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chore: remove qwen_tts before subtree setup

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bdim404
2026-03-06 17:35:16 +08:00
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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"]

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qwen_tts/core/tokenizer_12hz/modeling_qwen3_tts_tokenizer_v2.py
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