()
+ const escapeInlineScript = (code: string) => code.replace(/<\/script/gi, '<\\/script')
+ const escapeInlineStyle = (code: string) => code.replace(/<\/style/gi, '<\\/style')
+ const normalizeFileName = (assetPath: string) =>
+ assetPath.replace(/^\//, '').replace(/^\.\//, '')
+ const readBuiltAsset = (assetPath: string) => {
+ const fileName = normalizeFileName(assetPath)
+ const absolutePath = path.join(resolvedOutDir, fileName)
+ if (!fs.existsSync(absolutePath)) {
+ return null
+ }
+
+ filesToDelete.add(absolutePath)
+ return fs.readFileSync(absolutePath, 'utf8')
+ }
+
+ let html = fs.readFileSync(indexHtmlPath, 'utf8')
+
+ html = html.replace(
+ /
+
Fish Speech
+
+[English](../README.md) | [简体中文](README.zh.md) | [Portuguese](README.pt-BR.md) | [日本語](README.ja.md) | [한국어](README.ko.md) | **العربية** | [Español](docs/README.es.md)
+
+
+
+
+
+
+
+
+
Fish Speech
+
+[English](../README.md) | [简体中文](docs/README.zh.md) | [Portuguese](docs/README.pt-BR.md) | [日本語](docs/README.ja.md) | [한국어](docs/README.ko.md) | [العربية](docs/README.ar.md) | **Español**
+
+
+
+
+
+
+
Fish Speech
+
+[English](../README.md) | [简体中文](README.zh.md) | [Portuguese](README.pt-BR.md) | **日本語** | [한국어](README.ko.md) | [العربية](README.ar.md) | [Español](docs/README.es.md)
+
+
+
+
+
+
+
+
+
Fish Speech
+
+[English](../README.md) | [简体中文](README.zh.md) | [Portuguese](README.pt-BR.md) | [日本語](README.ja.md) | **한국어** | [العربية](README.ar.md) | [Español](docs/README.es.md)
+
+
+
+
+
+
+
+
+
Fish Speech
+
+[English](../README.md) | [简体中文](README.zh.md) | **Portuguese** | [日本語](README.ja.md) | [한국어](README.ko.md) | [العربية](README.ar.md) | [Español](docs/README.es.md)
+
+
+
+
+
+
+
+
+
Fish Speech
+
+[English](../README.md) | **简体中文** | [Portuguese](README.pt-BR.md) | [日本語](README.ja.md) | [한국어](README.ko.md) | [العربية](README.ar.md) | [Español](docs/README.es.md)
+
+
+
+
+
+
+
+
[INST] [SPK: speaker] text [/INST] ... [INST] text [/INST]
+
+ For non-interactive mode, we use the following format (one long sequence):
+ [INST] text [/INST] ...
+ """
+
+ def __init__(
+ self,
+ proto_files: list[str],
+ seed: int = 42,
+ interactive_prob: float = 0.5,
+ max_length: int = 1024,
+ tokenizer: FishTokenizer = 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.groups = None
+
+ def __iter__(self):
+ while True:
+ yield self.augment()
+
+ 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 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 pack_sentences(
+ self,
+ sentences: list[str],
+ semantics: list,
+ # speaker: Optional[str] = None, # speaker is now handled by tokens
+ skip_text: bool = False,
+ ):
+
+ seq = ContentSequence()
+
+ seq.append(TextPart(text="Speak out the provided text."))
+
+ # User's turn
+ cated_sentences = " ".join(sentences)
+ if skip_text:
+ cated_sentences = "<|skip_text|>"
+
+ seq.append(
+ TextPart(text=f"<|speaker:user|> {cated_sentences}"),
+ add_end=True,
+ )
+
+ # Assistant's turn
+ vq_codes = [x.values for x in semantics[0]]
+ vq_codes_tensor = torch.tensor(vq_codes).to(torch.int32)
+
+ # 将 cal_loss=True 直接关联到 VQPart 上，这比之前更精确
+ vq_part = VQPart(codes=vq_codes_tensor, cal_loss=True)
+
+ # 将多个 parts 一起添加，最后也加上 <|im_end|>
+ seq.append(
+ [TextPart(text="<|speaker:assistant|> <|voice|>"), vq_part],
+ add_end=True,
+ )
+
+ encoded = seq.encode(
+ tokenizer=self.tokenizer,
+ )
+
+ num_codebooks = (
+ len(semantics[0]) if self.num_codebooks is None else self.num_codebooks
+ )
+
+ tokens_raw = encoded.tokens
+ tokens = torch.zeros((num_codebooks + 1, len(tokens_raw)), dtype=torch.int)
+ tokens[0] = tokens_raw
+
+ vq_parts = encoded.vq_parts
+ vq_parts = [part.to(tokens.device) for part in vq_parts]
+ vq_parts = torch.cat(vq_parts, dim=1)
+ tokens[1:, encoded.vq_mask_tokens] = vq_parts
+
+ labels_raw = encoded.labels
+ labels = torch.full((num_codebooks + 1, len(labels_raw)), -100, dtype=torch.int)
+ labels[0, :] = labels_raw
+ labels[1:, encoded.vq_mask_labels] = vq_parts
+ labels[1:, -1:] = CODEBOOK_PAD_TOKEN_ID
+
+ tokens = tokens.long()
+ labels = labels.long()
+
+ # Verify the padding is correct, and the last token is eos
+ assert (tokens[1:, ~(encoded.vq_mask_tokens)] == CODEBOOK_PAD_TOKEN_ID).all()
+ assert (labels[1:, -1:] == CODEBOOK_PAD_TOKEN_ID).all()
+
+ return tokens, labels
+
+ def augment(self):
+ response = self.sample_data()
+ if len(response.samples) == 0:
+ # Invalid group
+ return None
+
+ samples = list(response.samples)
+ all_tokens, all_labels = [], []
+
+ while len(samples) > 0:
+ sentence = samples.pop(0)
+ text = clean_text(random.choice(sentence.texts))
+
+ 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)
+
+ 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
+
+
+class AutoTextSemanticInstructionDataset(Dataset):
+ """
+ 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):
+ [INST] [SPK: speaker] text [/INST] ... [INST] text [/INST]
+
+ For non-interactive mode, we use the following format (one long sequence):
+ [INST] text [/INST] ...
+ """
+
+ def __init__(
+ self,
+ proto_files: list[str],
+ seed: int = 42,
+ interactive_prob: float = 0.5,
+ max_length: int = 1024,
+ tokenizer: FishTokenizer = 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.data = []
+ self._init_data()
+
+ def _init_data(self):
+ 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)
+
+ 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):
+ groups.append(text_data)
+ count += 1
+
+ log.info(f"Read total {count} groups of data")
+
+ for group in groups:
+ if len(group.sentences) == 0:
+ continue
+
+ samples = list(group.sentences)
+ for sentence in samples:
+ text = clean_text(random.choice(sentence.texts))
+
+ tokens, labels = self.pack_sentences(
+ sentences=[text],
+ semantics=[sentence.semantics],
+ skip_text=random.random() < self.skip_text_prob,
+ )
+
+ self.data.append({"tokens": tokens, "labels": labels})
+
+ random.Random(self.seed).shuffle(self.data)
+
+ def __len__(self):
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ return self.data[idx]
+
+ def pack_sentences(
+ self,
+ sentences: list[str],
+ semantics: list,
+ skip_text: bool = False,
+ ):
+ messages = [
+ Message(
+ role="system",
+ parts=[TextPart(text="Speak out the provided text.")],
+ )
+ ]
+
+ cated_sentences = " ".join(sentences)
+ if skip_text:
+ cated_sentences = "<|skip_text|>"
+
+ messages.append(
+ Message(
+ role="user",
+ parts=[TextPart(text=cated_sentences)],
+ )
+ )
+
+ vq_codes = [x.values for x in semantics[0]]
+ vq_codes_tensor = torch.tensor(vq_codes).to(torch.int32)
+ vqpart = VQPart(codes=vq_codes_tensor)
+ messages.append(
+ Message(
+ role="assistant",
+ parts=[TextPart(text="<|voice|>"), vqpart],
+ cal_loss=True,
+ )
+ )
+
+ num_codebooks = (
+ len(semantics[0]) if self.num_codebooks is None else self.num_codebooks
+ )
+
+ conversation = Conversation(messages=messages)
+ encoded = conversation.encode(
+ tokenizer=self.tokenizer,
+ )
+
+ tokens_raw = encoded.tokens
+ tokens = torch.zeros((num_codebooks + 1, len(tokens_raw)), dtype=torch.int)
+ tokens[0] = tokens_raw
+
+ vq_parts = encoded.vq_parts
+ vq_parts = [part.to(tokens.device) for part in vq_parts]
+ vq_parts = torch.cat(vq_parts, dim=1)
+ tokens[1:, encoded.vq_mask_tokens] = vq_parts
+
+ labels_raw = encoded.labels
+ labels = torch.full((num_codebooks + 1, len(labels_raw)), -100, dtype=torch.int)
+ labels[0, :] = labels_raw
+ labels[1:, encoded.vq_mask_labels] = vq_parts
+ labels[1:, -1:] = CODEBOOK_PAD_TOKEN_ID
+
+ tokens = tokens.long()
+ labels = labels.long()
+
+ assert (tokens[1:, ~(encoded.vq_mask_tokens)] == CODEBOOK_PAD_TOKEN_ID).all()
+ assert (labels[1:, -1:] == CODEBOOK_PAD_TOKEN_ID).all()
+
+ return tokens, 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])
+
+
+@dataclass
+class TextDataCollator:
+ tokenizer: FishTokenizer
+ 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.get_token_id("<|end_of_text|>"),
+ )
+ _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 SemanticDataModule(LightningDataModule):
+ def __init__(
+ self,
+ train_dataset: Union[
+ AutoTextSemanticInstructionDataset,
+ AutoTextSemanticInstructionIterableDataset,
+ InterleaveDataset,
+ ],
+ val_dataset: Union[
+ AutoTextSemanticInstructionDataset,
+ AutoTextSemanticInstructionIterableDataset,
+ InterleaveDataset,
+ ],
+ batch_size: int = 32,
+ tokenizer: FishTokenizer = 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=FishTokenizer("checkpoints/fish-speech-1.5/tokenizer.tiktoken"),
+ use_speaker=False,
+ interactive_prob=1.0,
+ skip_text_prob=0.5,
+ )
+
+ for i in range(100):
+ # Please uncomment line 235 to visualize the tokenized message
+ print(ds[i])
diff --git a/vendor/fish-speech/fish_speech/datasets/vqgan.py b/vendor/fish-speech/fish_speech/datasets/vqgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7f84cffeb85c2734fef7ad581128624e1f523d2
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/datasets/vqgan.py
@@ -0,0 +1,145 @@
+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["audio_lengths"])
+ break
diff --git a/vendor/fish-speech/fish_speech/i18n/README.md b/vendor/fish-speech/fish_speech/i18n/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..700902b09db20911ef1ad678cbdce5644b84aea2
--- /dev/null
+++ b/vendor/fish-speech/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.
diff --git a/vendor/fish-speech/fish_speech/i18n/__init__.py b/vendor/fish-speech/fish_speech/i18n/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..981dbb3b3ecf28043ec9ff5757f947182821a246
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/i18n/__init__.py
@@ -0,0 +1,3 @@
+from .core import i18n
+
+__all__ = ["i18n"]
diff --git a/vendor/fish-speech/fish_speech/i18n/core.py b/vendor/fish-speech/fish_speech/i18n/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f793ec95669228f7f4e8f9a7a5fe38da85c74bd
--- /dev/null
+++ b/vendor/fish-speech/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()
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/ar_SA.json b/vendor/fish-speech/fish_speech/i18n/locale/ar_SA.json
new file mode 100644
index 0000000000000000000000000000000000000000..352189247209e4f33f6d663360c5fa6d27bd3322
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/i18n/locale/ar_SA.json
@@ -0,0 +1,123 @@
+{
+ "16-mixed is recommended for 10+ series GPU": "يُنصح بـ 16-mixed لبطاقات الرسوميات من سلسلة 10+ وما فوق",
+ "5 to 10 seconds of reference audio, useful for specifying speaker.": "من 5 إلى 10 ثواني من الصوت المرجعي، مفيد لتحديد المتحدث.",
+ "A text-to-speech model based on VQ-GAN and Llama developed by [Fish Audio](https://fish.audio).": "نموذج Text-to-Speech مبني على VQ-GAN و Llama، طوّرته [Fish Audio](https://fish.audio).",
+ "Accumulate Gradient Batches": "تراكم دفعات الـ Gradient",
+ "Add to Processing Area": "أضف إلى منطقة المعالجة",
+ "Added path successfully!": "تمت إضافة المسار بنجاح!",
+ "Advanced Config": "إعدادات متقدمة",
+ "Base LLAMA Model": "نموذج LLAMA الأساسي",
+ "Batch Inference": "Batch Inference",
+ "Batch Size": "حجم الـ Batch",
+ "Changing with the Model Path": "يتغير مع مسار النموذج",
+ "Chinese": "الصينية",
+ "Compile Model": "تجميع النموذج",
+ "Compile the model can significantly reduce the inference time, but will increase cold start time": "تجميع النموذج يقلل وقت الـ Inference بشكل ملحوظ، لكنه يزيد من وقت بدء التشغيل",
+ "Copy": "نسخ",
+ "Data Preprocessing": "معالجة البيانات المسبقة",
+ "Data Preprocessing Path": "مسار معالجة البيانات",
+ "Data Source": "مصدر البيانات",
+ "Decoder Model Config": "إعدادات نموذج الـ Decoder",
+ "Decoder Model Path": "مسار نموذج الـ Decoder",
+ "Disabled": "معطّل",
+ "Enable Reference Audio": "تفعيل الصوت المرجعي",
+ "English": "الإنجليزية",
+ "Error Message": "رسالة الخطأ",
+ "File Preprocessing": "معالجة الملفات المسبقة",
+ "Generate": "توليد",
+ "Generated Audio": "الصوت المولَّد",
+ "If there is no corresponding text for the audio, apply ASR for assistance, support .txt or .lab format": "إذا لم يكن هناك نص مطابق للصوت، استخدم الـ ASR للمساعدة، يدعم صيغة .txt أو .lab",
+ "Infer interface is closed": "تم إغلاق واجهة الـ Inference",
+ "Inference Configuration": "إعدادات الـ Inference",
+ "Inference Server Configuration": "إعدادات خادم الـ Inference",
+ "Inference Server Error": "خطأ في خادم الـ Inference",
+ "Inferring interface is launched at {}": "تم تشغيل واجهة الـ Inference على {}",
+ "Initial Learning Rate": "معدل التعلم الابتدائي",
+ "Input Audio & Source Path for Transcription": "الصوت المدخل ومسار المصدر للنسخ",
+ "Input Text": "النص المدخل",
+ "Invalid path: {}": "المسار غير صالح: {}",
+ "It is recommended to use CUDA, if you have low configuration, use CPU": "يُنصح باستخدام CUDA، أما إذا كان جهازك ضعيفًا فاستخدم CPU",
+ "Iterative Prompt Length, 0 means off": "طول الـ Prompt التكراري، 0 يعني إيقاف",
+ "Japanese": "اليابانية",
+ "LLAMA Configuration": "إعدادات LLAMA",
+ "LLAMA Model Config": "إعدادات نموذج LLAMA",
+ "LLAMA Model Path": "مسار نموذج LLAMA",
+ "Labeling Device": "جهاز التسمية",
+ "LoRA Model to be merged": "نموذج LoRA المراد دمجه",
+ "Maximum Audio Duration": "الحد الأقصى لمدة الصوت",
+ "Maximum Length per Sample": "الحد الأقصى لطول العينة",
+ "Maximum Training Steps": "الحد الأقصى لخطوات التدريب",
+ "Maximum tokens per batch, 0 means no limit": "الحد الأقصى للـ Tokens لكل Batch، 0 يعني بدون حد",
+ "Merge": "دمج",
+ "Merge LoRA": "دمج LoRA",
+ "Merge successfully": "تم الدمج بنجاح",
+ "Minimum Audio Duration": "الحد الأدنى لمدة الصوت",
+ "Model Output Path": "مسار إخراج النموذج",
+ "Model Size": "حجم النموذج",
+ "Move": "نقل",
+ "Move files successfully": "تم نقل الملفات بنجاح",
+ "No audio generated, please check the input text.": "لم يتم توليد أي صوت، تحقق من النص المدخل.",
+ "No selected options": "لم يتم تحديد أي خيار",
+ "Number of Workers": "عدد الـ Workers",
+ "Open Inference Server": "تشغيل خادم الـ Inference",
+ "Open Labeler WebUI": "فتح واجهة التسمية",
+ "Open Tensorboard": "فتح Tensorboard",
+ "Opened labeler in browser": "تم فتح التسمية في المتصفح",
+ "Optional Label Language": "لغة التسمية (اختياري)",
+ "Optional online ver": "الإصدار الأونلاين (اختياري)",
+ "Output Path": "مسار الإخراج",
+ "Path error, please check the model file exists in the corresponding path": "خطأ في المسار، تأكد من وجود ملف النموذج في المسار المحدد",
+ "Precision": "الـ Precision",
+ "Probability of applying Speaker Condition": "احتمالية تطبيق شرط المتحدث",
+ "Put your text here.": "ضع نصك هنا.",
+ "Reference Audio": "الصوت المرجعي",
+ "Reference Text": "النص المرجعي",
+ "Related code and weights are released under FISH AUDIO RESEARCH LICENSE.": "الكود والأوزان المرتبطة متاحة تحت رخصة FISH AUDIO RESEARCH.",
+ "Remove Selected Data": "حذف البيانات المحددة",
+ "Removed path successfully!": "تمت إزالة المسار بنجاح!",
+ "Repetition Penalty": "Repetition Penalty",
+ "Save model every n steps": "حفظ النموذج كل n خطوة",
+ "Select LLAMA ckpt": "اختر ملف الـ LLAMA Checkpoint",
+ "Select VITS ckpt": "اختر ملف الـ VITS Checkpoint",
+ "Select VQGAN ckpt": "اختر ملف الـ VQGAN Checkpoint",
+ "Select source file processing method": "اختر طريقة معالجة الملف المصدر",
+ "Select the model to be trained (Depending on the Tab page you are on)": "اختر النموذج للتدريب (حسب التبويب الحالي)",
+ "Selected: {}": "المحدد: {}",
+ "Speaker": "المتحدث",
+ "Speaker is identified by the folder name": "يتم التعرف على المتحدث من اسم المجلد",
+ "Start Training": "بدء التدريب",
+ "Streaming Audio": "بث الصوت",
+ "Streaming Generate": "توليد بث",
+ "Tensorboard Host": "Host الـ Tensorboard",
+ "Tensorboard Log Path": "مسار سجل Tensorboard",
+ "Tensorboard Port": "Port الـ Tensorboard",
+ "Tensorboard interface is closed": "تم إغلاق واجهة Tensorboard",
+ "Tensorboard interface is launched at {}": "تم تشغيل Tensorboard على {}",
+ "Text is too long, please keep it under {} characters.": "النص طويل جدًا، يرجى إبقاؤه أقل من {} حرف.",
+ "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.": "مسار المجلد المدخل على اليسار أو قائمة الملفات. سواء كان محددًا أم لا، سيُستخدم في التدريب اللاحق.",
+ "Training Configuration": "إعدادات التدريب",
+ "Training Error": "خطأ في التدريب",
+ "Training stopped": "توقف التدريب",
+ "Type name of the speaker": "اكتب اسم المتحدث",
+ "Type the path or select from the dropdown": "اكتب المسار أو اختر من القائمة",
+ "Use LoRA": "استخدام LoRA",
+ "Use LoRA can save GPU memory, but may reduce the quality of the model": "استخدام LoRA يوفر ذاكرة الـ GPU لكنه قد يقلل من جودة النموذج",
+ "Use filelist": "استخدام قائمة الملفات",
+ "Use large for 10G+ GPU, medium for 5G, small for 2G": "استخدم Large لبطاقات 10G+، Medium لـ 5G، Small لـ 2G",
+ "VITS Configuration": "إعدادات VITS",
