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chore: sync qwen_tts from upstream QwenLM/Qwen3-TTS@main

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bdim404
2026-03-06 17:36:53 +08:00
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qwen_tts/core/models/__init__.py Normal file
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# coding=utf-8
# Copyright 2026 The Alibaba Qwen team.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from .configuration_qwen3_tts import Qwen3TTSConfig
from .modeling_qwen3_tts import Qwen3TTSForConditionalGeneration
from .processing_qwen3_tts import Qwen3TTSProcessor

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qwen_tts/core/models/configuration_qwen3_tts.py Normal file
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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.
from transformers.configuration_utils import PretrainedConfig, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class Qwen3TTSSpeakerEncoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3TTSSpeakerEncoder`].
It is used to instantiate a Qwen3TTS speaker encoder model according to the specified arguments, defining the model
architecture. The architecture is based on the ECAPA-TDNN model.
Args:
mel_dim (`int`, *optional*, defaults to 128):
The dimension of the input mel-spectrogram.
enc_dim (`int`, *optional*, defaults to 192):
The dimension of the final speaker embedding.
enc_channels (`list[int]`, *optional*, defaults to `[512, 512, 512, 512, 1536]`):
A list of output channels for each TDNN/SERes2Net layer in the encoder. The first channel size is for the initial TDNN layer,
the intermediate ones for the `SqueezeExcitationRes2NetBlock` layers, and the last one for the multi-layer feature aggregation.
enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`):
A list of kernel sizes for each layer in the encoder, corresponding to `enc_channels`.
enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`):
A list of dilations for each layer in the encoder, corresponding to `enc_channels`.
enc_attention_channels (`int`, *optional*, defaults to 128):
The number of attention channels in the `AttentiveStatisticsPooling` layer.
enc_res2net_scale (`int`, *optional*,defaults to 8):
The scale of the `Res2NetBlock` in the encoder.
enc_se_channels (`int`, *optional*, defaults to 128):
The number of channels in the squeeze part of the `SqueezeExcitationBlock`.
"""
def __init__(
self,
mel_dim=128,
enc_dim=1024,
enc_channels=[512, 512, 512, 512, 1536],
enc_kernel_sizes=[5, 3, 3, 3, 1],
enc_dilations=[1, 2, 3, 4, 1],
enc_attention_channels=128,
enc_res2net_scale=8,
enc_se_channels=128,
sample_rate=24000,
):
self.mel_dim = mel_dim
self.enc_dim = enc_dim
self.enc_channels = enc_channels
self.enc_kernel_sizes = enc_kernel_sizes
self.enc_dilations = enc_dilations
self.enc_attention_channels = enc_attention_channels
self.enc_res2net_scale = enc_res2net_scale
self.enc_se_channels = enc_se_channels
self.sample_rate = sample_rate
class Qwen3TTSTalkerCodePredictorConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3TTSTalkerCodePredictorModel`]. It is used to instantiate a
Qwen3TTSTalkerCodePredictor model according to the specified arguments, 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:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Qwen3TTSTalkerCodePredictor model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen3TTSTalkerCodePredictorModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 22016):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 32):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
head_dim (`int`, *optional*, defaults to 128):
The attention head dimension.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 28):
The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any
additional layer afterwards will use SWA (Sliding Window Attention).
layer_types (`list`, *optional*):
Attention pattern for each layer.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
"""
model_type = "qwen3_tts_talker_code_predictor"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3TTSTalkerCodePredictor`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=2048,
hidden_size=1024,
intermediate_size=3072,
num_hidden_layers=5,
num_attention_heads=16,
num_key_value_heads=8,
head_dim=128,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=0.000001,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000,
rope_scaling=None,
attention_bias=False,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=28,
layer_types=None,
attention_dropout=0,
num_code_groups=32,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if self.use_sliding_window else None
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.head_dim = head_dim
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
self.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types)
self.num_code_groups = num_code_groups
class Qwen3TTSTalkerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3TTSTalkerModel`]. It is used to instantiate a
Qwen3TTSTalker model according to the specified arguments, 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:
vocab_size (`int`, *optional*, defaults to 151936):
Vocabulary size of the Qwen3TTSTalker model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen3TTSTalkerModel`]
hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 6144):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
"""
model_type = "qwen3_tts_talker"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3TTSTalker`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
sub_configs = {"code_predictor_config": Qwen3TTSTalkerCodePredictorConfig}
def __init__(
self,
code_predictor_config=None,
vocab_size=3072,
hidden_size=1024,
intermediate_size=2048,
num_hidden_layers=20,
num_attention_heads=16,
num_key_value_heads=2,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=0.000001,
use_cache=True,
tie_word_embeddings=False,
rope_theta=10000,
rope_scaling=None,
attention_bias=False,
use_sliding_window=False,
sliding_window=4096,
attention_dropout=0,
num_code_groups=32,
text_hidden_size=2048,
codec_eos_token_id=4198,
codec_think_id=4202,
codec_nothink_id=4203,
codec_think_bos_id=4204,
codec_think_eos_id=4205,
codec_pad_id=4196,
codec_bos_id=4197,
spk_id=None,
spk_is_dialect=None,
codec_language_id=None,
**kwargs,
):
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if use_sliding_window else None
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
if code_predictor_config is None:
code_predictor_config = {}
self.code_predictor_config = Qwen3TTSTalkerCodePredictorConfig()
logger.info("code_predictor_config is None. Initializing code_predictor model with default values")
elif isinstance(code_predictor_config, Qwen3TTSTalkerCodePredictorConfig):
self.code_predictor_config = code_predictor_config
else:
self.code_predictor_config = Qwen3TTSTalkerCodePredictorConfig(**code_predictor_config)
self.num_code_groups = num_code_groups
self.text_hidden_size = text_hidden_size
self.codec_eos_token_id = codec_eos_token_id
self.codec_think_id = codec_think_id
self.codec_language_id = codec_language_id
self.codec_nothink_id = codec_nothink_id
self.codec_think_bos_id = codec_think_bos_id
self.codec_think_eos_id = codec_think_eos_id
self.codec_pad_id = codec_pad_id
self.codec_bos_id = codec_bos_id
self.spk_id = spk_id
self.spk_is_dialect = spk_is_dialect
class Qwen3TTSConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Qwen3TTSForConditionalGeneration`].
"""
model_type = "qwen3_tts"
sub_configs = {
"talker_config": Qwen3TTSTalkerConfig,
"speaker_encoder_config": Qwen3TTSSpeakerEncoderConfig,
}
def __init__(
self,
talker_config=None,
speaker_encoder_config=None,
tokenizer_type=None,
tts_model_size=None,
tts_model_type=None,
im_start_token_id=151644,
im_end_token_id=151645,
tts_pad_token_id=151671,
tts_bos_token_id=151672,
tts_eos_token_id=151673,
**kwargs,
):
super().__init__(**kwargs)
if talker_config is None:
talker_config = {}
logger.info("talker_config is None. Initializing talker model with default values")
if speaker_encoder_config is None:
speaker_encoder_config = {}
logger.info("speaker_encoder_config is None. Initializing talker model with default values")
self.talker_config = Qwen3TTSTalkerConfig(**talker_config)
self.speaker_encoder_config = Qwen3TTSSpeakerEncoderConfig(**speaker_encoder_config)
self.tokenizer_type = tokenizer_type
self.tts_model_size = tts_model_size
self.tts_model_type = tts_model_type
self.im_start_token_id = im_start_token_id
self.im_end_token_id = im_end_token_id
self.tts_pad_token_id = tts_pad_token_id
self.tts_bos_token_id = tts_bos_token_id
self.tts_eos_token_id = tts_eos_token_id
__all__ = ["Qwen3TTSConfig", "Qwen3TTSTalkerConfig", "Qwen3TTSSpeakerEncoderConfig"]

