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# coding=utf-8
# Copyright 2024 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.
""" Parler-TTS model configuration"""
from transformers import AutoConfig, logging
from transformers.configuration_utils import PretrainedConfig
logger = logging.get_logger(__name__)
MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/parler_tts-small": "https://huggingface.co/facebook/parler_tts-small/resolve/main/config.json",
# See all ParlerTTS models at https://huggingface.co/models?filter=parler_tts
}
class ParlerTTSDecoderConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`ParlerTTSDecoder`]. It is used to instantiate a
Parler-TTS decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Parler-TTS
[facebook/parler_tts-small](https://huggingface.co/facebook/parler_tts-small) 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 2049):
Vocabulary size of the ParlerTTSDecoder model. Defines the number of different tokens that can be
represented by the `inputs_ids` passed when calling [`ParlerTTSDecoder`].
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of decoder layers.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer block.
ffn_dim (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer block.
activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the decoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, text_encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for activations inside the fully connected layer.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically, set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_factor (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.
scale_embedding (`bool`, *optional*, defaults to `False`):
Scale embeddings by diving by sqrt(hidden_size).
use_cache (`bool`, *optional*, defaults to `True`):
Whether the model should return the last key/values attentions (not used by all models)
num_codebooks (`int`, *optional*, defaults to 4):
The number of parallel codebooks forwarded to the model.
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
Whether input and output word embeddings should be tied.
rope_embeddings (`bool`, *optional*, defaults to `False`):
Whether to use ROPE or absolute positional embeddings.
rope_theta (`float`, *optional*, defaults to 100000.0):
The base period of the RoPE embeddings.
"""
model_type = "parler_tts_decoder"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=2049, # vocab size = 2048 (encodec vocab size) + 1 (eos)
max_position_embeddings=2048,
num_hidden_layers=24,
ffn_dim=4096,
num_attention_heads=16,
layerdrop=0.0,
use_cache=True,
activation_function="gelu",
hidden_size=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
initializer_factor=0.02,
scale_embedding=False,
num_codebooks=4,
pad_token_id=2048,
bos_token_id=2049,
eos_token_id=2048,
tie_word_embeddings=False,
rope_embeddings=False,
rope_theta=10_000.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.ffn_dim = ffn_dim
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.activation_function = activation_function
self.initializer_factor = initializer_factor
self.layerdrop = layerdrop
self.use_cache = use_cache
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.num_codebooks = num_codebooks
self.rope_embeddings = rope_embeddings
self.rope_theta = rope_theta
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
class ParlerTTSConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ParlerTTSModel`]. It is used to instantiate a
Parler-TTS model according to the specified arguments, defining the text encoder, audio encoder and Parler-TTS decoder
configs.
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 1024):
Vocabulary size of the prompt token ids. Defines the number of different tokens that can be
represented by the `prompt_inputs_ids`.
prompt_cross_attention (`bool`, *optional*, defaults to `False`):
Whether to use cross-attention conditioning for the prompt (as well as the description).
kwargs (*optional*):
Dictionary of keyword arguments. Notably:
- **text_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the text encoder config.
- **audio_encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that
defines the audio encoder config.
- **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
the decoder config.
Example:
```python
>>> from transformers import (
... ParlerTTSConfig,
... ParlerTTSDecoderConfig,
... T5Config,
... EncodecConfig,
... ParlerTTSForConditionalGeneration,
... )
>>> # Initializing text encoder, audio encoder, and decoder model configurations
>>> text_encoder_config = T5Config()
>>> audio_encoder_config = EncodecConfig()
>>> decoder_config = ParlerTTSDecoderConfig()
>>> configuration = ParlerTTSConfig.from_sub_models_config(
... text_encoder_config, audio_encoder_config, decoder_config
... )
>>> # Initializing a ParlerTTSForConditionalGeneration (with random weights) from the facebook/parler_tts-small style configuration
>>> model = ParlerTTSForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> config_text_encoder = model.config.text_encoder
>>> config_audio_encoder = model.config.audio_encoder
>>> config_decoder = model.config.decoder
>>> # Saving the model, including its configuration
>>> model.save_pretrained("parler_tts-model")
>>> # loading model and config from pretrained folder
>>> parler_tts_config = ParlerTTSConfig.from_pretrained("parler_tts-model")
>>> model = ParlerTTSForConditionalGeneration.from_pretrained("parler_tts-model", config=parler_tts_config)
```"""
model_type = "parler_tts"
is_composition = True
def __init__(self, vocab_size=1024, prompt_cross_attention=False, **kwargs):
super().__init__(**kwargs)
if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config")
text_encoder_config = kwargs.pop("text_encoder")
text_encoder_model_type = text_encoder_config.pop("model_type")
audio_encoder_config = kwargs.pop("audio_encoder")
audio_encoder_model_type = audio_encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
self.vocab_size = vocab_size
self.prompt_cross_attention = prompt_cross_attention
self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **text_encoder_config)
self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **audio_encoder_config)
self.decoder = ParlerTTSDecoderConfig(**decoder_config)
self.is_encoder_decoder = True
@classmethod
def from_sub_models_config(
cls,
text_encoder_config: PretrainedConfig,
audio_encoder_config: PretrainedConfig,
decoder_config: ParlerTTSDecoderConfig,
**kwargs,
):
r"""
Instantiate a [`ParlerTTSConfig`] (or a derived class) from text encoder, audio encoder and decoder
configurations.
Returns:
[`ParlerTTSConfig`]: An instance of a configuration object
"""
return cls(
text_encoder=text_encoder_config.to_dict(),
audio_encoder=audio_encoder_config.to_dict(),
decoder=decoder_config.to_dict(),
**kwargs,
)
@property
# This is a property because you might want to change the codec model on the fly
def sampling_rate(self):
return self.audio_encoder.sampling_rate
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