+ "VQGAN Configuration": "إعدادات VQGAN",
+ "Validation Batch Size": "حجم دفعة التحقق",
+ "View the status of the preprocessing folder (use the slider to control the depth of the tree)": "عرض حالة مجلد المعالجة المسبقة (استخدم الـ Slider للتحكم في عمق الشجرة)",
+ "We are not responsible for any misuse of the model, please consider your local laws and regulations before using it.": "نحن غير مسؤولين عن أي استخدام خاطئ للنموذج، يرجى مراعاة القوانين واللوائح المحلية قبل استخدامه.",
+ "WebUI Host": "Host الـ WebUI",
+ "WebUI Port": "Port الـ WebUI",
+ "Whisper Model": "نموذج Whisper",
+ "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.5).": "يمكنك إيجاد الكود المصدري [هنا](https://github.com/fishaudio/fish-speech) والنماذج [هنا](https://huggingface.co/fishaudio/fish-speech-1.5).",
+ "bf16-true is recommended for 30+ series GPU, 16-mixed is recommended for 10+ series GPU": "يُنصح بـ bf16-true لبطاقات سلسلة 30+ وما فوق، و16-mixed لسلسلة 10+ وما فوق",
+ "latest": "الأحدث",
+ "new": "جديد",
+ "Realtime Transform Text": "تحويل النص في الوقت الفعلي",
+ "Normalization Result Preview (Currently Only Chinese)": "معاينة نتيجة الـ Normalization (حاليًا للصينية فقط)",
+ "Text Normalization": "Normalization النص",
+ "Select Example Audio": "اختر مثال صوتي"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/en_US.json b/vendor/fish-speech/fish_speech/i18n/locale/en_US.json
new file mode 100644
index 0000000000000000000000000000000000000000..8f7a8eb6efe18b7a7469263365b1e528b8834ab8
--- /dev/null
+++ b/vendor/fish-speech/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 FISH AUDIO RESEARCH LICENSE.": "Related code and weights are released under FISH AUDIO RESEARCH 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-1.5).": "You can find the source code [here](https://github.com/fishaudio/fish-speech) and models [here](https://huggingface.co/fishaudio/fish-speech-1.5).",
+ "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"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/es_ES.json b/vendor/fish-speech/fish_speech/i18n/locale/es_ES.json
new file mode 100644
index 0000000000000000000000000000000000000000..c1b0906153804502aea6222bbc46663627ca868d
--- /dev/null
+++ b/vendor/fish-speech/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 FISH AUDIO RESEARCH LICENSE.": "El código relacionado y los pesos se publican bajo la FISH AUDIO RESEARCH LICENSE.",
+ "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-1.5).": "Puede encontrar el código fuente [aquí](https://github.com/fishaudio/fish-speech) y los modelos [aquí](https://huggingface.co/fishaudio/fish-speech-1.5).",
+ "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"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/ja_JP.json b/vendor/fish-speech/fish_speech/i18n/locale/ja_JP.json
new file mode 100644
index 0000000000000000000000000000000000000000..7746d424eb695a89e3451d58cf25b3f6e73c69ea
--- /dev/null
+++ b/vendor/fish-speech/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 FISH AUDIO RESEARCH LICENSE.": "関連コードと重みはFISH AUDIO RESEARCH LICENSE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-1.5).": "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-1.5)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"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/ko_KR.json b/vendor/fish-speech/fish_speech/i18n/locale/ko_KR.json
new file mode 100644
index 0000000000000000000000000000000000000000..6ba763abb3cdcb7eeb2298f63cc36217af2b1b6d
--- /dev/null
+++ b/vendor/fish-speech/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 FISH AUDIO RESEARCH LICENSE.": "관련 코드 및 가중치는 FISH AUDIO RESEARCH LICENSE 하에 배포됩니다.",
+ "Remove Selected Data": "선택한 데이터 제거",
+ "Removed path successfully!": "경로가 성공적으로 제거되었습니다!",
+ "Repetition Penalty": "반복 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-1.5).": "소스 코드는 [이곳](https://github.com/fishaudio/fish-speech)에서, 모델은 [이곳](https://huggingface.co/fishaudio/fish-speech-1.5)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"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/pt_BR.json b/vendor/fish-speech/fish_speech/i18n/locale/pt_BR.json
new file mode 100644
index 0000000000000000000000000000000000000000..09dabf2419460a2aa119d5ce3799660920aa1634
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/i18n/locale/pt_BR.json
@@ -0,0 +1,132 @@
+{
+ "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 FISH AUDIO RESEARCH LICENSE.": "O código relacionado e os pesos são licenciados sob a FISH AUDIO RESEARCH LICENSE.",
+ "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-1.5).": "Você pode encontrar o código fonte [aqui](https://github.com/fishaudio/fish-speech) e os modelos [aqui](https://huggingface.co/fishaudio/fish-speech-1.5).",
+ "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:"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/locale/zh_CN.json b/vendor/fish-speech/fish_speech/i18n/locale/zh_CN.json
new file mode 100644
index 0000000000000000000000000000000000000000..f10e47cecde96a1b7db661317658f0c91cd0019a
--- /dev/null
+++ b/vendor/fish-speech/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 FISH AUDIO RESEARCH LICENSE.": "相关代码和权重使用 FISH AUDIO RESEARCH LICENSE 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-1.5).": "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-1.5) 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"
+}
diff --git a/vendor/fish-speech/fish_speech/i18n/scan.py b/vendor/fish-speech/fish_speech/i18n/scan.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0194c0f1a31dc95309c64626d13f04751a44ba1
--- /dev/null
+++ b/vendor/fish-speech/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")
diff --git a/vendor/fish-speech/fish_speech/inference_engine/__init__.py b/vendor/fish-speech/fish_speech/inference_engine/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9aa6f58b3e44be2639b0bdc1d258bb1581159df8
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/inference_engine/__init__.py
@@ -0,0 +1,192 @@
+import gc
+import queue
+from typing import Generator
+
+import numpy as np
+import torch
+from loguru import logger
+
+from fish_speech.inference_engine.reference_loader import ReferenceLoader
+from fish_speech.inference_engine.utils import InferenceResult, wav_chunk_header
+from fish_speech.inference_engine.vq_manager import VQManager
+from fish_speech.models.dac.modded_dac import DAC
+from fish_speech.models.text2semantic.inference import (
+ GenerateRequest,
+ GenerateResponse,
+ WrappedGenerateResponse,
+)
+from fish_speech.utils import autocast_exclude_mps, set_seed
+from fish_speech.utils.schema import ServeTTSRequest
+
+
+class TTSInferenceEngine(ReferenceLoader, VQManager):
+
+ def __init__(
+ self,
+ llama_queue: queue.Queue,
+ decoder_model: DAC,
+ precision: torch.dtype,
+ compile: bool,
+ ) -> None:
+
+ super().__init__()
+
+ self.llama_queue = llama_queue
+ self.decoder_model = decoder_model
+ self.precision = precision
+ self.compile = compile
+
+ @torch.inference_mode()
+ def inference(self, req: ServeTTSRequest) -> Generator[InferenceResult, None, None]:
+ """
+ Main inference function:
+ - Loads the reference audio and text.
+ - Calls the LLAMA model for inference.
+ - Decodes the VQ tokens to audio.
+ """
+
+ ref_id: str | None = req.reference_id
+ prompt_tokens, prompt_texts = [], []
+ # Load the reference audio and text based on id or hash
+ if ref_id is not None:
+ prompt_tokens, prompt_texts = self.load_by_id(ref_id, req.use_memory_cache)
+
+ elif req.references:
+ prompt_tokens, prompt_texts = self.load_by_hash(
+ req.references, req.use_memory_cache
+ )
+
+ # Set the random seed if provided
+ if req.seed is not None:
+ set_seed(req.seed)
+ logger.warning(f"set seed: {req.seed}")
+
+ # Get the symbolic tokens from the LLAMA model
+ response_queue = self.send_Llama_request(req, prompt_tokens, prompt_texts)
+
+ # Get the sample rate from the decoder model
+ if hasattr(self.decoder_model, "spec_transform"):
+ sample_rate = self.decoder_model.spec_transform.sample_rate
+ else:
+ sample_rate = self.decoder_model.sample_rate
+
+ # If streaming, send the header
+ if req.streaming:
+ yield InferenceResult(
+ code="header",
+ audio=(
+ sample_rate,
+ np.array(wav_chunk_header(sample_rate=sample_rate)),
+ ),
+ error=None,
+ )
+
+ segments = []
+
+ while True:
+ # Get the response from the LLAMA model
+ wrapped_result: WrappedGenerateResponse = response_queue.get()
+ if wrapped_result.status == "error":
+ yield InferenceResult(
+ code="error",
+ audio=None,
+ error=(
+ wrapped_result.response
+ if isinstance(wrapped_result.response, Exception)
+ else Exception("Unknown error")
+ ),
+ )
+ break
+
+ # Check the response type
+ if not isinstance(wrapped_result.response, GenerateResponse):
+ raise TypeError(
+ f"Expected GenerateResponse, got {type(wrapped_result.response).__name__}"
+ )
+
+ result: GenerateResponse = wrapped_result.response
+ if result.action != "next":
+ segment = self.get_audio_segment(result)
+
+ if req.streaming: # Used only by the API server
+ yield InferenceResult(
+ code="segment",
+ audio=(sample_rate, segment),
+ error=None,
+ )
+ segments.append(segment)
+ else:
+ break
+
+ # Clean up the memory
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+ gc.collect()
+
+ # Edge case: no audio generated
+ if len(segments) == 0:
+ yield InferenceResult(
+ code="error",
+ audio=None,
+ error=RuntimeError("No audio generated, please check the input text."),
+ )
+ else:
+ # Streaming or not, return the final audio
+ audio = np.concatenate(segments, axis=0)
+ yield InferenceResult(
+ code="final",
+ audio=(sample_rate, audio),
+ error=None,
+ )
+
+ return None
+
+ def send_Llama_request(
+ self, req: ServeTTSRequest, prompt_tokens: list, prompt_texts: list
+ ) -> queue.Queue:
+ """
+ Send a request to the LLAMA model to generate the symbolic tokens.
+ """
+
+ # Prepare the request
+ request = dict(
+ device=self.decoder_model.device,
+ max_new_tokens=req.max_new_tokens,
+ text=req.text,
+ top_p=req.top_p,
+ repetition_penalty=req.repetition_penalty,
+ temperature=req.temperature,
+ compile=self.compile,
+ iterative_prompt=req.chunk_length > 0,
+ chunk_length=req.chunk_length,
+ prompt_tokens=prompt_tokens,
+ prompt_text=prompt_texts,
+ )
+
+ # Create a queue to get the response
+ response_queue = queue.Queue()
+
+ # Send the request to the LLAMA model
+ self.llama_queue.put(
+ GenerateRequest(
+ request=request,
+ response_queue=response_queue,
+ )
+ )
+
+ return response_queue
+
+ def get_audio_segment(self, result: GenerateResponse) -> np.ndarray:
+ """
+ Decode the VQ tokens to audio.
+ """
+
+ # Don't use autocast on MPS devices
+ with autocast_exclude_mps(
+ device_type=self.decoder_model.device.type, dtype=self.precision
+ ):
+ # Decode the symbolic tokens to audio
+ segment = self.decode_vq_tokens(codes=result.codes)
+
+ # Convert the audio to numpy
+ return segment.float().cpu().numpy()
diff --git a/vendor/fish-speech/fish_speech/inference_engine/reference_loader.py b/vendor/fish-speech/fish_speech/inference_engine/reference_loader.py
new file mode 100644
index 0000000000000000000000000000000000000000..93bea4fa34c29be607b6707c817834fa1367883f
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/inference_engine/reference_loader.py
@@ -0,0 +1,285 @@
+import io
+import re
+from hashlib import sha256
+from pathlib import Path
+from typing import Callable, Literal, Tuple
+
+import torch
+import torchaudio
+from loguru import logger
+
+from fish_speech.models.dac.modded_dac import DAC
+from fish_speech.utils.file import (
+ AUDIO_EXTENSIONS,
+ audio_to_bytes,
+ list_files,
+ read_ref_text,
+)
+from fish_speech.utils.schema import ServeReferenceAudio
+
+_ID_PATTERN = re.compile(r"^[a-zA-Z0-9\-_ ]+$")
+
+
+class ReferenceLoader:
+ def __init__(self) -> None:
+ """
+ Component of the TTSInferenceEngine class.
+ Loads and manages the cache for the reference audio and text.
+ """
+ self.ref_by_id: dict = {}
+ self.ref_by_hash: dict = {}
+
+ # Make Pylance happy (attribut/method not defined...)
+ self.decoder_model: DAC
+ self.encode_reference: Callable
+
+ # Define the torchaudio backend
+ # list_audio_backends() was removed in torchaudio 2.9
+ try:
+ backends = torchaudio.list_audio_backends()
+ if "ffmpeg" in backends:
+ self.backend = "ffmpeg"
+ else:
+ self.backend = "soundfile"
+ except AttributeError:
+ # torchaudio 2.9+ removed list_audio_backends()
+ # Try ffmpeg first, fallback to soundfile
+ try:
+ __import__("torchaudio.io._load_audio_fileobj")
+
+ self.backend = "ffmpeg"
+ except (ImportError, ModuleNotFoundError):
+ self.backend = "soundfile"
+
+ @staticmethod
+ def _validate_id(id: str) -> None:
+ if not _ID_PATTERN.match(id) or len(id) > 255:
+ raise ValueError(
+ "Reference ID contains invalid characters or is too long. "
+ "Only alphanumeric, hyphens, underscores, and spaces are allowed."
+ )
+
+ def load_by_id(
+ self,
+ id: str,
+ use_cache: Literal["on", "off"],
+ ) -> Tuple:
+ self._validate_id(id)
+
+ # Load the references audio and text by id
+ ref_folder = Path("references") / id
+ ref_folder.mkdir(parents=True, exist_ok=True)
+ ref_audios = list_files(
+ ref_folder, AUDIO_EXTENSIONS, recursive=True, sort=False
+ )
+
+ if use_cache == "off" or id not in self.ref_by_id:
+ # If the references are not already loaded, encode them
+ prompt_tokens = [
+ self.encode_reference(
+ # decoder_model=self.decoder_model,
+ reference_audio=audio_to_bytes(str(ref_audio)),
+ enable_reference_audio=True,
+ )
+ for ref_audio in ref_audios
+ ]
+ prompt_texts = [
+ read_ref_text(str(ref_audio.with_suffix(".lab")))
+ for ref_audio in ref_audios
+ ]
+ self.ref_by_id[id] = (prompt_tokens, prompt_texts)
+
+ else:
+ # Reuse already encoded references
+ logger.info("Use same references")
+ prompt_tokens, prompt_texts = self.ref_by_id[id]
+
+ return prompt_tokens, prompt_texts
+
+ def load_by_hash(
+ self,
+ references: list[ServeReferenceAudio],
+ use_cache: Literal["on", "off"],
+ ) -> Tuple:
+ # Load the references audio and text by hash
+ audio_hashes = [sha256(ref.audio).hexdigest() for ref in references]
+
+ cache_used = False
+ prompt_tokens, prompt_texts = [], []
+ for i, ref in enumerate(references):
+ if use_cache == "off" or audio_hashes[i] not in self.ref_by_hash:
+ # If the references are not already loaded, encode them
+ prompt_tokens.append(
+ self.encode_reference(
+ reference_audio=ref.audio,
+ enable_reference_audio=True,
+ )
+ )
+ prompt_texts.append(ref.text)
+ self.ref_by_hash[audio_hashes[i]] = (prompt_tokens[-1], ref.text)
+
+ else:
+ # Reuse already encoded references
+ cached_token, cached_text = self.ref_by_hash[audio_hashes[i]]
+ prompt_tokens.append(cached_token)
+ prompt_texts.append(cached_text)
+ cache_used = True
+
+ if cache_used:
+ logger.info("Use same references")
+
+ return prompt_tokens, prompt_texts
+
+ def load_audio(self, reference_audio: bytes | str, sr: int):
+ """
+ Load the audio data from a file or bytes.
+ """
+ if len(reference_audio) > 255 or not Path(reference_audio).exists():
+ audio_data = reference_audio
+ reference_audio = io.BytesIO(audio_data)
+
+ waveform, original_sr = torchaudio.load(reference_audio, backend=self.backend)
+
+ if waveform.shape[0] > 1:
+ waveform = torch.mean(waveform, dim=0, keepdim=True)
+
+ if original_sr != sr:
+ resampler = torchaudio.transforms.Resample(
+ orig_freq=original_sr, new_freq=sr
+ )
+ waveform = resampler(waveform)
+
+ audio = waveform.squeeze().numpy()
+ return audio
+
+ def list_reference_ids(self) -> list[str]:
+ """
+ List all valid reference IDs (subdirectory names containing valid audio and .lab files).
+
+ Returns:
+ list[str]: List of valid reference IDs
+ """
+ ref_base_path = Path("references")
+ if not ref_base_path.exists():
+ return []
+
+ valid_ids = []
+ for ref_dir in ref_base_path.iterdir():
+ if not ref_dir.is_dir():
+ continue
+
+ # Check if directory contains at least one audio file and corresponding .lab file
+ audio_files = list_files(
+ ref_dir, AUDIO_EXTENSIONS, recursive=False, sort=False
+ )
+ if not audio_files:
+ continue
+
+ # Check if corresponding .lab file exists for at least one audio file
+ has_valid_pair = False
+ for audio_file in audio_files:
+ lab_file = audio_file.with_suffix(".lab")
+ if lab_file.exists():
+ has_valid_pair = True
+ break
+
+ if has_valid_pair:
+ valid_ids.append(ref_dir.name)
+
+ return sorted(valid_ids)
+
+ def add_reference(self, id: str, wav_file_path: str, reference_text: str) -> None:
+ """
+ Add a new reference voice by creating a new directory and copying files.
