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edit//Qwen3-TTS-test//qwen_tts//core//tokenizer_12hz//configuration_qwen3_tts_tokenizer_v2.py

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+# coding=utf-8
+# Copyright 2026 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Qwen3TTSTokenizerV2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+from transformers import MimiConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen3TTSTokenizerV2DecoderConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen3TTSTokenizerV2DecoderConfig`].
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        codebook_size (`int`, *optional*, defaults to 2048):
+            Number of entries in each residual codebook used for acoustic token quantization.
+        hidden_size (`int`, *optional*, defaults to 1024):
+            Dimensionality of the hidden states and embeddings in the autoregressive transformer decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 8000):
+            Maximum sequence length that the autoregressive decoder can handle. Determines positional embedding size.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period for rotary position embeddings (RoPE) applied to attention layers.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 16):
+            Number of key and value attention heads used in grouped-query attention (if applicable).
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the attention projection layers.
+        sliding_window (`int`, *optional*, defaults to 72):
+            Window size for local attention mechanism, limiting attention context to improve efficiency.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the feed-forward (intermediate) layer in each transformer block.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function used in the feed-forward layers. Supports `"silu"`, `"relu"`, `"gelu"`, etc.
+        layer_scale_initial_scale (`float`, *optional*, defaults to 0.01):
+            Initial value for LayerScale applied in transformer blocks, helping stabilize training.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-5):
+            Epsilon value for RMS normalization layers to prevent division by zero.
+        num_hidden_layers (`int`, *optional*, defaults to 8):
+            Number of transformer blocks in the autoregressive decoder.
+        num_quantizers (`int`, *optional*, defaults to 16):
+            Number of residual vector quantizers used in the vocoder for fine-grained audio reconstruction.
+        upsample_rates (`Tuple[int]`, *optional*, defaults to `(8, 5, 4, 3)`):
+            Rate at which features are upsampled in the final waveform synthesis stage.
+        upsampling_ratios (`Tuple[int]`, *optional*, defaults to `(2, 2)`):
+            Ratios used in transposed convolutional layers to progressively upsample feature maps to waveform.
+        decoder_dim (`int`, *optional*, defaults to 1536):
+            Final dimensionality of the decoder's output before waveform generation.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            Dropout probability applied to attention weights in the decoder.
+    """
+
+    def __init__(
+        self,
+        codebook_size=2048,
+        hidden_size=1024,
+        latent_dim=1024,
+        max_position_embeddings=8000,
+        rope_theta=10000,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        attention_bias=False,
+        sliding_window=72,
+        intermediate_size=3072,
+        hidden_act="silu",
+        layer_scale_initial_scale=0.01,
+        rms_norm_eps=1e-5,
+        num_hidden_layers=8,
+        num_quantizers=16,
+        upsample_rates=(8, 5, 4, 3),
+        upsampling_ratios=(2, 2),
+        decoder_dim=1536,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.codebook_size = codebook_size
+        self.hidden_size = hidden_size
+        self.latent_dim = latent_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.rope_theta = rope_theta
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.attention_bias = attention_bias
+        self.sliding_window = sliding_window
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.layer_scale_initial_scale = layer_scale_initial_scale
+        self.rms_norm_eps = rms_norm_eps
+        self.num_hidden_layers = num_hidden_layers
+        self.num_quantizers = num_quantizers
+        self.upsample_rates = upsample_rates
+        self.upsampling_ratios = upsampling_ratios
+        self.decoder_dim = decoder_dim
+        self.attention_dropout = attention_dropout
+
+    @property
+    def layer_types(self):
+        """
+        All layer in code2wav should be sliding attention
+        """
+        return ["sliding_attention"] * self.num_hidden_layers
+
+
+class Qwen3TTSTokenizerV2Config(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`Qwen3TTSTokenizerV2Config`]. It is used to instantiate a Qwen3TTSTokenizerV2Model
+    model according to the specified sub-models configurations, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        encoder_config (`dict`, *optional*): Configuration of the underlying encoder sub-model.
+        decoder_config (`dict`, *optional*): Configuration of the underlying decoder sub-model.
+    """
+
+    model_type = "qwen3_tts_tokenizer_12hz"
+    sub_configs = {
+        "encoder_config": MimiConfig,
+        "decoder_config": Qwen3TTSTokenizerV2DecoderConfig,
+    }
+
+    def __init__(
+        self,
+        encoder_config=None,
+        decoder_config=None,
+        encoder_valid_num_quantizers=16,
+        input_sample_rate=24000,
+        output_sample_rate=24000,
+        decode_upsample_rate=1920,
+        encode_downsample_rate=1920,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        if encoder_config is None:
+            encoder_config = {}
+            logger.info("encoder_config is None. Initializing encoder with default values")
+        if decoder_config is None:
+            decoder_config = {}
+            logger.info("decoder_config is None. Initializing decoder with default values")
+
+        self.encoder_config = MimiConfig(**encoder_config)
+        self.decoder_config = Qwen3TTSTokenizerV2DecoderConfig(**decoder_config)
+
+        self.encoder_valid_num_quantizers = encoder_valid_num_quantizers
+        self.input_sample_rate = input_sample_rate
+        self.output_sample_rate = output_sample_rate
+        self.decode_upsample_rate = decode_upsample_rate
+        self.encode_downsample_rate = encode_downsample_rate
+
+
+__all__ = ["Qwen3TTSTokenizerV2Config", "Qwen3TTSTokenizerV2DecoderConfig"]