                    upsample_initial_channel // (2**i),
+                    upsample_initial_channel // (2 ** (i + 1)),
+                    k,
+                    stride=u,
+                ).weight_norm()
+            )
+
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            self.resblocks.append(
+                ParallelBlock(ch, resblock_kernel_sizes, resblock_dilation_sizes)
+            )
+
+        self.activation_post = post_activation()
+        self.conv_post = FishConvNet(
+            ch, 1, post_conv_kernel_size, stride=1
+        ).weight_norm()
+        self.ups.apply(init_weights)
+        self.conv_post.apply(init_weights)
+
+    def forward(self, x):
+        x = self.conv_pre(x)
+
+        for i in range(self.num_upsamples):
+            x = F.silu(x, inplace=True)
+            x = self.ups[i](x)
+
+            if self.training and self.checkpointing:
+                x = checkpoint(
+                    self.resblocks[i],
+                    x,
+                    use_reentrant=False,
+                )
+            else:
+                x = self.resblocks[i](x)
+
+        x = self.activation_post(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x
+
+    def remove_parametrizations(self):
+        for up in self.ups:
+            up.remove_parametrizations()
+        for block in self.resblocks:
+            block.remove_parametrizations()
+        self.conv_pre.remove_parametrizations()
+        self.conv_post.remove_parametrizations()
+
+
+# DropPath copied from timm library
+def drop_path(
+    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
+):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+
+    """  # noqa: E501
+
+    if drop_prob == 0.0 or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (
+        x.ndim - 1
+    )  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+    if keep_prob > 0.0 and scale_by_keep:
+        random_tensor.div_(keep_prob)
+    return x * random_tensor
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""  # noqa: E501
+
+    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
+
+    def extra_repr(self):
+        return f"drop_prob={round(self.drop_prob,3):0.3f}"
+
+
+class LayerNorm(nn.Module):
+    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
+    shape (batch_size, height, width, channels) while channels_first corresponds to inputs
+    with shape (batch_size, channels, height, width).
+    """  # noqa: E501
+
+    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(normalized_shape))
+        self.bias = nn.Parameter(torch.zeros(normalized_shape))
+        self.eps = eps
+        self.data_format = data_format
+        if self.data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError
+        self.normalized_shape = (normalized_shape,)
+
+    def forward(self, x):
+        if self.data_format == "channels_last":
+            return F.layer_norm(
+                x, self.normalized_shape, self.weight, self.bias, self.eps
+            )
+        elif self.data_format == "channels_first":
+            u = x.mean(1, keepdim=True)
+            s = (x - u).pow(2).mean(1, keepdim=True)
+            x = (x - u) / torch.sqrt(s + self.eps)
+            x = self.weight[:, None] * x + self.bias[:, None]
+            return x
+
+
+# ConvNeXt Block copied from https://github.com/fishaudio/fish-diffusion/blob/main/fish_diffusion/modules/convnext.py
+class ConvNeXtBlock(nn.Module):
+    r"""ConvNeXt Block. There are two equivalent implementations:
+    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+    We use (2) as we find it slightly faster in PyTorch
+
+    Args:
+        dim (int): Number of input channels.
+        drop_path (float): Stochastic depth rate. Default: 0.0
+        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
+        kernel_size (int): Kernel size for depthwise conv. Default: 7.
+        dilation (int): Dilation for depthwise conv. Default: 1.
+    """  # noqa: E501
+
+    def __init__(
+        self,
+        dim: int,
+        drop_path: float = 0.0,
+        layer_scale_init_value: float = 1e-6,
+        mlp_ratio: float = 4.0,
+        kernel_size: int = 7,
+        dilation: int = 1,
+    ):
+        super().__init__()
+
+        self.dwconv = FishConvNet(
+            dim,
+            dim,
+            kernel_size=kernel_size,
+            # padding=int(dilation * (kernel_size - 1) / 2),
+            groups=dim,
+        )  # depthwise conv
+        self.norm = LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(
+            dim, int(mlp_ratio * dim)
+        )  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(int(mlp_ratio * dim), dim)
+        self.gamma = (
+            nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
+            if layer_scale_init_value > 0
+            else None
+        )
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+
+    def forward(self, x, apply_residual: bool = True):
+        input = x
+
+        x = self.dwconv(x)
+        x = x.permute(0, 2, 1)  # (N, C, L) -> (N, L, C)
+        x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+
+        if self.gamma is not None:
+            x = self.gamma * x
+
+        x = x.permute(0, 2, 1)  # (N, L, C) -> (N, C, L)
+        x = self.drop_path(x)
+
+        if apply_residual:
+            x = input + x
+
+        return x
+
+
+class ConvNeXtEncoder(nn.Module):
+    def __init__(
+        self,
+        input_channels: int = 3,
+        depths: list[int] = [3, 3, 9, 3],
+        dims: list[int] = [96, 192, 384, 768],
+        drop_path_rate: float = 0.0,
+        layer_scale_init_value: float = 1e-6,
+        kernel_size: int = 7,
+    ):
+        super().__init__()
+        assert len(depths) == len(dims)
+
+        self.downsample_layers = nn.ModuleList()
+        stem = nn.Sequential(
+            FishConvNet(
+                input_channels,
+                dims[0],
+                kernel_size=7,
+                # padding=3,
+                # padding_mode="replicate",
+                # padding_mode="zeros",
+            ),
+            LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
+        )
+        self.downsample_layers.append(stem)
+
+        for i in range(len(depths) - 1):
+            mid_layer = nn.Sequential(
+                LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
+                nn.Conv1d(dims[i], dims[i + 1], kernel_size=1),
+            )
+            self.downsample_layers.append(mid_layer)
+
+        self.stages = nn.ModuleList()
+        dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+
+        cur = 0
+        for i in range(len(depths)):
+            stage = nn.Sequential(
+                *[
+                    ConvNeXtBlock(
+                        dim=dims[i],
+                        drop_path=dp_rates[cur + j],
+                        layer_scale_init_value=layer_scale_init_value,
+                        kernel_size=kernel_size,
+                    )
+                    for j in range(depths[i])
+                ]
+            )
+            self.stages.append(stage)
+            cur += depths[i]
+
+        self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first")
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv1d, nn.Linear)):
+            nn.init.trunc_normal_(m.weight, std=0.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+    ) -> torch.Tensor:
+        for i in range(len(self.downsample_layers)):
+            x = self.downsample_layers[i](x)
+            x = self.stages[i](x)
+
+        return self.norm(x)
+
+
+class FireflyArchitecture(nn.Module):
+    def __init__(
+        self,
+        backbone: nn.Module,
+        head: nn.Module,
+        quantizer: nn.Module,
+        spec_transform: nn.Module,
+    ):
+        super().__init__()
+
+        self.backbone = backbone
+        self.head = head
+        self.quantizer = quantizer
+        self.spec_transform = spec_transform
+        self.downsample_factor = math.prod(self.quantizer.downsample_factor)
+
+    def forward(self, x: torch.Tensor, template=None, mask=None) -> torch.Tensor:
+        if self.spec_transform is not None:
+            x = self.spec_transform(x)
+
+        x = self.backbone(x)
+        if mask is not None:
+            x = x * mask
+
+        if self.quantizer is not None:
+            vq_result = self.quantizer(x)
+            x = vq_result.z
+
+            if mask is not None:
+                x = x * mask
+
+        x = self.head(x, template=template)
+
+        if x.ndim == 2:
+            x = x[:, None, :]
+
+        if self.vq is not None:
+            return x, vq_result
+
+        return x
+
+    def encode(self, audios, audio_lengths):
+        audios = audios.float()
+
+        mels = self.spec_transform(audios)
+        mel_lengths = audio_lengths // self.spec_transform.hop_length
+        mel_masks = sequence_mask(mel_lengths, mels.shape[2])
+        mel_masks_float_conv = mel_masks[:, None, :].float()
+        mels = mels * mel_masks_float_conv
+
+        # Encode
+        encoded_features = self.backbone(mels) * mel_masks_float_conv
+        feature_lengths = mel_lengths // self.downsample_factor
+
+        return self.quantizer.encode(encoded_features), feature_lengths
+
+    def decode(self, indices, feature_lengths) -> torch.Tensor:
+        mel_masks = sequence_mask(
+            feature_lengths * self.downsample_factor,
+            indices.shape[2] * self.downsample_factor,
+        )
+        mel_masks_float_conv = mel_masks[:, None, :].float()
+        audio_lengths = (
+            feature_lengths * self.downsample_factor * self.spec_transform.hop_length
+        )
+
+        audio_masks = sequence_mask(
+            audio_lengths,
+            indices.shape[2] * self.downsample_factor * self.spec_transform.hop_length,
+        )
+        audio_masks_float_conv = audio_masks[:, None, :].float()
+
+        z = self.quantizer.decode(indices) * mel_masks_float_conv
+        x = self.head(z) * audio_masks_float_conv
+
+        return x, audio_lengths
+
+    def remove_parametrizations(self):
+        if hasattr(self.backbone, "remove_parametrizations"):
+            self.backbone.remove_parametrizations()
+
+        if hasattr(self.head, "remove_parametrizations"):
+            self.head.remove_parametrizations()
+
+    @property
+    def device(self):
+        return next(self.parameters()).device