+
+ Args:
+ id: Reference ID (directory name)
+ wav_file_path: Path to the audio file to copy
+ reference_text: Text content for the .lab file
+
+ Raises:
+ FileExistsError: If the reference ID already exists
+ FileNotFoundError: If the audio file doesn't exist
+ OSError: If file operations fail
+ """
+ self._validate_id(id)
+
+ # Check if reference already exists
+ ref_dir = Path("references") / id
+ if ref_dir.exists():
+ raise FileExistsError(f"Reference ID '{id}' already exists")
+
+ # Check if audio file exists
+ audio_path = Path(wav_file_path)
+ if not audio_path.exists():
+ raise FileNotFoundError(f"Audio file not found: {wav_file_path}")
+
+ # Validate audio file extension
+ if audio_path.suffix.lower() not in AUDIO_EXTENSIONS:
+ raise ValueError(
+ f"Unsupported audio format: {audio_path.suffix}. Supported formats: {', '.join(AUDIO_EXTENSIONS)}"
+ )
+
+ try:
+ # Create reference directory
+ ref_dir.mkdir(parents=True, exist_ok=False)
+
+ # Determine the target audio filename with original extension
+ target_audio_path = ref_dir / f"sample{audio_path.suffix}"
+
+ # Copy audio file
+ import shutil
+
+ shutil.copy2(audio_path, target_audio_path)
+
+ # Create .lab file
+ lab_path = ref_dir / "sample.lab"
+ with open(lab_path, "w", encoding="utf-8") as f:
+ f.write(reference_text)
+
+ # Clear cache for this ID if it exists
+ if id in self.ref_by_id:
+ del self.ref_by_id[id]
+
+ logger.info(f"Successfully added reference voice with ID: {id}")
+
+ except Exception as e:
+ # Clean up on failure
+ if ref_dir.exists():
+ import shutil
+
+ shutil.rmtree(ref_dir)
+ raise e
+
+ def delete_reference(self, id: str) -> None:
+ """
+ Delete a reference voice by removing its directory and files.
+
+ Args:
+ id: Reference ID (directory name) to delete
+
+ Raises:
+ FileNotFoundError: If the reference ID doesn't exist
+ OSError: If file operations fail
+ """
+ self._validate_id(id)
+
+ ref_dir = Path("references") / id
+ if not ref_dir.exists():
+ raise FileNotFoundError(f"Reference ID '{id}' does not exist")
+
+ try:
+ # Remove the entire reference directory
+ import shutil
+
+ shutil.rmtree(ref_dir)
+
+ # Clear cache for this ID if it exists
+ if id in self.ref_by_id:
+ del self.ref_by_id[id]
+
+ logger.info(f"Successfully deleted reference voice with ID: {id}")
+
+ except Exception as e:
+ logger.error(f"Failed to delete reference '{id}': {e}")
+ raise OSError(f"Failed to delete reference '{id}': {e}")
diff --git a/vendor/fish-speech/fish_speech/inference_engine/utils.py b/vendor/fish-speech/fish_speech/inference_engine/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c11af150dc4a0985b11ba7675d93cf165490f35
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/inference_engine/utils.py
@@ -0,0 +1,29 @@
+import io
+import wave
+from dataclasses import dataclass
+from typing import Literal, Optional, Tuple
+
+import numpy as np
+
+
+@dataclass
+class InferenceResult:
+ code: Literal["header", "segment", "error", "final"]
+ audio: Optional[Tuple[int, np.ndarray]]
+ error: Optional[Exception]
+
+
+def wav_chunk_header(
+ sample_rate: int = 44100, bit_depth: int = 16, channels: int = 1
+) -> bytes:
+ buffer = io.BytesIO()
+
+ with wave.open(buffer, "wb") as wav_file:
+ wav_file.setnchannels(channels)
+ wav_file.setsampwidth(bit_depth // 8)
+ wav_file.setframerate(sample_rate)
+
+ wav_header_bytes = buffer.getvalue()
+ buffer.close()
+
+ return wav_header_bytes
diff --git a/vendor/fish-speech/fish_speech/inference_engine/vq_manager.py b/vendor/fish-speech/fish_speech/inference_engine/vq_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..4481580233ec6d2ad1ab1d5c99579793cea0a4d6
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/inference_engine/vq_manager.py
@@ -0,0 +1,53 @@
+from typing import Callable
+
+import torch
+from loguru import logger
+
+from fish_speech.models.dac.modded_dac import DAC
+
+
+class VQManager:
+
+ def __init__(self):
+ # Make Pylance happy (attribut/method not defined...)
+ self.decoder_model: DAC
+ self.load_audio: Callable
+
+ def decode_vq_tokens(self, codes):
+ logger.info(f"VQ features: {codes.shape}")
+
+ if isinstance(self.decoder_model, DAC):
+ return self.decoder_model.from_indices(codes[None])[0].squeeze()
+
+ raise ValueError(f"Unknown model type: {type(self.decoder_model)}")
+
+ def encode_reference(self, reference_audio, enable_reference_audio):
+ if enable_reference_audio and reference_audio is not None:
+ # Load audios, and prepare basic info here
+ if hasattr(self.decoder_model, "spec_transform"):
+ sample_rate = self.decoder_model.spec_transform.sample_rate
+ else:
+ sample_rate = self.decoder_model.sample_rate
+ reference_audio_content = self.load_audio(reference_audio, sample_rate)
+
+ audios = torch.from_numpy(reference_audio_content).to(
+ self.decoder_model.device
+ )[None, None, :]
+ audio_lengths = torch.tensor(
+ [audios.shape[2]], device=self.decoder_model.device, dtype=torch.long
+ )
+ logger.info(
+ f"Loaded audio with {audios.shape[2] / sample_rate:.2f} seconds"
+ )
+
+ # VQ Encoder
+ if isinstance(self.decoder_model, DAC):
+ prompt_tokens = self.decoder_model.encode(audios, audio_lengths)[0][0]
+ logger.info(f"Encoded prompt: {prompt_tokens.shape}")
+ else:
+ raise ValueError(f"Unknown model type: {type(self.decoder_model)}")
+ else:
+ prompt_tokens = None
+ logger.info("No reference audio provided")
+
+ return prompt_tokens
diff --git a/vendor/fish-speech/fish_speech/models/text2semantic/inference.py b/vendor/fish-speech/fish_speech/models/text2semantic/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..59653d56c052b362d197a1ad9abf025065b0f75d
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/models/text2semantic/inference.py
@@ -0,0 +1,966 @@
+import os
+import queue
+import re
+import threading
+import time
+import traceback
+from copy import deepcopy
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Callable, Literal, Optional, Tuple, Union
+
+import click
+import numpy as np
+import torch
+import torch._inductor.config
+from loguru import logger
+from tqdm import tqdm
+
+from fish_speech.content_sequence import (
+ TextPart,
+ VQPart,
+)
+from fish_speech.conversation import Conversation, Message
+from fish_speech.tokenizer import IM_END_TOKEN
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+torch._inductor.config.coordinate_descent_tuning = True
+torch._inductor.config.triton.unique_kernel_names = True
+
+if hasattr(torch._inductor.config, "fx_graph_cache"):
+ torch._inductor.config.fx_graph_cache = True
+
+
+from torch.nn.attention import SDPBackend, sdpa_kernel
+
+from fish_speech.models.text2semantic.llama import (
+ BaseTransformer,
+ DualARTransformer,
+ NaiveTransformer,
+)
+
+
+def multinomial_sample_one_no_sync(probs_sort):
+ q = torch.rand_like(probs_sort)
+ q = -torch.log(q)
+ return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int)
+
+
+RAS_WIN_SIZE = 10 # window for Repetition Aware Sampling
+RAS_HIGH_TEMP = 1.0
+RAS_HIGH_TOP_P = 0.9
+
+
+def logits_to_probs(
+ logits,
+ temperature: torch.Tensor,
+ top_p: torch.Tensor,
+ top_k: int, # 注意: 我看到你传进来的是 int，这很关键
+) -> torch.Tensor:
+ sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+ cum_probs = torch.cumsum(torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1)
+
+ indices = torch.arange(sorted_logits.shape[-1], device=sorted_logits.device)
+ top_k_mask = indices >= top_k
+ sorted_indices_to_remove = (cum_probs > top_p) | top_k_mask
+ sorted_indices_to_remove[0] = False # 单元素修改问题不大，或者写成 | (indices != 0)
+
+ indices_to_remove = sorted_indices_to_remove.scatter(
+ dim=-1, index=sorted_indices, src=sorted_indices_to_remove
+ )
+ logits = torch.where(
+ indices_to_remove, float("-Inf"), logits
+ ) # 同样替换 masked_fill_ 为 torch.where
+ logits = logits / torch.clip(temperature, min=1e-5)
+
+ probs = torch.nn.functional.softmax(logits, dim=-1)
+ return probs
+
+
+def sample(
+ logits,
+ temperature: torch.Tensor,
+ top_p: torch.Tensor,
+ top_k: int,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+ probs = logits_to_probs(
+ logits=logits[0, -1],
+ temperature=temperature,
+ top_p=top_p,
+ top_k=top_k,
+ )
+ idx_next = multinomial_sample_one_no_sync(probs)
+ return idx_next, probs
+
+
+def decode_one_token_ar(
+ model: DualARTransformer,
+ x: torch.Tensor,
+ input_pos: torch.Tensor,
+ temperature: torch.Tensor,
+ top_p: torch.Tensor,
+ top_k: int,
+ semantic_logit_bias: torch.Tensor,
+ audio_masks: torch.Tensor,
+ audio_parts: torch.Tensor,
+ previous_tokens: Optional[torch.Tensor] = None,
+) -> torch.Tensor:
+ forward_result = model.forward_generate(
+ x,
+ input_pos,
+ audio_masks=audio_masks,
+ audio_parts=audio_parts,
+ )
+ logits = forward_result.logits # (1, 1, vocab_size)
+ hidden_states = forward_result.hidden_states
+
+ # Apply constrained decoding: only allow semantic tokens + im_end
+ biased_logits = logits + semantic_logit_bias
+
+ # Normal sample
+ main_token_normal = sample(
+ biased_logits, temperature=temperature, top_p=top_p, top_k=top_k
+ )[0]
+
+ # RAS: also sample with high temp to use as fallback if token repeats
+ high_temp = torch.tensor(
+ RAS_HIGH_TEMP, device=temperature.device, dtype=temperature.dtype
+ )
+ high_top_p = torch.tensor(RAS_HIGH_TOP_P, device=top_p.device, dtype=top_p.dtype)
+ main_token_high = sample(
+ biased_logits, temperature=high_temp, top_p=high_top_p, top_k=top_k
+ )[0]
+
+ # Use high-temp sample if: token is semantic AND token is in previous window
+ if previous_tokens is not None:
+ in_window = (previous_tokens[0] == main_token_normal).any()
+ # Use tensor ops (&, torch.where) instead of Python (and, if) — torch.compile requires no data-dependent branching
+ is_semantic = (main_token_normal >= model.config.semantic_begin_id) & (
+ main_token_normal <= model.config.semantic_end_id
+ )
+ should_use_high = in_window & is_semantic
+ main_token_normal = torch.where(
+ should_use_high, main_token_high, main_token_normal
+ )
+
+ codebooks = [main_token_normal]
+
+ input_pos = torch.tensor([0], device=hidden_states.device, dtype=torch.long)
+ model.forward_generate_fast(hidden_states, input_pos)
+
+ a = codebooks[0] - model.config.semantic_begin_id
+ a = torch.clamp(a, min=0, max=model.config.codebook_size - 1)
+
+ hidden_states = model.fast_embeddings(a)
+ codebooks.append(a)
+
+ for codebook_idx in range(1, model.config.num_codebooks):
+ input_pos = torch.tensor(
+ [codebook_idx], device=hidden_states.device, dtype=torch.long
+ )
+ logits = model.forward_generate_fast(hidden_states, input_pos)
+
+ short_logits = logits # DualAR predicts config.codebook_size number of tokens
+
+ # Convert logits to probs (no constrain for fast codebooks)
+ a = sample(
+ short_logits,
+ temperature=temperature,
+ top_p=top_p,
+ top_k=top_k,
+ )[0]
+
+ hidden_states = model.fast_embeddings(a)
+ codebooks.append(a)
+
+ codebooks = torch.stack(codebooks, dim=1)
+
+ # Only delete references, let Python GC handle cleanup
+ del logits, hidden_states, forward_result
+
+ return codebooks.T
+
+
+def decode_n_tokens(
+ model: DualARTransformer,
+ cur_token: torch.Tensor,
+ input_pos: torch.Tensor,
+ num_new_tokens: int,
+ temperature: torch.Tensor,
+ top_p: torch.Tensor,
+ top_k: int,
+ semantic_logit_bias: torch.Tensor,
+ audio_masks: torch.Tensor,
+ audio_parts: torch.Tensor,
+ decode_one_token=decode_one_token_ar,
+):
+ # Rolling window for RAS (Repetition Aware Sampling)
+ previous_tokens = torch.zeros(
+ (model.config.num_codebooks + 1, RAS_WIN_SIZE),
+ dtype=torch.int,
+ device=cur_token.device,
+ )
+ # Accumulate all generated tokens (the actual output)
+ new_tokens = []
+
+ # [MODIFIED] Pre-fetch ID for efficiency loop
+ im_end_id = model.tokenizer.get_token_id(IM_END_TOKEN)
+
+ for i in tqdm(range(num_new_tokens)):
+ with sdpa_kernel(SDPBackend.MATH):
+ next_token = decode_one_token(
+ model=model,
+ x=cur_token,
+ input_pos=input_pos,
+ previous_tokens=previous_tokens,
+ temperature=temperature,
+ top_p=top_p,
+ top_k=top_k,
+ semantic_logit_bias=semantic_logit_bias,
+ audio_masks=audio_masks,
+ audio_parts=audio_parts,
+ ).clone()
+
+ input_pos += 1
+ cur_token = next_token.view(1, model.config.num_codebooks + 1, -1)
+ # Roll RAS window left and insert new token at end
+ previous_tokens = previous_tokens.roll(-1, dims=1)
+ previous_tokens[:, -1] = next_token.view(model.config.num_codebooks + 1, -1)[
+ :, 0
+ ]
+ new_tokens.append(next_token)
+
+ if cur_token[0, 0, -1] == im_end_id:
+ break
+
+ del cur_token
+
+ return torch.cat(new_tokens, dim=1)
+
+
+@torch.no_grad()
+@torch.inference_mode()
+def generate(
+ *,
+ model: DualARTransformer,
+ prompt: torch.Tensor,
+ max_new_tokens: int,
+ audio_masks: torch.Tensor,
+ audio_parts: torch.Tensor,
+ decode_one_token=decode_one_token_ar,
+ num_samples: int = 1,
+ **sampling_kwargs,
+):
+ """
+ Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
+ """
+
+ # create an empty tensor of the expected final shape and fill in the current tokens
+ T = prompt.size(1)
+ prompt = prompt[None].repeat(num_samples, 1, 1)
+
+ if T >= model.config.max_seq_len:
+ raise ValueError(
+ f"Input sequence length {T} exceeds max_seq_len {model.config.max_seq_len}"
+ )
+
+ if max_new_tokens:
+ if T + max_new_tokens > model.config.max_seq_len:
+ max_new_tokens = model.config.max_seq_len - T
+
+ T_new = T + max_new_tokens
+ else:
+ T_new = model.config.max_seq_len
+ max_new_tokens = T_new - T
+
+ device = prompt.device
+ dtype = next(
+ model.parameters()
+ ).dtype # model weight dtype (bfloat16), NOT prompt dtype (int32)
+
+ # Critical fix: Only set up cache on first run or when necessary
+ if not hasattr(model, "_cache_setup_done") or not model._cache_setup_done:
+ with torch.device(device):
+ model.setup_caches(
+ max_batch_size=1, # Fixed to 1, avoid dynamic changes
+ max_seq_len=model.config.max_seq_len,
+ dtype=next(model.parameters()).dtype,
+ )
+ model._cache_setup_done = True
+
+ codebook_dim = 1 + model.config.num_codebooks
+
+ # Create new tensor each time, but try to reuse memory
+ input_pos = torch.arange(0, T, device=device, dtype=torch.long)
+ empty = torch.empty(
+ (codebook_dim, model.config.max_seq_len), dtype=prompt.dtype, device=device
+ )
+ empty[:, :T] = prompt
+ seq = empty
+
+ temp_val = sampling_kwargs.get("temperature", 1.0)
+ top_p_val = sampling_kwargs.get("top_p", 0.9)
+ top_k_val = sampling_kwargs.get("top_k", 30)
+
+ temperature = torch.tensor(temp_val, device=device, dtype=dtype)
+ top_p = torch.tensor(top_p_val, device=device, dtype=dtype)
+
+ # Build semantic logit bias: 0 for semantic tokens + im_end, -inf for all others
+ vocab_size = model.config.vocab_size
+ semantic_logit_bias = torch.full(
+ (1, 1, vocab_size), float("-inf"), device=device, dtype=dtype
+ )
+
+ # [MODIFIED] Use config for semantic range
+ semantic_logit_bias[
+ 0, 0, model.config.semantic_begin_id : model.config.semantic_end_id + 1
+ ] = 0.0
+
+ # [MODIFIED] Use tokenizer.get_token_id (Wrapper method)
+ semantic_logit_bias[0, 0, model.tokenizer.get_token_id(IM_END_TOKEN)] = 0.0
+
+ prefill_decode = decode_one_token_ar
+
+ first_token = prefill_decode(
+ model,
+ prompt.view(1, codebook_dim, -1),
+ input_pos,
+ temperature,
+ top_p,
+ top_k_val,
+ semantic_logit_bias,
+ audio_masks,
+ audio_parts,
+ )
+ seq[:, T : T + 1] = first_token
+
+ # Recreate input_pos
+ input_pos = torch.tensor([T], device=device, dtype=torch.int)
+
+ x = decode_n_tokens(
+ model,
+ first_token.view(1, codebook_dim, -1),
+ input_pos,
+ max_new_tokens - 1,
+ temperature=temperature,
+ top_p=top_p,
+ top_k=top_k_val,
+ semantic_logit_bias=semantic_logit_bias,
+ audio_masks=audio_masks,
+ audio_parts=audio_parts,
+ decode_one_token=decode_one_token,
+ )
+ seq = seq[:, : T + 1 + x.size(1)]
+ seq[:, T + 1 :] = x
+
+ # Clean up temporary variables
+ del first_token, x, prompt, empty, input_pos
+
+ return seq
+
+
+def init_model(checkpoint_path, device, precision, compile=False):
+ model = DualARTransformer.from_pretrained(checkpoint_path, load_weights=True)
+
+ model = model.to(device=device, dtype=precision)
+ logger.info(f"Restored model from checkpoint")
+
+ if isinstance(model, DualARTransformer):
+ decode_one_token = decode_one_token_ar
+ # prefill_n_tokens = decode_one_token_ar
+ logger.info("Using DualARTransformer")
+ else:
+ raise ValueError("Unsupported model type")
+
+ # Pre-create fixed parameter tensors to avoid runtime creation
+ model.fixed_temperature = torch.tensor(0.7, device=device, dtype=torch.float)
+ model.fixed_top_p = torch.tensor(0.7, device=device, dtype=torch.float)
+ model.fixed_repetition_penalty = torch.tensor(1.5, device=device, dtype=torch.float)
+
+ # Mark whether cache has been initialized
+ model._cache_setup_done = False
+
+ if compile:
+ logger.info("Compiling function...")
+ decode_one_token = torch.compile(
+ decode_one_token,
+ backend="inductor" if torch.cuda.is_available() else "aot_eager",
+ mode="default" if torch.cuda.is_available() else None,
+ fullgraph=True,
+ )
+
+ return model.eval(), decode_one_token
+
+
+@torch.inference_mode()
+def load_codec_model(codec_checkpoint_path, device, precision=torch.bfloat16):
+ """Load the DAC codec model for audio encoding/decoding."""
