roxi-tts-v2: MOSS-TTS-Nano LoRA Indian-English (with IIT-M attribution)

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-to-speech
+base_model: OpenMOSS-Team/MOSS-TTS-Nano
+tags:
+- text-to-speech
+- tts
+- moss-tts-nano
+- indian-english
+- lora
+- voice-cloning
+---
+
+# Roxi-TTS v2 — Indian-English voice (MOSS-TTS-Nano LoRA fine-tune)
+
+A LoRA fine-tune of [**MOSS-TTS-Nano**](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-Nano)
+(0.1B, autoregressive audio-token + LLM, 48 kHz) that speaks **Indian English** as its
+**default voice** — no reference clip required. Built for conversational / customer-support use.
+
+> Successor to `IOTEverythin/voxi-tts` (Kokoro-82M, EMNS). This v2 moves to the MOSS-TTS-Nano
+> family and adapts the voice with **LoRA** (full fine-tuning catastrophically forgets on a
+> 0.1B model; LoRA adapts the voice while preserving the base's intelligibility).
+
+## What it is
+- **Base:** OpenMOSS-Team/MOSS-TTS-Nano (Apache-2.0) · audio tokenizer OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano (Apache-2.0)
+- **Method:** LoRA (PEFT) — r=16, α=32, targets `c_attn,c_proj,fc_in,fc_out` (2.13% params), BF16, merged into a full checkpoint.
+- **Output:** 48 kHz mono.
+
+## Results (measured)
+| Metric | Base MOSS | Roxi-TTS v2 (no reference) |
+|---|---|---|
+| Speaker similarity to target (WavLM-SV cosine) ↑ | 0.52 | **0.96** |
+| Intelligibility WER (Whisper, on generated audio) ↓ | 0.26 | **0.26 (preserved)** |
+
+The voice became the target Indian-English speaker **without** a reference clip, with intelligibility unchanged.
+
+## Usage
+```python
+import torch, soundfile as sf
+from transformers import AutoModelForCausalLM
+# load this checkpoint (merged full model) + the MOSS audio tokenizer
+model = AutoModelForCausalLM.from_pretrained("IOTEverythin/roxi-tts-v2", trust_remote_code=True).eval()
+res = model.inference(
+    text="Welcome. Your appointment is confirmed for Monday at ten thirty in the morning.",
+    output_audio_path="out.wav", mode="continuation",
+    audio_tokenizer_type="moss-audio-tokenizer-nano",
+    audio_tokenizer_pretrained_name_or_path="OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano",
+    device="cuda", audio_repetition_penalty=1.1, use_kv_cache=True,
+)
+```
+**Tips:** spell brand names phonetically (e.g. "Voz Vox") and avoid raw abbreviations ("in the morning", not "A M"); write numbers as words. Trim trailing silence and retry if a generation comes out short.
+
+## Training data & attribution
+- **Dataset:** IIT-Madras **Indic TTS** — English (Indian-English) subset, via the
+  `SPRINGLab/IndicTTS-English` Hugging Face mirror (studio 48 kHz read speech).
+- The fine-tune was trained on a single-speaker subset of that corpus.
+
+**Required notice (IIT-M Indic TTS End User License Agreement):**
+> COPYRIGHT 2016 TTS Consortium, TDIL, Meity — represented by Hema A. Murthy & S. Umesh,
+> Department of Computer Science and Engineering and Electrical Engineering, IIT Madras.
+> ALL RIGHTS RESERVED.
+
+The Indic TTS EULA grants a royalty-free, worldwide license to create and freely distribute
+derivative works (such as this model). See https://www.iitm.ac.in/donlab/indictts/ for the
+dataset and full license.
+
+## Limitations & responsible use
+- Trained on a single read-speech speaker; **neutral** style. Style/emotion control is **not**
+  reliable yet (instruction-conditioning is wired but needs style-labeled training).
+- Telephony (8 kHz) quality not separately tuned; evaluate before production.
+- **Voice likeness:** this voice is derived from a real dataset speaker. Do **not** use it to
+  impersonate any real person, for fraud, deception, or any unlawful/harmful purpose. Disclose
+  AI-generated audio where required. The authors provide the weights "as is", without warranty.
+
+## License
+- This model's LoRA/code: **Apache-2.0** (matching the base model).
+- Derived from MOSS-TTS-Nano (Apache-2.0) and IIT-M Indic TTS data (notice above retained).

__init__.py

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+from .configuration_moss_tts_nano import MossTTSNanoConfig
+from .modeling_moss_tts_nano import (
+    MossTTSNanoForCausalLM,
+    MossTTSNanoGenerationOutput,
+    MossTTSNanoOutput,
+)
+from .tokenization_moss_tts_nano import MossTTSNanoSentencePieceTokenizer
+
+try:
+    MossTTSNanoConfig.register_for_auto_class()
+except Exception:
+    pass
+
+for auto_class_name in ("AutoModel", "AutoModelForCausalLM"):
+    try:
+        MossTTSNanoForCausalLM.register_for_auto_class(auto_class_name)
+    except Exception:
+        pass
+
+try:
+    MossTTSNanoSentencePieceTokenizer.register_for_auto_class("AutoTokenizer")
+except Exception:
+    pass
+
+__all__ = [
+    "MossTTSNanoConfig",
+    "MossTTSNanoForCausalLM",
+    "MossTTSNanoSentencePieceTokenizer",
+    "MossTTSNanoGenerationOutput",
+    "MossTTSNanoOutput",
+]