fish_speech/models/vqgan/modules/fsq.py

@@ -0,0 +1,116 @@
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from vector_quantize_pytorch import GroupedResidualFSQ
+
+from .firefly import ConvNeXtBlock, FishConvNet, FishTransConvNet
+
+
+@dataclass
+class FSQResult:
+    z: torch.Tensor
+    codes: torch.Tensor
+    latents: torch.Tensor
+
+
+class DownsampleFiniteScalarQuantize(nn.Module):
+    def __init__(
+        self,
+        input_dim: int = 512,
+        n_codebooks: int = 9,
+        n_groups: int = 1,
+        levels: tuple[int] = (8, 5, 5, 5),  # Approximate 2**10
+        downsample_factor: tuple[int] = (2, 2),
+        downsample_dims: tuple[int] | None = None,
+    ):
+        super().__init__()
+
+        if downsample_dims is None:
+            downsample_dims = [input_dim for _ in range(len(downsample_factor))]
+
+        all_dims = (input_dim,) + tuple(downsample_dims)
+
+        self.residual_fsq = GroupedResidualFSQ(
+            dim=all_dims[-1],
+            levels=levels,
+            num_quantizers=n_codebooks,
+            groups=n_groups,
+        )
+
+        self.downsample_factor = downsample_factor
+        self.downsample_dims = downsample_dims
+
+        self.downsample = nn.Sequential(
+            *[
+                nn.Sequential(
+                    FishConvNet(
+                        all_dims[idx],
+                        all_dims[idx + 1],
+                        kernel_size=factor,
+                        stride=factor,
+                    ),
+                    ConvNeXtBlock(dim=all_dims[idx + 1]),
+                )
+                for idx, factor in enumerate(downsample_factor)
+            ]
+        )
+
+        self.upsample = nn.Sequential(
+            *[
+                nn.Sequential(
+                    FishTransConvNet(
+                        all_dims[idx + 1],
+                        all_dims[idx],
+                        kernel_size=factor,
+                        stride=factor,
+                    ),
+                    ConvNeXtBlock(dim=all_dims[idx]),
+                )
+                for idx, factor in reversed(list(enumerate(downsample_factor)))
+            ]
+        )
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv1d, nn.Linear)):
+            nn.init.trunc_normal_(m.weight, std=0.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward(self, z) -> FSQResult:
+        original_shape = z.shape
+        z = self.downsample(z)
+        quantized, indices = self.residual_fsq(z.mT)
+        result = FSQResult(
+            z=quantized.mT,
+            codes=indices.mT,
+            latents=z,
+        )
+        result.z = self.upsample(result.z)
+
+        # Pad or crop z to match original shape
+        diff = original_shape[-1] - result.z.shape[-1]
+        left = diff // 2
+        right = diff - left
+
+        if diff > 0:
+            result.z = F.pad(result.z, (left, right))
+        elif diff < 0:
+            result.z = result.z[..., left:-right]
+
+        return result
+
+    def encode(self, z):
+        z = self.downsample(z)
+        _, indices = self.residual_fsq(z.mT)
+        indices = rearrange(indices, "g b l r -> b (g r) l")
+        return indices
+
+    def decode(self, indices: torch.Tensor):
+        indices = rearrange(indices, "b (g r) l -> g b l r", g=self.residual_fsq.groups)
+        z_q = self.residual_fsq.get_output_from_indices(indices)
+        z_q = self.upsample(z_q.mT)
+        return z_q