+ from hydra.utils import instantiate
+ from omegaconf import OmegaConf
+
+ config_path = Path(__file__).parent.parent.parent / "configs" / "modded_dac_vq.yaml"
+ cfg = OmegaConf.load(str(config_path))
+ codec = instantiate(cfg)
+
+ state_dict = torch.load(codec_checkpoint_path, map_location="cpu")
+ if "state_dict" in state_dict:
+ state_dict = state_dict["state_dict"]
+ if any("generator" in k for k in state_dict):
+ state_dict = {
+ k.replace("generator.", ""): v
+ for k, v in state_dict.items()
+ if "generator." in k
+ }
+ codec.load_state_dict(state_dict, strict=False)
+ codec.eval()
+ codec.to(device=device, dtype=precision)
+ return codec
+
+
+@torch.inference_mode()
+def encode_audio(audio_path, codec, device):
+ """Encode an audio file to VQ codes."""
+ import torchaudio
+
+ wav, sr = torchaudio.load(str(audio_path))
+ if wav.shape[0] > 1:
+ wav = wav.mean(dim=0, keepdim=True)
+ wav = torchaudio.functional.resample(wav.to(device), sr, codec.sample_rate)[0]
+
+ # Match codec model dtype (e.g. bfloat16)
+ model_dtype = next(codec.parameters()).dtype
+ audios = wav[None, None].to(dtype=model_dtype) # (1, 1, T)
+ audio_lengths = torch.tensor([len(wav)], device=device, dtype=torch.long)
+
+ indices, feature_lengths = codec.encode(audios, audio_lengths)
+ return indices[0, :, : feature_lengths[0]] # (num_codebooks, T)
+
+
+@torch.inference_mode()
+def decode_to_audio(codes, codec):
+ """Decode VQ codes to audio waveform."""
+ # codes: (num_codebooks, T) -> (1, num_codebooks, T)
+ audio = codec.from_indices(codes[None])
+ return audio[0, 0] # (T,) mono waveform
+
+
+@dataclass
+class GenerateResponse:
+ action: Literal["sample", "next"]
+ codes: Optional[torch.Tensor] = None
+ text: Optional[str] = None
+
+
+def split_text_by_speaker(text: str) -> list[str]:
+ """
+ Split text into turns based on <|speaker:X|> tags.
+
+ Args:
+ text: The full text with speaker tags
+
+ Returns:
+ List of speaker turns, each starting with <|speaker:X|>
+ """
+ pattern = r"(<\|speaker:\d+\|>)"
+ parts = re.split(pattern, text)
+
+ turns = []
+ i = 0
+ while i < len(parts):
+ part = parts[i].strip()
+ if re.match(pattern, part):
+ if i + 1 < len(parts):
+ turn = part + parts[i + 1]
+ turns.append(turn.strip())
+ i += 2
+ else:
+ turns.append(part)
+ i += 1
+ else:
+ i += 1
+
+ return turns
+
+
+def group_turns_into_batches(
+ turns: list[str], max_speakers: int = 3, max_bytes: int = 300
+) -> list[str]:
+ """
+ Group turns into batches based on speaker count or byte limit.
+
+ Args:
+ turns: List of speaker turns
+ max_speakers: Maximum number of speakers per batch (default 3)
+ max_bytes: Maximum UTF-8 bytes per batch (default 300)
+
+ Returns:
+ List of batched text strings
+ """
+ batches = []
+ current_batch = []
+ current_bytes = 0
+
+ for turn in turns:
+ turn_bytes = len(turn.encode("utf-8"))
+
+ would_exceed_speakers = len(current_batch) >= max_speakers
+ would_exceed_bytes = current_bytes + turn_bytes > max_bytes and current_batch
+
+ if would_exceed_speakers or would_exceed_bytes:
+ batches.append("\n".join(current_batch))
+ current_batch = [turn]
+ current_bytes = turn_bytes
+ else:
+ current_batch.append(turn)
+ current_bytes += turn_bytes
+
+ if current_batch:
+ batches.append("\n".join(current_batch))
+
+ return batches
+
+
+def generate_long(
+ *,
+ model,
+ device: Union[str, torch.device],
+ decode_one_token: Callable,
+ text: str,
+ num_samples: int = 1,
+ max_new_tokens: int = 0,
+ top_p: float = 0.9,
+ top_k: int = 30,
+ repetition_penalty: float = 1.1,
+ temperature: float = 1.0,
+ compile: bool = False,
+ iterative_prompt: bool = True,
+ chunk_length: int = 512,
+ prompt_text: Optional[Union[str, list[str]]] = None,
+ prompt_tokens: Optional[Union[torch.Tensor, list[torch.Tensor]]] = None,
+):
+ assert 0 < top_p <= 1, "top_p must be in (0, 1]"
+ assert 0 < temperature < 2, "temperature must be in (0, 2)"
+
+ use_prompt = bool(prompt_text) and bool(prompt_tokens)
+ if use_prompt and isinstance(prompt_text, str):
+ prompt_text = [prompt_text]
+ prompt_tokens = [prompt_tokens]
+
+ if use_prompt:
+ assert len(prompt_text) == len(
+ prompt_tokens
+ ), "Prompt text and tokens must have the same length"
+
+ if prompt_tokens:
+ prompt_tokens = [i.cpu() for i in prompt_tokens]
+
+ model_size = sum(p.numel() for p in model.parameters() if p.requires_grad)
+ tokenizer = model.tokenizer
+ max_length = model.config.max_seq_len
+
+ # Build base conversation with system message
+ base_conversation = Conversation()
+
+ if use_prompt:
+ # Auto-add speaker tags to prompt texts that don't have them
+ tagged_prompt_text = []
+ for i, t in enumerate(prompt_text):
+ if not re.search(r"<\|speaker:\d+\|>", t):
+ tagged_prompt_text.append(f"<|speaker:{i}|>{t}")
+ else:
+ tagged_prompt_text.append(t)
+
+ system_parts = [
+ TextPart(
+ text="convert the provided text to speech reference to the following:\n\nText:\n",
+ cal_loss=False,
+ ),
+ ]
+ reference_text = "\n".join(tagged_prompt_text)
+ system_parts.append(TextPart(text=reference_text, cal_loss=False))
+ system_parts.append(TextPart(text="\n\nSpeech:\n", cal_loss=False))
+ all_codes = torch.cat([c for c in prompt_tokens], dim=1)
+ system_parts.append(VQPart(codes=all_codes, cal_loss=False))
+ # torch.save(all_codes, "debug_vq_codes.pt")
+ else:
+ system_parts = [
+ TextPart(text="convert the provided text to speech", cal_loss=False)
+ ]
+
+ base_conversation.append(
+ Message(
+ role="system",
+ parts=system_parts,
+ cal_loss=False,
+ add_im_start=True,
+ add_im_end=True,
+ )
+ )
+
+ # Split text by speaker and group into batches
+ turns = split_text_by_speaker(text)
+ if turns:
+ batches = group_turns_into_batches(
+ turns, max_speakers=5, max_bytes=chunk_length
+ )
+ else:
+ batches = [text]
+
+ logger.info(f"Split into {len(turns)} turns, grouped into {len(batches)} batches")
+
+ for sample_idx in range(num_samples):
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+
+ t0 = time.perf_counter()
+
+ # Deep copy base conversation for this sample
+ conversation = deepcopy(base_conversation)
+
+ for batch_idx, batch_text in enumerate(batches):
+ logger.info(
+ f"--- Sample {sample_idx}, Batch {batch_idx} "
+ f"({len(batch_text.encode('utf-8'))} bytes) ---"
+ )
+ logger.info(f"Batch text: {batch_text}")
+
+ # Add user message
+ conversation.append(
+ Message(
+ role="user",
+ parts=[TextPart(text=batch_text, cal_loss=False)],
+ cal_loss=False,
+ add_im_start=True,
+ add_im_end=True,
+ )
+ )
+
+ # Deep copy for generation (don't pollute original conversation)
+ conversation_gen = deepcopy(conversation)
+ conversation_gen.append(
+ Message(
+ role="assistant",
+ parts=[],
+ cal_loss=False,
+ modality="voice",
+ add_im_start=True,
+ add_im_end=False,
+ )
+ )
+
+ logger.info("Visualizing prompt structure:")
+ conversation_gen.visualize(
+ tokenizer,
+ merge_audio_tokens=True,
+ merge_semantic_tokens=True,
+ )
+
+ encoded, audio_masks, audio_parts = conversation_gen.encode_for_inference(
+ tokenizer, num_codebooks=model.config.num_codebooks
+ )
+
+ logger.info(f"Encoded prompt shape: {encoded.shape}")
+ if audio_parts is not None:
+ logger.info(f"Audio parts shape: {audio_parts.shape}")
+ if audio_masks is not None:
+ logger.info(
+ f"Audio masks non-zero count: {torch.count_nonzero(audio_masks)}"
+ )
+
+ if encoded.size(1) > max_length - 2048:
+ raise ValueError(
+ f"Prompt is too long: {encoded.size(1)} > {max_length - 2048}"
+ )
+
+ encoded = encoded.to(device=device)
+ prompt_length = encoded.size(1)
+
+ y = generate(
+ model=model,
+ prompt=encoded,
+ max_new_tokens=max_new_tokens,
+ audio_masks=audio_masks,
+ audio_parts=audio_parts,
+ decode_one_token=decode_one_token,
+ temperature=temperature,
+ top_p=top_p,
+ top_k=top_k,
+ )
+
+ if sample_idx == 0 and batch_idx == 0 and compile:
+ logger.info(f"Compilation time: {time.perf_counter() - t0:.2f} seconds")
+
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+
+ t_batch = time.perf_counter() - t0
+ tokens_generated = y.size(1) - prompt_length
+ tokens_sec = tokens_generated / t_batch if t_batch > 0 else 0
+ logger.info(
+ f"Batch {batch_idx}: Generated {tokens_generated} tokens in "
+ f"{t_batch:.02f} seconds, {tokens_sec:.02f} tokens/sec"
+ )
+ logger.info(
+ f"Bandwidth achieved: {model_size * tokens_sec / 1e9:.02f} GB/s"
+ )
+
+ # Extract generated codes
+ codes = y[1:, prompt_length:-1].clone()
+ assert (codes >= 0).all(), f"Negative code found: {codes}"
+
+ # Add assistant message with generated codes back to conversation
+ conversation.append(
+ Message(
+ role="assistant",
+ parts=[VQPart(codes=codes.cpu(), cal_loss=False)],
+ cal_loss=False,
+ modality="voice",
+ add_im_start=True,
+ add_im_end=True,
+ )
+ )
+
+ yield GenerateResponse(action="sample", codes=codes, text=batch_text)
+
+ # Cleanup
+ del y, encoded
+
+ if torch.cuda.is_available():
+ logger.info(
+ f"GPU Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB"
+ )
+
+ yield GenerateResponse(action="next")
+
+
+@dataclass
+class WrappedGenerateResponse:
+ status: Literal["success", "error"]
+ response: Optional[Union[GenerateResponse, Exception]] = None
+
+
+@dataclass
+class GenerateRequest:
+ request: dict
+ response_queue: queue.Queue
+
+
+def launch_thread_safe_queue(
+ checkpoint_path,
+ device,
+ precision,
+ compile: bool = False,
+):
+ input_queue = queue.Queue()
+ init_event = threading.Event()
+
+ def worker():
+ model, decode_one_token = init_model(
+ checkpoint_path, device, precision, compile=compile
+ )
+ with torch.device(device):
+ model.setup_caches(
+ max_batch_size=1,
+ max_seq_len=model.config.max_seq_len,
+ dtype=next(model.parameters()).dtype,
+ )
+ init_event.set()
+
+ while True:
+ item: GenerateRequest | None = input_queue.get()
+ if item is None:
+ break
+
+ kwargs = item.request
+ response_queue = item.response_queue
+
+ try:
+ for chunk in generate_long(
+ model=model, decode_one_token=decode_one_token, **kwargs
+ ):
+ response_queue.put(
+ WrappedGenerateResponse(status="success", response=chunk)
+ )
+
+ # Only clear cache after complete request batch
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+
+ except Exception as e:
+ logger.error(traceback.format_exc())
+ response_queue.put(WrappedGenerateResponse(status="error", response=e))
+ # Clear cache on error
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+
+ threading.Thread(target=worker, daemon=True).start()
+ init_event.wait()
+
+ return input_queue
+
+
+@click.command()
+@click.option(
+ "--text",
+ type=str,
+ default="<|speaker:0|>你说的对, 但是原神是一款由米哈游自主研发的开放世界手游.",
+)
+@click.option("--prompt-text", type=str, default=None, multiple=True)
+@click.option(
+ "--prompt-tokens",
+ type=click.Path(path_type=Path, exists=True),
+ default=None,
+ multiple=True,
+)
+@click.option(
+ "--prompt-audio",
+ type=click.Path(path_type=Path, exists=True),
+ default=None,
+ multiple=True,
+)
+@click.option("--output", type=click.Path(path_type=Path), default=None)
+@click.option("--num-samples", type=int, default=1)
+@click.option("--max-new-tokens", type=int, default=0)
+@click.option("--top-p", type=float, default=0.9)
+@click.option("--top-k", type=int, default=30)
+@click.option("--temperature", type=float, default=1.0)
+@click.option(
+ "--checkpoint-path",
+ type=click.Path(path_type=Path, exists=True),
+ default="checkpoints/s2-pro",
+)
+@click.option("--device", type=str, default="cuda")
+@click.option("--compile/--no-compile", default=False)
+@click.option("--seed", type=int, default=42)
+@click.option("--half/--no-half", default=False)
+@click.option("--iterative-prompt/--no-iterative-prompt", default=True)
+@click.option("--chunk-length", type=int, default=300)
+@click.option("--output-dir", type=Path, default="output")
+def main(
+ text: str,
+ prompt_text: Optional[tuple[str, ...]],
+ prompt_tokens: Optional[tuple[Path, ...]],
+ prompt_audio: Optional[tuple[Path, ...]],
+ output: Optional[Path],
+ num_samples: int,
+ max_new_tokens: int,
+ top_p: float,
+ top_k: int,
+ temperature: float,
+ checkpoint_path: Path,
+ device: str,
+ compile: bool,
+ seed: int,
+ half: bool,
+ iterative_prompt: bool,
+ chunk_length: int,
+ output_dir: Path,
+) -> None:
+ os.makedirs(output_dir, exist_ok=True)
+ precision = torch.half if half else torch.bfloat16
+
+ if prompt_text and not prompt_audio and not prompt_tokens:
+ raise ValueError(
+ "--prompt-text requires either --prompt-audio or --prompt-tokens"
+ )
+ if prompt_text and prompt_tokens and len(prompt_text) != len(prompt_tokens):
+ raise ValueError(
+ f"Number of prompt text ({len(prompt_text)}) and prompt tokens ({len(prompt_tokens)}) should be the same"
+ )
+ if prompt_text and prompt_audio and len(prompt_text) != len(prompt_audio):
+ raise ValueError(
+ f"Number of prompt text ({len(prompt_text)}) and prompt audio ({len(prompt_audio)}) should be the same"
+ )
+
+ logger.info("Loading model ...")
+ t0 = time.time()
+ model, decode_one_token = init_model(
+ checkpoint_path, device, precision, compile=compile
+ )
+ with torch.device(device):
+ model.setup_caches(
+ max_batch_size=1,
+ max_seq_len=model.config.max_seq_len,
+ dtype=next(model.parameters()).dtype,
+ )
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+
+ logger.info(f"Time to load model: {time.time() - t0:.02f} seconds")
+
+ codec = None
+ codec_checkpoint = checkpoint_path / "codec.pth"
+
+ # Handle prompt: --prompt-audio takes priority over --prompt-tokens
+ prompt_tokens_list = None
+ if prompt_audio:
+ logger.info("Loading codec model for audio encoding...")
+ codec = load_codec_model(codec_checkpoint, device, precision)
+ prompt_tokens_list = [
+ encode_audio(p, codec, device).cpu() for p in prompt_audio
+ ]
+ logger.info(f"Encoded {len(prompt_audio)} audio file(s) to VQ codes")
+ elif prompt_tokens is not None:
+ prompt_tokens_list = [torch.from_numpy(np.load(p)) for p in prompt_tokens]
+
+ torch.manual_seed(seed)
+
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed(seed)
+
+ generator = generate_long(
+ model=model,
+ device=device,
+ decode_one_token=decode_one_token,
+ text=text,
+ num_samples=num_samples,
+ max_new_tokens=max_new_tokens,
+ top_p=top_p,
+ top_k=top_k,
+ temperature=temperature,
+ compile=compile,
+ iterative_prompt=iterative_prompt,
+ chunk_length=chunk_length,
+ prompt_text=list(prompt_text) if prompt_text else None,
+ prompt_tokens=prompt_tokens_list,
+ )
+
+ idx = 0
+ codes = []
+
+ for response in generator:
+ if response.action == "sample":
+ codes.append(response.codes)
+ logger.info(f"Sampled text: {response.text}")
+ elif response.action == "next":
+ if codes:
+ merged_codes = torch.cat(codes, dim=1)
+ codes_npy_path = os.path.join(output_dir, f"codes_{idx}.npy")
+ np.save(codes_npy_path, merged_codes.cpu().numpy())
+ logger.info(f"Saved codes to {codes_npy_path}")
+
+ # Decode to wav if --output is specified
+ if output:
+ if codec is None:
+ logger.info("Loading codec model for audio decoding...")