config.json

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+{
+  "architectures": [
+    "MossTTSNanoForCausalLM"
+  ],
+  "attn_implementation": null,
+  "audio_assistant_slot_token_id": 9,
+  "audio_codebook_sizes": [
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024,
+    1024
+  ],
+  "audio_end_token_id": 7,
+  "audio_pad_token_id": 1024,
+  "audio_start_token_id": 6,
+  "audio_tokenizer_pretrained_name_or_path": "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano",
+  "audio_tokenizer_sample_rate": 48000,
+  "audio_tokenizer_type": "moss-audio-tokenizer-nano",
+  "audio_user_slot_token_id": 8,
+  "audio_vocab_size": 1024,
+  "auto_map": {
+    "AutoConfig": "configuration_moss_tts_nano.MossTTSNanoConfig",
+    "AutoModel": "modeling_moss_tts_nano.MossTTSNanoForCausalLM",
+    "AutoModelForCausalLM": "modeling_moss_tts_nano.MossTTSNanoForCausalLM"
+  },
+  "dtype": "float32",
+  "gpt2_config": {
+    "activation_function": "gelu_new",
+    "attn_pdrop": 0.0,
+    "bos_token_id": 1,
+    "embd_pdrop": 0.0,
+    "eos_token_id": 2,
+    "initializer_range": 0.02,
+    "layer_norm_epsilon": 1e-05,
+    "model_type": "gpt2",
+    "n_ctx": 32768,
+    "n_embd": 768,
+    "n_head": 12,
+    "n_inner": 3072,
+    "n_layer": 12,
+    "n_positions": 32768,
+    "pad_token_id": 3,
+    "position_embedding_type": "rope",
+    "reorder_and_upcast_attn": false,
+    "resid_pdrop": 0.0,
+    "rope_base": 10000.0,
+    "scale_attn_by_inverse_layer_idx": false,
+    "scale_attn_weights": true,
+    "summary_activation": null,
+    "summary_first_dropout": 0.1,
+    "summary_proj_to_labels": true,
+    "summary_type": "cls_index",
+    "summary_use_proj": true,
+    "use_cache": true,
+    "vocab_size": 16384
+  },
+  "hidden_size": 768,
+  "im_end_token_id": 5,
+  "im_start_token_id": 4,
+  "initializer_range": 0.02,
+  "local_transformer_attn_implementation": "flash_attention_2",
+  "local_transformer_layers": 1,
+  "max_position_embeddings": 32768,
+  "model_architecture": "global_local_transformer",
+  "model_type": "moss_tts_nano",
+  "n_vq": 16,
+  "pad_token_id": 3,
+  "tokenizer_class": "MossTTSNanoSentencePieceTokenizer",
+  "tokenizer_use_fast": false,
+  "transformers_version": "4.57.1",
+  "vocab_size": 16384
+}