fish_speech/models/vqgan/utils.py

@@ -0,0 +1,94 @@
+import matplotlib
+import torch
+from matplotlib import pyplot as plt
+
+matplotlib.use("Agg")
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+
+def plot_mel(data, titles=None):
+    fig, axes = plt.subplots(len(data), 1, squeeze=False)
+
+    if titles is None:
+        titles = [None for i in range(len(data))]
+
+    plt.tight_layout()
+
+    for i in range(len(data)):
+        mel = data[i]
+
+        if isinstance(mel, torch.Tensor):
+            mel = mel.float().detach().cpu().numpy()
+
+        axes[i][0].imshow(mel, origin="lower")
+        axes[i][0].set_aspect(2.5, adjustable="box")
+        axes[i][0].set_ylim(0, mel.shape[0])
+        axes[i][0].set_title(titles[i], fontsize="medium")
+        axes[i][0].tick_params(labelsize="x-small", left=False, labelleft=False)
+        axes[i][0].set_anchor("W")
+
+    return fig
+
+
+def slice_segments(x, ids_str, segment_size=4):
+    ret = torch.zeros_like(x[:, :, :segment_size])
+    for i in range(x.size(0)):
+        idx_str = ids_str[i]
+        idx_end = idx_str + segment_size
+        ret[i] = x[i, :, idx_str:idx_end]
+
+    return ret
+
+
+def rand_slice_segments(x, x_lengths=None, segment_size=4):
+    b, d, t = x.size()
+    if x_lengths is None:
+        x_lengths = t
+    ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0)
+    ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long)
+    ret = slice_segments(x, ids_str, segment_size)
+    return ret, ids_str
+
+
+@torch.jit.script
+def fused_add_tanh_sigmoid_multiply(in_act, n_channels):
+    n_channels_int = n_channels[0]
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+
+    return acts
+
+
+def avg_with_mask(x, mask):
+    assert mask.dtype == torch.float, "Mask should be float"
+
+    if mask.ndim == 2:
+        mask = mask.unsqueeze(1)
+
+    if mask.shape[1] == 1:
+        mask = mask.expand_as(x)
+
+    return (x * mask).sum() / mask.sum()

fish_speech/scheduler.py

@@ -0,0 +1,40 @@
+import math
+
+
+def get_cosine_schedule_with_warmup_lr_lambda(
+    current_step: int,
+    *,
+    num_warmup_steps: int | float,
+    num_training_steps: int,
+    num_cycles: float = 0.5,
+    final_lr_ratio: float = 0.0,
+):
+    if 0 < num_warmup_steps < 1:  # float mode
+        num_warmup_steps = int(num_warmup_steps * num_training_steps)
+
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+
+    progress = float(current_step - num_warmup_steps) / float(
+        max(1, num_training_steps - num_warmup_steps)
+    )
+
+    return max(
+        final_lr_ratio,
+        0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)),
+    )
+
+
+def get_constant_schedule_with_warmup_lr_lambda(
+    current_step: int,
+    *,
+    num_warmup_steps: int | float,
+    num_training_steps: int | None = None,
+):
+    if 0 < num_warmup_steps < 1:  # float mode
+        num_warmup_steps = int(num_warmup_steps * num_training_steps)
+
+    if current_step < num_warmup_steps:
+        return float(current_step) / float(max(1, num_warmup_steps))
+
+    return 1.0

fish_speech/text/__init__.py

@@ -0,0 +1,4 @@
+from .clean import clean_text
+from .spliter import split_text
+
+__all__ = ["clean_text", "split_text"]

fish_speech/text/chn_text_norm/.gitignore

@@ -0,0 +1,114 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# JetBrains PyCharm
+.idea
+
+# Customize
+references
+url.txt
+
+# Git
+.git

fish_speech/text/chn_text_norm/README.md

@@ -0,0 +1,36 @@
+# This account is no longer in use, see [Atomicoo](https://github.com/atomicoo) for my latest works.
+
+# Chn Text Norm
+
+this is a repository for chinese text normalization (no longer maintained).
+
+## Quick Start ##
+
+### Git Clone Repo ###
+
+git clone this repo to the root directory of your project which need to use it.
+
+    cd /path/to/proj
+    git clone https://github.com/Joee1995/chn-text-norm.git
+
+after that, your doc tree should be:
+```
+proj                     # root of your project
+|--- chn_text_norm       # this chn-text-norm tool
+     |--- text.py
+     |--- ...
+|--- text_normalize.py   # your text normalization code
+|--- ...
+```
+
+### How to Use ? ###
+
+    # text_normalize.py
+    from chn_text_norm.text import *
+    
+    raw_text = 'your raw text'
+    text = Text(raw_text=raw_text).normalize()
+
+### How to add quantums ###
+
+打开test.py，然后你就知道怎么做了。