+ codec = load_codec_model(codec_checkpoint, device, precision)
+ audio = decode_to_audio(merged_codes.to(device), codec)
+ import soundfile as sf
+
+ out_path = (
+ str(output)
+ if num_samples == 1
+ else str(output.with_stem(f"{output.stem}_{idx}"))
+ )
+ sf.write(out_path, audio.cpu().float().numpy(), codec.sample_rate)
+ logger.info(f"Saved audio to {out_path}")
+
+ logger.info(f"Next sample")
+ codes = []
+ idx += 1
+ else:
+ logger.error(f"Error: {response}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/vendor/fish-speech/fish_speech/scheduler.py b/vendor/fish-speech/fish_speech/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..43bed6a2210723a7d5e1ea0a48ba61140047ca29
--- /dev/null
+++ b/vendor/fish-speech/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
diff --git a/vendor/fish-speech/fish_speech/text/__init__.py b/vendor/fish-speech/fish_speech/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad704d79a881c50d68b4a0fc1490104ee933dec1
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/text/__init__.py
@@ -0,0 +1,3 @@
+from .clean import clean_text
+
+__all__ = ["clean_text"]
diff --git a/vendor/fish-speech/fish_speech/text/clean.py b/vendor/fish-speech/fish_speech/text/clean.py
new file mode 100644
index 0000000000000000000000000000000000000000..68428c406c018a5bb156908b80341429a78c0301
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/text/clean.py
@@ -0,0 +1,37 @@
+import re
+
+SYMBOLS_MAPPING = {
+ "‘": "'",
+ "’": "'",
+}
+
+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
diff --git a/vendor/fish-speech/fish_speech/tokenizer.py b/vendor/fish-speech/fish_speech/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5cc1998aa2ded211692c01003f6971ab6ca97b1
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/tokenizer.py
@@ -0,0 +1,129 @@
+import json
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, List, Union
+
+import torch
+from transformers import AutoTokenizer
+
+if TYPE_CHECKING:
+ from transformers import PreTrainedTokenizerFast
+
+logger = logging.getLogger(__name__)
+
+# Constants definitions
+EOS_TOKEN = "<|endoftext|>"
+PAD_TOKEN = "<|pad|>"
+IM_START_TOKEN = "<|im_start|>"
+IM_END_TOKEN = "<|im_end|>"
+PHONEME_START_TOKEN = "<|phoneme_start|>"
+PHONEME_END_TOKEN = "<|phoneme_end|>"
+
+MODALITY_TEXT_TOKEN = "<|text|>"
+MODALITY_VOICE_TOKEN = "<|voice|>"
+MODALITY_INTERLEAVE_TOKEN = "<|interleave|>"
+AUDIO_START_TOKEN = "<|audio_start|>"
+AUDIO_END_TOKEN = "<|audio_end|>"
+AUDIO_EMBED_TOKEN = "<|audio_pad|>"
+
+MODALITY_TOKENS = {
+ "text": MODALITY_TEXT_TOKEN,
+ "voice": MODALITY_VOICE_TOKEN,
+ "interleave": MODALITY_INTERLEAVE_TOKEN,
+}
+
+SEMANTIC_TOKEN_TEMPLATE = "<|semantic:{i}|>"
+SEMANTIC_TOKENS = [SEMANTIC_TOKEN_TEMPLATE.format(i=i) for i in range(4096)]
+
+ALL_SPECIAL_TOKENS = [
+ EOS_TOKEN,
+ PAD_TOKEN,
+ IM_START_TOKEN,
+ IM_END_TOKEN,
+ PHONEME_START_TOKEN,
+ PHONEME_END_TOKEN,
+ MODALITY_TEXT_TOKEN,
+ MODALITY_VOICE_TOKEN,
+ MODALITY_INTERLEAVE_TOKEN,
+ AUDIO_START_TOKEN,
+ AUDIO_END_TOKEN,
+ AUDIO_EMBED_TOKEN,
+ *SEMANTIC_TOKENS,
+]
+
+
+class FishTokenizer:
+ def __init__(self, model_path: str):
+ self._tokenizer = AutoTokenizer.from_pretrained(model_path)
+ self.semantic_id_to_token_id = {}
+
+ vocab = self._tokenizer.get_vocab()
+ valid_ids = []
+
+ for code_idx in range(4096):
+ token = SEMANTIC_TOKEN_TEMPLATE.format(i=code_idx)
+ if token in vocab:
+ token_id = vocab[token]
+ self.semantic_id_to_token_id[code_idx] = token_id
+ valid_ids.append(token_id)
+
+ if not valid_ids:
+ logger.error(
+ "CRITICAL ERROR: No semantic tokens found in vocab! Audio cannot be synthesized."
+ )
+ self.semantic_begin_id = 0
+ self.semantic_end_id = 0
+ # Dummy tensor to prevent crash, though generation will fail
+ self.semantic_map_tensor = torch.zeros(4096, dtype=torch.long)
+ else:
+ self.semantic_begin_id = min(valid_ids)
+ self.semantic_end_id = max(valid_ids)
+ # Create a lookup tensor to handle potential gaps in token IDs safely
+ self.semantic_map_tensor = torch.zeros(4096, dtype=torch.long)
+ for k, v in self.semantic_id_to_token_id.items():
+ self.semantic_map_tensor[k] = v
+
+ logger.info(
+ f"Loaded Tokenizer. Semantic Range: {self.semantic_begin_id} -> {self.semantic_end_id}"
+ )
+
+ @property
+ def vocab_size(self):
+ return self._tokenizer.vocab_size
+
+ @property
+ def pad_token_id(self):
+ return self._tokenizer.pad_token_id
+
+ @property
+ def eos_token_id(self):
+ return self._tokenizer.eos_token_id
+
+ def get_token_id(self, token: str) -> int:
+ return self._tokenizer.convert_tokens_to_ids(token)
+
+ def encode(
+ self, text: str, add_special_tokens: bool = False, **kwargs
+ ) -> List[int]:
+ # [FIX] Force Qwen/Tiktoken backends to parse special tokens inline
+ import inspect
+
+ sig = inspect.signature(self._tokenizer.encode)
+ if "allowed_special" in sig.parameters and "allowed_special" not in kwargs:
+ kwargs["allowed_special"] = "all"
+ return self._tokenizer.encode(
+ text, add_special_tokens=add_special_tokens, **kwargs
+ )
+
+ def decode(self, tokens: Union[List[int], int], **kwargs) -> str:
+ return self._tokenizer.decode(tokens, **kwargs)
+
+ def save_pretrained(self, path: str):
+ self._tokenizer.save_pretrained(path)
+
+ @classmethod
+ def from_pretrained(cls, path: str):
+ return cls(path)
+
+ def __getattr__(self, name):
+ return getattr(self._tokenizer, name)
diff --git a/vendor/fish-speech/fish_speech/train.py b/vendor/fish-speech/fish_speech/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..e693f3adc4dda787bdd587aec29f53355f2b1653
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/train.py
@@ -0,0 +1,141 @@
+import os
+
+os.environ["USE_LIBUV"] = "0"
+import sys
+from typing import Optional
+
+import hydra
+import lightning as L
+import pyrootutils
+import torch
+from lightning import Callback, LightningDataModule, LightningModule, Trainer
+from lightning.pytorch.loggers import Logger
+from lightning.pytorch.strategies import DDPStrategy
+from omegaconf import DictConfig, OmegaConf
+
+os.environ.pop("SLURM_NTASKS", None)
+os.environ.pop("SLURM_JOB_NAME", None)
+os.environ.pop("SLURM_NTASKS_PER_NODE", None)
+
+# register eval resolver and root
+pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+# Allow TF32 on Ampere GPUs
+torch.set_float32_matmul_precision("high")
+torch.backends.cudnn.allow_tf32 = True
+
+# register eval resolver
+OmegaConf.register_new_resolver("eval", eval)
+
+import fish_speech.utils as utils
+
+log = utils.RankedLogger(__name__, rank_zero_only=True)
+
+
+@utils.task_wrapper
+def train(cfg: DictConfig) -> tuple[dict, dict]:
+ """Trains the model. Can additionally evaluate on a testset, using best weights obtained during
+ training.
+ This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
+ failure. Useful for multiruns, saving info about the crash, etc.
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+ Returns:
+ Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
+ """ # noqa: E501
+
+ # set seed for random number generators in pytorch, numpy and python.random
+ if cfg.get("seed"):
+ L.seed_everything(cfg.seed, workers=False)
+
+ if cfg.get("deterministic"):
+ torch.use_deterministic_algorithms(True)
+
+ log.info(f"Instantiating datamodule <{cfg.data._target_}>")
+ datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
+
+ log.info(f"Instantiating model <{cfg.model._target_}>")
+ model: LightningModule = hydra.utils.instantiate(cfg.model)
+
+ log.info("Instantiating callbacks...")
+ callbacks: list[Callback] = utils.instantiate_callbacks(cfg.get("callbacks"))
+
+ log.info("Instantiating loggers...")
+ logger: list[Logger] = utils.instantiate_loggers(cfg.get("logger"))
+
+ log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
+ trainer: Trainer = hydra.utils.instantiate(
+ cfg.trainer,
+ callbacks=callbacks,
+ logger=logger,
+ )
+
+ object_dict = {
+ "cfg": cfg,
+ "datamodule": datamodule,
+ "model": model,
+ "callbacks": callbacks,
+ "logger": logger,
+ "trainer": trainer,
+ }
+
+ if logger:
+ log.info("Logging hyperparameters!")
+ utils.log_hyperparameters(object_dict)
+
+ if cfg.get("train"):
+ log.info("Starting training!")
+
+ ckpt_path = cfg.get("ckpt_path")
+ auto_resume = False
+
+ resume_ckpt_path = utils.get_latest_checkpoint(cfg.paths.ckpt_dir)
+ if resume_ckpt_path is not None:
+ ckpt_path = resume_ckpt_path
+ auto_resume = True
+
+ if ckpt_path is not None:
+ log.info(f"Resuming from checkpoint: {ckpt_path}")
+
+ # resume weights only is disabled for auto-resume
+ if cfg.get("resume_weights_only") and auto_resume is False:
+ log.info("Resuming weights only!")
+ ckpt = torch.load(ckpt_path, map_location=model.device)
+ if "state_dict" in ckpt:
+ ckpt = ckpt["state_dict"]
+ err = model.load_state_dict(ckpt, strict=False)
+ log.info(f"Error loading state dict: {err}")
+ ckpt_path = None
+
+ trainer.fit(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
+
+ train_metrics = trainer.callback_metrics
+
+ if cfg.get("test"):
+ log.info("Starting testing!")
+ ckpt_path = trainer.checkpoint_callback.best_model_path
+ if ckpt_path == "":
+ log.warning("Best ckpt not found! Using current weights for testing...")
+ ckpt_path = cfg.get("ckpt_path")
+
+ trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
+ log.info(f"Best ckpt path: {ckpt_path}")
+
+ test_metrics = trainer.callback_metrics
+
+ # merge train and test metrics
+ metric_dict = {**train_metrics, **test_metrics}
+
+ return metric_dict, object_dict
+
+
+@hydra.main(
+ version_base="1.3", config_path="./configs", config_name="llama_pretrain.yaml"
+)
+def main(cfg: DictConfig) -> Optional[float]:
+ # train the model
+ train(cfg)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/vendor/fish-speech/fish_speech/utils/__init__.py b/vendor/fish-speech/fish_speech/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..53cf2f23174ddac9bf523730aca2f6a9965d134a
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/__init__.py
@@ -0,0 +1,24 @@
+from .braceexpand import braceexpand
+from .context import autocast_exclude_mps
+from .file import get_latest_checkpoint
+from .instantiators import instantiate_callbacks, instantiate_loggers
+from .logger import RankedLogger
+from .logging_utils import log_hyperparameters
+from .rich_utils import enforce_tags, print_config_tree
+from .utils import extras, get_metric_value, set_seed, task_wrapper
+
+__all__ = [
+ "enforce_tags",
+ "extras",
+ "get_metric_value",
+ "RankedLogger",
+ "instantiate_callbacks",
+ "instantiate_loggers",
+ "log_hyperparameters",
+ "print_config_tree",
+ "task_wrapper",
+ "braceexpand",
+ "get_latest_checkpoint",
+ "autocast_exclude_mps",
+ "set_seed",
+]
diff --git a/vendor/fish-speech/fish_speech/utils/braceexpand.py b/vendor/fish-speech/fish_speech/utils/braceexpand.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3ac739f01f7e10e039c68c1157d6c761064f974
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/braceexpand.py
@@ -0,0 +1,217 @@
+"""
+Bash-style brace expansion
+Copied from: https://github.com/trendels/braceexpand/blob/main/src/braceexpand/__init__.py
+License: MIT
+"""
+
+import re
+import string
+from itertools import chain, product
+from typing import Iterable, Iterator, Optional
+
+__all__ = ["braceexpand", "alphabet", "UnbalancedBracesError"]
+
+
+class UnbalancedBracesError(ValueError):
+ pass
+
+
+alphabet = string.ascii_uppercase + string.ascii_lowercase
+
+int_range_re = re.compile(r"^(-?\d+)\.\.(-?\d+)(?:\.\.-?(\d+))?$")
+char_range_re = re.compile(r"^([A-Za-z])\.\.([A-Za-z])(?:\.\.-?(\d+))?$")
+escape_re = re.compile(r"\\(.)")
+
+
+def braceexpand(pattern: str, escape: bool = True) -> Iterator[str]:
+ """braceexpand(pattern) -> iterator over generated strings
+
+ Returns an iterator over the strings resulting from brace expansion
+ of pattern. This function implements Brace Expansion as described in
+ bash(1), with the following limitations:
+
+ * A pattern containing unbalanced braces will raise an
+ UnbalancedBracesError exception. In bash, unbalanced braces will either
+ be partly expanded or ignored.
+
+ * A mixed-case character range like '{Z..a}' or '{a..Z}' will not
+ include the characters '[]^_`' between 'Z' and 'a'.
+
+ When escape is True (the default), characters in pattern can be
+ prefixed with a backslash to cause them not to be interpreted as
+ special characters for brace expansion (such as '{', '}', ',').
+ To pass through a a literal backslash, double it ('\\\\').
+
+ When escape is False, backslashes in pattern have no special
+ meaning and will be preserved in the output.
+
+ Examples:
+
+ >>> from braceexpand import braceexpand
+
+ # Integer range
+ >>> list(braceexpand('item{1..3}'))
+ ['item1', 'item2', 'item3']
+
+ # Character range
+ >>> list(braceexpand('{a..c}'))
+ ['a', 'b', 'c']
+
+ # Sequence
+ >>> list(braceexpand('index.html{,.backup}'))
+ ['index.html', 'index.html.backup']
+
+ # Nested patterns
+ >>> list(braceexpand('python{2.{5..7},3.{2,3}}'))
+ ['python2.5', 'python2.6', 'python2.7', 'python3.2', 'python3.3']
+
+ # Prefixing an integer with zero causes all numbers to be padded to
+ # the same width.
+ >>> list(braceexpand('{07..10}'))
+ ['07', '08', '09', '10']
+
+ # An optional increment can be specified for ranges.
+ >>> list(braceexpand('{a..g..2}'))
+ ['a', 'c', 'e', 'g']
+
+ # Ranges can go in both directions.
+ >>> list(braceexpand('{4..1}'))
+ ['4', '3', '2', '1']
+
+ # Numbers can be negative
+ >>> list(braceexpand('{2..-1}'))
+ ['2', '1', '0', '-1']
+
+ # Unbalanced braces raise an exception.
+ >>> list(braceexpand('{1{2,3}'))
+ Traceback (most recent call last):
+ ...
+ UnbalancedBracesError: Unbalanced braces: '{1{2,3}'
+
+ # By default, the backslash is the escape character.
+ >>> list(braceexpand(r'{1\\{2,3}'))
+ ['1{2', '3']
+
+ # Setting 'escape' to False disables backslash escaping.
+ >>> list(braceexpand(r'\\{1,2}', escape=False))
+ ['\\\\1', '\\\\2']
+
+ """
+ return (
+ escape_re.sub(r"\1", s) if escape else s for s in parse_pattern(pattern, escape)
+ )
+
+
+def parse_pattern(pattern: str, escape: bool) -> Iterator[str]:
+ start = 0
+ pos = 0
+ bracketdepth = 0
+ items: list[Iterable[str]] = []
+
+ # print 'pattern:', pattern
+ while pos < len(pattern):
+ if escape and pattern[pos] == "\\":
+ pos += 2
+ continue
+ elif pattern[pos] == "{":
+ if bracketdepth == 0 and pos > start:
+ # print 'literal:', pattern[start:pos]
+ items.append([pattern[start:pos]])
+ start = pos
+ bracketdepth += 1
+ elif pattern[pos] == "}":
+ bracketdepth -= 1
+ if bracketdepth == 0:
+ # print 'expression:', pattern[start+1:pos]
+ expr = pattern[start + 1 : pos]
+ item = parse_expression(expr, escape)
+ if item is None: # not a range or sequence
+ items.extend([["{"], parse_pattern(expr, escape), ["}"]])
+ else:
+ items.append(item)
+ start = pos + 1 # skip the closing brace
+ pos += 1
+
+ if bracketdepth != 0: # unbalanced braces
+ raise UnbalancedBracesError("Unbalanced braces: '%s'" % pattern)
+
+ if start < pos:
+ items.append([pattern[start:]])
+
+ return ("".join(item) for item in product(*items))
+
+
+def parse_expression(expr: str, escape: bool) -> Optional[Iterable[str]]:
+ int_range_match = int_range_re.match(expr)
+ if int_range_match:
+ return make_int_range(*int_range_match.groups())
+
+ char_range_match = char_range_re.match(expr)
+ if char_range_match:
+ return make_char_range(*char_range_match.groups())
+
+ return parse_sequence(expr, escape)
+
+
+def parse_sequence(seq: str, escape: bool) -> Optional[Iterator[str]]:
+ # sequence -> chain(*sequence_items)
+ start = 0
+ pos = 0
+ bracketdepth = 0
+ items: list[Iterable[str]] = []
+
+ # print 'sequence:', seq
+ while pos < len(seq):
+ if escape and seq[pos] == "\\":
+ pos += 2
+ continue
+ elif seq[pos] == "{":
+ bracketdepth += 1
+ elif seq[pos] == "}":
+ bracketdepth -= 1
+ elif seq[pos] == "," and bracketdepth == 0:
+ items.append(parse_pattern(seq[start:pos], escape))
+ start = pos + 1 # skip the comma
+ pos += 1
+
+ if bracketdepth != 0:
+ raise UnbalancedBracesError
+ if not items:
+ return None
+
+ # part after the last comma (may be the empty string)
+ items.append(parse_pattern(seq[start:], escape))
+ return chain(*items)
+
+
+def make_int_range(left: str, right: str, incr: Optional[str] = None) -> Iterator[str]:
+ if any([s.startswith(("0", "-0")) for s in (left, right) if s not in ("0", "-0")]):
+ padding = max(len(left), len(right))
+ else:
+ padding = 0
+ step = (int(incr) or 1) if incr else 1
+ start = int(left)
+ end = int(right)
+ r = range(start, end + 1, step) if start < end else range(start, end - 1, -step)
+ fmt = "%0{}d".format(padding)
+ return (fmt % i for i in r)
+
+
+def make_char_range(left: str, right: str, incr: Optional[str] = None) -> str:
+ step = (int(incr) or 1) if incr else 1
+ start = alphabet.index(left)
+ end = alphabet.index(right)
+ if start < end:
+ return alphabet[start : end + 1 : step]
+ else:
+ end = end or -len(alphabet)
+ return alphabet[start : end - 1 : -step]
+
+
+if __name__ == "__main__":
+ import doctest
+ import sys
+
+ failed, _ = doctest.testmod(optionflags=doctest.IGNORE_EXCEPTION_DETAIL)
+ if failed:
+ sys.exit(1)
diff --git a/vendor/fish-speech/fish_speech/utils/context.py b/vendor/fish-speech/fish_speech/utils/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..f04a99290ab32f7fe5b60656075a2d03af8468d6
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/context.py
@@ -0,0 +1,13 @@
+from contextlib import nullcontext
+
+import torch
+
+
+def autocast_exclude_mps(
+ device_type: str, dtype: torch.dtype
+) -> nullcontext | torch.autocast:
+ return (
+ nullcontext()
+ if torch.backends.mps.is_available()
+ else torch.autocast(device_type, dtype)
+ )
diff --git a/vendor/fish-speech/fish_speech/utils/file.py b/vendor/fish-speech/fish_speech/utils/file.py
new file mode 100644
index 0000000000000000000000000000000000000000..a54c22655fc52d55db8932f7c6edabe017b965f4
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/file.py
@@ -0,0 +1,139 @@
+import os
+from pathlib import Path
+from typing import Union
+
+from loguru import logger
+from natsort import natsorted
+
+AUDIO_EXTENSIONS = {
+ ".mp3",
+ ".wav",
+ ".flac",
+ ".ogg",
+ ".m4a",
+ ".wma",
+ ".aac",
+ ".aiff",
+ ".aif",
+ ".aifc",
+}
+
+VIDEO_EXTENSIONS = {
+ ".mp4",
+ ".avi",
+}
+
+
+def get_latest_checkpoint(path: Path | str) -> Path | None:
+ # Find the latest checkpoint
+ ckpt_dir = Path(path)
+
+ if ckpt_dir.exists() is False:
+ return None
+
+ ckpts = sorted(ckpt_dir.glob("*.ckpt"), key=os.path.getmtime)
+ if len(ckpts) == 0:
+ return None
+
+ return ckpts[-1]
+
+
+def audio_to_bytes(file_path):
+ if not file_path or not Path(file_path).exists():
+ return None
+ with open(file_path, "rb") as wav_file:
+ wav = wav_file.read()
+ return wav
+
+
+def read_ref_text(ref_text):
+ path = Path(ref_text)
+ if path.exists() and path.is_file():
+ with path.open("r", encoding="utf-8") as file:
+ return file.read()
+ return ref_text
+
+
+def list_files(
+ path: Union[Path, str],
+ extensions: set[str] = set(),
+ recursive: bool = False,
+ sort: bool = True,
+) -> list[Path]:
+ """List files in a directory.