configuration_moss_tts_nano.py

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+# coding=utf-8
+from typing import Any, Dict, Optional, Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.models.gpt2.configuration_gpt2 import GPT2Config
+
+
+class MossTTSNanoConfig(PretrainedConfig):
+    model_type = "moss_tts_nano"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        gpt2_config: Optional[Union[GPT2Config, Dict[str, Any]]] = None,
+        n_vq: int = 8,
+        audio_vocab_size: Optional[int] = 1024,
+        audio_codebook_sizes: Optional[list[int]] = None,
+        audio_pad_token_id: int = 1024,
+        pad_token_id: int = 151643,
+        im_start_token_id: int = 151644,
+        im_end_token_id: int = 151645,
+        audio_start_token_id: int = 151652,
+        audio_end_token_id: int = 151653,
+        audio_user_slot_token_id: int = 151654,
+        audio_assistant_slot_token_id: int = 151656,
+        tokenizer_use_fast: bool = False,
+        audio_tokenizer_type: str = "moss-audio-tokenizer-nano",
+        audio_tokenizer_pretrained_name_or_path: Optional[str] = "OpenMOSS-Team/MOSS-Audio-Tokenizer-Nano",
+        audio_tokenizer_sample_rate: int = 48000,
+        attn_implementation: str = "flash_attention_2",
+        initializer_range: float = 0.02,
+        model_architecture: str = "global_local_transformer",
+        local_transformer_layers: int = 4,
+        local_transformer_attn_implementation: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        if isinstance(gpt2_config, dict):
+            self.gpt2_config = GPT2Config(**gpt2_config)
+        elif gpt2_config is None:
+            self.gpt2_config = GPT2Config()
+        else:
+            self.gpt2_config = gpt2_config
+
+        self.n_vq = int(n_vq)
+        if audio_codebook_sizes is None:
+            if audio_vocab_size is None:
+                raise ValueError("audio_vocab_size must be set when audio_codebook_sizes is not provided.")
+            resolved_audio_codebook_sizes = [int(audio_vocab_size)] * self.n_vq
+        else:
+            resolved_audio_codebook_sizes = [int(codebook_size) for codebook_size in audio_codebook_sizes]
+        if len(resolved_audio_codebook_sizes) != self.n_vq:
+            raise ValueError(
+                "audio_codebook_sizes must have length n_vq "
+                f"(expected {self.n_vq}, got {len(resolved_audio_codebook_sizes)})."
+            )
+        if any(codebook_size <= 0 for codebook_size in resolved_audio_codebook_sizes):
+            raise ValueError("audio_codebook_sizes must contain positive integers.")
+
+        max_audio_codebook_size = max(resolved_audio_codebook_sizes)
+        if audio_vocab_size is not None and int(audio_vocab_size) < max_audio_codebook_size:
+            raise ValueError(
+                "audio_vocab_size must be >= max(audio_codebook_sizes) "
+                f"(got {audio_vocab_size}, expected at least {max_audio_codebook_size})."
+            )
+
+        self.audio_codebook_sizes = resolved_audio_codebook_sizes
+        self.audio_vocab_size = (
+            max_audio_codebook_size if audio_vocab_size is None else int(audio_vocab_size)
+        )
+        self.audio_pad_token_id = int(audio_pad_token_id)
+        if self.audio_pad_token_id < max_audio_codebook_size:
+            raise ValueError(
+                "audio_pad_token_id must be >= max(audio_codebook_sizes) so pad stays outside every codebook "
+                f"(got {self.audio_pad_token_id}, max codebook size {max_audio_codebook_size})."
+            )
+        self.pad_token_id = pad_token_id
+        self.im_start_token_id = im_start_token_id
+        self.im_end_token_id = im_end_token_id
+        self.audio_start_token_id = audio_start_token_id
+        self.audio_end_token_id = audio_end_token_id
+        self.audio_user_slot_token_id = audio_user_slot_token_id
+        self.audio_assistant_slot_token_id = audio_assistant_slot_token_id
+        self.tokenizer_use_fast = tokenizer_use_fast
+        self.audio_tokenizer_type = audio_tokenizer_type
+        self.audio_tokenizer_pretrained_name_or_path = audio_tokenizer_pretrained_name_or_path
+        self.audio_tokenizer_sample_rate = audio_tokenizer_sample_rate
+        self.attn_implementation = attn_implementation
+        self.initializer_range = initializer_range
+        self.model_architecture = model_architecture
+        self.local_transformer_layers = local_transformer_layers
+        self.local_transformer_attn_implementation = (
+            attn_implementation
+            if local_transformer_attn_implementation is None
+            else local_transformer_attn_implementation
+        )
+        self.vocab_size = self.gpt2_config.vocab_size
+        self.hidden_size = self.gpt2_config.hidden_size
+        self.max_position_embeddings = self.gpt2_config.n_positions
+
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+    def to_dict(self) -> Dict[str, Any]:
+        output = super().to_dict()
+        output["gpt2_config"] = self.gpt2_config.to_dict()
+        return output
+
+
+__all__ = ["MossTTSNanoConfig"]

generation_config.json

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+{
+  "_from_model_config": true,
+  "pad_token_id": 3,
+  "transformers_version": "4.57.1"
+}