fish_speech/text/chn_text_norm/basic_class.py

@@ -0,0 +1,172 @@
+# -*- coding: utf-8 -*-
+"""基本类
+中文字符类
+中文数字/数位类
+中文数字类
+中文数位类
+中文数字系统类
+中文数学符号类
+*中文其他符号类
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-02"
+
+from fish_speech.text.chn_text_norm.basic_constant import NUMBERING_TYPES
+
+
+class ChineseChar(object):
+    """
+    中文字符
+    每个字符对应简体和繁体,
+    e.g. 简体 = '负', 繁体 = '負'
+    转换时可转换为简体或繁体
+    """
+
+    def __init__(self, simplified, traditional):
+        self.simplified = simplified
+        self.traditional = traditional
+        self.__repr__ = self.__str__
+
+    def __str__(self):
+        return self.simplified or self.traditional or None
+
+    def __repr__(self):
+        return self.__str__()
+
+
+class ChineseNumberUnit(ChineseChar):
+    """
+    中文数字/数位字符
+    每个字符除繁简体外还有一个额外的大写字符
+    e.g. '陆' 和 '陸'
+    """
+
+    def __init__(self, power, simplified, traditional, big_s, big_t):
+        super(ChineseNumberUnit, self).__init__(simplified, traditional)
+        self.power = power
+        self.big_s = big_s
+        self.big_t = big_t
+
+    def __str__(self):
+        return "10^{}".format(self.power)
+
+    @classmethod
+    def create(cls, index, value, numbering_type=NUMBERING_TYPES[1], small_unit=False):
+
+        if small_unit:
+            return ChineseNumberUnit(
+                power=index + 1,
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[1],
+                big_t=value[1],
+            )
+        elif numbering_type == NUMBERING_TYPES[0]:
+            return ChineseNumberUnit(
+                power=index + 8,
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[0],
+                big_t=value[1],
+            )
+        elif numbering_type == NUMBERING_TYPES[1]:
+            return ChineseNumberUnit(
+                power=(index + 2) * 4,
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[0],
+                big_t=value[1],
+            )
+        elif numbering_type == NUMBERING_TYPES[2]:
+            return ChineseNumberUnit(
+                power=pow(2, index + 3),
+                simplified=value[0],
+                traditional=value[1],
+                big_s=value[0],
+                big_t=value[1],
+            )
+        else:
+            raise ValueError(
+                "Counting type should be in {0} ({1} provided).".format(
+                    NUMBERING_TYPES, numbering_type
+                )
+            )
+
+
+class ChineseNumberDigit(ChineseChar):
+    """
+    中文数字字符
+    """
+
+    def __init__(
+        self, value, simplified, traditional, big_s, big_t, alt_s=None, alt_t=None
+    ):
+        super(ChineseNumberDigit, self).__init__(simplified, traditional)
+        self.value = value
+        self.big_s = big_s
+        self.big_t = big_t
+        self.alt_s = alt_s
+        self.alt_t = alt_t
+
+    def __str__(self):
+        return str(self.value)
+
+    @classmethod
+    def create(cls, i, v):
+        return ChineseNumberDigit(i, v[0], v[1], v[2], v[3])
+
+
+class ChineseMath(ChineseChar):
+    """
+    中文数位字符
+    """
+
+    def __init__(self, simplified, traditional, symbol, expression=None):
+        super(ChineseMath, self).__init__(simplified, traditional)
+        self.symbol = symbol
+        self.expression = expression
+        self.big_s = simplified
+        self.big_t = traditional
+
+
+CC, CNU, CND, CM = ChineseChar, ChineseNumberUnit, ChineseNumberDigit, ChineseMath
+
+
+class NumberSystem(object):
+    """
+    中文数字系统
+    """
+
+    pass
+
+
+class MathSymbol(object):
+    """
+    用于中文数字系统的数学符号 (繁/简体), e.g.
+    positive = ['正', '正']
+    negative = ['负', '負']
+    point = ['点', '點']
+    """
+
+    def __init__(self, positive, negative, point):
+        self.positive = positive
+        self.negative = negative
+        self.point = point
+
+    def __iter__(self):
+        for v in self.__dict__.values():
+            yield v
+
+
+# class OtherSymbol(object):
+#     """
+#     其他符号
+#     """
+#
+#     def __init__(self, sil):
+#         self.sil = sil
+#
+#     def __iter__(self):
+#         for v in self.__dict__.values():
+#             yield v

fish_speech/text/chn_text_norm/basic_constant.py

@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+"""基本常量
+中文数字/数位/符号字符常量
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-02"
+
+CHINESE_DIGIS = "零一二三四五六七八九"
+BIG_CHINESE_DIGIS_SIMPLIFIED = "零壹贰叁肆伍陆柒捌玖"
+BIG_CHINESE_DIGIS_TRADITIONAL = "零壹貳參肆伍陸柒捌玖"
+SMALLER_BIG_CHINESE_UNITS_SIMPLIFIED = "十百千万"
+SMALLER_BIG_CHINESE_UNITS_TRADITIONAL = "拾佰仟萬"
+LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED = "亿兆京垓秭穰沟涧正载"
+LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL = "億兆京垓秭穰溝澗正載"
+SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED = "十百千万"
+SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL = "拾佰仟萬"
+
+ZERO_ALT = "〇"
+ONE_ALT = "幺"
+TWO_ALTS = ["两", "兩"]
+
+POSITIVE = ["正", "正"]
+NEGATIVE = ["负", "負"]
+POINT = ["点", "點"]
+# PLUS = [u'加', u'加']
+# SIL = [u'杠', u'槓']
+
+# 中文数字系统类型
+NUMBERING_TYPES = ["low", "mid", "high"]