+
+ Args:
+ path (Path): Path to the directory.
+ extensions (set, optional): Extensions to filter. Defaults to None.
+ recursive (bool, optional): Whether to search recursively. Defaults to False.
+ sort (bool, optional): Whether to sort the files. Defaults to True.
+
+ Returns:
+ list: List of files.
+ """
+
+ if isinstance(path, str):
+ path = Path(path)
+
+ if not path.exists():
+ raise FileNotFoundError(f"Directory {path} does not exist.")
+
+ files = [file for ext in extensions for file in path.rglob(f"*{ext}")]
+
+ if sort:
+ files = natsorted(files)
+
+ return files
+
+
+def load_filelist(path: Path | str) -> list[tuple[Path, str, str, str]]:
+ """
+ Load a Bert-VITS2 style filelist.
+ """
+
+ files = set()
+ results = []
+ count_duplicated, count_not_found = 0, 0
+
+ LANGUAGE_TO_LANGUAGES = {
+ "zh": ["zh", "en"],
+ "jp": ["jp", "en"],
+ "en": ["en"],
+ }
+
+ with open(path, "r", encoding="utf-8") as f:
+ for line in f.readlines():
+ splits = line.strip().split("|", maxsplit=3)
+ if len(splits) != 4:
+ logger.warning(f"Invalid line: {line}")
+ continue
+
+ filename, speaker, language, text = splits
+ file = Path(filename)
+ language = language.strip().lower()
+
+ if language == "ja":
+ language = "jp"
+
+ assert language in ["zh", "jp", "en"], f"Invalid language {language}"
+ languages = LANGUAGE_TO_LANGUAGES[language]
+
+ if file in files:
+ logger.warning(f"Duplicated file: {file}")
+ count_duplicated += 1
+ continue
+
+ if not file.exists():
+ logger.warning(f"File not found: {file}")
+ count_not_found += 1
+ continue
+
+ results.append((file, speaker, languages, text))
+
+ if count_duplicated > 0:
+ logger.warning(f"Total duplicated files: {count_duplicated}")
+
+ if count_not_found > 0:
+ logger.warning(f"Total files not found: {count_not_found}")
+
+ return results
diff --git a/vendor/fish-speech/fish_speech/utils/instantiators.py b/vendor/fish-speech/fish_speech/utils/instantiators.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6ee463924f588a35477937fbe3c3364043bdf3e
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/instantiators.py
@@ -0,0 +1,50 @@
+from typing import List
+
+import hydra
+from omegaconf import DictConfig
+from pytorch_lightning import Callback
+from pytorch_lightning.loggers import Logger
+
+from .logger import RankedLogger
+
+log = RankedLogger(__name__, rank_zero_only=True)
+
+
+def instantiate_callbacks(callbacks_cfg: DictConfig) -> List[Callback]:
+ """Instantiates callbacks from config."""
+
+ callbacks: List[Callback] = []
+
+ if not callbacks_cfg:
+ log.warning("No callback configs found! Skipping..")
+ return callbacks
+
+ if not isinstance(callbacks_cfg, DictConfig):
+ raise TypeError("Callbacks config must be a DictConfig!")
+
+ for _, cb_conf in callbacks_cfg.items():
+ if isinstance(cb_conf, DictConfig) and "_target_" in cb_conf:
+ log.info(f"Instantiating callback <{cb_conf._target_}>")
+ callbacks.append(hydra.utils.instantiate(cb_conf))
+
+ return callbacks
+
+
+def instantiate_loggers(logger_cfg: DictConfig) -> List[Logger]:
+ """Instantiates loggers from config."""
+
+ logger: List[Logger] = []
+
+ if not logger_cfg:
+ log.warning("No logger configs found! Skipping...")
+ return logger
+
+ if not isinstance(logger_cfg, DictConfig):
+ raise TypeError("Logger config must be a DictConfig!")
+
+ for _, lg_conf in logger_cfg.items():
+ if isinstance(lg_conf, DictConfig) and "_target_" in lg_conf:
+ log.info(f"Instantiating logger <{lg_conf._target_}>")
+ logger.append(hydra.utils.instantiate(lg_conf))
+
+ return logger
diff --git a/vendor/fish-speech/fish_speech/utils/logger.py b/vendor/fish-speech/fish_speech/utils/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..94f94f738d1d87404354d086c30ef0ad9ab04cdc
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/logger.py
@@ -0,0 +1,55 @@
+import logging
+from typing import Mapping, Optional
+
+from lightning_utilities.core.rank_zero import rank_prefixed_message, rank_zero_only
+
+
+class RankedLogger(logging.LoggerAdapter):
+ """A multi-GPU-friendly python command line logger."""
+
+ def __init__(
+ self,
+ name: str = __name__,
+ rank_zero_only: bool = True,
+ extra: Optional[Mapping[str, object]] = None,
+ ) -> None:
+ """Initializes a multi-GPU-friendly python command line logger that logs on all processes
+ with their rank prefixed in the log message.
+
+ :param name: The name of the logger. Default is ``__name__``.
+ :param rank_zero_only: Whether to force all logs to only occur on the rank zero process. Default is `False`.
+ :param extra: (Optional) A dict-like object which provides contextual information. See `logging.LoggerAdapter`.
+ """
+ logger = logging.getLogger(name)
+ super().__init__(logger=logger, extra=extra)
+ self.rank_zero_only = rank_zero_only
+
+ def log(
+ self, level: int, msg: str, rank: Optional[int] = None, *args, **kwargs
+ ) -> None:
+ """Delegate a log call to the underlying logger, after prefixing its message with the rank
+ of the process it's being logged from. If `'rank'` is provided, then the log will only
+ occur on that rank/process.
+
+ :param level: The level to log at. Look at `logging.__init__.py` for more information.
+ :param msg: The message to log.
+ :param rank: The rank to log at.
+ :param args: Additional args to pass to the underlying logging function.
+ :param kwargs: Any additional keyword args to pass to the underlying logging function.
+ """
+ if self.isEnabledFor(level):
+ msg, kwargs = self.process(msg, kwargs)
+ current_rank = getattr(rank_zero_only, "rank", None)
+ if current_rank is None:
+ raise RuntimeError(
+ "The `rank_zero_only.rank` needs to be set before use"
+ )
+ msg = rank_prefixed_message(msg, current_rank)
+ if self.rank_zero_only:
+ if current_rank == 0:
+ self.logger.log(level, msg, *args, **kwargs)
+ else:
+ if rank is None:
+ self.logger.log(level, msg, *args, **kwargs)
+ elif current_rank == rank:
+ self.logger.log(level, msg, *args, **kwargs)
diff --git a/vendor/fish-speech/fish_speech/utils/logging_utils.py b/vendor/fish-speech/fish_speech/utils/logging_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e3b0a2519e12845f09e5fbe86dfccbf5b345429
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/logging_utils.py
@@ -0,0 +1,48 @@
+from lightning.pytorch.utilities import rank_zero_only
+
+from fish_speech.utils import logger as log
+
+
+@rank_zero_only
+def log_hyperparameters(object_dict: dict) -> None:
+ """Controls which config parts are saved by lightning loggers.
+
+ Additionally saves:
+ - Number of model parameters
+ """
+
+ hparams = {}
+
+ cfg = object_dict["cfg"]
+ model = object_dict["model"]
+ trainer = object_dict["trainer"]
+
+ if not trainer.logger:
+ log.warning("Logger not found! Skipping hyperparameter logging...")
+ return
+
+ hparams["model"] = cfg["model"]
+
+ # save number of model parameters
+ hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
+ hparams["model/params/trainable"] = sum(
+ p.numel() for p in model.parameters() if p.requires_grad
+ )
+ hparams["model/params/non_trainable"] = sum(
+ p.numel() for p in model.parameters() if not p.requires_grad
+ )
+
+ hparams["data"] = cfg["data"]
+ hparams["trainer"] = cfg["trainer"]
+
+ hparams["callbacks"] = cfg.get("callbacks")
+ hparams["extras"] = cfg.get("extras")
+
+ hparams["task_name"] = cfg.get("task_name")
+ hparams["tags"] = cfg.get("tags")
+ hparams["ckpt_path"] = cfg.get("ckpt_path")
+ hparams["seed"] = cfg.get("seed")
+
+ # send hparams to all loggers
+ for logger in trainer.loggers:
+ logger.log_hyperparams(hparams)
diff --git a/vendor/fish-speech/fish_speech/utils/rich_utils.py b/vendor/fish-speech/fish_speech/utils/rich_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a465f54d610779766d51e3d1a020a3b1517fd1f
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/rich_utils.py
@@ -0,0 +1,100 @@
+from pathlib import Path
+from typing import Sequence
+
+import rich
+import rich.syntax
+import rich.tree
+from hydra.core.hydra_config import HydraConfig
+from lightning.pytorch.utilities import rank_zero_only
+from omegaconf import DictConfig, OmegaConf, open_dict
+from rich.prompt import Prompt
+
+from fish_speech.utils import logger as log
+
+
+@rank_zero_only
+def print_config_tree(
+ cfg: DictConfig,
+ print_order: Sequence[str] = (
+ "data",
+ "model",
+ "callbacks",
+ "logger",
+ "trainer",
+ "paths",
+ "extras",
+ ),
+ resolve: bool = False,
+ save_to_file: bool = False,
+) -> None:
+ """Prints content of DictConfig using Rich library and its tree structure.
+
+ Args:
+ cfg (DictConfig): Configuration composed by Hydra.
+ print_order (Sequence[str], optional): Determines in what order config components are printed.
+ resolve (bool, optional): Whether to resolve reference fields of DictConfig.
+ save_to_file (bool, optional): Whether to export config to the hydra output folder.
+ """ # noqa: E501
+
+ style = "dim"
+ tree = rich.tree.Tree("CONFIG", style=style, guide_style=style)
+
+ queue = []
+
+ # add fields from `print_order` to queue
+ for field in print_order:
+ (
+ queue.append(field)
+ if field in cfg
+ else log.warning(
+ f"Field '{field}' not found in config. "
+ + f"Skipping '{field}' config printing..."
+ )
+ )
+
+ # add all the other fields to queue (not specified in `print_order`)
+ for field in cfg:
+ if field not in queue:
+ queue.append(field)
+
+ # generate config tree from queue
+ for field in queue:
+ branch = tree.add(field, style=style, guide_style=style)
+
+ config_group = cfg[field]
+ if isinstance(config_group, DictConfig):
+ branch_content = OmegaConf.to_yaml(config_group, resolve=resolve)
+ else:
+ branch_content = str(config_group)
+
+ branch.add(rich.syntax.Syntax(branch_content, "yaml"))
+
+ # print config tree
+ rich.print(tree)
+
+ # save config tree to file
+ if save_to_file:
+ with open(Path(cfg.paths.output_dir, "config_tree.log"), "w") as file:
+ rich.print(tree, file=file)
+
+
+@rank_zero_only
+def enforce_tags(cfg: DictConfig, save_to_file: bool = False) -> None:
+ """Prompts user to input tags from command line if no tags are provided in config.""" # noqa: E501
+
+ if not cfg.get("tags"):
+ if "id" in HydraConfig().cfg.hydra.job:
+ raise ValueError("Specify tags before launching a multirun!")
+
+ log.warning("No tags provided in config. Prompting user to input tags...")
+ tags = Prompt.ask("Enter a list of comma separated tags", default="dev")
+ tags = [t.strip() for t in tags.split(",") if t != ""]
+
+ with open_dict(cfg):
+ cfg.tags = tags
+
+ log.info(f"Tags: {cfg.tags}")
+
+ if save_to_file:
+ with open(Path(cfg.paths.output_dir, "tags.log"), "w") as file:
+ rich.print(cfg.tags, file=file)
diff --git a/vendor/fish-speech/fish_speech/utils/schema.py b/vendor/fish-speech/fish_speech/utils/schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ef4dcd372379fe1a4e64a0025b0c63de7c2ec96
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/schema.py
@@ -0,0 +1,138 @@
+import base64
+import os
+import queue
+from dataclasses import dataclass
+from typing import Literal
+
+import torch
+from pydantic import BaseModel, Field, conint, model_validator
+from pydantic.functional_validators import SkipValidation
+from typing_extensions import Annotated
+
+from fish_speech.content_sequence import TextPart, VQPart
+
+
+class ServeVQPart(BaseModel):
+ type: Literal["vq"] = "vq"
+ codes: SkipValidation[list[list[int]]]
+
+
+class ServeTextPart(BaseModel):
+ type: Literal["text"] = "text"
+ text: str
+
+
+class ServeAudioPart(BaseModel):
+ type: Literal["audio"] = "audio"
+ audio: bytes
+
+
+class ServeRequest(BaseModel):
+ # Raw content sequence dict that we can use with ContentSequence(**content)
+ content: dict
+ max_new_tokens: int = 600
+ top_p: float = 0.7
+ repetition_penalty: float = 1.2
+ temperature: float = 0.7
+ streaming: bool = False
+ num_samples: int = 1
+ early_stop_threshold: float = 1.0
+
+
+class ServeVQGANEncodeRequest(BaseModel):
+ # The audio here should be in wav, mp3, etc
+ audios: list[bytes]
+
+
+class ServeVQGANEncodeResponse(BaseModel):
+ tokens: SkipValidation[list[list[list[int]]]]
+
+
+class ServeVQGANDecodeRequest(BaseModel):
+ tokens: SkipValidation[list[list[list[int]]]]
+
+
+class ServeVQGANDecodeResponse(BaseModel):
+ # The audio here should be in PCM float16 format
+ audios: list[bytes]
+
+
+class ServeReferenceAudio(BaseModel):
+ audio: bytes
+ text: str
+
+ @model_validator(mode="before")
+ def decode_audio(cls, values):
+ audio = values.get("audio")
+ if (
+ isinstance(audio, str) and len(audio) > 255
+ ): # Check if audio is a string (Base64)
+ try:
+ values["audio"] = base64.b64decode(audio)
+ except Exception:
+ # If the audio is not a valid base64 string, we will just ignore it and let the server handle it
+ pass
+ return values
+
+ def __repr__(self) -> str:
+ return f"ServeReferenceAudio(text={self.text!r}, audio_size={len(self.audio)})"
+
+
+class ServeTTSRequest(BaseModel):
+ text: str
+ chunk_length: Annotated[int, conint(ge=100, le=1000, strict=True)] = 200
+ # Audio format
+ format: Literal["wav", "pcm", "mp3", "opus"] = "wav"
+ # Latency mode (used by api.fish.audio; "normal" or "balanced")
+ latency: Literal["normal", "balanced"] = "normal"
+ # References audios for in-context learning
+ references: list[ServeReferenceAudio] = []
+ # Reference id
+ # For example, if you want use https://fish.audio/m/7f92f8afb8ec43bf81429cc1c9199cb1/
+ # Just pass 7f92f8afb8ec43bf81429cc1c9199cb1
+ reference_id: str | None = None
+ seed: int | None = None
+ use_memory_cache: Literal["on", "off"] = "off"
+ # Normalize text for en & zh, this increase stability for numbers
+ normalize: bool = True
+ # not usually used below
+ streaming: bool = False
+ max_new_tokens: int = 1024
+ top_p: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.8
+ repetition_penalty: Annotated[float, Field(ge=0.9, le=2.0, strict=True)] = 1.1
+ temperature: Annotated[float, Field(ge=0.1, le=1.0, strict=True)] = 0.8
+
+ class Config:
+ # Allow arbitrary types for pytorch related types
+ arbitrary_types_allowed = True
+
+
+class AddReferenceRequest(BaseModel):
+ id: str = Field(..., min_length=1, max_length=255, pattern=r"^[a-zA-Z0-9\-_ ]+$")
+ audio: bytes
+ text: str = Field(..., min_length=1)
+
+
+class AddReferenceResponse(BaseModel):
+ success: bool
+ message: str
+ reference_id: str
+
+
+class ListReferencesResponse(BaseModel):
+ success: bool
+ reference_ids: list[str]
+ message: str = "Success"
+
+
+class DeleteReferenceResponse(BaseModel):
+ success: bool
+ message: str
+ reference_id: str
+
+
+class UpdateReferenceResponse(BaseModel):
+ success: bool
+ message: str
+ old_reference_id: str
+ new_reference_id: str
diff --git a/vendor/fish-speech/fish_speech/utils/spectrogram.py b/vendor/fish-speech/fish_speech/utils/spectrogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..19ea435c996f6862da8885c3e8c9e8ca2b291e32
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/spectrogram.py
@@ -0,0 +1,124 @@
+import torch
+import torchaudio.functional as F
+from torch import Tensor, nn
+from torchaudio.transforms import MelScale
+
+
+class LinearSpectrogram(nn.Module):
+ def __init__(
+ self,
+ n_fft=2048,
+ win_length=2048,
+ hop_length=512,
+ center=False,
+ mode="pow2_sqrt",
+ ):
+ super().__init__()
+
+ self.n_fft = n_fft
+ self.win_length = win_length
+ self.hop_length = hop_length
+ self.center = center
+ self.mode = mode
+ self.return_complex = True
+
+ self.register_buffer("window", torch.hann_window(win_length), persistent=False)
+
+ def forward(self, y: Tensor) -> Tensor:
+ if y.ndim == 3:
+ y = y.squeeze(1)
+
+ y = torch.nn.functional.pad(
+ y.unsqueeze(1),
+ (
+ (self.win_length - self.hop_length) // 2,
+ (self.win_length - self.hop_length + 1) // 2,
+ ),
+ mode="reflect",
+ ).squeeze(1)
+
+ spec = torch.stft(
+ y,
+ self.n_fft,
+ hop_length=self.hop_length,
+ win_length=self.win_length,
+ window=self.window,
+ center=self.center,
+ pad_mode="reflect",
+ normalized=False,
+ onesided=True,
+ return_complex=self.return_complex,
+ )
+
+ if self.return_complex:
+ spec = torch.view_as_real(spec)
+
+ if self.mode == "pow2_sqrt":
+ spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+
+ return spec
+
+
+class LogMelSpectrogram(nn.Module):
+ def __init__(
+ self,
+ sample_rate=44100,
+ n_fft=2048,
+ win_length=2048,
+ hop_length=512,
+ n_mels=128,
+ center=False,
+ f_min=0.0,
+ f_max=None,
+ ):
+ super().__init__()
+
+ self.sample_rate = sample_rate
+ self.n_fft = n_fft
+ self.win_length = win_length
+ self.hop_length = hop_length
+ self.center = center
+ self.n_mels = n_mels
+ self.f_min = f_min
+ self.f_max = f_max or float(sample_rate // 2)
+
+ self.spectrogram = LinearSpectrogram(n_fft, win_length, hop_length, center)
+
+ fb = F.melscale_fbanks(
+ n_freqs=self.n_fft // 2 + 1,
+ f_min=self.f_min,
+ f_max=self.f_max,
+ n_mels=self.n_mels,
+ sample_rate=self.sample_rate,
+ norm="slaney",
+ mel_scale="slaney",
+ )
+ self.register_buffer(
+ "fb",
+ fb,
+ persistent=False,
+ )
+
+ def compress(self, x: Tensor) -> Tensor:
+ return torch.log(torch.clamp(x, min=1e-5))
+
+ def decompress(self, x: Tensor) -> Tensor:
+ return torch.exp(x)
+
+ def apply_mel_scale(self, x: Tensor) -> Tensor:
+ return torch.matmul(x.transpose(-1, -2), self.fb).transpose(-1, -2)
+
+ def forward(
+ self, x: Tensor, return_linear: bool = False, sample_rate: int = None
+ ) -> Tensor:
+ if sample_rate is not None and sample_rate != self.sample_rate:
+ x = F.resample(x, orig_freq=sample_rate, new_freq=self.sample_rate)
+
+ linear = self.spectrogram(x)
+ x = self.apply_mel_scale(linear)
+ x = self.compress(x)
+
+ if return_linear:
+ return x, self.compress(linear)
+
+ return x
diff --git a/vendor/fish-speech/fish_speech/utils/utils.py b/vendor/fish-speech/fish_speech/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a34bdcfedff76c333f50ed8be050d0dd5a8f98a
--- /dev/null
+++ b/vendor/fish-speech/fish_speech/utils/utils.py
@@ -0,0 +1,136 @@
+import random
+import warnings
+from importlib.util import find_spec
+from typing import Callable
+
+import numpy as np
+import torch
+from omegaconf import DictConfig
+
+from .logger import RankedLogger
+from .rich_utils import enforce_tags, print_config_tree
+
+log = RankedLogger(__name__, rank_zero_only=True)
+
+
+def extras(cfg: DictConfig) -> None:
+ """Applies optional utilities before the task is started.