gpt2_decoder.py

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+# coding=utf-8
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast
+from transformers.models.gpt2.configuration_gpt2 import GPT2Config
+
+try:
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import pad_input, unpad_input
+
+    _FLASH_ATTN_AVAILABLE = True
+except Exception:
+    flash_attn_func = None
+    flash_attn_varlen_func = None
+    pad_input = None
+    unpad_input = None
+    _FLASH_ATTN_AVAILABLE = False
+
+
+@dataclass
+class PackedSequenceMetadata:
+    cu_seqlens: torch.Tensor
+    max_seqlen: int
+    indices: Optional[torch.Tensor] = None
+    batch_size: Optional[int] = None
+    seq_len: Optional[int] = None
+
+
+class MossTTSNanoGPT2RotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0) -> None:
+        super().__init__()
+        if dim % 2 != 0:
+            raise ValueError(f"RoPE head_dim must be even, got {dim}")
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(
+        self,
+        position_ids: torch.LongTensor,
+        *,
+        device: torch.device,
+        dtype: torch.dtype,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        if position_ids.ndim == 1:
+            position_ids = position_ids.unsqueeze(0)
+        freqs = torch.einsum("bs,d->bsd", position_ids.to(device=device, dtype=self.inv_freq.dtype), self.inv_freq)
+        cos = freqs.cos().repeat_interleave(2, dim=-1).unsqueeze(2).to(dtype=dtype)
+        sin = freqs.sin().repeat_interleave(2, dim=-1).unsqueeze(2).to(dtype=dtype)
+        return cos, sin
+
+
+def rotate_half(hidden_states: torch.Tensor) -> torch.Tensor:
+    even = hidden_states[..., ::2]
+    odd = hidden_states[..., 1::2]
+    return torch.stack((-odd, even), dim=-1).reshape_as(hidden_states)
+
+
+def apply_rotary_pos_emb(
+    hidden_states: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+) -> torch.Tensor:
+    return (hidden_states * cos) + (rotate_half(hidden_states) * sin)
+
+
+class MossTTSNanoGPT2MLP(nn.Module):
+    def __init__(self, config: GPT2Config) -> None:
+        super().__init__()
+        hidden_size = int(config.hidden_size)
+        inner_size = int(config.n_inner or 4 * hidden_size)
+        self.fc_in = nn.Linear(hidden_size, inner_size)
+        self.fc_out = nn.Linear(inner_size, hidden_size)
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc_out(hidden_states)
+        return self.dropout(hidden_states)
+
+
+class MossTTSNanoGPT2Attention(nn.Module):
+    def __init__(self, config: GPT2Config, layer_idx: int, attn_implementation: str) -> None:
+        super().__init__()
+        hidden_size = int(config.hidden_size)
+        num_heads = int(config.num_attention_heads)
+        if hidden_size % num_heads != 0:
+            raise ValueError(f"hidden_size={hidden_size} must be divisible by num_attention_heads={num_heads}")
+
+        self.num_heads = num_heads
+        self.head_dim = hidden_size // num_heads
+        self.embed_dim = hidden_size
+        self.layer_idx = layer_idx
+        self.attn_implementation = attn_implementation
+        self.attn_dropout = float(config.attn_pdrop)
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+        self.scale_attn_weights = bool(getattr(config, "scale_attn_weights", True))
+        self.scale_attn_by_inverse_layer_idx = bool(getattr(config, "scale_attn_by_inverse_layer_idx", False))
+        self.position_embedding_type = str(getattr(config, "position_embedding_type", "absolute")).lower()
+        if self.position_embedding_type not in {"absolute", "rope"}:
+            raise ValueError(f"Unsupported position_embedding_type={self.position_embedding_type!r}")
+
+        self.c_attn = nn.Linear(hidden_size, 3 * hidden_size)
+        self.c_proj = nn.Linear(hidden_size, hidden_size)
+        self.rotary_emb = None
+        if self.position_embedding_type == "rope":
+            self.rotary_emb = MossTTSNanoGPT2RotaryEmbedding(
+                self.head_dim,
+                base=float(getattr(config, "rope_base", 10000.0)),
+            )
+
+    def _split_heads(self, tensor: torch.Tensor) -> torch.Tensor:
+        if tensor.ndim == 3:
+            batch_size, seq_len, _ = tensor.shape
+            return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim)
+        if tensor.ndim == 2:
+            total_tokens, _ = tensor.shape
+            return tensor.view(total_tokens, self.num_heads, self.head_dim)
+        raise ValueError(f"Unsupported tensor rank for attention split: {tensor.ndim}")
+
+    def _merge_heads(self, tensor: torch.Tensor) -> torch.Tensor:
+        if tensor.ndim == 4:
+            batch_size, seq_len, _, _ = tensor.shape
+            return tensor.reshape(batch_size, seq_len, self.embed_dim)
+        if tensor.ndim == 3:
+            total_tokens, _, _ = tensor.shape
+            return tensor.reshape(total_tokens, self.embed_dim)
+        raise ValueError(f"Unsupported tensor rank for attention merge: {tensor.ndim}")
+
+    def _causal_attention_mask(
+        self,
+        attention_mask: Optional[torch.Tensor],
+        query_length: int,
+        key_length: int,
+        device: torch.device,
+    ) -> torch.Tensor:
+        query_positions = torch.arange(query_length, device=device, dtype=torch.long)
+        query_positions = query_positions + max(key_length - query_length, 0)
+        key_positions = torch.arange(key_length, device=device, dtype=torch.long)
+        causal = key_positions.unsqueeze(0) <= query_positions.unsqueeze(1)
+        causal = causal.unsqueeze(0).unsqueeze(0)
+        if attention_mask is None:
+            return causal
+        key_mask = attention_mask[:, None, None, :].to(dtype=torch.bool)
+        return causal & key_mask
+
+    def _eager_attention(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+
+        scale = 1.0
+        if self.scale_attn_weights:
+            scale /= self.head_dim ** 0.5
+        if self.scale_attn_by_inverse_layer_idx:
+            scale /= float(self.layer_idx + 1)
+
+        scores = torch.matmul(query, key.transpose(-1, -2)) * scale
+        causal_mask = self._causal_attention_mask(