fish_speech/text/chn_text_norm/basic_util.py

@@ -0,0 +1,342 @@
+# -*- coding: utf-8 -*-
+"""基本方法
+创建中文数字系统 方法
+中文字符串 <=> 数字串 方法
+数字串 <=> 中文字符串 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-02"
+
+from fish_speech.text.chn_text_norm.basic_class import *
+from fish_speech.text.chn_text_norm.basic_constant import *
+
+
+def create_system(numbering_type=NUMBERING_TYPES[1]):
+    """
+    根据数字系统类型返回创建相应的数字系统，默认为 mid
+    NUMBERING_TYPES = ['low', 'mid', 'high']: 中文数字系统类型
+        low:  '兆' = '亿' * '十' = $10^{9}$,  '京' = '兆' * '十', etc.
+        mid:  '兆' = '亿' * '万' = $10^{12}$, '京' = '兆' * '万', etc.
+        high: '兆' = '亿' * '亿' = $10^{16}$, '京' = '兆' * '兆', etc.
+    返回对应的数字系统
+    """
+
+    # chinese number units of '亿' and larger
+    all_larger_units = zip(
+        LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED,
+        LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL,
+    )
+    larger_units = [
+        CNU.create(i, v, numbering_type, False) for i, v in enumerate(all_larger_units)
+    ]
+    # chinese number units of '十, 百, 千, 万'
+    all_smaller_units = zip(
+        SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED,
+        SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL,
+    )
+    smaller_units = [
+        CNU.create(i, v, small_unit=True) for i, v in enumerate(all_smaller_units)
+    ]
+    # digis
+    chinese_digis = zip(
+        CHINESE_DIGIS,
+        CHINESE_DIGIS,
+        BIG_CHINESE_DIGIS_SIMPLIFIED,
+        BIG_CHINESE_DIGIS_TRADITIONAL,
+    )
+    digits = [CND.create(i, v) for i, v in enumerate(chinese_digis)]
+    digits[0].alt_s, digits[0].alt_t = ZERO_ALT, ZERO_ALT
+    digits[1].alt_s, digits[1].alt_t = ONE_ALT, ONE_ALT
+    digits[2].alt_s, digits[2].alt_t = TWO_ALTS[0], TWO_ALTS[1]
+
+    # symbols
+    positive_cn = CM(POSITIVE[0], POSITIVE[1], "+", lambda x: x)
+    negative_cn = CM(NEGATIVE[0], NEGATIVE[1], "-", lambda x: -x)
+    point_cn = CM(POINT[0], POINT[1], ".", lambda x, y: float(str(x) + "." + str(y)))
+    # sil_cn = CM(SIL[0], SIL[1], '-', lambda x, y: float(str(x) + '-' + str(y)))
+    system = NumberSystem()
+    system.units = smaller_units + larger_units
+    system.digits = digits
+    system.math = MathSymbol(positive_cn, negative_cn, point_cn)
+    # system.symbols = OtherSymbol(sil_cn)
+    return system
+
+
+def chn2num(chinese_string, numbering_type=NUMBERING_TYPES[1]):
+
+    def get_symbol(char, system):
+        for u in system.units:
+            if char in [u.traditional, u.simplified, u.big_s, u.big_t]:
+                return u
+        for d in system.digits:
+            if char in [
+                d.traditional,
+                d.simplified,
+                d.big_s,
+                d.big_t,
+                d.alt_s,
+                d.alt_t,
+            ]:
+                return d
+        for m in system.math:
+            if char in [m.traditional, m.simplified]:
+                return m
+
+    def string2symbols(chinese_string, system):
+        int_string, dec_string = chinese_string, ""
+        for p in [system.math.point.simplified, system.math.point.traditional]:
+            if p in chinese_string:
+                int_string, dec_string = chinese_string.split(p)
+                break
+        return [get_symbol(c, system) for c in int_string], [
+            get_symbol(c, system) for c in dec_string
+        ]
+
+    def correct_symbols(integer_symbols, system):
+        """
+        一百八 to 一百八十
+        一亿一千三百万 to 一亿 一千万 三百万
+        """
+
+        if integer_symbols and isinstance(integer_symbols[0], CNU):
+            if integer_symbols[0].power == 1:
+                integer_symbols = [system.digits[1]] + integer_symbols
+
+        if len(integer_symbols) > 1:
+            if isinstance(integer_symbols[-1], CND) and isinstance(
+                integer_symbols[-2], CNU
+            ):
+                integer_symbols.append(
+                    CNU(integer_symbols[-2].power - 1, None, None, None, None)
+                )
+
+        result = []
+        unit_count = 0
+        for s in integer_symbols:
+            if isinstance(s, CND):
+                result.append(s)
+                unit_count = 0
+            elif isinstance(s, CNU):
+                current_unit = CNU(s.power, None, None, None, None)
+                unit_count += 1
+
+            if unit_count == 1:
+                result.append(current_unit)
+            elif unit_count > 1:
+                for i in range(len(result)):
+                    if (
+                        isinstance(result[-i - 1], CNU)
+                        and result[-i - 1].power < current_unit.power
+                    ):
+                        result[-i - 1] = CNU(
+                            result[-i - 1].power + current_unit.power,
+                            None,
+                            None,
+                            None,
+                            None,
+                        )
+        return result
+
+    def compute_value(integer_symbols):
+        """
+        Compute the value.
+        When current unit is larger than previous unit, current unit * all previous units will be used as all previous units.
+        e.g. '两千万' = 2000 * 10000 not 2000 + 10000
+        """
+        value = [0]
+        last_power = 0
+        for s in integer_symbols:
+            if isinstance(s, CND):
+                value[-1] = s.value
+            elif isinstance(s, CNU):
+                value[-1] *= pow(10, s.power)
+                if s.power > last_power:
+                    value[:-1] = list(map(lambda v: v * pow(10, s.power), value[:-1]))
+                    last_power = s.power
+                value.append(0)
+        return sum(value)
+
+    system = create_system(numbering_type)
+    int_part, dec_part = string2symbols(chinese_string, system)
+    int_part = correct_symbols(int_part, system)
+    int_str = str(compute_value(int_part))
+    dec_str = "".join([str(d.value) for d in dec_part])
+    if dec_part:
+        return "{0}.{1}".format(int_str, dec_str)
+    else:
+        return int_str
+
+
+def num2chn(
+    number_string,
+    numbering_type=NUMBERING_TYPES[1],
+    big=False,
+    traditional=False,
+    alt_zero=False,
+    alt_one=False,
+    alt_two=True,
+    use_zeros=True,
+    use_units=True,
+):
+
+    def get_value(value_string, use_zeros=True):
+
+        striped_string = value_string.lstrip("0")
+
+        # record nothing if all zeros
+        if not striped_string:
+            return []
+
+        # record one digits
+        elif len(striped_string) == 1:
+            if use_zeros and len(value_string) != len(striped_string):
+                return [system.digits[0], system.digits[int(striped_string)]]
+            else:
+                return [system.digits[int(striped_string)]]
+
+        # recursively record multiple digits
+        else:
+            result_unit = next(
+                u for u in reversed(system.units) if u.power < len(striped_string)
+            )
+            result_string = value_string[: -result_unit.power]
+            return (
+                get_value(result_string)
+                + [result_unit]
+                + get_value(striped_string[-result_unit.power :])
+            )
+
+    system = create_system(numbering_type)
+
+    int_dec = number_string.split(".")
+    if len(int_dec) == 1:
+        int_string = int_dec[0]
+        dec_string = ""
+    elif len(int_dec) == 2:
+        int_string = int_dec[0]
+        dec_string = int_dec[1]
+    else:
+        raise ValueError(
+            "invalid input num string with more than one dot: {}".format(number_string)
+        )
+
+    if use_units and len(int_string) > 1:
+        result_symbols = get_value(int_string)
+    else:
+        result_symbols = [system.digits[int(c)] for c in int_string]
+    dec_symbols = [system.digits[int(c)] for c in dec_string]
+    if dec_string:
+        result_symbols += [system.math.point] + dec_symbols
+
+    if alt_two:
+        liang = CND(
+            2,
+            system.digits[2].alt_s,
+            system.digits[2].alt_t,
+            system.digits[2].big_s,
+            system.digits[2].big_t,
+        )
+        for i, v in enumerate(result_symbols):
+            if isinstance(v, CND) and v.value == 2:
+                next_symbol = (
+                    result_symbols[i + 1] if i < len(result_symbols) - 1 else None
+                )
+                previous_symbol = result_symbols[i - 1] if i > 0 else None
+                if isinstance(next_symbol, CNU) and isinstance(
+                    previous_symbol, (CNU, type(None))
+                ):
+                    if next_symbol.power != 1 and (
+                        (previous_symbol is None) or (previous_symbol.power != 1)
+                    ):
+                        result_symbols[i] = liang
+
+    # if big is True, '两' will not be used and `alt_two` has no impact on output
+    if big:
+        attr_name = "big_"
+        if traditional:
+            attr_name += "t"
+        else:
+            attr_name += "s"
+    else:
+        if traditional:
+            attr_name = "traditional"
+        else:
+            attr_name = "simplified"
+
+    result = "".join([getattr(s, attr_name) for s in result_symbols])
+
+    # if not use_zeros:
+    #     result = result.strip(getattr(system.digits[0], attr_name))
+
+    if alt_zero:
+        result = result.replace(
+            getattr(system.digits[0], attr_name), system.digits[0].alt_s
+        )
+
+    if alt_one:
+        result = result.replace(
+            getattr(system.digits[1], attr_name), system.digits[1].alt_s
+        )
+
+    for i, p in enumerate(POINT):
+        if result.startswith(p):
+            return CHINESE_DIGIS[0] + result
+
+    # ^10, 11, .., 19
+    if (
+        len(result) >= 2
+        and result[1]
+        in [
+            SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED[0],
+            SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL[0],
+        ]
+        and result[0]
+        in [
+            CHINESE_DIGIS[1],
+            BIG_CHINESE_DIGIS_SIMPLIFIED[1],
+            BIG_CHINESE_DIGIS_TRADITIONAL[1],
+        ]
+    ):
+        result = result[1:]
+
+    return result
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    all_chinese_number_string = (
+        CHINESE_DIGIS
+        + BIG_CHINESE_DIGIS_SIMPLIFIED
+        + BIG_CHINESE_DIGIS_TRADITIONAL
+        + LARGER_CHINESE_NUMERING_UNITS_SIMPLIFIED
+        + LARGER_CHINESE_NUMERING_UNITS_TRADITIONAL
+        + SMALLER_CHINESE_NUMERING_UNITS_SIMPLIFIED
+        + SMALLER_CHINESE_NUMERING_UNITS_TRADITIONAL
+        + ZERO_ALT
+        + ONE_ALT
+        + "".join(TWO_ALTS + POSITIVE + NEGATIVE + POINT)
+    )
+
+    print("num:", chn2num("一万零四百零三点八零五"))
+    print("num:", chn2num("一亿六点三"))
+    print("num:", chn2num("一亿零六点三"))
+    print("num:", chn2num("两千零一亿六点三"))
+    # print('num:', chn2num('一零零八六'))
+    print("txt:", num2chn("10260.03", alt_zero=True))
+    print("txt:", num2chn("20037.090", numbering_type="low", traditional=True))
+    print("txt:", num2chn("100860001.77", numbering_type="high", big=True))
+    print(
+        "txt:",
+        num2chn(
+            "059523810880",
+            alt_one=True,
+            alt_two=False,
+            use_lzeros=True,
+            use_rzeros=True,
+            use_units=False,
+        ),
+    )
+
+    print(all_chinese_number_string)