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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.
from transformers.feature_extraction_utils import BatchFeature
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin
class Qwen3TTSProcessorKwargs(ProcessingKwargs, total=False):
_defaults = {
"text_kwargs": {
"padding": False,
"padding_side": "left",
}
}
class Qwen3TTSProcessor(ProcessorMixin):
r"""
Constructs a Qwen3TTS processor.
Args:
tokenizer ([`Qwen2TokenizerFast`], *optional*):
The text tokenizer.
chat_template (`Optional[str]`, *optional*):
The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
"""
attributes = ["tokenizer"]
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(
self, tokenizer=None, chat_template=None
):
super().__init__(tokenizer, chat_template=chat_template)
def __call__(self, text=None, **kwargs) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and audio(s). This method forwards the `text`
and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
the text.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
"""
if text is None:
raise ValueError("You need to specify either a `text` input to process.")
output_kwargs = self._merge_kwargs(
Qwen3TTSProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if not isinstance(text, list):
text = [text]
texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
return BatchFeature(
data={**texts_inputs},
tensor_type=kwargs.get("return_tensors"),
)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def apply_chat_template(self, conversations, chat_template=None, **kwargs):
if isinstance(conversations[0], dict):
conversations = [conversations]
return super().apply_chat_template(conversations, chat_template, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
return list(
dict.fromkeys(
tokenizer_input_names
)
)
__all__ = ["Qwen3TTSProcessor"]