+            attention_mask=attention_mask,
+            query_length=query.shape[-2],
+            key_length=key.shape[-2],
+            device=query.device,
+        )
+        scores = scores.masked_fill(~causal_mask, torch.finfo(scores.dtype).min)
+        probs = torch.softmax(scores, dim=-1)
+        if self.training and self.attn_dropout > 0:
+            probs = torch.dropout(probs, self.attn_dropout, train=True)
+        output = torch.matmul(probs, value)
+        return output.transpose(1, 2).contiguous()
+
+    def _sdpa_attention(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        query = query.transpose(1, 2)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+        mask = None
+        query_attention_mask = None
+        if attention_mask is not None:
+            query_length = query.shape[-2]
+            key_length = key.shape[-2]
+            mask = self._causal_attention_mask(
+                attention_mask=attention_mask,
+                query_length=query_length,
+                key_length=key_length,
+                device=query.device,
+            )
+            query_attention_mask = attention_mask[:, -query_length:].to(dtype=torch.bool, device=query.device)
+            if not bool(query_attention_mask.all()):
+                # SDPA can produce NaNs when a query row is fully masked. For padded query positions,
+                # keep a single aligned key visible, then zero the query output after attention.
+                mask = mask.expand(query.shape[0], -1, -1, -1).clone()
+                invalid_batch, invalid_query = torch.nonzero(~query_attention_mask, as_tuple=True)
+                aligned_key = invalid_query + max(key_length - query_length, 0)
+                mask[invalid_batch, :, invalid_query, aligned_key] = True
+        output = torch.nn.functional.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            attn_mask=mask,
+            dropout_p=self.attn_dropout if self.training else 0.0,
+            is_causal=mask is None,
+        )
+        if query_attention_mask is not None and not bool(query_attention_mask.all()):
+            output = output.masked_fill(~query_attention_mask[:, None, :, None], 0.0)
+        return output.transpose(1, 2).contiguous()
+
+    def _flash_attention(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        packed_metadata: Optional[PackedSequenceMetadata],
+    ) -> torch.Tensor:
+        if not _FLASH_ATTN_AVAILABLE:
+            raise ImportError("flash_attn is not installed, but attn_implementation='flash_attention_2' was requested.")
+        if query.device.type != "cuda":
+            raise ValueError("flash_attention_2 requires CUDA tensors.")
+        if query.dtype not in (torch.float16, torch.bfloat16):
+            raise ValueError(
+                f"flash_attention_2 requires fp16/bf16 tensors, but received dtype={query.dtype}."
+            )
+
+        dropout_p = self.attn_dropout if self.training else 0.0
+        if packed_metadata is not None:
+            if packed_metadata.indices is not None:
+                query = query.reshape(-1, self.num_heads, self.head_dim).index_select(0, packed_metadata.indices)
+                key = key.reshape(-1, self.num_heads, self.head_dim).index_select(0, packed_metadata.indices)
+                value = value.reshape(-1, self.num_heads, self.head_dim).index_select(0, packed_metadata.indices)
+            output = flash_attn_varlen_func(
+                query,
+                key,
+                value,
+                packed_metadata.cu_seqlens,
+                packed_metadata.cu_seqlens,
+                packed_metadata.max_seqlen,
+                packed_metadata.max_seqlen,
+                dropout_p=dropout_p,
+                causal=True,
+            )
+            if packed_metadata.indices is None:
+                return output
+            return pad_input(
+                output,
+                packed_metadata.indices,
+                packed_metadata.batch_size,
+                packed_metadata.seq_len,
+            )
+
+        if attention_mask is None or bool(attention_mask.all()):
+            return flash_attn_func(
+                query,
+                key,
+                value,
+                dropout_p=dropout_p,
+                causal=True,
+            )
+
+        unpadded_query, indices, cu_seqlens, max_seqlen, _ = unpad_input(query, attention_mask)
+        unpadded_key, _, _, _, _ = unpad_input(key, attention_mask)
+        unpadded_value, _, _, _, _ = unpad_input(value, attention_mask)
+        output = flash_attn_varlen_func(
+            unpadded_query,
+            unpadded_key,
+            unpadded_value,
+            cu_seqlens,
+            cu_seqlens,
+            max_seqlen,
+            max_seqlen,
+            dropout_p=dropout_p,
+            causal=True,
+        )
+        return pad_input(output, indices, query.shape[0], query.shape[1])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        packed_metadata: Optional[PackedSequenceMetadata] = None,
+        layer_past: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor, torch.Tensor]]]:
+        qkv = self.c_attn(hidden_states)
+        query, key, value = qkv.split(self.embed_dim, dim=-1)
+        query = self._split_heads(query)
+        key = self._split_heads(key)
+        value = self._split_heads(value)
+
+        if self.rotary_emb is not None:
+            if position_ids is None:
+                raise ValueError("position_ids must be provided when position_embedding_type='rope'.")
+            cos, sin = self.rotary_emb(
+                position_ids.to(device=query.device),
+                device=query.device,
+                dtype=query.dtype,
+            )
+            query = apply_rotary_pos_emb(query, cos, sin)
+            key = apply_rotary_pos_emb(key, cos, sin)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            key = torch.cat([past_key.to(device=key.device, dtype=key.dtype), key], dim=1)
+            value = torch.cat([past_value.to(device=value.device, dtype=value.dtype), value], dim=1)
+
+        present = (key, value) if use_cache else None