fish_speech/text/chn_text_norm/cardinal.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+"""CARDINAL类 (包含小数DECIMAL类)
+纯数 <=> 中文字符串 方法
+中文字符串 <=> 纯数 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-03"
+
+from fish_speech.text.chn_text_norm.basic_util import *
+
+
+class Cardinal:
+    """
+    CARDINAL类
+    """
+
+    def __init__(self, cardinal=None, chntext=None):
+        self.cardinal = cardinal
+        self.chntext = chntext
+
+    def chntext2cardinal(self):
+        return chn2num(self.chntext)
+
+    def cardinal2chntext(self):
+        return num2chn(self.cardinal)
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    print(Cardinal(cardinal="21357.230").cardinal2chntext())

fish_speech/text/chn_text_norm/date.py

@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+"""DATE类
+日期 <=> 中文字符串 方法
+中文字符串 <=> 日期 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-07"
+
+from fish_speech.text.chn_text_norm.cardinal import Cardinal
+from fish_speech.text.chn_text_norm.digit import Digit
+
+
+class Date:
+    """
+    DATE类
+    """
+
+    def __init__(self, date=None, chntext=None):
+        self.date = date
+        self.chntext = chntext
+
+    # def chntext2date(self):
+    #     chntext = self.chntext
+    #     try:
+    #         year, other = chntext.strip().split('年', maxsplit=1)
+    #         year = Digit(chntext=year).digit2chntext() + '年'
+    #     except ValueError:
+    #         other = chntext
+    #         year = ''
+    #     if other:
+    #         try:
+    #             month, day = other.strip().split('月', maxsplit=1)
+    #             month = Cardinal(chntext=month).chntext2cardinal() + '月'
+    #         except ValueError:
+    #             day = chntext
+    #             month = ''
+    #         if day:
+    #             day = Cardinal(chntext=day[:-1]).chntext2cardinal() + day[-1]
+    #     else:
+    #         month = ''
+    #         day = ''
+    #     date = year + month + day
+    #     self.date = date
+    #     return self.date
+
+    def date2chntext(self):
+        date = self.date
+        try:
+            year, other = date.strip().split("年", maxsplit=1)
+            year = Digit(digit=year).digit2chntext() + "年"
+        except ValueError:
+            other = date
+            year = ""
+        if other:
+            try:
+                month, day = other.strip().split("月", maxsplit=1)
+                month = Cardinal(cardinal=month).cardinal2chntext() + "月"
+            except ValueError:
+                day = date
+                month = ""
+            if day:
+                day = Cardinal(cardinal=day[:-1]).cardinal2chntext() + day[-1]
+        else:
+            month = ""
+            day = ""
+        chntext = year + month + day
+        self.chntext = chntext
+        return self.chntext
+
+
+if __name__ == "__main__":
+
+    # 测试
+    print(Date(date="09年3月16日").date2chntext())

fish_speech/text/chn_text_norm/digit.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+"""DIGIT类
+数字串 <=> 中文字符串 方法
+中文字符串 <=> 数字串 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-03"
+
+from fish_speech.text.chn_text_norm.basic_util import *
+
+
+class Digit:
+    """
+    DIGIT类
+    """
+
+    def __init__(self, digit=None, chntext=None):
+        self.digit = digit
+        self.chntext = chntext
+
+    # def chntext2digit(self):
+    #     return chn2num(self.chntext)
+
+    def digit2chntext(self):
+        return num2chn(self.digit, alt_two=False, use_units=False)
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    print(Digit(digit="2016").digit2chntext())