+
+ Utilities:
+ - Ignoring python warnings
+ - Setting tags from command line
+ - Rich config printing
+ """
+
+ # return if no `extras` config
+ if not cfg.get("extras"):
+ log.warning("Extras config not found! ")
+ return
+
+ # disable python warnings
+ if cfg.extras.get("ignore_warnings"):
+ log.info("Disabling python warnings! ")
+ warnings.filterwarnings("ignore")
+
+ # prompt user to input tags from command line if none are provided in the config
+ if cfg.extras.get("enforce_tags"):
+ log.info("Enforcing tags! ")
+ enforce_tags(cfg, save_to_file=True)
+
+ # pretty print config tree using Rich library
+ if cfg.extras.get("print_config"):
+ log.info("Printing config tree with Rich! ")
+ print_config_tree(cfg, resolve=True, save_to_file=True)
+
+
+def task_wrapper(task_func: Callable) -> Callable:
+ """Optional decorator that controls the failure behavior when executing the task function.
+
+ This wrapper can be used to:
+ - make sure loggers are closed even if the task function raises an exception (prevents multirun failure)
+ - save the exception to a `.log` file
+ - mark the run as failed with a dedicated file in the `logs/` folder (so we can find and rerun it later)
+ - etc. (adjust depending on your needs)
+
+ Example:
+ ```
+ @utils.task_wrapper
+ def train(cfg: DictConfig) -> Tuple[dict, dict]:
+
+ ...
+
+ return metric_dict, object_dict
+ ```
+ """ # noqa: E501
+
+ def wrap(cfg: DictConfig):
+ # execute the task
+ try:
+ metric_dict, object_dict = task_func(cfg=cfg)
+
+ # things to do if exception occurs
+ except Exception as ex:
+ # save exception to `.log` file
+ log.exception("")
+
+ # some hyperparameter combinations might be invalid or
+ # cause out-of-memory errors so when using hparam search
+ # plugins like Optuna, you might want to disable
+ # raising the below exception to avoid multirun failure
+ raise ex
+
+ # things to always do after either success or exception
+ finally:
+ # display output dir path in terminal
+ log.info(f"Output dir: {cfg.paths.run_dir}")
+
+ # always close wandb run (even if exception occurs so multirun won't fail)
+ if find_spec("wandb"): # check if wandb is installed
+ import wandb
+
+ if wandb.run:
+ log.info("Closing wandb!")
+ wandb.finish()
+
+ return metric_dict, object_dict
+
+ return wrap
+
+
+def get_metric_value(metric_dict: dict, metric_name: str) -> float:
+ """Safely retrieves value of the metric logged in LightningModule."""
+
+ if not metric_name:
+ log.info("Metric name is None! Skipping metric value retrieval...")
+ return None
+
+ if metric_name not in metric_dict:
+ raise Exception(
+ f"Metric value not found! \n"
+ "Make sure metric name logged in LightningModule is correct!\n"
+ "Make sure `optimized_metric` name in `hparams_search` config is correct!"
+ )
+
+ metric_value = metric_dict[metric_name].item()
+ log.info(f"Retrieved metric value! <{metric_name}={metric_value}>")
+
+ return metric_value
+
+
+def set_seed(seed: int):
+ if seed < 0:
+ seed = -seed
+ if seed > (1 << 31):
+ seed = 1 << 31
+
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+
+ if torch.backends.cudnn.is_available():
+ torch.backends.cudnn.deterministic = True
+ torch.backends.cudnn.benchmark = False
diff --git a/vendor/fish-speech/pyproject.toml b/vendor/fish-speech/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..6b9b58a736056a295f55a277db678655cab6c740
--- /dev/null
+++ b/vendor/fish-speech/pyproject.toml
@@ -0,0 +1,140 @@
+[project]
+name = "fish-speech"
+version = "2.0.0"
+authors = [
+ {name = "Fish Audio", email = "oss@fish.audio"},
+]
+description = "Fish Speech"
+readme = "README.md"
+requires-python = ">=3.10"
+keywords = ["TTS", "Speech"]
+license = {text = "Fish Audio Research License"}
+classifiers = [
+ "Programming Language :: Python :: 3",
+]
+dependencies = [
+ "numpy",
+ "torch==2.8.0",
+ "torchaudio==2.8.0",
+ "transformers<=4.57.3",
+ "datasets==2.18.0",
+ "lightning>=2.1.0",
+ "hydra-core>=1.3.2",
+ "tensorboard>=2.14.1",
+ "natsort>=8.4.0",
+ "einops>=0.7.0",
+ "librosa>=0.10.1",
+ "rich>=13.5.3",
+ "gradio>5.0.0",
+ "wandb>=0.19.0",
+ "grpcio>=1.58.0",
+ "kui>=1.6.0",
+ "uvicorn>=0.30.0",
+ "loguru>=0.6.0",
+ "loralib>=0.1.2",
+ "pyrootutils>=1.0.4",
+ "resampy>=0.4.3",
+ "einx[torch]==0.2.2",
+ "zstandard>=0.22.0",
+ "pydub",
+ "pyaudio",
+ "modelscope==1.17.1",
+ "opencc-python-reimplemented==0.1.7",
+ "silero-vad",
+ "ormsgpack",
+ "tiktoken>=0.8.0",
+ "pydantic==2.9.2",
+ "cachetools",
+ "descript-audio-codec",
+ "safetensors"
+]
+
+[project.optional-dependencies]
+stable = [
+ "torch==2.8.0",
+ "torchaudio",
+]
+cpu = [
+ "torch==2.8.0",
+ "torchaudio",
+]
+cu126 = [
+ "torch==2.8.0",
+ "torchaudio",
+]
+cu128 = [
+ "torch==2.8.0",
+ "torchaudio",
+]
+cu129 = [
+ "torch==2.8.0",
+ "torchaudio",
+]
+
+[tool.uv]
+override-dependencies = [
+ # descript-audiotools (transitive via descript-audio-codec) pins protobuf<3.20,
+ # but fish-speech's generated proto code requires >=3.20; cap at <6 for API stability
+ "protobuf>=3.20.0,<6.0.0",
+]
+conflicts = [
+ [
+ { extra = "cpu" },
+ { extra = "cu126" },
+ { extra = "cu128" },
+ { extra = "cu129" },
+ ],
+]
+
+[tool.uv.sources]
+torch = [
+ { index = "pytorch-cpu", extra = "cpu" },
+ { index = "pytorch-cu126", extra = "cu126" },
+ { index = "pytorch-cu128", extra = "cu128" },
+ { index = "pytorch-cu129", extra = "cu129" },
+]
+torchaudio = [
+ { index = "pytorch-cpu", extra = "cpu" },
+ { index = "pytorch-cu126", extra = "cu126" },
+ { index = "pytorch-cu128", extra = "cu128" },
+ { index = "pytorch-cu129", extra = "cu129" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu126"
+url = "https://download.pytorch.org/whl/cu126"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu129"
+url = "https://download.pytorch.org/whl/cu129"
+explicit = true
+
+[build-system]
+requires = ["setuptools", "setuptools-scm"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+include = ["fish_speech*", "tools*"]
+namespaces = true
+
+[tool.setuptools.package-data]
+fish_speech = [
+ "configs/*.yaml",
+ "configs/lora/*.yaml",
+ "datasets/protos/*.proto",
+ "i18n/*.md",
+ "i18n/locale/*.json",
+]
+
+[tool.setuptools_scm]
diff --git a/vendor/fish-speech/tools/api_client.py b/vendor/fish-speech/tools/api_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..31898aed011b61eb328ea67b9ebd2284515ced16
--- /dev/null
+++ b/vendor/fish-speech/tools/api_client.py
@@ -0,0 +1,239 @@
+import argparse
+import base64
+import time
+import wave
+
+import ormsgpack
+import pyaudio
+import requests
+from pydub import AudioSegment
+from pydub.playback import play
+
+from fish_speech.utils.file import audio_to_bytes, read_ref_text
+from fish_speech.utils.schema import ServeReferenceAudio, ServeTTSRequest
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(
+ description="Send text to a Fish Speech TTS server and receive synthesized audio.",
+ epilog=(
+ "Model selection note:\n"
+ " The base TTS model is selected by the server you call. For example, if the\n"
+ " server was started with checkpoints/s2-pro, this client will use S2-Pro\n"
+ " automatically. There is no separate per-request --model flag.\n\n"
+ "Examples:\n"
+ ' python tools/api_client.py -u http://127.0.0.1:8080/v1/tts -t "Hello from Fish Speech"\n'
+ ' python tools/api_client.py -u http://127.0.0.1:8080/v1/tts -t "Hello" --reference_id my-speaker'
+ ),
+ formatter_class=argparse.RawTextHelpFormatter,
+ )
+
+ parser.add_argument(
+ "--url",
+ "-u",
+ type=str,
+ default="http://127.0.0.1:8080/v1/tts",
+ help="URL of the TTS server. The server decides which base model is loaded.",
+ )
+ parser.add_argument(
+ "--text", "-t", type=str, required=True, help="Text to be synthesized"
+ )
+ parser.add_argument(
+ "--reference_id",
+ "-id",
+ type=str,
+ default=None,
+ help="ID of the reference voice to use for synthesis\n(Local: name of folder containing audios and files)",
+ )
+ parser.add_argument(
+ "--reference_audio",
+ "-ra",
+ type=str,
+ nargs="+",
+ default=None,
+ help="Path to the audio file",
+ )
+ parser.add_argument(
+ "--reference_text",
+ "-rt",
+ type=str,
+ nargs="+",
+ default=None,
+ help="Reference text for voice synthesis",
+ )
+ parser.add_argument(
+ "--output",
+ "-o",
+ type=str,
+ default="generated_audio",
+ help="Output audio file name",
+ )
+ parser.add_argument(
+ "--play",
+ action=argparse.BooleanOptionalAction,
+ default=True,
+ help="Whether to play audio after receiving data",
+ )
+ parser.add_argument(
+ "--format", type=str, choices=["wav", "pcm", "mp3", "opus"], default="wav"
+ )
+ parser.add_argument(
+ "--latency",
+ type=str,
+ default="normal",
+ choices=["normal", "balanced"],
+ help="Used in api.fish.audio/v1/tts",
+ )
+ parser.add_argument(
+ "--max_new_tokens",
+ type=int,
+ default=1024,
+ help="Maximum new tokens to generate. \n0 means no limit.",
+ )
+ parser.add_argument(
+ "--chunk_length", type=int, default=300, help="Chunk length for synthesis"
+ )
+ parser.add_argument(
+ "--top_p", type=float, default=0.8, help="Top-p sampling for synthesis"
+ )
+ parser.add_argument(
+ "--repetition_penalty",
+ type=float,
+ default=1.1,
+ help="Repetition penalty for synthesis",
+ )
+ parser.add_argument(
+ "--temperature", type=float, default=0.8, help="Temperature for sampling"
+ )
+
+ # parser.add_argument("--streaming", type=bool, default=False, help="Enable streaming response")
+ parser.add_argument(
+ "--streaming", action="store_true", help="Enable streaming response"
+ )
+ parser.add_argument(
+ "--channels", type=int, default=1, help="Number of audio channels"
+ )
+ parser.add_argument("--rate", type=int, default=44100, help="Sample rate for audio")
+ parser.add_argument(
+ "--use_memory_cache",
+ type=str,
+ default="off",
+ choices=["on", "off"],
+ help="Cache encoded references codes in memory.\n",
+ )
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=None,
+ help="`None` means randomized inference, otherwise deterministic.\nIt can't be used for fixing a timbre.",
+ )
+ parser.add_argument(
+ "--api_key",
+ type=str,
+ default="YOUR_API_KEY",
+ help="API key for authentication",
+ )
+
+ return parser.parse_args()
+
+
+if __name__ == "__main__":
+ args = parse_args()
+
+ idstr: str | None = args.reference_id
+ # priority: ref_id > [{text, audio},...]
+ if idstr is None:
+ ref_audios = args.reference_audio
+ ref_texts = args.reference_text
+ if ref_audios is None:
+ byte_audios = []
+ else:
+ byte_audios = [audio_to_bytes(ref_audio) for ref_audio in ref_audios]
+ if ref_texts is None:
+ ref_texts = []
+ else:
+ ref_texts = [read_ref_text(ref_text) for ref_text in ref_texts]
+ else:
+ byte_audios = []
+ ref_texts = []
+ pass # in api.py
+
+ data = {
+ "text": args.text,
+ "references": [
+ ServeReferenceAudio(
+ audio=ref_audio if ref_audio is not None else b"", text=ref_text
+ )
+ for ref_text, ref_audio in zip(ref_texts, byte_audios)
+ ],
+ "reference_id": idstr,
+ "format": args.format,
+ "latency": args.latency,
+ "max_new_tokens": args.max_new_tokens,
+ "chunk_length": args.chunk_length,
+ "top_p": args.top_p,
+ "repetition_penalty": args.repetition_penalty,
+ "temperature": args.temperature,
+ "streaming": args.streaming,
+ "use_memory_cache": args.use_memory_cache,
+ "seed": args.seed,
+ }
+
+ pydantic_data = ServeTTSRequest(**data)
+
+ print("Sending request")
+ start_time = time.time()
+ response = requests.post(
+ args.url,
+ params={"format": "msgpack"},
+ data=ormsgpack.packb(pydantic_data, option=ormsgpack.OPT_SERIALIZE_PYDANTIC),
+ stream=args.streaming,
+ headers={
+ "authorization": f"Bearer {args.api_key}",
+ "content-type": "application/msgpack",
+ },
+ )
+ end_time = time.time()
+ print(f"Request took {end_time - start_time} seconds")
+
+ if response.status_code == 200:
+ if args.streaming:
+ p = pyaudio.PyAudio()
+ audio_format = pyaudio.paInt16 # Assuming 16-bit PCM format
+ stream = p.open(
+ format=audio_format, channels=args.channels, rate=args.rate, output=True
+ )
+
+ wf = wave.open(f"{args.output}.wav", "wb")
+ wf.setnchannels(args.channels)
+ wf.setsampwidth(p.get_sample_size(audio_format))
+ wf.setframerate(args.rate)
+
+ stream_stopped_flag = False
+
+ try:
+ for chunk in response.iter_content(chunk_size=1024):
+ if chunk:
+ stream.write(chunk)
+ wf.writeframesraw(chunk)
+ else:
+ if not stream_stopped_flag:
+ stream.stop_stream()
+ stream_stopped_flag = True
+ finally:
+ stream.close()
+ p.terminate()
+ wf.close()
+ else:
+ audio_content = response.content
+ audio_path = f"{args.output}.{args.format}"
+ with open(audio_path, "wb") as audio_file:
+ audio_file.write(audio_content)
+
+ audio = AudioSegment.from_file(audio_path, format=args.format)
+ if args.play:
+ play(audio)
+ print(f"Audio has been saved to '{audio_path}'.")
+ else:
+ print(f"Request failed with status code {response.status_code}")
+ print(response.json())
diff --git a/vendor/fish-speech/tools/api_server.py b/vendor/fish-speech/tools/api_server.py
new file mode 100644
index 0000000000000000000000000000000000000000..f30f1c79a46a1f8f8d666309c7d547f490f4ca9b
--- /dev/null
+++ b/vendor/fish-speech/tools/api_server.py
@@ -0,0 +1,145 @@
+import json
+import multiprocessing
+import os
+import re
+from argparse import Namespace
+from threading import Lock
+
+import pyrootutils
+import uvicorn
+from kui.asgi import (
+ Depends,
+ FactoryClass,
+ HTTPException,
+ HttpRoute,
+ Kui,
+ OpenAPI,
+ Routes,
+)
+from kui.cors import CORSConfig
+from kui.openapi.specification import Info
+from kui.security import bearer_auth
+from loguru import logger
+from typing_extensions import Annotated
+
+pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+from tools.server.api_utils import MsgPackRequest, parse_args
+from tools.server.exception_handler import ExceptionHandler
+from tools.server.model_manager import ModelManager
+from tools.server.views import routes
+
+ENV_ARGS_KEY = "FISH_API_SERVER_ARGS"
+
+
+class API(ExceptionHandler):
+ def __init__(self, args: Namespace | None = None):
+ self.args = args or parse_args()
+
+ def api_auth(endpoint):
+ async def verify(token: Annotated[str, Depends(bearer_auth)]):
+ if token != self.args.api_key:
+ raise HTTPException(401, None, "Invalid token")
+ return await endpoint()
+
+ async def passthrough():
+ return await endpoint()
+
+ if self.args.api_key is not None:
+ return verify
+ else:
+ return passthrough
+
+ self.routes = Routes(
+ routes, # keep existing routes
+ http_middlewares=[api_auth], # apply api_auth middleware
+ )
+
+ # OpenAPIの設定
+ self.openapi = OpenAPI(
+ Info(
+ {
+ "title": "Fish Speech API",
+ "version": "1.5.0",
+ }
+ ),
+ ).routes
+
+ # Initialize the app
+ self.app = Kui(
+ routes=self.routes + self.openapi[1:], # Remove the default route
+ exception_handlers={
+ HTTPException: self.http_exception_handler,
+ Exception: self.other_exception_handler,
+ },
+ factory_class=FactoryClass(http=MsgPackRequest),
+ cors_config=CORSConfig(),
+ )
+
+ # Add the state variables
+ self.app.state.lock = Lock()
+ self.app.state.device = self.args.device
+ self.app.state.max_text_length = self.args.max_text_length
+
+ # Associate the app with the model manager
+ self.app.on_startup(self.initialize_app)
+
+ async def initialize_app(self, app: Kui):
+ # Make the ModelManager available to the views
+ app.state.model_manager = ModelManager(
+ mode=self.args.mode,
+ device=self.args.device,
+ half=self.args.half,
+ compile=self.args.compile,
+ llama_checkpoint_path=self.args.llama_checkpoint_path,
+ decoder_checkpoint_path=self.args.decoder_checkpoint_path,
+ decoder_config_name=self.args.decoder_config_name,
+ )
+
+ logger.info(f"Startup done, listening server at http://{self.args.listen}")
+
+
+def create_app():
+ args_env = os.environ.get(ENV_ARGS_KEY)
+ args = None
+
+ if args_env:
+ try:
+ args = Namespace(**json.loads(args_env))
+ except Exception as exc:
+ logger.warning(f"Failed to load args from {ENV_ARGS_KEY}: {exc}")
+
+ return API(args=args).app
+
+
+# Each worker process created by Uvicorn has its own memory space,
+# meaning that models and variables are not shared between processes.