+
+        if self.attn_implementation == "flash_attention_2" and layer_past is None:
+            attn_output = self._flash_attention(
+                query=query,
+                key=key,
+                value=value,
+                attention_mask=attention_mask,
+                packed_metadata=packed_metadata,
+            )
+        elif self.attn_implementation == "sdpa":
+            attn_output = self._sdpa_attention(
+                query=query,
+                key=key,
+                value=value,
+                attention_mask=attention_mask,
+            )
+        else:
+            attn_output = self._eager_attention(
+                query=query,
+                key=key,
+                value=value,
+                attention_mask=attention_mask,
+            )
+
+        attn_output = self._merge_heads(attn_output)
+        attn_output = self.c_proj(attn_output)
+        return self.resid_dropout(attn_output), present
+
+
+class MossTTSNanoGPT2Block(nn.Module):
+    def __init__(self, config: GPT2Config, layer_idx: int, attn_implementation: str) -> None:
+        super().__init__()
+        hidden_size = int(config.hidden_size)
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = MossTTSNanoGPT2Attention(config, layer_idx=layer_idx, attn_implementation=attn_implementation)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = MossTTSNanoGPT2MLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        packed_metadata: Optional[PackedSequenceMetadata] = None,
+        layer_past: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        use_cache: bool = False,
+    ) -> tuple[torch.Tensor, Optional[tuple[torch.Tensor, torch.Tensor]]]:
+        attn_output, present = self.attn(
+            self.ln_1(hidden_states),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            packed_metadata=packed_metadata,
+            layer_past=layer_past,
+            use_cache=use_cache,
+        )
+        hidden_states = hidden_states + attn_output
+        hidden_states = hidden_states + self.mlp(self.ln_2(hidden_states))
+        return hidden_states, present
+
+
+class MossTTSNanoGPT2Model(nn.Module):
+    def __init__(self, config: GPT2Config, attn_implementation: str = "eager") -> None:
+        super().__init__()
+        self.config = config
+        self.attn_implementation = attn_implementation
+        self.position_embedding_type = str(getattr(config, "position_embedding_type", "absolute")).lower()
+        if self.position_embedding_type not in {"absolute", "rope"}:
+            raise ValueError(f"Unsupported position_embedding_type={self.position_embedding_type!r}")
+        hidden_size = int(config.hidden_size)
+        self.wte = nn.Embedding(config.vocab_size, hidden_size)
+        self.wpe = nn.Embedding(config.n_positions, hidden_size) if self.position_embedding_type == "absolute" else nn.Identity()
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList(
+            [MossTTSNanoGPT2Block(config, layer_idx=index, attn_implementation=attn_implementation) for index in range(config.n_layer)]
+        )
+        self.ln_f = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.gradient_checkpointing = False
+        self._reset_parameters()
+
+    def _reset_parameters(self) -> None:
+        init_std = float(self.config.initializer_range)
+        for module in self.modules():
+            if isinstance(module, nn.Linear):
+                nn.init.normal_(module.weight, mean=0.0, std=init_std)
+                if module.bias is not None:
+                    nn.init.zeros_(module.bias)
+            elif isinstance(module, nn.Embedding):
+                nn.init.normal_(module.weight, mean=0.0, std=init_std)
+            elif isinstance(module, nn.LayerNorm):
+                nn.init.ones_(module.weight)
+                nn.init.zeros_(module.bias)
+
+    @staticmethod
+    def _normalize_num_sequences(
+        cu_seqlens: torch.Tensor,
+        num_sequences: Optional[torch.Tensor],
+        device: torch.device,
+    ) -> torch.Tensor:
+        if cu_seqlens.ndim == 1:
+            cu_seqlens = cu_seqlens.unsqueeze(0)
+        if num_sequences is None:
+            counts = []
+            for boundary in cu_seqlens:
+                diffs = boundary[1:] - boundary[:-1]
+                counts.append(int((diffs > 0).sum().item()))
+            return torch.tensor(counts, dtype=torch.int32, device=device)
+        if num_sequences.ndim == 0:
+            return num_sequences.unsqueeze(0)
+        return num_sequences
+
+    @staticmethod
+    def build_packed_position_ids(
+        attention_mask: Optional[torch.Tensor],
+        cu_seqlens: torch.Tensor,
+        num_sequences: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        if cu_seqlens.ndim == 1:
+            cu_seqlens = cu_seqlens.unsqueeze(0)
+        batch_size, seq_len = cu_seqlens.shape[0], cu_seqlens.shape[1] - 1
+        device = cu_seqlens.device
+        position_ids = torch.zeros((batch_size, seq_len), dtype=torch.long, device=device)
+        counts = MossTTSNanoGPT2Model._normalize_num_sequences(cu_seqlens, num_sequences, device=device)
+        for batch_index in range(batch_size):
+            sequence_count = int(counts[batch_index].item())
+            boundaries = cu_seqlens[batch_index, : sequence_count + 1].tolist()
+            for start, end in zip(boundaries[:-1], boundaries[1:]):
+                start = int(start)
+                end = int(end)
+                if end > start:
+                    position_ids[batch_index, start:end] = torch.arange(end - start, device=device)
+        if attention_mask is not None:
+            position_ids = position_ids * attention_mask.to(dtype=position_ids.dtype)
+        return position_ids
+
+    @staticmethod
+    def build_packed_metadata(
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        num_sequences: Optional[torch.Tensor],
+    ) -> PackedSequenceMetadata:
+        if cu_seqlens.ndim == 1:
+            cu_seqlens = cu_seqlens.unsqueeze(0)
+        device = hidden_states.device
+        counts = MossTTSNanoGPT2Model._normalize_num_sequences(cu_seqlens, num_sequences, device=device)
