BeatSeatandYueqian Lin
28ce618f0e
Signed-off-by: BeatSeat <wendavid552@gmail.com> Signed-off-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com> Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>
95 lines
3.2 KiB
Python
95 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""GLM-TTS config registration with transformers AutoConfig.
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Registers GLMTTSConfig (model_type="glm_tts") so that
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``AutoConfig.from_pretrained("path/to/glm-tts")`` returns the correct config class.
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Note: GLM-TTS uses a Llama backbone, but we register a custom config
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to handle the special token IDs and flow model parameters.
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"""
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from __future__ import annotations
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from typing import Any
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from transformers import AutoConfig, PretrainedConfig
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class GLMTTSConfig(PretrainedConfig):
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"""Llama-based AR model for text-to-speech token generation.
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Special token IDs are loaded dynamically from the tokenizer at init time.
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"""
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model_type: str = "glm_tts"
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def __init__(
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self,
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vocab_size: int = 98304,
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hidden_size: int = 2048,
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intermediate_size: int = 6144,
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num_hidden_layers: int = 28,
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num_attention_heads: int = 16,
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num_key_value_heads: int = 4,
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hidden_act: str = "silu",
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max_position_embeddings: int = 8192,
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rms_norm_eps: float = 1e-5,
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rope_theta: float = 10000.0,
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audio_token_start: int = -1,
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audio_token_end: int = -1,
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boa_token_id: int = -1,
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eoa_token_id: int = -1,
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pad_token_id: int = -1,
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bos_token_id: int = -1,
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speech_token_vocab_size: int = 32768,
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speech_token_dim: int = 512,
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spk_embed_dim: int = 192,
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mel_dim: int = 80,
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input_frame_rate: float = 25.0,
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mel_framerate: int = 50,
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max_token_text_ratio: float = 20.0,
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min_token_text_ratio: float = 2.0,
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sample_method: str = "ras",
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ras_top_p: float = 0.8,
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ras_top_k: int = 25,
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ras_win_size: int = 10,
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ras_tau_r: float = 0.1,
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**kwargs: Any,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.rms_norm_eps = rms_norm_eps
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self.rope_theta = rope_theta
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self.audio_token_start = audio_token_start
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self.audio_token_end = audio_token_end
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self.boa_token_id = boa_token_id
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self.eoa_token_id = eoa_token_id
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self.speech_token_vocab_size = speech_token_vocab_size
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self.speech_token_dim = speech_token_dim
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self.spk_embed_dim = spk_embed_dim
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self.mel_dim = mel_dim
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self.input_frame_rate = input_frame_rate
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self.mel_framerate = mel_framerate
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self.max_token_text_ratio = max_token_text_ratio
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self.min_token_text_ratio = min_token_text_ratio
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self.sample_method = sample_method
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self.ras_top_p = ras_top_p
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self.ras_top_k = ras_top_k
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self.ras_win_size = ras_win_size
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self.ras_tau_r = ras_tau_r
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
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AutoConfig.register("glm_tts", GLMTTSConfig)
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__all__ = [
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"GLMTTSConfig",
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]
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