fish_speech/text/chn_text_norm/fraction.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""FRACTION类
+分数 <=> 中文字符串 方法
+中文字符串 <=> 分数 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-03"
+
+from fish_speech.text.chn_text_norm.basic_util import *
+
+
+class Fraction:
+    """
+    FRACTION类
+    """
+
+    def __init__(self, fraction=None, chntext=None):
+        self.fraction = fraction
+        self.chntext = chntext
+
+    def chntext2fraction(self):
+        denominator, numerator = self.chntext.split("分之")
+        return chn2num(numerator) + "/" + chn2num(denominator)
+
+    def fraction2chntext(self):
+        numerator, denominator = self.fraction.split("/")
+        return num2chn(denominator) + "分之" + num2chn(numerator)
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    print(Fraction(fraction="2135/7230").fraction2chntext())
+    print(Fraction(chntext="五百八十一分之三百六十九").chntext2fraction())

fish_speech/text/chn_text_norm/money.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""MONEY类
+金钱 <=> 中文字符串 方法
+中文字符串 <=> 金钱 方法
+"""
+import re
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-08"
+
+from fish_speech.text.chn_text_norm.cardinal import Cardinal
+
+
+class Money:
+    """
+    MONEY类
+    """
+
+    def __init__(self, money=None, chntext=None):
+        self.money = money
+        self.chntext = chntext
+
+    # def chntext2money(self):
+    #     return self.money
+
+    def money2chntext(self):
+        money = self.money
+        pattern = re.compile(r"(\d+(\.\d+)?)")
+        matchers = pattern.findall(money)
+        if matchers:
+            for matcher in matchers:
+                money = money.replace(
+                    matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext()
+                )
+        self.chntext = money
+        return self.chntext
+
+
+if __name__ == "__main__":
+
+    # 测试
+    print(Money(money="21.5万元").money2chntext())
+    print(Money(money="230块5毛").money2chntext())

fish_speech/text/chn_text_norm/percentage.py

@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+"""PERCENTAGE类
+百分数 <=> 中文字符串 方法
+中文字符串 <=> 百分数 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-06"
+
+from fish_speech.text.chn_text_norm.basic_util import *
+
+
+class Percentage:
+    """
+    PERCENTAGE类
+    """
+
+    def __init__(self, percentage=None, chntext=None):
+        self.percentage = percentage
+        self.chntext = chntext
+
+    def chntext2percentage(self):
+        return chn2num(self.chntext.strip().strip("百分之")) + "%"
+
+    def percentage2chntext(self):
+        return "百分之" + num2chn(self.percentage.strip().strip("%"))
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    print(Percentage(chntext="百分之五十六点零三").chntext2percentage())
+    print(Percentage(percentage="65.3%").percentage2chntext())

fish_speech/text/chn_text_norm/telephone.py

@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+"""TELEPHONE类
+电话号码 <=> 中文字符串 方法
+中文字符串 <=> 电话号码 方法
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-03"
+
+from fish_speech.text.chn_text_norm.basic_util import *
+
+
+class TelePhone:
+    """
+    TELEPHONE类
+    """
+
+    def __init__(self, telephone=None, raw_chntext=None, chntext=None):
+        self.telephone = telephone
+        self.raw_chntext = raw_chntext
+        self.chntext = chntext
+
+    # def chntext2telephone(self):
+    #     sil_parts = self.raw_chntext.split('<SIL>')
+    #     self.telephone = '-'.join([
+    #         str(chn2num(p)) for p in sil_parts
+    #     ])
+    #     return self.telephone
+
+    def telephone2chntext(self, fixed=False):
+
+        if fixed:
+            sil_parts = self.telephone.split("-")
+            self.raw_chntext = "<SIL>".join(
+                [num2chn(part, alt_two=False, use_units=False) for part in sil_parts]
+            )
+            self.chntext = self.raw_chntext.replace("<SIL>", "")
+        else:
+            sp_parts = self.telephone.strip("+").split()
+            self.raw_chntext = "<SP>".join(
+                [num2chn(part, alt_two=False, use_units=False) for part in sp_parts]
+            )
+            self.chntext = self.raw_chntext.replace("<SP>", "")
+        return self.chntext
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    print(TelePhone(telephone="0595-23980880").telephone2chntext())
+    # print(TelePhone(raw_chntext='零五九五杠二三八六五零九八').chntext2telephone())