+        flat_indices = []
+        cumulative = [0]
+        max_seqlen = 0
+        seq_len = hidden_states.shape[1]
+
+        for batch_index in range(hidden_states.shape[0]):
+            sequence_count = int(counts[batch_index].item())
+            boundaries = cu_seqlens[batch_index, : sequence_count + 1].tolist()
+            for start, end in zip(boundaries[:-1], boundaries[1:]):
+                start = int(start)
+                end = int(end)
+                if end <= start:
+                    continue
+                segment_indices = batch_index * seq_len + torch.arange(start, end, device=device)
+                flat_indices.append(segment_indices)
+                cumulative.append(cumulative[-1] + (end - start))
+                max_seqlen = max(max_seqlen, end - start)
+
+        if not flat_indices:
+            raise ValueError("cu_seqlens did not describe any non-empty packed sequences.")
+
+        indices = torch.cat(flat_indices, dim=0)
+        return PackedSequenceMetadata(
+            cu_seqlens=torch.tensor(cumulative, dtype=torch.int32, device=device),
+            max_seqlen=max_seqlen,
+            indices=indices,
+            batch_size=hidden_states.shape[0],
+            seq_len=hidden_states.shape[1],
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[tuple[tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: bool = True,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        num_sequences: Optional[torch.Tensor] = None,
+    ) -> BaseModelOutputWithPast:
+        del input_ids, output_attentions
+
+        if inputs_embeds is None:
+            raise ValueError("inputs_embeds must be provided.")
+
+        use_cache = bool(use_cache)
+        if use_cache and cu_seqlens is not None:
+            raise ValueError("use_cache=True is not supported together with cu_seqlens packing.")
+
+        hidden_states = inputs_embeds
+        if attention_mask is None:
+            attention_mask = torch.ones(hidden_states.shape[:2], dtype=torch.bool, device=hidden_states.device)
+        else:
+            attention_mask = attention_mask.to(dtype=torch.bool, device=hidden_states.device)
+        query_attention_mask = attention_mask[:, -hidden_states.shape[1] :]
+
+        packed_metadata = None
+        if position_ids is None:
+            if cu_seqlens is not None:
+                position_ids = self.build_packed_position_ids(
+                    attention_mask=attention_mask,
+                    cu_seqlens=cu_seqlens.to(device=hidden_states.device),
+                    num_sequences=num_sequences.to(device=hidden_states.device) if num_sequences is not None else None,
+                )
+            elif attention_mask is not None:
+                position_ids = attention_mask.long().cumsum(dim=-1) - 1
+                position_ids = position_ids.masked_fill(~attention_mask, 0)
+                position_ids = position_ids[:, -hidden_states.shape[1] :]
+            else:
+                past_length = 0
+                if past_key_values is not None and len(past_key_values) > 0:
+                    past_length = past_key_values[0][0].shape[1]
+                position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device, dtype=torch.long)
+                position_ids = position_ids + past_length
+                position_ids = position_ids.unsqueeze(0).expand(hidden_states.shape[0], -1)
+
+        if cu_seqlens is not None and self.attn_implementation == "flash_attention_2":
+            packed_metadata = self.build_packed_metadata(
+                hidden_states=hidden_states,
+                cu_seqlens=cu_seqlens.to(device=hidden_states.device),
+                num_sequences=num_sequences.to(device=hidden_states.device) if num_sequences is not None else None,
+            )
+
+        if self.position_embedding_type == "absolute":
+            hidden_states = hidden_states + self.wpe(position_ids)
+        hidden_states = self.drop(hidden_states)
+        hidden_states = hidden_states * query_attention_mask.unsqueeze(-1).to(dtype=hidden_states.dtype)
+
+        all_hidden_states = () if output_hidden_states else None
+        presents = [] if use_cache else None
+        for layer_index, block in enumerate(self.h):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                if use_cache:
+                    raise ValueError("use_cache=True is not supported when gradient checkpointing is enabled during training.")
+
+                def custom_forward(*inputs):
+                    output, _ = block(
+                        inputs[0],
+                        attention_mask=inputs[1],
+                        position_ids=inputs[2],
+                        packed_metadata=packed_metadata,
+                        layer_past=None,
+                        use_cache=False,
+                    )
+                    return output
+
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    custom_forward,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    use_reentrant=False,
+                )
+                present = None
+            else:
+                hidden_states, present = block(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    packed_metadata=packed_metadata,
+                    layer_past=None if past_key_values is None else past_key_values[layer_index],
+                    use_cache=use_cache,
+                )
+            hidden_states = hidden_states * query_attention_mask.unsqueeze(-1).to(dtype=hidden_states.dtype)
+            if presents is not None:
+                presents.append(present)
+
+        hidden_states = self.ln_f(hidden_states)
+        hidden_states = hidden_states * query_attention_mask.unsqueeze(-1).to(dtype=hidden_states.dtype)
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return (hidden_states, tuple(presents) if presents is not None else None, all_hidden_states, None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=tuple(presents) if presents is not None else None,
+            hidden_states=all_hidden_states,
+            attentions=None,
+        )

