110 lines
4.5 KiB
Python
110 lines
4.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""GLM-TTS serving adapter."""
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from typing import TYPE_CHECKING, Any
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from vllm.logger import init_logger
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from vllm_omni.entrypoints.openai.tts_adapters import register_tts_adapter
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from vllm_omni.entrypoints.openai.tts_adapters.base import ARTTSAdapter, PreparedRequest
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if TYPE_CHECKING:
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from vllm_omni.entrypoints.openai.protocol.audio import OpenAICreateSpeechRequest
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logger = init_logger(__name__)
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@register_tts_adapter
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class GlmTTSAdapter(ARTTSAdapter):
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stage_keys = frozenset({"glm_tts"})
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name = "glm_tts"
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def validate(self, request: "OpenAICreateSpeechRequest") -> str | None:
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"""Validate GLM-TTS request — requires ref_audio for voice cloning."""
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server = self.ctx.server
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err = server._apply_uploaded_speaker(request)
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if err:
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return err
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if not request.input or not request.input.strip():
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return "Input text cannot be empty"
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if request.ref_audio is None:
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return "GLM-TTS requires 'ref_audio' for zero-shot voice cloning"
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fmt_err = server._validate_ref_audio_format(request.ref_audio)
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if fmt_err:
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return fmt_err
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if not request.ref_text or not request.ref_text.strip():
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return "GLM-TTS voice cloning requires 'ref_text' (transcript of the reference audio)"
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if request.max_new_tokens is not None:
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if request.max_new_tokens < self.max_new_tokens_min:
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return f"max_new_tokens must be >= {self.max_new_tokens_min}"
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if request.max_new_tokens > self.max_new_tokens_max:
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return f"max_new_tokens cannot exceed {self.max_new_tokens_max}"
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return None
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async def build(
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self, request: "OpenAICreateSpeechRequest", sampling_params_list: list, has_inline_ref_audio: bool
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) -> PreparedRequest:
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prompt = await self.ctx.server._build_glm_tts_prompt(request, has_inline_ref_audio=has_inline_ref_audio)
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return PreparedRequest(prompt=prompt, tts_params={}, model_type="glm_tts")
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def apply_sampling_overrides(
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self,
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sampling_params_list: list,
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request: "OpenAICreateSpeechRequest",
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prompt: dict[str, Any] | None = None,
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request_id: str | None = None,
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) -> list:
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# GLM-TTS: set dynamic min/max tokens based on text length.
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import copy
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server = self.ctx.server
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sampling_params_list = copy.deepcopy(sampling_params_list)
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glm_metadata = prompt.get("additional_information") if isinstance(prompt, dict) else None
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text_len_value = None
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if isinstance(glm_metadata, dict):
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text_len_value = glm_metadata.get("glm_tts_text_token_len")
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if isinstance(text_len_value, list) and text_len_value:
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text_len_value = text_len_value[0]
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text_token_len = (
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int(text_len_value)
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if text_len_value is not None
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else server._estimate_glm_tts_text_token_len(request.input)
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)
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hf_cfg = server.model_config.hf_config
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min_ratio = getattr(hf_cfg, "min_token_text_ratio", 2)
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max_ratio = getattr(hf_cfg, "max_token_text_ratio", 20)
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stage_min_tokens = getattr(sampling_params_list[0], "min_tokens", None)
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stage_max_tokens = getattr(sampling_params_list[0], "max_tokens", None)
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cap_candidates = [int(cap) for cap in (stage_max_tokens, request.max_new_tokens) if cap is not None]
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hard_cap = min(cap_candidates) if cap_candidates else None
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min_tokens = max(1, int(text_token_len * min_ratio))
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if stage_min_tokens is not None:
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min_tokens = max(min_tokens, int(stage_min_tokens))
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if hard_cap is not None:
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min_tokens = min(min_tokens, hard_cap)
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max_tokens = max(min_tokens, int(text_token_len * max_ratio))
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if hard_cap is not None:
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max_tokens = min(max_tokens, hard_cap)
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sampling_params_list[0].min_tokens = min_tokens
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sampling_params_list[0].max_tokens = max_tokens
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seed = getattr(request, "seed", None)
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if seed is not None:
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sampling_params_list[0].seed = seed
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logger.info(
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"GLM-TTS dynamic tokens: text_tokens=%d, min_ratio=%s, max_ratio=%s, "
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"stage_min=%s, stage_max=%s, request_max=%s, min_tokens=%d, max_tokens=%d",
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text_token_len,
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min_ratio,
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max_ratio,
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stage_min_tokens,
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stage_max_tokens,
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request.max_new_tokens,
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min_tokens,
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max_tokens,
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)
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return sampling_params_list
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