fish_speech/text/chn_text_norm/text.py

@@ -0,0 +1,177 @@
+# -*- coding: utf-8 -*-
+"""
+TEXT类
+"""
+
+__author__ = "Zhiyang Zhou <zyzhou@stu.xmu.edu.cn>"
+__data__ = "2019-05-03"
+
+import re
+
+from fish_speech.text.chn_text_norm.cardinal import Cardinal
+from fish_speech.text.chn_text_norm.date import Date
+from fish_speech.text.chn_text_norm.digit import Digit
+from fish_speech.text.chn_text_norm.fraction import Fraction
+from fish_speech.text.chn_text_norm.money import Money
+from fish_speech.text.chn_text_norm.percentage import Percentage
+from fish_speech.text.chn_text_norm.telephone import TelePhone
+
+CURRENCY_NAMES = (
+    "(人民币|美元|日元|英镑|欧元|马克|法郎|加拿大元|澳元|港币|先令|芬兰马克|爱尔兰镑|"
+    "里拉|荷兰盾|埃斯库多|比塞塔|印尼盾|林吉特|新西兰元|比索|卢布|新加坡元|韩元|泰铢)"
+)
+CURRENCY_UNITS = "((亿|千万|百万|万|千|百)|(亿|千万|百万|万|千|百|)元|(亿|千万|百万|万|千|百|)块|角|毛|分)"
+COM_QUANTIFIERS = (
+    "(匹|张|座|回|场|尾|条|个|首|阙|阵|网|炮|顶|丘|棵|只|支|袭|辆|挑|担|颗|壳|窠|曲|墙|群|腔|"
+    "砣|座|客|贯|扎|捆|刀|令|打|手|罗|坡|山|岭|江|溪|钟|队|单|双|对|出|口|头|脚|板|跳|枝|件|贴|"
+    "针|线|管|名|位|身|堂|课|本|页|家|户|层|丝|毫|厘|分|钱|两|斤|担|铢|石|钧|锱|忽|(千|毫|微)克|"
+    "毫|厘|分|寸|尺|丈|里|寻|常|铺|程|(千|分|厘|毫|微)米|撮|勺|合|升|斗|石|盘|碗|碟|叠|桶|笼|盆|"
+    "盒|杯|钟|斛|锅|簋|篮|盘|桶|罐|瓶|壶|卮|盏|箩|箱|煲|啖|袋|钵|年|月|日|季|刻|时|周|天|秒|分|旬|"
+    "纪|岁|世|更|夜|春|夏|秋|冬|代|伏|辈|丸|泡|粒|颗|幢|堆|条|根|支|道|面|片|张|颗|块|人|抽)"
+)
+
+
+class Text:
+    """
+    Text类
+    """
+
+    def __init__(self, raw_text, norm_text=None):
+        self.raw_text = "^" + raw_text + "$"
+        self.norm_text = norm_text
+
+    def _particular(self):
+        text = self.norm_text
+        pattern = re.compile(r"(([a-zA-Z]+)二([a-zA-Z]+))")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('particular')
+            for matcher in matchers:
+                text = text.replace(matcher[0], matcher[1] + "2" + matcher[2], 1)
+        self.norm_text = text
+        return self.norm_text
+
+    def normalize(self):
+        text = self.raw_text
+
+        # 规范化日期
+        pattern = re.compile(
+            r"\D+((([089]\d|(19|20)\d{2})年)?(\d{1,2}月(\d{1,2}[日号])?)?)"
+        )
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('date')
+            for matcher in matchers:
+                text = text.replace(matcher[0], Date(date=matcher[0]).date2chntext(), 1)
+
+        # 规范化金钱
+        pattern = re.compile(
+            r"\D+((\d+(\.\d+)?)[多余几]?"
+            + CURRENCY_UNITS
+            + "(\d"
+            + CURRENCY_UNITS
+            + "?)?)"
+        )
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('money')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], Money(money=matcher[0]).money2chntext(), 1
+                )
+
+        # 规范化固话/手机号码
+        # 手机
+        # http://www.jihaoba.com/news/show/13680
+        # 移动：139、138、137、136、135、134、159、158、157、150、151、152、188、187、182、183、184、178、198
+        # 联通：130、131、132、156、155、186、185、176
+        # 电信：133、153、189、180、181、177
+        pattern = re.compile(r"\D((\+?86 ?)?1([38]\d|5[0-35-9]|7[678]|9[89])\d{8})\D")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('telephone')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], TelePhone(telephone=matcher[0]).telephone2chntext(), 1
+                )
+        # 固话
+        pattern = re.compile(r"\D((0(10|2[1-3]|[3-9]\d{2})-?)?[1-9]\d{6,7})\D")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('fixed telephone')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0],
+                    TelePhone(telephone=matcher[0]).telephone2chntext(fixed=True),
+                    1,
+                )
+
+        # 规范化分数
+        pattern = re.compile(r"(\d+/\d+)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('fraction')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher, Fraction(fraction=matcher).fraction2chntext(), 1
+                )
+
+        # 规范化百分数
+        text = text.replace("％", "%")
+        pattern = re.compile(r"(\d+(\.\d+)?%)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('percentage')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0],
+                    Percentage(percentage=matcher[0]).percentage2chntext(),
+                    1,
+                )
+
+        # 规范化纯数+量词
+        pattern = re.compile(r"(\d+(\.\d+)?)[多余几]?" + COM_QUANTIFIERS)
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('cardinal+quantifier')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1
+                )
+
+        # 规范化数字编号
+        pattern = re.compile(r"(\d{4,32})")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('digit')
+            for matcher in matchers:
+                text = text.replace(matcher, Digit(digit=matcher).digit2chntext(), 1)
+
+        # 规范化纯数
+        pattern = re.compile(r"(\d+(\.\d+)?)")
+        matchers = pattern.findall(text)
+        if matchers:
+            # print('cardinal')
+            for matcher in matchers:
+                text = text.replace(
+                    matcher[0], Cardinal(cardinal=matcher[0]).cardinal2chntext(), 1
+                )
+
+        self.norm_text = text
+        self._particular()
+
+        return self.norm_text.lstrip("^").rstrip("$")
+
+
+if __name__ == "__main__":
+
+    # 测试程序
+    print(Text(raw_text="固话：0595-23865596或23880880。").normalize())
+    print(Text(raw_text="手机：+86 19859213959或15659451527。").normalize())
+    print(Text(raw_text="分数：32477/76391。").normalize())
+    print(Text(raw_text="百分数：80.03%。").normalize())
+    print(Text(raw_text="编号：31520181154418。").normalize())
+    print(Text(raw_text="纯数：2983.07克或12345.60米。").normalize())
+    print(Text(raw_text="日期：1999年2月20日或09年3月15号。").normalize())
+    print(Text(raw_text="金钱：12块5，34.5元，20.1万").normalize())
+    print(Text(raw_text="特殊：O2O或B2C。").normalize())

fish_speech/text/clean.py

@@ -0,0 +1,62 @@
+import re
+
+SYMBOLS_MAPPING = {
+    "\n": "",
+    "…": ".",
+    "“": "'",
+    "”": "'",
+    "‘": "'",
+    "’": "'",
+    "【": "",
+    "】": "",
+    "[": "",
+    "]": "",
+    "（": "",
+    "）": "",
+    "(": "",
+    ")": "",
+    "・": "",
+    "·": "",
+    "「": "'",
+    "」": "'",
+    "《": "'",
+    "》": "'",
+    "—": "",
+    "～": "",
+    "~": "",
+    "：": ",",
+    "；": ",",
+    ";": ",",
+    ":": ",",
+}
+
+REPLACE_SYMBOL_REGEX = re.compile(
+    "|".join(re.escape(p) for p in SYMBOLS_MAPPING.keys())
+)
+
+
+EMOJI_REGEX = re.compile(
+    "["
+    "\U0001F600-\U0001F64F"  # emoticons
+    "\U0001F300-\U0001F5FF"  # symbols & pictographs
+    "\U0001F680-\U0001F6FF"  # transport & map symbols
+    "\U0001F1E0-\U0001F1FF"  # flags (iOS)
+    "]+",
+    flags=re.UNICODE,
+)
+
+
+def clean_text(text):
+    # Clean the text
+    text = text.strip()
+
+    # Replace all chinese symbols with their english counterparts
+    text = REPLACE_SYMBOL_REGEX.sub(lambda x: SYMBOLS_MAPPING[x.group()], text)
+
+    # Remove emojis
+    text = EMOJI_REGEX.sub(r"", text)
+
+    # Remove continuous periods (...) and commas (,,,)
+    text = re.sub(r"[.,]{2,}", lambda m: m.group()[0], text)
+
+    return text