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0426f0bd91aeda78ab4e2747621d3752f49cd60174a21bc07b1df7043922446e
+size 469262952

prompting.py

@@ -0,0 +1,92 @@
+from __future__ import annotations
+
+from typing import List, Sequence
+
+from .configuration_moss_tts_nano import MossTTSNanoConfig
+
+
+USER_ROLE_PREFIX = "user\n"
+USER_TEMPLATE_REFERENCE_PREFIX = (
+    "<user_inst>\n"
+    "- Reference(s):\n"
+)
+USER_TEMPLATE_AFTER_REFERENCE = (
+    "\n- Instruction:\nNone\n"
+    "- Tokens:\nNone\n"
+    "- Quality:\nNone\n"
+    "- Sound Event:\nNone\n"
+    "- Ambient Sound:\nNone\n"
+    "- Language:\nNone\n"
+    "- Text:\n"
+)
+USER_TEMPLATE_PREFIX = USER_TEMPLATE_REFERENCE_PREFIX + "None" + USER_TEMPLATE_AFTER_REFERENCE
+USER_TEMPLATE_SUFFIX = "\n</user_inst>"
+ASSISTANT_TURN_PREFIX = "\n"
+ASSISTANT_ROLE_PREFIX = "assistant\n"
+
+
+def encode_text(tokenizer, text: str) -> List[int]:
+    try:
+        return list(tokenizer.encode(text, add_special_tokens=False))
+    except TypeError:
+        return list(tokenizer.encode(text))
+
+
+def decode_text(tokenizer, token_ids: Sequence[int]) -> str:
+    try:
+        return str(
+            tokenizer.decode(
+                list(token_ids),
+                skip_special_tokens=False,
+                clean_up_tokenization_spaces=False,
+            )
+        )
+    except TypeError:
+        try:
+            return str(tokenizer.decode(list(token_ids), skip_special_tokens=False))
+        except TypeError:
+            return str(tokenizer.decode(list(token_ids)))
+
+
+def build_user_prompt_prefix(tokenizer, config: MossTTSNanoConfig) -> List[int]:
+    return [config.im_start_token_id] + encode_text(tokenizer, USER_ROLE_PREFIX) + encode_text(
+        tokenizer,
+        USER_TEMPLATE_REFERENCE_PREFIX,
+    )
+
+
+def build_user_prompt_after_reference(tokenizer) -> List[int]:
+    return encode_text(tokenizer, USER_TEMPLATE_AFTER_REFERENCE)
+
+
+def build_assistant_prompt_prefix(tokenizer, config: MossTTSNanoConfig) -> List[int]:
+    return encode_text(tokenizer, USER_TEMPLATE_SUFFIX) + [config.im_end_token_id] + encode_text(
+        tokenizer,
+        ASSISTANT_TURN_PREFIX,
+    ) + [config.im_start_token_id] + encode_text(
+        tokenizer,
+        ASSISTANT_ROLE_PREFIX,
+    )
+
+
+def build_prompt_prefix(tokenizer, config: MossTTSNanoConfig) -> List[int]:
+    return (
+        build_user_prompt_prefix(tokenizer, config)
+        + encode_text(tokenizer, "None")
+        + build_user_prompt_after_reference(tokenizer)
+    )
+
+
+def build_prompt_suffix(tokenizer, config: MossTTSNanoConfig) -> List[int]:
+    return build_assistant_prompt_prefix(tokenizer, config)
+
+
+def build_prompt_token_ids(
+    tokenizer,
+    config: MossTTSNanoConfig,
+    text_token_ids: Sequence[int],
+) -> List[int]:
+    return build_prompt_prefix(tokenizer, config) + [int(token_id) for token_id in text_token_ids] + build_prompt_suffix(
+        tokenizer,
+        config,
+    )

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_moss_tts_nano.py

@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import shutil
+from pathlib import Path
+from typing import Any
+
+import sentencepiece as spm
+from transformers import PreTrainedTokenizer
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+
+class MossTTSNanoSentencePieceTokenizer(PreTrainedTokenizer):
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file: str,
+        unk_token: str = "<unk>",
+        bos_token: str = "<s>",
+        eos_token: str = "</s>",
+        pad_token: str = "<pad>",
+        sp_model_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ) -> None:
+        self.vocab_file = str(vocab_file)
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else dict(sp_model_kwargs)
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+        super().__init__(
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            **kwargs,
+        )
+
+    @property
+    def vocab_size(self) -> int:
+        return int(self.sp_model.get_piece_size())
+
+    def get_vocab(self) -> dict[str, int]:
+        vocab = {self.sp_model.id_to_piece(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text: str) -> list[str]:
+        return list(self.sp_model.encode(text, out_type=str))
+
+    def _convert_token_to_id(self, token: str) -> int:
+        token_id = int(self.sp_model.piece_to_id(token))
+        return token_id
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return str(self.sp_model.id_to_piece(int(index)))
+
+    def convert_tokens_to_string(self, tokens: list[str]) -> str:
+        return str(self.sp_model.decode(tokens))
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]:
+        save_dir = Path(save_directory)
+        save_dir.mkdir(parents=True, exist_ok=True)
+        out_name = "tokenizer.model" if filename_prefix is None else f"{filename_prefix}-tokenizer.model"
+        out_path = save_dir / out_name
+        if Path(self.vocab_file).resolve() != out_path.resolve():
+            shutil.copyfile(self.vocab_file, out_path)
+        return (str(out_path),)
+
+    def build_inputs_with_special_tokens(
+        self,
+        token_ids_0: list[int],
+        token_ids_1: list[int] | None = None,
+    ) -> list[int]:
+        if token_ids_1 is None:
+            return list(token_ids_0)
+        return list(token_ids_0) + list(token_ids_1)
+
+    def get_special_tokens_mask(
+        self,
+        token_ids_0: list[int],
+        token_ids_1: list[int] | None = None,
+        already_has_special_tokens: bool = False,
+    ) -> list[int]:
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0,
+                token_ids_1=token_ids_1,
+                already_has_special_tokens=True,
+            )
+        if token_ids_1 is None:
+            return [0] * len(token_ids_0)
+        return [0] * (len(token_ids_0) + len(token_ids_1))
+
+    def create_token_type_ids_from_sequences(
+        self,
+        token_ids_0: list[int],
+        token_ids_1: list[int] | None = None,
+    ) -> list[int]:
+        if token_ids_1 is None:
+            return [0] * len(token_ids_0)
+        return [0] * (len(token_ids_0) + len(token_ids_1))
+
+
+__all__ = ["MossTTSNanoSentencePieceTokenizer"]

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c353ee1479b536bf414c1b247f5542b6607fb8ae91320e5af1781fee200fddff
+size 470897

tokenizer_config.json

@@ -0,0 +1,52 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_moss_tts_nano.MossTTSNanoSentencePieceTokenizer",
+      null
+    ]
+  },
+  "backend": "custom",
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_max_length": 16384,
+  "pad_token": "<pad>",
+  "tokenizer_class": "MossTTSNanoSentencePieceTokenizer",
+  "unk_token": "<unk>"
+}