6a0bae2a0b
## Voice Pipeline (P3) - Replace openWakeWord daemon with Wyoming Satellite approach - Add Wyoming Satellite service on port 10700 for HA voice pipeline - Update setup.sh with cross-platform sed compatibility (macOS/Linux) - Add version field to Kokoro TTS voice info - Update launchd service loader to use Wyoming Satellite ## Home Assistant Integration (P4) - Add custom conversation agent component (openclaw_conversation) - Fix: Use IntentResponse instead of plain strings (HA API requirement) - Support both HTTP API and CLI fallback modes - Config flow for easy HA UI setup - Add OpenClaw bridge scripts (Python + Bash) - Add ha-ctl utility for HA entity control - Fix: Use context manager for token file reading - Add HA configuration examples and documentation ## Infrastructure - Add mem0 backup automation (launchd + script) - Add n8n workflow templates (morning briefing, notification router) - Add VS Code workspace configuration - Reorganize model files into categorized folders: - lmstudio-community/ - mlx-community/ - bartowski/ - mradermacher/ ## Documentation - Update PROJECT_PLAN.md with Wyoming Satellite architecture - Update TODO.md with completed Wyoming integration tasks - Add OPENCLAW_INTEGRATION.md for HA setup guide ## Testing - Verified Wyoming services running (STT:10300, TTS:10301, Satellite:10700) - Verified OpenClaw CLI accessibility - Confirmed cross-platform compatibility fixes
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
from typing import List, Optional, Union
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from transformers.models.llama import LlamaTokenizerFast
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class DeepseekTokenizerFast(LlamaTokenizerFast):
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def convert_ids_to_tokens(
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self, ids: Union[int, List[int]], skip_special_tokens: bool = False
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) -> Union[str, List[str]]:
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"""
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Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
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added tokens.
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Args:
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ids (`int` or `List[int]`):
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The token id (or token ids) to convert to tokens.
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skip_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not to remove special tokens in the decoding.
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Returns:
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`str` or `List[str]`: The decoded token(s).
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"""
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if isinstance(ids, int):
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return self._convert_id_to_token(ids)
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tokens = []
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for index in ids:
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index = int(index)
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if skip_special_tokens and index in self.all_special_ids:
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continue
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token = self._tokenizer.id_to_token(index)
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tokens.append(token if token is not None else "")
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return tokens
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def _convert_id_to_token(self, index: int) -> Optional[str]:
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token = self._tokenizer.id_to_token(int(index))
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return token if token is not None else ""
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