+# Therefore, any variables (like `llama_queue` or `decoder_model`)
+# will not be shared across workers.
+
+# Multi-threading for deep learning can cause issues, such as inconsistent
+# outputs if multiple threads access the same buffers simultaneously.
+# Instead, it's better to use multiprocessing or independent models per thread.
+
+if __name__ == "__main__":
+
+ multiprocessing.set_start_method("spawn", force=True)
+
+ args = parse_args()
+ os.environ[ENV_ARGS_KEY] = json.dumps(vars(args))
+
+ # IPv6 address format is [xxxx:xxxx::xxxx]:port
+ match = re.search(r"\[([^\]]+)\]:(\d+)$", args.listen)
+ if match:
+ host, port = match.groups() # IPv6
+ else:
+ host, port = args.listen.split(":") # IPv4
+
+ uvicorn.run(
+ "tools.api_server:create_app",
+ host=host,
+ port=int(port),
+ workers=args.workers,
+ log_level="info",
+ factory=True,
+ )
diff --git a/vendor/fish-speech/tools/run_webui.py b/vendor/fish-speech/tools/run_webui.py
new file mode 100644
index 0000000000000000000000000000000000000000..37130a090147b6e72acd1ba961cac91ad0d06a59
--- /dev/null
+++ b/vendor/fish-speech/tools/run_webui.py
@@ -0,0 +1,107 @@
+import os
+from argparse import ArgumentParser
+from pathlib import Path
+
+import pyrootutils
+import torch
+from loguru import logger
+
+pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+from fish_speech.inference_engine import TTSInferenceEngine
+from fish_speech.models.dac.inference import load_model as load_decoder_model
+from fish_speech.models.text2semantic.inference import launch_thread_safe_queue
+from fish_speech.utils.schema import ServeTTSRequest
+from tools.webui import build_app
+from tools.webui.inference import get_inference_wrapper
+
+# Make einx happy
+os.environ["EINX_FILTER_TRACEBACK"] = "false"
+
+
+def parse_args():
+ parser = ArgumentParser()
+ parser.add_argument(
+ "--llama-checkpoint-path",
+ type=Path,
+ default="checkpoints/s2-pro",
+ )
+ parser.add_argument(
+ "--decoder-checkpoint-path",
+ type=Path,
+ default="checkpoints/s2-pro/codec.pth",
+ )
+ parser.add_argument("--decoder-config-name", type=str, default="modded_dac_vq")
+ parser.add_argument("--device", type=str, default="cuda")
+ parser.add_argument("--half", action="store_true")
+ parser.add_argument("--compile", action="store_true")
+ parser.add_argument("--max-gradio-length", type=int, default=0)
+ parser.add_argument("--theme", type=str, default="light")
+
+ return parser.parse_args()
+
+
+if __name__ == "__main__":
+ args = parse_args()
+ args.precision = torch.half if args.half else torch.bfloat16
+
+ # Check if MPS or CUDA is available
+ if torch.backends.mps.is_available():
+ args.device = "mps"
+ logger.info("mps is available, running on mps.")
+ elif torch.xpu.is_available():
+ args.device = "xpu"
+ logger.info("XPU is available, running on XPU.")
+ elif not torch.cuda.is_available():
+ logger.info("CUDA is not available, running on CPU.")
+ args.device = "cpu"
+
+ logger.info("Loading Llama model...")
+ llama_queue = launch_thread_safe_queue(
+ checkpoint_path=args.llama_checkpoint_path,
+ device=args.device,
+ precision=args.precision,
+ compile=args.compile,
+ )
+
+ logger.info("Loading VQ-GAN model...")
+ decoder_model = load_decoder_model(
+ config_name=args.decoder_config_name,
+ checkpoint_path=args.decoder_checkpoint_path,
+ device=args.device,
+ )
+
+ logger.info("Decoder model loaded, warming up...")
+
+ # Create the inference engine
+ inference_engine = TTSInferenceEngine(
+ llama_queue=llama_queue,
+ decoder_model=decoder_model,
+ compile=args.compile,
+ precision=args.precision,
+ )
+
+ # Dry run to check if the model is loaded correctly and avoid the first-time latency
+ list(
+ inference_engine.inference(
+ ServeTTSRequest(
+ text="Hello world.",
+ references=[],
+ reference_id=None,
+ max_new_tokens=1024,
+ chunk_length=200,
+ top_p=0.7,
+ repetition_penalty=1.5,
+ temperature=0.7,
+ format="wav",
+ )
+ )
+ )
+
+ logger.info("Warming up done, launching the web UI...")
+
+ # Get the inference function with the immutable arguments
+ inference_fct = get_inference_wrapper(inference_engine)
+
+ app = build_app(inference_fct, args.theme)
+ app.launch()
diff --git a/vendor/fish-speech/uv.lock b/vendor/fish-speech/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..1d6e0d7da2daf95005bd2034d9e7eb82577945ab
--- /dev/null
+++ b/vendor/fish-speech/uv.lock
@@ -0,0 +1,5852 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+ "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra == 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra == 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra == 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform == 'darwin' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version >= '3.13' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
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+ "python_full_version == '3.12.*' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
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+ "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
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+ "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+ "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-11-fish-speech-cpu' and extra != 'extra-11-fish-speech-cu126' and extra != 'extra-11-fish-speech-cu128' and extra != 'extra-11-fish-speech-cu129'",
+]
+conflicts = [[
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+ { name = "transformers" },
+ { name = "uvicorn" },
+ { name = "wandb" },
+ { name = "zstandard" },
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+ { name = "torchaudio", version = "2.8.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" } },
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+ { name = "torchaudio", version = "2.8.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" } },
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+
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+ "python_full_version >= '3.13' and sys_platform != 'linux'",
+ "python_full_version == '3.12.*' and sys_platform == 'linux'",
+ "python_full_version == '3.11.*' and sys_platform == 'linux'",
+ "python_full_version < '3.11' and sys_platform == 'linux'",
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+ "python_full_version == '3.11.*' and sys_platform != 'linux'",
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+]
+
+ 
+
+
+ 
+ 
+ 
+ 
+ 
+ 
+
+### تحكم دقيق للغاية عبر اللغة الطبيعية
+
+يمنح S2 Pro الصوت "روحاً" لا مثيل لها. من خلال صيغة `[tag]` البسيطة، يمكنك تضمين تعليمات عاطفية بدقة في أي موضع من النص.
+- **دعم أكثر من 15,000 وسم فريد**: لا يقتصر على الإعدادات المسبقة الثابتة، بل يدعم **أوصاف النص الحر**. يمكنك تجربة `[whisper in small voice]` (همس بصوت منخفض)، أو `[professional broadcast tone]` (نبرة إذاعية احترافية)، أو `[pitch up]` (رفع طبقة الصوت).
+- **مكتبة عواطف غنية**:
+ `[pause]` `[emphasis]` `[laughing]` `[inhale]` `[chuckle]` `[tsk]` `[singing]` `[excited]` `[laughing tone]` `[interrupting]` `[chuckling]` `[excited tone]` `[volume up]` `[echo]` `[angry]` `[low volume]` `[sigh]` `[low voice]` `[whisper]` `[screaming]` `[shouting]` `[loud]` `[surprised]` `[short pause]` `[exhale]` `[delight]` `[panting]` `[audience laughter]` `[with strong accent]` `[volume down]` `[clearing throat]` `[sad]` `[moaning]` `[shocked]`
+
+### بنية مبتكرة ثنائية الانحدار الذاتي (Dual-Autoregressive)
+
+يعتمد S2 Pro بنية Dual-AR بنظام "رئيسي-تابع"، تتكون من Decoder-only Transformer وترميز صوتي RVQ (10 قواميس أكواد، بمعدل إطارات يبلغ حوالي 21 هرتز):
+
+- **Slow AR (4 مليار معلمة)**: يعمل على طول المحور الزمني، ويتنبأ بقاموس الأكواد الدلالي الأساسي.
+- **Fast AR (400 مليون معلمة)**: يولد الـ 9 قواميس المتبقية في كل خطوة زمنية، لاستعادة أدق التفاصيل الصوتية ببراعة.
+
+يحقق هذا التصغير غير المتماثل أقصى درجات الدقة الصوتية مع زيادة سرعة الاستدلال بشكل كبير.
+
+### توافق التعلم التعزيزي (RL Alignment)
+
+يستخدم S2 Pro تقنية **Group Relative Policy Optimization (GRPO)** للتوافق بعد التدريب. نستخدم نفس مجموعة النماذج المستخدمة في تنظيف البيانات وتصنيفها مباشرة كنماذج مكافأة (Reward Model)، مما يحل بشكل مثالي مشكلة عدم التطابق بين توزيع بيانات ما قبل التدريب وأهداف ما بعد التدريب.
+- **إشارات مكافأة متعددة الأبعاد**: تقييم شامل للدقة الدلالية، والقدرة على اتباع التعليمات، وتسجيل التفضيل الصوتي، وتماثل نبرة الصوت، لضمان أن كل ثانية من الكلام المولد تتوافق مع الحدس البشري.
+
+### أداء استدلال تدفقي فائق (يعتمد على SGLang)
+
+نظراً لأن بنية Dual-AR تتماثل هيكلياً مع بنية LLM القياسية، فإن S2 Pro يدعم أصلاً جميع ميزات تسريع الاستدلال في SGLang، بما في ذلك الدفعات المستمرة (Continuous Batching)، و Paged KV Cache، و CUDA Graph، والتخزين المؤقت للبادئة القائم على RadixAttention.
+
+**أداء وحدة معالجة رسومات NVIDIA H200 واحدة:**
+- **عامل الوقت الحقيقي (RTF)**: 0.195
+- **تأخر الصوت الأول (TTFA)**: حوالي 100 مللي ثانية
+- **إنتاجية فائقة السرعة**: تصل إلى 3000+ وسم صوتي/ثانية مع الحفاظ على RTF < 0.5
+
+### دعم قوي للغات المتعددة
+
+يدعم S2 Pro أكثر من 80 لغة، مما يتيح تركيباً عالياً الجودة دون الحاجة إلى وحدات صوتية (phonemes) أو معالجة محددة لكل لغة:
+
+- **المستوى الأول (Tier 1)**: اليابانية (ja)، الإنجليزية (en)، الصينية (zh)
+- **المستوى الثاني (Tier 2)**: الكورية (ko)، الإسبانية (es)، البرتغالية (pt)، العربية (ar)، الروسية (ru)، الفرنسية (fr)، الألمانية (de)
+- **تغطية عالمية**: sv, it, tr, no, nl, cy, eu, ca, da, gl, ta, hu, fi, pl, et, hi, la, ur, th, vi, jw, bn, yo, xsl, cs, sw, nn, he, ms, uk, id, kk, bg, lv, my, tl, sk, ne, fa, af, el, bo, hr, ro, sn, mi, yi, am, be, km, is, az, sd, br, sq, ps, mn, ht, ml, sr, sa, te, ka, bs, pa, lt, kn, si, hy, mr, as, gu, fo والمزيد.
+
+### توليد متحدثين متعددين أصلي
+
+
+
+يسمح Fish Audio S2 للمستخدمين بتحميل عينة مرجعية تحتوي على متحدثين متعددين، وسيقوم النموذج بمعالجة ميزات كل متحدث عبر وسم `<|speaker:i|>`. بعد ذلك، يمكنك التحكم في أداء النموذج عبر وسم معرف المتحدث، مما يتيح لتوليد واحد أن يتضمن متحدثين متعددين. لم تعد هناك حاجة لتحميل عينة مرجعية منفصلة وتوليد صوت لكل متحدث على حدة كما كان في السابق.
+
+### توليد حوارات متعددة الجولات
+
+بفضل توسيع سياق النموذج، يمكن لنموذجنا الآن الاستفادة من المعلومات السابقة لتحسين التعبير في المحتوى المولد لاحقاً، مما يعزز من طبيعية المحتوى.
+
+### استنساخ الصوت السريع
+
+يدعم Fish Audio S2 استنساخاً دقيقاً للصوت باستخدام عينات مرجعية قصيرة (عادةً 10-30 ثانية). يلتقط النموذج نبرة الصوت وأسلوب الكلام والميول العاطفية، مما يولد أصواتاً مستنسخة واقعية ومتسقة دون الحاجة إلى ضبط دقيق إضافي.
+لاستخدام خادم SGLang، يرجى الرجوع إلى [SGLang-Omni README](https://github.com/sgl-project/sglang-omni/blob/main/sglang_omni/models/fishaudio_s2_pro/README.md).
+
+---
+
+## شكر وتقدير
+
+- [VITS2 (daniilrobnikov)](https://github.com/daniilrobnikov/vits2)
+- [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)
+- [GPT VITS](https://github.com/innnky/gpt-vits)
+- [MQTTS](https://github.com/b04901014/MQTTS)
+- [GPT Fast](https://github.com/pytorch-labs/gpt-fast)
+- [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)
+- [Qwen3](https://github.com/QwenLM/Qwen3)
+
+## التقرير الفني
+
+```bibtex
+@misc{fish-speech-v1.4,
+ title={Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis},
+ author={Shijia Liao and Yuxuan Wang and Tianyu Li and Yifan Cheng and Ruoyi Zhang and Rongzhi Zhou and Yijin Xing},
+ year={2024},
+ eprint={2411.01156},
+ archivePrefix={arXiv},
+ primaryClass={cs.SD},
+ url={https://arxiv.org/abs/2411.01156},
+}
+
+@misc{liao2026fishaudios2technical,
+ title={Fish Audio S2 Technical Report},
+ author={Shijia Liao and Yuxuan Wang and Songting Liu and Yifan Cheng and Ruoyi Zhang and Tianyu Li and Shidong Li and Yisheng Zheng and Xingwei Liu and Qingzheng Wang and Zhizhuo Zhou and Jiahua Liu and Xin Chen and Dawei Han},
+ year={2026},
+ eprint={2603.08823},
+ archivePrefix={arXiv},
+ primaryClass={cs.SD},
+ url={https://arxiv.org/abs/2603.08823},
+}
+```
diff --git a/vendor/fish-speech/docs/README.es.md b/vendor/fish-speech/docs/README.es.md
new file mode 100644
index 0000000000000000000000000000000000000000..df1fc8660c9a0061eef74af3ae410a6b6d1688a4
--- /dev/null
+++ b/vendor/fish-speech/docs/README.es.md
@@ -0,0 +1,188 @@
+
+
+### Control fino inline mediante lenguaje natural
+
+S2 Pro aporta un nivel de “alma” sin precedentes a la voz. Usando sintaxis `[tag]`, puedes insertar instrucciones emocionales con precisión en cualquier parte del texto.
+
+* **Más de 15,000 tags únicos soportados**
+* Soporta descripciones libres como `[whisper in small voice]`, `[professional broadcast tone]`, `[pitch up]`
+
+### Arquitectura Dual-Autoregressive (Dual-AR)
+
+* **Slow AR (4B parámetros)**: modela la estructura temporal
+* **Fast AR (400M parámetros)**: reconstruye detalles acústicos finos
+
+### Alineación mediante RL
+
+* Usa GRPO
+* Señales de recompensa multidimensionales
+
+### Rendimiento extremo en streaming
+
+* RTF: 0.195
+* TTFA: ~100 ms
+* +3000 tokens/s
+
+### Soporte multilingüe robusto
+
+* Más de 80 idiomas
+* Sin necesidad de phonemes específicos
+
+### Generación multi-speaker nativa
+
+
+
+Permite múltiples hablantes usando `<|speaker:i|>` en una sola generación.
+
+### Generación multi-turno
+
+Mantiene contexto para mejorar la naturalidad.
+
+### Clonación de voz rápida
+
+* Solo 10–30 segundos de audio
+* Alta fidelidad de timbre y estilo
+
+Para usar con SGLang Server, consulta el README correspondiente.
+
+---
+
+## Créditos
+
+* [VITS2 (daniilrobnikov)](https://github.com/daniilrobnikov/vits2)
+* [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)
+* [GPT VITS](https://github.com/innnky/gpt-vits)
+* [MQTTS](https://github.com/b04901014/MQTTS)
+* [GPT Fast](https://github.com/pytorch-labs/gpt-fast)
+* [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)
+* [Qwen3](https://github.com/QwenLM/Qwen3)
+
+## Informe Técnico
+
+```bibtex
+@misc{fish-speech-v1.4,
+ title={Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis},
+ author={Shijia Liao and Yuxuan Wang and Tianyu Li and Yifan Cheng and Ruoyi Zhang and Rongzhi Zhou and Yijin Xing},
+ year={2024},
+ eprint={2411.01156},
+ archivePrefix={arXiv},
+ primaryClass={cs.SD},
+ url={https://arxiv.org/abs/2411.01156},
+}
+
+@misc{liao2026fishaudios2technical,
+ title={Fish Audio S2 Technical Report},
+ author={Shijia Liao and Yuxuan Wang and Songting Liu and Yifan Cheng and Ruoyi Zhang and Tianyu Li and Shidong Li and Yisheng Zheng and Xingwei Liu and Qingzheng Wang and Zhizhuo Zhou and Jiahua Liu and Xin Chen and Dawei Han},
+ year={2026},
+ eprint={2603.08823},
+ archivePrefix={arXiv},
+ primaryClass={cs.SD},
+ url={https://arxiv.org/abs/2603.08823},
+}
+```
diff --git a/vendor/fish-speech/docs/README.ja.md b/vendor/fish-speech/docs/README.ja.md
new file mode 100644
index 0000000000000000000000000000000000000000..dc8c3f37b4e2fb0ba4431d0bb239002ae0036ec9
--- /dev/null
+++ b/vendor/fish-speech/docs/README.ja.md
@@ -0,0 +1,199 @@
+